Liver cancer screening system based on platelet alpha particle super-resolution imaging

Abstract

The invention discloses a liver cancer screening system based on platelet alpha particle super-resolution imaging, which combines the proportion of alpha particle'regular distribution 'platelets and clinical indexes AFP and DCP to screen liver cancer, and can improve the sensitivity of early hepatocellular carcinoma screening. Besides, according to the cancer broad-spectrum screening system provided by the invention, the proportion of alpha-particle'regular distribution 'platelets or the proportion of alpha-particle'N < = 30' platelets is taken as a cancer broad-spectrum screening marker, and the higher the proportion of the alpha-particle'regular distribution 'platelets or the lower the proportion of alpha-particle'N < = 30' platelets is, the higher the proportion of alpha-particle'regular distribution 'platelets is or the lower the proportion of alpha-particle'N < = 30' platelets is. When the cancer risk is higher, cancer broad-spectrum screening is carried out, and high specificity and sensitivity are achieved.

Description

Technical Field

[0001] This invention belongs to the field of screening for malignant tumors of the digestive system, and more specifically, relates to a broad-spectrum cancer screening system based on platelet alpha particle super-resolution imaging, particularly a liver cancer screening system based on platelet alpha particle super-resolution imaging. Background Technology

[0002] "Broad-spectrum biomarkers" for cancer screening refer to one or a group of biomarkers that can detect multiple cancer types (rather than a single cancer). Traditional broad-spectrum cancer screening biomarkers mainly include a group of protein biomarkers, which screen by simultaneously detecting the concentration of multiple cancer-related proteins (such as CEA, CA19-9, CYFRA21-1, etc.). Despite their promising prospects, they still suffer from generally low sensitivity for stage I cancer, resulting in a high risk of missed diagnoses.

[0003] Research on broad-spectrum cancer screening biomarkers based on liquid biopsy is at a critical stage of translating from technological breakthroughs to clinical validation. For example, circulating tumor DNA (ctDNA) methylation mapping, which can simultaneously analyze millions of methylation sites through high-throughput sequencing, is currently the most mainstream and promising broad-spectrum screening strategy. However, early cancer signals in the blood are weak and easily masked by background noise. For instance, stage I or earlier tumors are small, and the amount of ctDNA released into the bloodstream from cancer cell apoptosis / necrosis is extremely low, often below the "noise threshold" of detection technologies. Therefore, finding more biomarkers that can serve as broad-spectrum cancer screening markers is crucial for broad-spectrum cancer screening, especially early screening, such as early hepatocellular carcinoma screening. Summary of the Invention

[0004] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a liver cancer screening system based on platelet alpha particle super-resolution imaging. Its purpose is to identify the proportion of platelets with "regularly distributed" alpha particles or the proportion of platelets with "N≤30" alpha particles as broad-spectrum cancer screening biomarkers. Screening is conducted according to the principle that a higher proportion of platelets with "regularly distributed" alpha particles or a lower proportion of platelets with "N≤30" alpha particles indicates a higher risk of cancer. This system can be used for broad-spectrum cancer screening. In particular, combining the proportion of platelets with "regularly distributed" alpha particles, AFP, and DCP can be used to screen for early-stage hepatocellular carcinoma with high sensitivity and specificity. This solves the technical problems of limited types of existing broad-spectrum cancer biomarkers and low sensitivity or specificity of existing tumor markers.

[0005] To achieve the above objectives, according to one aspect of the present invention, a broad-spectrum cancer screening system based on platelet alpha particle super-resolution imaging is provided, comprising a broad-spectrum biomarker acquisition module and a broad-spectrum cancer screening module. The broad-spectrum biomarker acquisition module is used to acquire broad-spectrum biomarkers of the subjects, including the proportion of platelets with "regular distribution" of α particles and / or the proportion of platelets with "N≤30" of α particles, and submits them to the cancer broad-spectrum screening module. The cancer broad-spectrum screening module uses the fact that the proportion of platelets with "regular distribution" of alpha particles is positively correlated with the risk of cancer to conduct broad-spectrum screening for high-risk individuals. Alternatively, based on the negative correlation between the proportion of platelets with alpha particles "N≤30" and cancer risk, broad-spectrum screening can be conducted for individuals at high cancer risk.

[0006] Preferably, in the cancer broad-spectrum screening system, the cancer broad-spectrum screening module screens individuals at high risk of cancer and outputs results according to the following method: Individuals with a higher proportion of platelets exhibiting a "regular distribution" of alpha granules than a first preset threshold are considered to be at high risk for cancer; or... Individuals with a high risk of cancer are identified based on the proportion of platelets with α-particle "N≤30" being less than the second preset threshold. The first preset threshold or the second preset threshold is determined using the ROC curve method.

[0007] Preferably, in the cancer broad-spectrum screening system, the cancer broad-spectrum screening module performs broad-spectrum screening on individuals at high risk of cancer based on the positive correlation between the proportion of platelets with "regular distribution" of alpha particles and the risk of cancer.

[0008] Preferably, in the cancer broad-spectrum screening system, the cancer broad-spectrum screening module determines individuals at high risk of cancer based on the proportion of platelets with "regular distribution" of α particles being greater than a first preset threshold; the first preset threshold is 23.3%.

[0009] Preferably, the cancer broad-spectrum screening system screens for cancers including hepatocellular carcinoma, cholangiocarcinoma, lung cancer, and ovarian cancer.

[0010] According to a second aspect of the present invention, an early liver cancer screening system is also provided, which includes a biomarker acquisition module, a biomarker analysis module, and a liver cancer screening module. The biomarker acquisition module is used to acquire biomarker data from the subjects and submit it to the biomarker analysis module; the biomarker data includes the proportion of platelets with "regular distribution" of α particles X1, the content of AFP X2, and the content of DCP X3; The biomarker analysis module inputs the acquired biomarker data into a multivariate logistic regression model to calculate joint factors and submits them to the liver cancer screening module. The liver cancer screening module determines the risk of liver cancer based on the principle that the higher the proportion of platelets with "regular distribution" of α particles (X1), the higher the risk, and the higher the content of AFP (X2) and DCP (X3), the higher the risk.

[0011] Preferably, in the early liver cancer screening system, the biomarker acquisition module acquires biomarker data including the proportion of platelets with "regularly distributed" α particles (X1), the content of AFP (X2), and the content of DCP (X3). The biomarker analysis module calculates the joint factor according to the multivariate logistic regression model y=k1X1+k2X2+k3X3+b, where k1~k3 are regression coefficients, all of which are greater than 0. The liver cancer screening module screens for early-stage hepatocellular carcinoma according to the principle that the higher the combined factor, the higher the risk of hepatocellular carcinoma.

[0012] Preferably, in the early liver cancer screening system, the screening module performs screening according to the following method: If the combined factor is greater than the third preset threshold, the subject is judged to be at high risk of early hepatocellular carcinoma; the third preset threshold is determined by the ROC curve method.

[0013] Preferably, the early liver cancer screening system has a multivariate logistic regression model of y=0.052X1+0.019X2+0.044X3-2.282.

[0014] Preferably, in the early liver cancer screening system, the third preset threshold is 0.91.

[0015] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: The cancer broad-spectrum screening system based on platelet α-particle super-resolution imaging provided by this invention, based on the acquired broad-spectrum biomarkers of the subjects (the proportion of platelets with "regularly distributed" α-particles and / or the proportion of platelets with "N≤30" α-particles), performs broad-spectrum screening according to the principle that the higher the proportion of platelets with "regularly distributed" α-particles or the lower the proportion of platelets with "N≤30" α-particles, the higher the risk of cancer. It has high specificity and sensitivity.

[0016] In particular, by combining the proportion of platelets with "regularly distributed" α-particles with clinical indicators AFP and DCP, and using a multivariate logistic regression model y=k1X1+k2X2+k3X3+b to calculate the joint factor y, where k1~k3 are all greater than 0, and judging the subject as a high-risk individual for early hepatocellular carcinoma based on the joint factor y being greater than the third preset threshold, the sensitivity of early hepatocellular carcinoma screening can be improved, which is conducive to the early detection of liver cancer patients and has important significance for improving the survival of liver cancer patients. Attached Figure Description

[0017] Figure 1 This is a super-resolution fluorescence image of the subcellular structure (α-granules) of platelets.

[0018] Figure 2 This is a schematic diagram of four types of platelets: "regular distribution", "N≤30", "N>30" and "aggregation".

[0019] Figure 3 This is a significance analysis of the differences in the percentage of different types of platelets among healthy individuals, patients with benign liver disease, and patients with liver cancer.

[0020] Figure 4 In Example 1, the ability to distinguish liver cancer was assessed based on the proportion of platelets with "regular distribution" of α particles.

[0021] Figure 5 In Example 1, the ability to distinguish liver cancer was assessed based on the proportion of platelets with α particles “N≤30”.

[0022] Figure 6 This is a significance analysis of the differences in the percentage of different types of platelets among healthy individuals, patients with benign liver disease, and patients with early-stage hepatocellular carcinoma.

[0023] Figure 7 In Example 2, the ability to distinguish early-stage hepatocellular carcinoma was assessed based on the proportion of platelets with "regular distribution" of α-particles.

[0024] Figure 8 It refers to the proportion of platelets with "regularly distributed" alpha granules in four types of early-stage hepatocellular carcinoma patients and subjects with benign liver diseases.

[0025] Figure 9 The sensitivity of the proportion of platelets with "regularly distributed" alpha granules in four types of early-stage hepatocellular carcinoma patients.

[0026] Figure 10 This is the ability of Example 2 to differentiate early-stage hepatocellular carcinoma based on combined indicators.

[0027] Figure 11 This is a significance analysis of the differences in the percentage of different types of platelets between healthy individuals and patients with bile duct cancer.

[0028] Figure 12 In Example 3, the ability to differentiate cholangiocarcinoma was assessed based on the proportion of platelets with "regular distribution" of α particles.

[0029] Figure 13 These are super-resolution fluorescence representations of platelet alpha particles from healthy individuals, patients with benign pulmonary nodules, and patients with lung cancer.

[0030] Figure 14 This is the result of classifying platelets according to the distribution pattern of alpha particles.

[0031] Figure 15 This is the result of an analysis of the differences in the proportion of platelets with different α-granule distribution characteristics among healthy individuals, patients with benign pulmonary nodules, and patients with lung cancer.

[0032] Figure 16 The ROC curve analysis of the proportion of platelets with "regularly distributed" alpha particles in distinguishing between non-cancer patients and lung cancer patients.

[0033] Figure 17 The ROC curve analysis of the proportion of platelets with "N<30" α particles in distinguishing between non-cancer patients and lung cancer patients.

[0034] Figure 18 The ROC curve analysis of the proportion of platelets with "regularly distributed" alpha particles in distinguishing between non-cancer patients and early-stage lung cancer patients.

[0035] Figure 19 The ROC curve analysis of the proportion of platelets with "n<30" α particles in distinguishing between non-cancer patients and early-stage lung cancer patients.

[0036] Figure 20 This study compares the differences in four α-granule distribution patterns between healthy individuals and those with preoperative and postoperative ovarian cancer.

[0037] Figure 21 This study compares the differences in four α-granule distribution patterns between postoperative recurrent ovarian cancer and postoperative non-recurrent ovarian cancer.

[0038] Figure 22 These are ROC curves plotted using the proportions of α-particles with "N<30" and "regular distribution" as classification indicators, respectively. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0040] Based on patent CN119027360A, this invention utilizes platelet α-particle super-resolution imaging technology to further analyze the differences in the proportion of different types of platelets among healthy individuals, patients with benign liver diseases (cirrhosis, hepatic hemangioma), liver cancer patients (hepatocellular carcinoma, cholangiocarcinoma), lung cancer patients, patients with benign pulmonary nodules, and patients with primary or recurrent ovarian cancer. Following the platelet classification method in patent CN115661074A, platelet α-particles are divided into four categories: "regular distribution," "N≤30," "N>30," and "aggregation," and the percentage of each category in the total platelet count is statistically analyzed. The "regular distribution" of α-particles is determined as follows: The search query identifies the ellipse with the smallest area, in which more than 95% of the α-particles are distributed within the α-particle image of a single platelet, as the outer ring. If the area of ​​this outer ring is above a preset threshold for the area of ​​a regularly distributed ellipse and the ratio of its minor axis to its major axis is within the preset range for a regularly distributed ellipse, then the distribution pattern of the α-particles in a single platelet is determined to be "regularly distributed." The preset threshold for the area of ​​a regularly distributed ellipse is within 0.15 μm. 2 20.0μm 2 The ratio of the minor axis to the major axis of the preset rule distribution ellipse is in the range of [0.2, 1].

[0041] The following criteria apply to "N≤30" and "N>30": Referring to Example 1 in patent CN115661074A, for the distribution image of individual platelet α-particles that are "scattered", the number of fluorescent pixel blocks is counted as the number N of α-particles, and they are divided into "N≤30" and "N>30".

[0042] Statistical analysis revealed significant differences in the proportions of platelets with "regularly distributed" alpha particles, "N≤30", "N>30", and "aggregated" alpha particles between cancer patients and healthy individuals. The differences were particularly pronounced in the "regularly distributed" and "N≤30" categories, while the other three categories showed no statistically significant differences between patients and healthy individuals. Further ROC curve analysis showed that using the proportion of platelets with "regularly distributed" or "N≤30" alpha particles as a classification indicator effectively distinguishes between cancer patients and non-cancer subjects, making it a potential biomarker for broad-spectrum cancer screening. In particular, combining the proportion of "regularly distributed" alpha particles with existing clinical indicators AFP and DCP as a classification basis can improve the diagnostic accuracy and sensitivity of existing combined indicators (AFP+DCP) in differentiating early-stage hepatocellular carcinoma from benign liver diseases, thus enabling its use in early-stage hepatocellular carcinoma screening.

[0043] Based on this, the present invention provides a cancer broad-spectrum screening system based on platelet α-particle super-resolution imaging, which includes a broad-spectrum biomarker acquisition module and a cancer broad-spectrum screening module. The broad-spectrum biomarker acquisition module is used to acquire broad-spectrum biomarkers of the subjects and submit them to the cancer broad-spectrum screening module. The broad-spectrum biomarkers include the proportion of platelets with "regular distribution" of α particles and / or the proportion of platelets with "N≤30" of α particles. The cancer broad-spectrum screening module uses the fact that the proportion of platelets with "regular distribution" of alpha particles is positively correlated with the risk of cancer to conduct broad-spectrum screening for high-risk individuals. Alternatively, based on the negative correlation between the proportion of platelets with alpha particles "N≤30" and cancer risk, broad-spectrum screening can be conducted for individuals at high cancer risk.

[0044] The broad-spectrum biomarker was obtained using the following method: Super-resolution images of platelet α-particles from subjects were acquired. Image segmentation was used to divide these images into individual platelets. Platelet α-particle distribution feature data were extracted, and platelets were categorized into four types based on their α-particle structure: "regular distribution," "N≤30," "N>30," and "aggregation." The proportion of platelets with "regular distribution" and / or "N≤30" α-particles was statistically analyzed. For example, the obtained super-resolution fluorescence images of platelet α-particle structure from subjects were used to classify platelets into four categories—"regular distribution," "N≤30," "N>30," and "aggregation"—according to the platelet classification method in patent CN115661074A. Each category was then labeled and counted. This can be done manually by labeling each type and counting the number of platelets in each type, or by using image recognition technology for classification and counting, or by automatically classifying and counting platelets based on the α-particle structure-based platelet classification system in patent CN116958694A. The total number of individual platelets, the number of platelets with "regularly distributed" α particles, and / or the number of platelets with "N≤30" α particles are counted. The proportion of platelets with "regularly distributed" α particles and / or the proportion of platelets with "N≤30" α particles are analyzed and calculated, and then submitted to the cancer broad-spectrum screening module.

[0045] In some embodiments, based on the classification results, the number of platelets with "regular distribution", "N≤30", "N>30", and "aggregation" α-particles is counted, the total number of platelets and the proportion of these four types of platelets are calculated, and the proportion of these four types of platelets is submitted to the screening module; wherein the total number of platelets is the sum of the number of the four types of platelets with "regular distribution", "N≤30", "N>30", and "aggregation" α-particles in the super-resolution fluorescence image of the platelet α-particle structure, and the proportion of "regular distribution" platelets is the number of platelets with "regular distribution" α-particles in the super-resolution fluorescence image of the subject's platelet α-particle structure. The percentage of platelets with "N≤30" is the percentage of platelets with "N≤30" α particles in the super-resolution fluorescence image of the platelet α particle structure of the subject out of the total platelet count; the percentage of platelets with "N>30" is the percentage of platelets with "N>30" α particles in the super-resolution fluorescence image of the platelet α particle structure of the subject out of the total platelet count; the percentage of "aggregated" platelets is the percentage of platelets with "aggregated" α particles in the super-resolution fluorescence image of the platelet α particle structure of the subject out of the total platelet count.

[0046] The cancer broad-spectrum screening module screens patients based on the principle that the higher the proportion of platelets with "regularly distributed" alpha particles or the lower the proportion of platelets with "N≤30" alpha particles, the higher the risk of cancer.

[0047] In some embodiments, the proportion of platelets with a "regularly distributed" α-particle pattern and the proportion of platelets with "N≤30" α-particle patterns are used to determine whether a subject is at high risk for cancer, provided that the proportion of platelets with a "regularly distributed" α-particle pattern is greater than a first preset threshold and / or the proportion of platelets with "N≤30" α-particle patterns is less than a second preset threshold. Preferably, the subject is determined to be at high risk for cancer based on the proportion of platelets with a "regularly distributed" α-particle pattern being greater than the first preset threshold.

[0048] The preset threshold in this invention is determined using the ROC curve method. In some embodiments, the preset threshold is determined according to the following method: ROC curves were plotted using the proportion of platelets with "regular distribution" of α particles as a binary classification index, and the threshold corresponding to the maximum value of the Youden index was used as the first preset threshold. ROC curves were plotted using the proportion of platelets with α particles “N≤30” as a binary classification index, and the threshold corresponding to the maximum value of the Youden index was used as the second preset threshold.

[0049] For example, the ROC curves of liver cancer patients and non-cancer subjects plotted based on the proportion of platelets with a "regular distribution" of alpha particles use the optimal classification threshold as the first preset threshold. Liver cancer patients include hepatocellular carcinoma and / or cholangiocarcinoma, while non-cancer subjects include healthy individuals and / or those with benign liver diseases. The ROC curves of liver cancer patients and non-cancer subjects plotted based on the proportion of platelets with an alpha particle size of "N≤30" use the optimal classification threshold as the second preset threshold. The optimal classification threshold is the classification threshold corresponding to the maximum value of the Youden index. For example, the ROC curves of liver cancer patients and non-cancer subjects plotted based on the proportion of platelets with a "regular distribution" of alpha particles use the proportion of platelets with a "regular distribution" of alpha particles corresponding to the maximum value of the Youden index as the first preset threshold; the ROC curves of liver cancer patients and non-cancer subjects plotted based on the proportion of platelets with an alpha particle size of "N≤30" use the proportion of platelets with an alpha particle size of "N≤30" corresponding to the maximum value of the Youden index as the second preset threshold.

[0050] In some embodiments, the proportion of platelets with a "regular distribution" of α particles is used as a classification index to plot the ROC curve between liver cancer patients (hepatocellular carcinoma and cholangiocarcinoma patients) and non-cancer subjects (healthy individuals and those with benign liver diseases). The proportion of platelets with a "regular distribution" of α particles corresponding to the maximum value of the Youden index is 20%, which is taken as the first preset threshold. If the proportion of platelets with a "regular distribution" of α particles is greater than 20%, the subject is judged to be at high risk of liver cancer.

[0051] In some implementations, the proportion of platelets with a "regular distribution" of alpha particles is used as a classification indicator. ROC curves are plotted between patients with cholangiocarcinoma and healthy individuals. The proportion of platelets with a "regular distribution" of alpha particles corresponding to the maximum value of the Youden index, which is 23.3%, is used as the first preset threshold. If the proportion of platelets with a "regular distribution" of alpha particles is greater than 23.3%, the subject is judged to be at high risk for cholangiocarcinoma.

[0052] In some implementations, the proportion of platelets with a "regular distribution" of alpha particles is used as a classification indicator. ROC curves are plotted between lung cancer patients and healthy individuals. The proportion of platelets with a "regular distribution" of alpha particles corresponding to the maximum value of the Youden index, which is 19.41%, is used as the first preset threshold. If the proportion of platelets with a "regular distribution" of alpha particles is greater than 19.41%, the subject is judged to be at high risk of lung cancer.

[0053] This confirms that the proportion of platelets with a "regularly distributed" alpha particle pattern can serve as a broad-spectrum biomarker for cancer screening. Specifically, individuals with a alpha particle pattern greater than 23.3% of their platelets can be identified as high-risk for cancer, allowing for broad-spectrum screening of these high-risk individuals. The cancers targeted for this broad-spectrum screening include hepatocellular carcinoma, cholangiocarcinoma, lung cancer, and ovarian cancer.

[0054] In some embodiments, the proportion of platelets with α-particle size "N≤30" is used as a classification index to plot the ROC curve between liver cancer patients (hepatocellular carcinoma and cholangiocarcinoma patients) and non-cancer subjects (healthy subjects and those with benign liver disease). The proportion of platelets with α-particle size "N≤30" corresponding to the maximum value of Youden's index is 76.5%, which is used as the second preset threshold. If the proportion of platelets with α-particle size "N≤30" is less than 76.5%, the subject is judged to be at high risk of liver cancer.

[0055] In addition, the present invention also provides an early liver cancer screening system, which includes a biomarker acquisition module, a biomarker analysis module and a liver cancer screening module; The biomarker acquisition module is used to acquire biomarker data from the subjects and submit it to the biomarker analysis module; the biomarker data includes the proportion of platelets with "regular distribution" of α particles X1, the content of AFP X2, and the content of DCP X3; The biomarker analysis module inputs the acquired biomarker data into a multivariate logistic regression model to calculate joint factors and submits them to the liver cancer screening module. The liver cancer screening module determines the risk of liver cancer based on the principle that the higher the proportion of platelets with "regular distribution" of α particles (X1), the higher the risk, and the higher the content of AFP (X2) and DCP (X3), the higher the risk.

[0056] In some embodiments, the biomarker analysis module is used to obtain the proportion of platelets with "regular distribution" of α particles X1, the content of AFP X2 and the content of DCP X3 of the subject, as biomarker data, and submit them to the biomarker analysis module. The biomarker analysis module calculates the joint factor according to the multivariate logistic regression model y=k1X1+k2X2+k3X3+b, where k1~k3 are regression coefficients, all of which are greater than 0; and submits the result to the liver cancer screening module. In some embodiments, the proportion of platelets with "regular distribution" of α particles and the clinical indicators AFP and DCP are combined as new joint factors for judgment to distinguish between benign liver diseases and early hepatocellular carcinoma.

[0057] The liver cancer screening module screens for early-stage hepatocellular carcinoma according to the principle that the higher the combined factor, the higher the risk of hepatocellular carcinoma.

[0058] In some embodiments, if the combined factor is greater than a third preset threshold, the subject is determined to be at high risk of early-stage hepatocellular carcinoma; if the combined factor is less than or equal to the third preset threshold, the subject is determined to have benign liver disease.

[0059] The multivariate logistic regression model is obtained as follows: According to the inclusion and exclusion criteria, the proportion of platelets with a "regular distribution" of α-granules, and the levels of AFP and DCP were collected from patients with early-stage hepatocellular carcinoma and benign liver diseases. A multivariate logistic regression analysis was performed using the proportion of platelets with a "regular distribution" of α-granules, AFP, and DCP as three independent variables. The resulting multivariate logistic regression equation was y = k1X1 + k2X2 + k3X3 + b, where y is the consortium factor, k1~k3 are the multivariate logistic regression coefficients, b is a constant, X1 is the proportion of platelets with a "regular distribution" of α-granules (%), X2 is the "AFP value," and X3 is the "DCP value."

[0060] The benign liver diseases include cirrhosis and hepatic hemangioma. In some embodiments, the multivariate logistic regression model is y=0.052X1+0.019X2+0.044X3-2.282.

[0061] The preset threshold in this invention is determined using the ROC curve method. In some embodiments, the preset threshold is determined according to the following method: ROC curves were plotted using the joint factor y as a binary classification index, and the threshold corresponding to the maximum value of its Youden index was used as the third preset threshold. In some implementations, the joint factor y is used as a classification index to plot ROC curves for patients with early-stage hepatocellular carcinoma and subjects with benign liver disease. The y-value corresponding to the maximum value of the Youden index is used as a third preset threshold. In some embodiments, the joint factor y is used as a classification index to plot ROC curves for patients with early-stage hepatocellular carcinoma and subjects with benign liver disease. The third preset threshold corresponding to the maximum value of the Youden index is 0.91. Subjects with a joint factor y greater than 0.91 are judged to be at high risk for early-stage hepatocellular carcinoma, and those with a joint factor y less than or equal to 0.91 are judged to have benign liver disease. The benign liver disease patients include patients with cirrhosis and patients with hepatic hemangioma.

[0062] The following are examples. The inclusion and exclusion criteria for the following subjects are as follows: ① Patients with liver cancer and benign liver diseases: Histologically confirmed malignant tumors or benign diseases (cirrhosis, hepatic hemangioma), who have not undergone surgery, chemotherapy or radiotherapy before blood collection, and have no history of other tumors in the past five years; among them, patients with benign liver diseases include patients with cirrhosis and hepatic hemangioma, and patients with liver cancer include patients with hepatocellular carcinoma and cholangiocarcinoma. The assessment criteria for early hepatocellular carcinoma is BCLC stage 0 / A.

[0063] ② Healthy subjects: blood donors who had no history of cancer at the time of or before blood collection and who had not taken antiplatelet drugs within two weeks prior to blood collection.

[0064] Example 1: Screening for Liver Cancer Subjects meeting the inclusion and exclusion criteria were included. A training set consisted of 95 liver cancer patients (83 hepatocellular carcinoma patients and 12 cholangiocarcinoma patients) and 114 non-cancer subjects (92 healthy individuals and 22 patients with benign liver disease). A validation set consisted of another group of 95 liver cancer patients (83 hepatocellular carcinoma patients and 12 cholangiocarcinoma patients) and 114 non-cancer subjects (91 healthy individuals and 23 patients with benign liver disease). Blood samples from both non-cancer subjects (healthy individuals and those with benign liver disease) and liver cancer patients (hepatocellular carcinoma and cholangiocarcinoma patients) were used for analysis. Super-resolution microscopy was employed to obtain super-resolution fluorescence images of platelet α-granules for each subject, as detailed below: (1) Obtaining super-resolution fluorescence images of platelet α-granules (1-1) Platelet sample preparation: First, 2-4 mL of whole blood containing EDTA-K2 anticoagulation was collected from the subjects and centrifuged at 200g for 12 min at room temperature to obtain platelet-rich plasma supernatant. The supernatant was then transferred to centrifuge tubes, and ACDT solution was added to prevent blood coagulation. The tubes were then incubated at 37℃ in a 5% CO2 incubator for 2 hours. After 2 hours, an equal volume of fixative was added to the centrifuge tubes to maintain platelet morphology and structure, and the tubes were incubated for 30 min. The plates were then washed three times by centrifugation at room temperature (1500g, 3 min each time), and finally resuspended in 1 mL of PBS solution.

[0065] (1-2) Immunochemical staining of α-granule structure: ① Platelet plating: Platelets are plated into a confocal dish treated with poly-L-lysine. After the platelet density reaches approximately 80%, the dish is rinsed three times with PBS.

[0066] ② Punching: Add 0.2 mL of 0.2% Triton X-100 solution to the petri dish and treat for 10 min.

[0067] ③ Blocking: Add 0.2 mL of blocking solution (containing 10% goat serum (NAS), 1% bovine serum albumin (BSA), 0.05% Triton X-100 and 0.05% Proclin 300, all diluted with PBS) to the culture dish and treat for 60 min.

[0068] ④ Primary antibody labeling: The α-particle primary antibody was diluted 1:1000 in the blocking buffer. The α-particle primary antibody was commercially available as Polyclonal Rabbit Anti-Human Von Willebrand Factor, catalog number A0082, brand name Dako. The blocking buffer contained 10% goat serum (NAS), 1% bovine serum albumin (BSA), 0.05% Triton X-100, and 0.05% Proclin 300, all diluted with PBS. Add 0.2 mL of the primary antibody dilution buffer to the culture dish and incubate overnight at 4°C.

[0069] ⑤ Primary antibody rinsing: Aspirate the primary antibody dilution solution from the culture dish, add washing buffer, and gently rinse on a shaker at room temperature for 5 minutes. Repeat 5 times. The washing buffer is phosphate buffered saline (PBS) containing 0.1% Tween 20.

[0070] ⑥ Secondary antibody labeling: The secondary antibody was diluted 1:500 into the blocking buffer. The α-particle secondary antibody was commercially available as Goat Anti-Rabbit IgG H&L (Alexa Fluor® 488), catalog number ab150077, brand name Abcam. 0.2 mL of the secondary antibody dilution buffer was added to the culture dish and incubated at room temperature for 1 hour.

[0071] ⑦ Secondary antibody rinsing: Aspirate the secondary antibody dilution solution from the culture dish, add antibody washing solution, place on a shaker and gently rinse at room temperature for 5 minutes. Repeat 5 times.

[0072] ⑧ Antibody fixation: Add 0.2 mL of 4% PFA solution to the culture dish and treat for 10 min.

[0073] ⑨ Rinse: Add 0.2 mL of PBS solution to the petri dish and gently rinse at room temperature for 5 min on a shaker. Repeat 3 times.

[0074] (1-3) Super-resolution fluorescence imaging of α-particle structure: The fluorescence intensity and exposure time were adjusted for each sample to ensure image quality. Super-resolution microscopy techniques (SIM, STED, STORM, etc.) were used to image platelets, obtaining at least 50 high-resolution fluorescence images of platelet subcellular structures for each sample, with a total platelet count exceeding 500. Representative super-resolution fluorescence images of platelet subcellular structures (α-particles) are shown below. Figure 1 As shown.

[0075] (2) Automated classification and statistical analysis of α-particle structure: The obtained super-resolution fluorescence images are input into the Res-UNet and ResNet-50 algorithm models for rapid classification, referring to the platelet classification system based on α-particle structure in patent CN116958694A, as follows; Digital bright-field images and corresponding digital fluorescence images of platelet cells with submicroscopic structure of platelet α-granules were obtained. The digital fluorescence images were then subjected to grayscale normalization and pixel size interpolation alignment to obtain microscopic images of individual platelet α-granules. Based on the platelet α-granule structure, and following the platelet classification in patent CN115661074A, individual platelets are divided into four categories: "regular distribution," "N≤30," "N>30," and "aggregation." The outer circle is defined as the ellipse within the α-granule image of a single platelet that satisfies condition A and has the smallest area. If the area of ​​this outer circle is above a preset threshold for the area of ​​a regular distribution ellipse and the ratio of its minor axis to its major axis is within a preset range for a regular distribution ellipse, then the platelets involved in the single platelet α-granule distribution image are classified as "regularly distributed." The threshold for the area of ​​a regular distribution ellipse is between 0.15 μm² and 20.0 μm². The range of the regular distribution ellipse is [0.2, 1]. Similarly, the outer circle is defined as the ellipse within the α-granule image of a single platelet that satisfies condition A and has the smallest area. If the area of ​​this outer circle is below a threshold for the area of ​​an aggregated distribution ellipse, then the platelets involved in the single platelet α-granule distribution image are classified as "aggregated." The threshold for the area of ​​an aggregated distribution ellipse is within 0.02 μm². 2 -7.5μm 2 Between; the area threshold of the regular distribution ellipse is greater than or equal to the area threshold of the clustered distribution ellipse. If the α-particle image within a single platelet does not belong to "regular distribution" or "clustered distribution", it is judged as "scattered distribution". For the distribution image of a single platelet α-particle in "scattered distribution", the number of fluorescent pixel blocks is counted as the number of α-particles N, and further divided into platelets with α-particles "N≤30" and platelets with α-particles "N>30". Typical images of these four categories are shown in the figure. Figure 2 As shown, the number of platelets in these four categories and the total number of platelets are counted, and the proportion of each type of platelet is calculated.

[0076] In this embodiment, a statistical comparative analysis was conducted to determine the significant differences in the percentages of different platelet types among 183 healthy individuals, 45 patients with benign liver disease, and 190 patients with liver cancer. The results are as follows: Figure 3 As shown. By Figure 3The results showed significant differences in the proportions of platelets with "regular distribution," "N≤30," "N>30," and "aggregation" alpha granules between liver cancer patients and healthy individuals, particularly in the "regular distribution" and "N≤30" types. However, there was a significant difference in the proportion of platelets with "N>30" between benign liver disease patients and healthy individuals, while the other three types showed no statistically significant differences. It is speculated that the proportions of "regular distribution," "N≤30," and "aggregation" platelets are potential biomarkers for distinguishing between liver cancer and non-cancer subjects (healthy individuals and those with benign liver disease). Generally, the greater the difference in an indicator, the more likely it is to become a clinical indicator. Considering the varying degrees of significance of differences in the proportions of different platelet types between liver cancer patients and healthy individuals, our analysis confirmed that the proportions of "regular distribution" and "N≤30" platelets with alpha granules, which showed the most significant differences, were more effective than the proportion of "aggregated" platelets. Therefore, the proportion of platelets with "regular distribution" of α-particles and "N≤30" was selected as two indicators to evaluate diagnostic efficacy in the later stage.

[0077] To evaluate the diagnostic efficacy of the proportions of these two platelet classes in differentiating between non-cancer subjects and liver cancer patients, we performed receiver operating characteristic (ROC) curve analysis to evaluate its effectiveness in classifying or diagnosing the two groups of test subjects (e.g., non-cancer subjects and cancer patients), and to find the optimal threshold value for the indicator, thereby determining the sensitivity and specificity of the evaluation indicator. Sensitivity refers to the ability of a test or diagnostic test to correctly identify individuals who are actually infected, also known as the true positive rate; its formula is: Sensitivity = True Positive (TP) / (True Positive (TP) + False Negative (FN)). Higher sensitivity indicates higher accuracy of the test method in identifying infected individuals and a lower possibility of missed diagnoses. Specificity refers to the ability of a test or diagnostic test to correctly identify individuals who are not actually infected, also known as the true negative rate; its formula is: Specificity = True Negative (TP) / (True Negative (TP) + False Positive (FN)). Higher specificity indicates higher accuracy of the test method in identifying uninfected individuals and a lower possibility of misdiagnosis.

[0078] In this embodiment, the proportion of platelets with "regularly distributed" α-particles and the proportion of platelets with "N≤30" α-particles were used as classification indicators. The diagnostic accuracy of these two indicators in differentiating between liver cancer patients and non-cancer subjects (including healthy individuals and patients with liver disease) was analyzed in both the training and validation sets. The training set consisted of 95 liver cancer patients and 114 non-cancer subjects (92 healthy individuals and 22 patients with benign liver disease); the validation set consisted of 95 liver cancer patients and 114 non-cancer subjects (91 healthy individuals and 23 patients with benign liver disease). The ROC curve analysis results based on the proportion of platelets with "regularly distributed" α-particles are shown below. Figure 4 As shown, the ROC curve analysis results, based on the proportion of platelets with α-particle "N≤30", were used to assess its ability to differentiate liver cancer. Figure 5 As shown, it was used to assess its ability to differentiate liver cancer.

[0079] Depend on Figure 4 The results show that, using the proportion of platelets with a "regularly distributed" alpha granule pattern as the diagnostic indicator, the AUC corresponding to the ROC curve in the training set was 0.809 (95% CI 0.746-0.873), and the AUC corresponding to the ROC curve in the validation set was 0.823 (95% CI 0.762-0.885), both above 0.8. A larger AUC indicates greater diagnostic efficacy, with an AUC between 0.7 and 0.9 indicating high diagnostic accuracy. Therefore, the proportion of platelets with a "regularly distributed" alpha granule pattern can effectively distinguish between liver cancer patients and non-cancer subjects, and can be used as a biochemical indicator for liver cancer screening.

[0080] The Youden Index (YOI) is a statistical measure commonly used to select the optimal classification threshold. It is defined as Youden Index = Sensitivity + Specificity - 1. The optimal classification threshold is the proportion of platelets with a "regularly distributed" alpha particle pattern corresponding to the maximum value of the YOI. In the validation set, the optimal classification threshold is 20%, corresponding to a sensitivity of 80.0% and a specificity of 78.9%. That is, if the proportion of platelets with a "regularly distributed" alpha particle pattern exceeds 20%, it can be classified as liver cancer, and if the proportion is less than 20%, it can be classified as non-cancer subjects.

[0081] Depend on Figure 5The results show that using the proportion of platelets with "N≤30" α-granules as the classification index, the AUC corresponding to the ROC curve in the training set is 0.797 (95% CI 0.737-0.856), and the AUC corresponding to the ROC curve in the validation set is 0.791 (95% CI 0.730-0.852), both above 0.75. This demonstrates a high diagnostic performance in distinguishing between non-cancer subjects and liver cancer patients. Using the proportion of platelets with "N≤30" α-granules corresponding to the maximum Youden index as the optimal classification threshold, the optimal classification threshold in the validation set is 76.5%, corresponding to a sensitivity of 84.2% and a specificity of 65.8%. That is, platelets with "N≤30" α-granules accounting for less than 76.5% can be classified as liver cancer, while those accounting for more than 76.5% can be classified as non-cancer subjects.

[0082] The above results indicate that the proportion of platelets with "regularly distributed" α-particles and the proportion of platelets with "N≤30" are both good tumor markers for liver cancer screening, with the proportion of platelets with "regularly distributed" α-particles being the preferred indicator.

[0083] Example 2: Screening for early-stage hepatocellular carcinoma A different group of subjects, distinct from those in Example 1, were included according to the inclusion and exclusion criteria. This group comprised 228 non-cancer patients (183 healthy individuals and 45 patients with benign liver disease) and 136 patients with early-stage hepatocellular carcinoma. They were randomly assigned to two groups: a training set of 66 patients with early-stage hepatocellular carcinoma and 114 non-cancer subjects (92 healthy individuals and 22 patients with liver disease), and a validation set of 70 patients with early-stage hepatocellular carcinoma and 114 non-cancer subjects (91 healthy individuals and 23 patients with liver disease). Blood samples from both non-cancer subjects (healthy individuals and those with benign liver disease) and early-stage hepatocellular carcinoma patients were analyzed. Super-resolution fluorescence images of platelet α-granules were obtained for each subject using super-resolution microscopy, as detailed below. (1) Obtain super-resolution fluorescence images of platelet α particles: Same as in Example 1.

[0084] (2) Automated classification and statistical analysis of α-particle structure: Platelet classification was the same as in Example 1. After classifying platelets according to the distribution pattern of α-particles, statistical analysis was performed. The analysis results are as follows: Figure 6 As shown.

[0085] Depend on Figure 6The results showed significant differences in the proportions of platelets with "regularly distributed" α-particles, "N≤30", "N>30", and "aggregated" α-particle types between early-stage hepatocellular carcinoma patients and healthy individuals, especially in the "regularly distributed" type. However, there was a significant difference in the proportion of platelets with "N>30" type between benign liver disease patients and healthy individuals, while the other three types showed no statistically significant differences. The results of Example 1 indicate that the proportion of platelets with "regularly distributed" α-particles can serve as a biomarker for liver cancer screening, and hepatocellular carcinoma is a common pathological type of liver cancer.

[0086] To verify whether this indicator can further distinguish between patients with early-stage hepatocellular carcinoma and healthy individuals, this embodiment uses the proportion of platelets with "regularly distributed" α-granules as the criterion. The diagnostic accuracy of this indicator in differentiating between patients with early-stage hepatocellular carcinoma and non-cancer subjects (including healthy individuals and patients with benign liver disease) is analyzed in both the training and validation sets. The training set included 66 patients with early-stage hepatocellular carcinoma and 114 non-cancer subjects (92 healthy individuals and 22 patients with benign liver disease); the validation set included 70 patients with early-stage hepatocellular carcinoma and 114 non-cancer subjects (91 healthy individuals and 23 patients with benign liver disease). The ROC curve based on the proportion of platelets with "regularly distributed" α-granules is shown below. Figure 7 As shown, it was used to assess its ability to differentiate early-stage hepatocellular carcinoma.

[0087] Depend on Figure 7 The results show that, using the proportion of platelets with "regularly distributed" α-particles as the criterion, the AUC corresponding to its ROC curve in the training set was 0.794 (95% CI 0.718-0.870), and the AUC corresponding to its ROC curve in the validation set was 0.837 (95% CI 0.773-0.901). This indicates that the proportion of platelets with "regularly distributed" α-particles can distinguish between non-cancer subjects and early-stage hepatocellular carcinoma patients with high diagnostic performance.

[0088] The optimal classification threshold was determined by the proportion of platelets with a "regularly distributed" α-particle pattern corresponding to the maximum value of the Youden index. In the validation set, the optimal classification threshold was 20%, with a sensitivity of 81.4% and a specificity of 78.9%. That is, if the proportion of platelets with a "regularly distributed" α-particle pattern exceeds 20%, the patient can be classified as having early-stage hepatocellular carcinoma, and if the proportion is less than 20%, the patient can be classified as a non-cancer subject.

[0089] Furthermore, considering the insufficient sensitivity of existing clinical tumor markers alpha-fetoprotein (AFP) and abnormal prothrombin, also known as de-γ-carboxyprothrombin (DCP), in early hepatocellular carcinoma screening, we investigated whether the proportion of platelets with "regularly distributed" α-granules could be combined with existing clinical tumor markers (AFP and DCP) for early hepatocellular carcinoma screening, and its impact on the diagnostic performance of existing clinical tumor markers (AFP and DCP).

[0090] First, we categorized 136 patients with early-stage hepatocellular carcinoma into four groups based on their clinical biomarker status: AFP(-), AFP(+), DCP(-), and DCP(+). The classification criteria were based on thresholds set by clinical testing. An AFP level above 7 ng / mL was considered AFP-positive (AFP(+)), otherwise AFP-negative (AFP(-)). Similarly, a DCP level above 40 ng / mL was considered DCP-positive (DCP(+)), otherwise DCP-negative (DCP(-)).

[0091] Next, the proportion of platelets with "regularly distributed" α-granules was analyzed in four categories of early-stage hepatocellular carcinoma patients (AFP(-), AFP(+), DCP(-), and DCP(+)) and subjects with benign liver disease. The results are as follows: Figure 8 As shown, the diagnostic sensitivity of platelets with a "regularly distributed" alpha particle pattern was further evaluated in different subtypes of hepatocellular carcinoma. In early-stage hepatocellular carcinoma patients, the black dashed line represents a threshold of 20% for platelets with a "regularly distributed" alpha particle pattern. Platelets above this threshold were considered early-stage hepatocellular carcinoma, meaning the alpha particle indicator was detected; those below this threshold were considered non-cancerous, meaning the alpha particle indicator was missed.

[0092] The sensitivity of the platelet percentage based on the "regular distribution" of α-granules in patients with early-stage hepatocellular carcinoma (AFP-), AFP-), DCP-), and DCP-), as shown by the following data, is as follows: Figure 9 As shown. By Figure 9 The analysis results show that the proportion of platelets with a "regularly distributed" alpha granule pattern has a sensitivity higher than 75% in all four categories, indicating high sensitivity. It is particularly noteworthy that the sensitivity of this indicator, the proportion of platelets with a "regularly distributed" alpha granule pattern, is higher than 80% in both AFP(-) and DCP(-), while in this embodiment, the sensitivity of alpha-fetoprotein (AFP) in early-stage hepatocellular carcinoma is 58%, demonstrating that this indicator can effectively compensate for the deficiencies of existing clinical indicators.

[0093] Meanwhile, to further evaluate the screening performance of the combined α-particle "regularly distributed" platelet count and existing clinical indicators AFP and de-γ-carboxyprothrombin (DCP) for early hepatocellular carcinoma, this combined indicator is abbreviated as α-particle + AFP + DCP. This embodiment uses the existing clinical combined indicator AFP + DCP as a control to explore whether the combined indicator α-particle + AFP + DCP can improve the screening performance for early hepatocellular carcinoma, as detailed below: A binary logistic regression analysis was performed on the three independent variables: the proportion of platelets with "regularly distributed" α particles, AFP, and DCP. The resulting binary logistic regression equation is: y1 = 0.052X1 + 0.019X2 + 0.044X3 - 2.282; where y1 is the "joint factor", X1 is the proportion of platelets with "regularly distributed" α particles (%), X2 is the "AFP value", and X3 is the "DCP value".

[0094] A binary logistic regression analysis was performed on the two independent variables, AFP and DCP, and a binary logistic regression equation was obtained: y2=0.012z1 +0.042z2–6.39; where y2 is the "joint factor", z1 is the "AFP value" and z2 is the "DCP value".

[0095] Based on a sample of 136 patients with early-stage hepatocellular carcinoma and 45 patients with benign liver disease, ROC curves for the combined diagnosis of α-granules + AFP + DCP and the dual diagnosis of AFP + DCP were plotted using the new combined factor y1 and the existing combined factor y2, respectively. Figure 10 As shown, this is used to assess the ability of combined indicators to differentiate early-stage hepatocellular carcinoma.

[0096] Depend on Figure 10The results showed that the AUC of diagnosing early hepatocellular carcinoma using the existing dual indicators of AFP and DCP was 0.915 (95% CI 0.865-0.964), with a sensitivity of 73.8% and a specificity of 100%. In contrast, the AUC of diagnosing early hepatocellular carcinoma using the combined indicators of α-granules, AFP, and DCP was 0.949 (95% CI 0.913-0.985), with a sensitivity of 81.1% and a specificity of 100%. The AUC increased from 0.915 to 0.949, and the sensitivity increased from 73.8% to 81.1%, a 10% improvement. This indicates that the proportion of platelets with "regularly distributed" α-granules has a good complementary effect with existing clinical indicators, and the combined use of these three indicators can significantly improve the diagnostic performance and sensitivity of early hepatocellular carcinoma. Based on the ROC curve of the combined diagnosis of α-particles, AFP, and DCP, the optimal classification threshold is the joint factor y1 value corresponding to the maximum value of Youden's index. The optimal classification threshold is 0.92. That is, if the joint factor y1 is greater than 0.92, it can be judged as early hepatocellular carcinoma, and if it is less than 0.92, it can be judged as benign liver disease.

[0097] Example 3: Screening for cholangiocarcinoma A different group of subjects from Example 1, who met the inclusion and exclusion criteria, were included, comprising 183 healthy individuals and 24 patients with cholangiocarcinoma. These subjects were randomly assigned to a training set and a validation set. Blood samples from both healthy individuals and cholangiocarcinoma patients were used for analysis. Super-resolution fluorescence images of platelet α-particles were obtained for each subject using super-resolution microscopy, as detailed below: (1) Obtain super-resolution fluorescence images of platelet α particles: Same as in Example 1.

[0098] (2) Automated classification and statistical analysis of α-particle structure: Platelet classification was the same as in Example 1. After classifying platelets according to the distribution pattern of α-particles, the proportion of each type of platelet was statistically analyzed, and the results are as follows. Figure 11 As shown.

[0099] Depend on Figure 11 The results showed significant differences in the proportions of platelets with "regularly distributed" α-particles, "N≤30", "N>30", and "aggregated" α-particle types between patients with cholangiocarcinoma and healthy individuals, especially in the "regularly distributed" type. The results of Example 1 indicate that the proportion of platelets with "regularly distributed" α-particles can serve as a biomarker for screening liver cancer, and cholangiocarcinoma is another pathological type of liver cancer.

[0100] To verify whether this indicator can further distinguish between patients with cholangiocarcinoma and healthy individuals, this embodiment uses the proportion of platelets with "regularly distributed" α-granules as the criterion. Based on data from healthy individuals and patients with cholangiocarcinoma in the validation set, the diagnostic accuracy of this indicator in differentiating between patients with cholangiocarcinoma and healthy individuals is analyzed. The ROC curve results are as follows: Figure 12 As shown, it was used to assess its ability to differentiate cholangiocarcinoma.

[0101] Depend on Figure 12 The results show that the AUC corresponding to the ROC curve in the validation set is 0.812 > 0.7, indicating that using the proportion of platelets with "regularly distributed" α-particles as the diagnostic indicator has high diagnostic performance in distinguishing between healthy individuals and patients with cholangiocarcinoma. In the validation set, the optimal classification threshold is the proportion of platelets with "regularly distributed" α-particles corresponding to the maximum value of the Youden index, which is 23.3%. The corresponding sensitivity is 79.2% and the specificity is 85.2%. That is, if the proportion of platelets with "regularly distributed" α-particles exceeds 23.3%, it is considered cholangiocarcinoma.

[0102] Example 4: Lung Cancer Screening The inclusion and exclusion criteria for subjects are as follows: ① Lung cancer patients: Patients whose pathology confirms malignant tumors through tissue biopsy, and who have not undergone surgery, chemotherapy or radiotherapy before blood collection, and have no other tumor history in the past five years; the evaluation criteria for early lung cancer are tissue biopsies obtained by puncture or surgery, and patients classified as stage IA or IB according to TNM.

[0103] ② Patients with benign pulmonary nodules: Patients with pulmonary nodules confirmed by tissue biopsy but without lung cancer, and with no history of tumors in the past five years; ③ Healthy subjects: Healthy individuals who have no prior history of cancer at the time of or before blood collection and who have not taken antiplatelet drugs within two weeks prior to blood collection.

[0104] Subjects meeting the inclusion and exclusion criteria were included. A training set consisted of 42 lung cancer patients and 96 non-cancer subjects (91 healthy individuals and 5 patients with benign pulmonary nodules), while a validation set consisted of another group of 41 lung cancer patients and 96 non-cancer subjects (92 healthy individuals and 4 patients with benign pulmonary nodules). Blood samples from both non-cancer subjects (healthy individuals and patients with benign pulmonary nodules) and lung cancer patients were used for analysis. Super-resolution microscopy was employed to obtain super-resolution fluorescence images of platelet α-particles for each subject, as detailed below: (1) Platelet preparation, staining and super-resolution imaging (1-1) Platelet sample preparation: Platelets were extracted from whole blood samples of the subjects. 2-4 mL of whole blood containing EDTA-K2 anticoagulation was centrifuged at 200 g for 12 min at room temperature to obtain platelet-rich supernatant. The supernatant was then transferred to centrifuge tubes and ACDT solution was added. The tubes were then incubated at 37 °C in a 5% CO2 incubator for 2 h to allow the platelets to recover.

[0105] (1-2) Platelet fixation: After 2 hours, add an equal volume of fixative (commercially available 8% paraformaldehyde dissolved in commercially available PHEM buffer) to the centrifuge tube and let it stand for 30 minutes. Then wash three times with 1 mL PBS solution at 1500 g for 3 minutes, and finally resuspend the platelets in 1 mL PBS solution.

[0106] (1-3) Immunochemical staining of platelet α-granules: ① Platelet plating: Platelets are plated into a confocal dish treated with poly-L-lysine. After the platelet density reaches approximately 80%, the dish is rinsed three times with PBS.

[0107] ② Punching: Add 0.2 mL of 0.2% Triton X-100 solution to the culture dish containing platelets and let it stand at room temperature for 10 min.

[0108] ③ Blocking: Add 0.2 mL of blocking solution (commercially available, Beyotime Biotechnology, P0102) to the culture dish and let it stand at room temperature for 60 min.

[0109] ④ Primary antibody labeling: Dilute the α-particle primary antibody (commercially available, Beyotime Biotechnology, AG8775) at a ratio of 1:1000 with blocking buffer. Add 0.2 mL of the primary antibody dilution buffer to a culture dish and incubate overnight at 4°C.

[0110] ⑤ Primary antibody washing: Aspirate the primary antibody dilution solution from the culture dish, add antibody washing buffer (0.1% commercially available Tween 20 solution dissolved in PBS), place on a shaker and gently wash at room temperature for 5 minutes, repeat 5 times.

[0111] ⑥ Secondary antibody labeling: Dilute the secondary antibody (commercially available, Beyotime Biotechnology, P0188) at a ratio of 1:500 in blocking buffer (commercially available, Beyotime Biotechnology, P0102). Add 0.2 mL of the secondary antibody dilution buffer to the culture dish and incubate at room temperature for 1 h.

[0112] ⑦ Secondary antibody rinsing: Aspirate the secondary antibody dilution solution from the culture dish, add antibody washing solution (0.1% commercially available Tween 20 solution dissolved in PBS), place on a shaker and gently rinse at room temperature for 5 minutes, repeat 5 times.

[0113] ⑧ Antibody fixation: Add 0.2 mL of 4% PFA solution to a culture dish and let it stand at room temperature for 10 min.

[0114] 9. Rinse: Add 0.2 mL of PBS solution to the petri dish and gently rinse at room temperature for 5 min on a shaker. Repeat 3 times.

[0115] (1-4) Super-resolution imaging of platelet α-granules: Fluorescence intensity and exposure time were adjusted according to the sample to ensure a high signal-to-noise ratio in the fluorescence images. Super-resolution microscopy techniques (SIM, STED, STORM, etc.) were used to image platelets, obtaining at least 50 high-resolution fluorescence images of platelet subcellular structures for each sample, with a total number of platelets exceeding 500. Representative super-resolution fluorescence images of platelet subcellular structures are shown below. Figure 13 As shown.

[0116] (2) Automated classification and statistical analysis of platelets: The obtained super-resolution fluorescence images are input into the Res-UNet and ResNet-50 algorithm models for rapid classification, referring to the platelet classification system based on α-granule structure in patent CN116958694A, as follows; Digital bright-field images and corresponding digital fluorescence images of platelet cells with submicroscopic structure of platelet α-granules were obtained. The digital fluorescence images were then subjected to grayscale normalization and pixel size interpolation alignment to obtain microscopic images of individual platelet α-granules. Based on the α-granule structure of platelets, and following the platelet classification in patent CN115661074A, individual platelets are divided into four categories: "regular distribution", "N < 30", "N ≥ 30", and "aggregation". Typical diagrams of these four categories are shown below. Figure 14 As shown; the number of platelets in these four categories was counted, and the percentage of each type of platelet was calculated. Subsequently, a statistical comparative analysis was conducted to determine the significant differences in the percentages of different platelet types among 183 healthy individuals, 9 patients with benign pulmonary nodules, and 83 patients with lung cancer. The results are as follows. Figure 15 As shown.

[0117] Depend on Figure 15The results showed no significant differences in platelet count among patients with benign pulmonary nodules and healthy individuals across all four platelet types. However, compared to non-cancer subjects (patients with benign pulmonary nodules and healthy individuals), the percentages of platelets with "regularly distributed" alpha granules and "N < 30" were significantly different in lung cancer patients. Specifically, the proportion of platelets with "regularly distributed" alpha granules was significantly higher in lung cancer patients, while the proportion of platelets with "N < 30" was significantly lower, and the proportion of platelets with "N ≥ 30" did not show a statistically significant difference. Compared to healthy individuals, the percentage of platelets with "aggregated" alpha granules was significantly lower in lung cancer patients, but this percentage did not show a statistically significant difference compared to patients with benign pulmonary nodules. Therefore, the proportions of platelets with "regularly distributed" alpha granules and "N < 30" will be selected separately for diagnostic efficacy assessment in future studies.

[0118] In this embodiment, the proportion of platelets with "regularly distributed" α-particles and the proportion of platelets with "N < 30" α-particles were used as classification indicators. The diagnostic efficacy and accuracy of these two indicators in differentiating lung cancer patients from non-cancer subjects were analyzed on both the training and validation sets. The ROC curve analysis results using the proportion of platelets with "regularly distributed" α-particles as the classification indicator are shown below. Figure 16 As shown, the ROC curve analysis results, using the proportion of platelets with α-particle "N<30" as the classification index, are as follows: Figure 17 As shown.

[0119] Depend on Figure 16 The results show that, using the proportion of platelets with "regularly distributed" α-particles as the classification index, the AUC value corresponding to its ROC curve in the training set is 0.722 (95% CI 0.623-0.822), and the AUC value corresponding to its ROC curve in the validation set is 0.737 (95% CI 0.632-0.842). The AUC values ​​are all greater than 0.7, indicating that this index has good diagnostic performance for lung cancer and can effectively distinguish between lung cancer patients and non-cancerous subjects. It can be used as a biochemical index for lung cancer screening.

[0120] The Youden Index is a statistical measure commonly used to select the optimal classification threshold. It is defined as Youden Index = Sensitivity + Specificity - 1. The optimal classification threshold is the proportion of platelets with a "regularly distributed" alpha particle pattern corresponding to the maximum value of the Youden Index. In the validation set, the optimal classification threshold is 19.41%, corresponding to a sensitivity of 61.0% and a specificity of 90.6%. In other words, a proportion of platelets with a "regularly distributed" alpha particle pattern exceeding 19.41% can be considered high-risk for lung cancer, while a proportion below 19.41% can be considered low-risk.

[0121] Depend on Figure 17 The results show that, using the proportion of platelets with α-particles "N < 30" as the classification index, the AUC values ​​corresponding to the ROC curves in the training and validation sets are 0.688 (95% CI 0.590-0.786) and 0.738 (95% CI 0.645-0.830), respectively. In the validation set, the optimal classification threshold is 81.7%, corresponding to a sensitivity of 64.6% and a specificity of 70.7%. In other words, a proportion of platelets with "N < 30" α-particles below 81.7% can be considered high-risk for lung cancer, while a proportion above 81.7% can be considered low-risk.

[0122] Furthermore, subjects meeting the inclusion and exclusion criteria were included. A training set consisted of 28 early-stage lung cancer patients and 96 non-cancer subjects (92 healthy individuals and 4 patients with benign pulmonary nodules), while a validation set consisted of another group of 28 early-stage lung cancer patients and 96 non-cancer subjects (91 healthy individuals and 5 patients with benign pulmonary nodules). Blood samples from non-cancer subjects (healthy individuals and patients with benign pulmonary nodules) and lung cancer patients were used for testing. Super-resolution microscopy was employed to obtain super-resolution fluorescence images of platelet α-particles for each subject. Based on the platelet α-particle distribution pattern, and according to the platelet classification in patent CN115661074A, the platelet α-particle distribution pattern was divided into four categories: "regular distribution," "N < 30," "N ≥ 30," and "aggregation." The proportion of platelets with "regular distribution" α-particles and the proportion of platelets with "n < 30" α-particles were statistically analyzed. Subsequently, the proportions of platelets with "regularly distributed" α-particles and those with "n < 30" α-particles were used as classification indicators. The diagnostic accuracy of these two indicators in differentiating lung cancer patients from non-cancer subjects was analyzed on both the training and validation sets. The ROC curve analysis results using the proportion of platelets with "regularly distributed" α-particles as the classification indicator are shown below. Figure 18 As shown, the ROC curve analysis results using the proportion of platelets with α-particles "n < 30" as the classification index are as follows: Figure 19 As shown.

[0123] Depend on Figure 18The results show that, using the proportion of platelets with a "regularly distributed" alpha particle pattern as the classification index, the AUC value corresponding to its ROC curve in the training set was 0.739 (95% CI 0.653-0.826), and the AUC value corresponding to its ROC curve in the validation set was 0.792 (95% CI 0.708-0.875). Both AUC values ​​are greater than 0.7, indicating that this index has good diagnostic performance for lung cancer and can effectively distinguish between early-stage lung cancer patients and non-cancerous subjects, making it a suitable biochemical indicator for early lung cancer screening. In the validation set, the optimal classification threshold was 19.41%, corresponding to a sensitivity of 66.1% and a specificity of 90.6%. That is, a proportion of platelets with a "regularly distributed" alpha particle pattern exceeding 19.41% can be considered high-risk for early-stage lung cancer, while a proportion below 19.41% can be considered low-risk.

[0124] Depend on Figure 19 The results showed that, using the proportion of platelets with α-particles "n<30" as the classification index, the AUC values ​​corresponding to the ROC curve in the training set were 0.716 (95% CI 0.631-0.802) and 0.772 (95% CI 0.694-0.849). In the validation set, the optimal classification threshold was 77.5%, corresponding to a sensitivity of 72.9% and a specificity of 69.6%. That is, when the proportion of platelets with "n<30" α-particles is below 77.5%, individuals can be identified as having a high risk of early-stage lung cancer, and when it exceeds 77.5%, individuals can be identified as having a low risk of lung cancer.

[0125] Furthermore, 83 patients with early-stage lung cancer and 9 patients with benign pulmonary nodules were included according to the inclusion and exclusion criteria. Platelet alpha granule distribution and four clinical tumor markers (carcinoembryonic antigen CEA, squamous cell carcinoma antigen SCCA, cytokeratin 19 fragment CK19, and neuron-specific enolase NSE) were measured. The threshold for platelet distribution was 19.41% with "regularly distributed" alpha granules. Based on clinically defined thresholds, CEA levels above 5 μg / L were considered CEA positive, otherwise CEA negative; SCCA levels above 1.5 ng / mL were considered SCCA positive, otherwise SCCA negative; CK19 levels above 1.5 ng / mL were considered CK19 positive, otherwise CK19 negative; and NSE levels above 16.3 μg / L were considered NSE positive, otherwise SCC negative.

[0126] Using the gold standard tissue biopsy results as a positive control, the proportion of lung cancer patients detected in this 83-patient lung cancer testing population was determined based on the proportion of these four clinical tumor markers and the proportion of platelets with "regularly distributed" alpha granules. The results showed that the sensitivity and specificity of tissue biopsy were both 100% in these 83 lung cancer patients. The sensitivities of the four clinical tumor markers (CEA, SCCA, CK19, and NSE) were 6.85%, 6.67%, 51.67%, and 19.61%, respectively. This indicates that the existing clinical tumor markers have poor sensitivity for lung cancer diagnosis, easily leading to missed or misdiagnosed cases.

[0127] Using platelets with a "regularly distributed" alpha particle pattern as a screening marker, with a threshold of 19.41% for platelets with a "regularly distributed" alpha particle pattern, the detection rate in lung cancer patients is high, with a sensitivity of 60.24% and a specificity of 77.78%. Its sensitivity and specificity are significantly higher than those of existing tumor markers such as SCCA, CK19, and NSE, especially its sensitivity is higher than the four existing clinical tumor markers.

[0128] The above results indicate that the proportion of platelets with "regularly distributed" α-particles and the proportion of platelets with "N<30" α-particles are both good biomarkers for lung cancer screening. The proportion of platelets with "regularly distributed" α-particles is preferred as a lung cancer screening indicator.

[0129] Example 5: Screening for Ovarian Cancer Patent CN119027360A has confirmed that the proportion of platelets with a "regularly distributed" alpha granule pattern can serve as a screening indicator for primary ovarian cancer. This embodiment further studies ovarian cancer recurrence screening. In this embodiment, ovarian cancer refers to a collective term for ovarian epithelial cancer, fallopian tube cancer, and peritoneal cancer; that is, ovarian cancer patients in this embodiment have ovarian epithelial cancer, fallopian tube cancer, or peritoneal cancer. The inclusion and exclusion criteria for the following subjects are as follows: ① Preoperative and postoperative ovarian cancer: Preoperative ovarian cancer refers to patients with a clear histological diagnosis of ovarian cancer as an adnexal malignancy, and who have no history of other cancers within the past 5 years and have never received treatment. Postoperative ovarian cancer refers to patients who have undergone ovarian cancer surgery at least one month after completing cytoreductive surgery.

[0130] ② Recurrent and Non-recurrent ovarian cancer: Recurrent ovarian cancer refers to ovarian cancer patients who have undergone cytoreductive surgery and completed adjuvant therapy, and are followed up every 3 months for up to 40 months after the completion of definitive treatment, during which time ovarian cancer recurrence occurs. Non-recurrent ovarian cancer refers to ovarian cancer patients who have undergone cytoreductive surgery and completed adjuvant therapy, and are followed up every 3 months for up to 40 months after the completion of definitive treatment, during which time ovarian cancer recurrence does not occur.

[0131] ③ Healthy subjects: Adult women with no history of cancer, no adnexal lesions, and who have not taken antiplatelet drugs within two weeks prior to blood collection.

[0132] Subjects meeting the inclusion and exclusion criteria were included, comprising 51 healthy individuals and 22 ovarian cancer patients. Blood samples were collected from the healthy individuals and ovarian cancer patients before surgery, and from the ovarian cancer patients after surgery (at least one month post-surgery). Platelets were separated and obtained, and super-resolution fluorescence images of platelet α-granules for each subject were obtained using super-resolution microscopy, as detailed below: (1) Platelet extraction from a small amount of whole blood from the test subject: Place 2-4 mL of whole blood containing EDTA-K2 anticoagulant in a medical centrifuge, centrifuge at 200 g for 12 min at room temperature, then gently remove the centrifuge tube and gently aspirate the supernatant. Next, add ACDT solution to the centrifuge tube containing the supernatant to prevent blood clotting. Finally, place the centrifuge tube in a 37°C incubator containing 5% CO2 and let it stand for 2 h to recover.

[0133] (2) Platelet fixation: Remove the centrifuge tubes from the incubator, and then add an equal amount of fixative to the centrifuge tubes to maintain the morphology and structure of the platelets. Let them stand for 30 minutes to fix. Place the centrifuge tubes in a horizontal centrifuge and centrifuge at 1500g at room temperature for 3 minutes. Then gently remove the tubes, add 1 mL of PBS solution, mix by pipetting, and centrifuge again at 1500g at room temperature for 3 minutes. Repeat 3 times.

[0134] (3) Immunostaining of platelet subcellular structures: a. Platelet plating: First, dilute the platelet suspension with PBS, then add a small amount of platelet suspension to a culture dish treated with poly-L-lysine. After standing for 1 hour, observe the platelet density under a microscope. If the density is too low, the solution in the dish needs to be aspirated and the process repeated. If the density is too high, the dilution ratio needs to be increased and the process repeated.

[0135] b. Punching: Add 0.2 mL of 0.2% Triton X-100 solution to the culture dish containing platelets, let it stand at room temperature for 10 min, and then aspirate the solution.

[0136] c. Blocking: Add 0.2 mL of blocking solution (including 10% goat serum (NAS), 1% bovine serum albumin (BSA), 0.05% Triton X-100 and 0.05% Proclin 300, all diluted with PBS) to a culture dish, let stand at room temperature for 60 min, and then aspirate the solution.

[0137] d. Primary antibody labeling: The α-particle primary antibody was diluted in blocking buffer at a ratio of 1:1000. The trade name of the α-particle primary antibody was Polyclonal Rabbit Anti-Human Von Willebrand Factor, catalog number A0082, brand name Dako. A certain amount of primary antibody dilution buffer was added to the culture dish and incubated overnight at 4°C. The blocking buffer contained: 10% goat serum (NAS), 1% bovine serum albumin (BSA), 0.05% Triton X-100, and 0.05% Proclin 300, all diluted with PBS.

[0138] e. Primary antibody washing: Aspirate the primary antibody dilution solution from the culture dish, add antibody washing solution, place on a shaker and gently wash at room temperature for 5 minutes. Repeat 5 times. The antibody washing solution is phosphate buffer (PBS) containing 0.1% Tween 20.

[0139] f. Secondary antibody labeling: Dilute the α-particle secondary antibody in blocking buffer at a dilution ratio of 1:500. The commercial name of the α-particle secondary antibody is Goat Anti-Rabbit IgG H&L (Alexa Fluor® 488), catalog number ab150077, brand name Abcam. Add a certain amount of secondary antibody dilution buffer to the culture dish and incubate at room temperature for 1 hour.

[0140] Secondary antibody rinsing: Aspirate the secondary antibody dilution solution from the culture dish, add antibody washing solution, place on a shaker and gently rinse at room temperature for 5 minutes, repeat 5 times.

[0141] g. Refixation: Add a small amount of 4% PFA solution to the petri dish, let it stand at room temperature for 10 minutes, and then aspirate the solution.

[0142] h. Rinsing: Add a certain amount of PBS solution to the petri dish, place it on a shaker and gently rinse at room temperature for 5 minutes, repeat 3 times.

[0143] (4) Super-resolution imaging of platelet α-particles: The fluorescence intensity and exposure time of each sample were adjusted to ensure that each reconstructed image had a high signal-to-noise ratio. Then, super-resolution microscopy (SIM, STED, STORM, etc.) was used to image a large number of individual platelets to obtain multiple super-resolution fluorescence images of platelet α-particles. Based on the distribution pattern of platelet α-particles, according to the classification of platelets in patent CN115661074A, the distribution pattern of α-particles in platelets was divided into four categories: "regular distribution", "N<30", "N≥30" and "aggregation". The specific classification method refers to Example 1 in patent CN115661074A. The differences in the distribution pattern of platelet α-particles between these 22 ovarian cancer patients before and after surgery and healthy individuals were analyzed. The results are as follows: Figure 20 As shown.

[0144] Depend on Figure 20 It was found that the distribution pattern of α-granules in ovarian cancer patients (preoperatively) differed significantly from that in healthy individuals. Compared to healthy individuals, the proportion of "N<30" α-granule distribution patterns in ovarian cancer patients (preoperatively) significantly decreased, while the proportion of "regular distribution" significantly increased, indicating that the preoperative α-granule distribution pattern in ovarian cancer patients was primarily "regular." However, after surgery, the proportion of "N<30" α-granule distribution patterns in ovarian cancer patients significantly increased, while the proportion of "regular distribution" significantly decreased, and there was no statistically significant difference in α-granule distribution patterns between postoperative patients and healthy individuals. The postoperative α-granule distribution pattern in ovarian cancer patients was primarily "N<30." These results suggest that the platelet α-granule distribution pattern has a good response to ovarian cancer treatment.

[0145] Furthermore, eligible subjects were included according to the inclusion and exclusion criteria, including 21 patients with non-recurrent ovarian cancer and 31 patients with recurrent ovarian cancer. Blood samples from these subjects were used for testing, and super-resolution fluorescence images of platelet α-granules were obtained for each subject using super-resolution microscopy. The proportion of each category was statistically analyzed, and the results are as follows: Figure 21 As shown.

[0146] ROC curves were used to evaluate the performance of α-granule “N<30” and “regular distribution” patterns in monitoring ovarian cancer recurrence. For indicators negatively correlated with the outcome (inverse association), we reversed the scoring direction (equivalently, defining smaller values ​​as suggestive of positivity) to ensure that the AUC truly reflects its discriminative ability. In this embodiment, the negative proportion of α-granule “N<30” and the proportion of “regular distribution” were used as classification indicators to distinguish between recurrent and non-recurrent ovarian cancer, and the results are as follows. Figure 22 As shown.

[0147] Depend on Figure 22The results showed that the area under the ROC curve for classifying α-particles with a negative "N<30" distribution pattern was 0.853 (95% confidence interval (95% CI): 0.741–0.964). In contrast, the area under the ROC curve for classifying α-particles with a regular distribution pattern was 0.895 (95% CI: 0.791–0.994), indicating that its discriminative performance was superior to the "N<30" pattern. Therefore, the proportion of α-particles with a regular distribution pattern or the proportion of α-particles with a "N<30" distribution pattern can be used as a biochemical indicator for screening ovarian cancer recurrence.

[0148] In addition, 10 patients with non-recurrent ovarian cancer and 15 patients with recurrent ovarian cancer were used as the training set, and another group of 11 patients with non-recurrent ovarian cancer and 16 patients with recurrent ovarian cancer were used as the validation set. This embodiment uses blood samples from the training and validation sets to detect the super-resolution fluorescence images of platelet α-particles and the serum tumor marker cancer antigen 125 (CA125) levels in each subject. The CA125 data for these subjects were obtained from clinical data from the hospital.

[0149] ROC curves were plotted using the proportion of α-particles with "regular distribution" and serum CA125 content as classification indicators. The results showed that, using CA125 content as a classification indicator, the AUC of the ROC curve distinguishing recurrent ovarian cancer from non-recurrent ovarian cancer in the training set was 0.721 (95% CI: 0.504-0.939); while in the training set, the AUC of the ROC curve distinguishing recurrent ovarian cancer from non-recurrent ovarian cancer using α-particles with "regular distribution" was 0.914 (95% CI: 0.765-1.000), which was significantly better than the existing tumor marker CA125.

[0150] Next, validation was performed using α-particle distribution data and CA125 content from the validation set. The results showed that, using CA125 content as a classification indicator, the AUC of the ROC curve distinguishing recurrent and non-recurrent ovarian cancer in the validation set was 0.705 (95% CI: 0.491-0.918); while in the validation set, the AUC of the ROC curve distinguishing recurrent and non-recurrent ovarian cancer by the "regular distribution" of α-particles was 0.875 (95% CI: 0.731-1.000). The maximum Youden index of the ROC curve plotted using the proportion of "regular distribution" of α-particles as the monitoring indicator was 0.665, corresponding to a proportion of 9.0% of "regular distribution" of α-particles, with a sensitivity of 93.8% and a specificity of 72.7%. This indicates that if the proportion of "regular distribution" of α-particles is greater than 9.0% during the follow-up period after ovarian cancer surgery, it can be assessed as an indication of ovarian cancer recurrence. It is evident that both primary and recurrent ovarian cancer are related to the proportion of platelets with "regularly distributed" alpha granules, and this indicator can be used for screening primary or recurrent ovarian cancer, thus serving as a screening indicator for primary or recurrent ovarian cancer.

[0151] Although cancers are highly heterogeneous in tissue origin, gene mutations, and clinical manifestations, they all share a series of basic biological capabilities known as "cancer markers." As demonstrated in the examples above, the proportion of platelets with a "regularly distributed" alpha granule distribution or the proportion of platelets with an alpha granule distribution of "N≤30" exhibits a consistent pattern of change in cholangiocarcinoma, hepatocellular carcinoma, lung cancer, and ovarian cancer (primary or recurrent). Specifically, the proportion of platelets with a "regularly distributed" alpha granule distribution significantly increases and the proportion of platelets with an alpha granule distribution of "N≤30" significantly decreases in cancer patients. ROC curve analysis confirms that the proportion of platelets with a "regularly distributed" alpha granule distribution or the proportion of platelets with an alpha granule distribution of "N≤30" can serve as a biomarker for screening these cancers. This indicates that this indicator can accurately assess the extent of cancer in the body, while being insensitive to factors such as type, cause, first-time occurrence, or recurrence, thus serving as a broad-spectrum cancer screening marker. In particular, the proportion of platelets with "regularly distributed" alpha particles has higher sensitivity or diagnostic efficacy compared to some existing tumor markers such as cancer antigen 125 (CA125), cytokeratin 19 fragment (CK19), carcinoembryonic antigen (CEA), AFP, and DCP, and can be used for early cancer screening, such as early screening for ovarian cancer, lung cancer, and hepatocellular carcinoma.

[0152] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A broad-spectrum cancer screening system based on platelet α-particle super-resolution imaging, characterized in that, Includes a broad-spectrum biomarker acquisition module and a broad-spectrum cancer screening module; The broad-spectrum biomarker acquisition module is used to acquire broad-spectrum biomarkers of the subjects, including the proportion of platelets with "regularly distributed" α particles and / or the proportion of platelets with "N≤30" α particles, and submits them to the cancer broad-spectrum screening module. The cancer broad-spectrum screening module uses the fact that the proportion of platelets with "regular distribution" of alpha particles is positively correlated with the risk of cancer to conduct broad-spectrum screening for high-risk individuals. Alternatively, based on the negative correlation between the proportion of platelets with α-particle "N≤30" and cancer risk, broad-spectrum screening can be conducted for individuals at high cancer risk.

2. The broad-spectrum cancer screening system as described in claim 1, characterized in that, The broad-spectrum cancer screening module screens individuals at high risk of cancer and outputs results using the following method: Individuals with a higher proportion of platelets exhibiting a "regular distribution" of alpha granules than a first preset threshold are considered to be at high risk for cancer; or... Individuals with a high risk of cancer are identified based on the proportion of platelets with α particles "N≤30" being less than the second preset threshold. The first preset threshold or the second preset threshold is determined using the ROC curve method.

3. The broad-spectrum cancer screening system as described in claim 2, characterized in that, The cancer broad-spectrum screening module uses the fact that the proportion of platelets with "regular distribution" of alpha particles is positively correlated with the risk of cancer to conduct broad-spectrum screening for high-risk individuals.

4. The broad-spectrum cancer screening system as described in claim 3, characterized in that, The cancer broad-spectrum screening module determines individuals at high risk of cancer based on the proportion of platelets with a "regular distribution" of alpha particles being greater than a first preset threshold; the first preset threshold is 23.3%.

5. The broad-spectrum cancer screening system as described in claim 4, characterized in that, The broad-spectrum screening covers cancers including hepatocellular carcinoma, bile duct cancer, lung cancer, and ovarian cancer.

6. A screening system for early-stage liver cancer, characterized in that, It includes a biomarker acquisition module, a biomarker analysis module, and a liver cancer screening module; The biomarker acquisition module is used to acquire biomarker data from the subject and submit it to the biomarker analysis module; the biomarker data includes the proportion of platelets with "regular distribution" of α particles X1, the content of AFP X2, and the content of DCP X3; The biomarker analysis module inputs the acquired biomarker data into a multivariate logistic regression model to calculate joint factors and submits them to the liver cancer screening module. The liver cancer screening module determines the risk of liver cancer based on the principle that the higher the proportion of platelets with "regular distribution" of α particles (X1), the higher the risk, and the higher the content of AFP (X2) and DCP (X3), the higher the risk.

7. The early liver cancer screening system as described in claim 6, characterized in that, The biomarker acquisition module acquires biomarker data including the proportion of platelets with "regular distribution" of α particles (X1), the content of AFP (X2), and the content of DCP (X3). The biomarker analysis module calculates the joint factor according to the multivariate logistic regression model y=k1X1+k2X2+k3X3+b, where k1~k3 are regression coefficients, all of which are greater than 0. The liver cancer screening module screens for early-stage hepatocellular carcinoma according to the principle that the higher the combined factor, the higher the risk of hepatocellular carcinoma.

8. The early liver cancer screening system as described in claim 7, characterized in that, The liver cancer screening module performs screening according to the following method: If the combined factor is greater than the third preset threshold, the subject is judged to be at high risk of early hepatocellular carcinoma; the third preset threshold is determined by the ROC curve method.

9. The early liver cancer screening system as described in claim 8, characterized in that, The multivariate logistic regression model is y = 0.052X1 + 0.019X2 + 0.044X3 - 2.

282.

10. The early liver cancer screening system as described in claim 9, characterized in that, The third preset threshold is 0.91.

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