Gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure

Abstract

The present invention discloses a gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure, which includes a super-resolution image acquisition module of platelet subcellular structure, a platelet classification and statistics module, and a gynecological tumor diagnosis module; the gynecological tumor diagnosis system is based on the super-resolution image of platelet α particles, classifies platelets according to the distribution form of α particles, and counts the proportion of platelets with "regular distribution" of α particles. The higher the proportion of platelets with "regular distribution" of α particles, the higher the risk of judging that the sample is a gynecological malignant tumor. In particular, if the proportion of platelets with "regular distribution" of α particles is greater than the proportion corresponding to the Youden maximum, the sample is judged to be at high risk of gynecological malignant tumor. The gynecological tumor diagnosis system has high sensitivity and good specificity, and can be used to quickly identify patients with benign gynecological tumors and gynecological malignant tumors.

Description

Technical Field

[0001] The present invention belongs to the field of gynecological malignant tumor diagnosis, and more specifically, relates to a gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure. Background Art

[0002] Gynecological tumors generally refer to benign and malignant tumors. Benign tumors include ovarian cysts, endometrial hyperplasia, and endometrial polyps, while malignant tumors primarily fall into three categories: ovarian cancer, endometrial cancer, and cervical cancer. The incidence and mortality rates of gynecological malignancies are increasing year by year, posing a significant threat to women's lives. Efficient, cost-effective, and accurate diagnosis and stratified treatment are crucial for timely treatment of patients. Accurate early classification of gynecological tumors has been a research hotspot.

[0003] Currently, common clinical methods for detecting ovarian cancer include tumor markers (such as serum cancer antigen 125 and human epididymis protein 4) combined with imaging tests such as ultrasound and CT. However, tissue biopsy remains the gold standard for diagnosing benign and malignant ovarian cancer. However, tissue biopsy has drawbacks such as invasiveness, high cost, poor reproducibility, and delayed diagnostic results. The diagnosis of endometrial cancer primarily focuses on cytology and transvaginal ultrasound, but both have poor specificity and fall short of clinical requirements. Currently, histopathology is also the gold standard for diagnosing benign and malignant endometrial cancer. However, due to the multifocal nature of endometrial lesions and limitations of sampling methods, endometrial biopsies still have an approximately 10% false-negative rate. Furthermore, there are no known sensitive tumor markers for the diagnosis and follow-up of endometrial cancer.

[0004] Furthermore, clinical screening for cervical cancer mostly involves HPV screening, exfoliative cytology, and colposcopy. However, for post-treatment follow-up and early detection of recurrence, cervical cancer lacks highly specific and sensitive biomarkers, resulting in poor diagnostic accuracy. This suggests that existing technologies for diagnosing and distinguishing benign and malignant gynecological tumors suffer from poor specificity, delayed diagnostic results, and even the tendency to produce false negatives, leading to inaccurate diagnostic results. Rapid diagnosis of benign and malignant gynecological tumors remains a clinical challenge.

[0005] In recent years, liquid biopsy, as an emerging diagnostic technology, has become a powerful tool for cancer diagnosis. Compared with traditional tissue biopsies, liquid biopsy samples are available from a wide range of sources and can minimize errors due to human manipulation and disease heterogeneity. Existing studies have shown that platelets have become a potentially valuable source of liquid biopsies, and platelet count, mean volume, distribution width, RNA, and protein can serve as effective biomarkers for cancer screening, diagnosis, prognosis, and treatment monitoring. However, these parameters, including platelet count, mean volume, and distribution width, are highly variable and vary in patients with inflammation, resulting in poor specificity for cancer diagnosis. Furthermore, platelet RNA and protein have drawbacks in cancer diagnosis, including low levels, complex analytical methods, and poor sensitivity. However, existing studies examining the subcellular structure of platelets (including microtubules, α-granules, mitochondria, and dense granules) in cancer-related studies are limited. Therefore, developing a diagnostic system based on platelet subcellular structure to rapidly and accurately identify gynecological tumors is of great significance. Summary of the Invention

[0006] In response to the above defects or improvement needs of the prior art, the present invention provides a gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure, which aims to classify platelets according to the "regular distribution" of α particles, the number of α particles "N<30", "N≥30" and "aggregated distribution" based on the super-resolution image of the platelet subcellular structure obtained by the detection sample blood, and to count the proportion of each type of platelet. It is found that the proportion of platelets with "regular distribution" of α particles in gynecological tumor patients is greater than that in healthy people, and the proportion of platelets with "regular distribution" of α particles in blood is greater than that in healthy people. The proportion of platelets can be used to distinguish between benign and malignant gynecological tumors. It has high sensitivity and good specificity, and can be used as a biomarker for quickly identifying benign and malignant gynecological tumors. The higher the proportion of platelets with "regular distribution" of α particles, the higher the risk of judging the sample as a gynecological tumor. In particular, if the proportion of platelets with "regular distribution" of α particles is greater than the proportion corresponding to the Youden maximum, the sample is judged to be at high risk of gynecological malignant tumors, thereby solving the existing technical problem of untimely and poor specificity in judging the benign and malignant status of tumors based on tissue biopsy results.

[0007] To achieve the above objectives, according to one aspect of the present invention, a gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure is provided, which includes a super-resolution image acquisition module for platelet subcellular structure, a platelet classification and statistics module, and a gynecological tumor diagnosis module;

[0008] The super-resolution image acquisition module of the platelet subcellular structure is used to obtain multiple super-resolution images of platelet α particles composed of fluorescent signal pixels and non-fluorescent signal pixels of the sample to be diagnosed, and submit them to the platelet classification and statistics module;

[0009] The platelet classification and statistics module counts the total number of platelets and the number of platelets with "regularly distributed" alpha particles based on all acquired alpha particle super-resolution images, calculates the proportion of "regularly distributed" platelets in the sample, and submits the results to the gynecological tumor diagnosis module;

[0010] The gynecological tumor diagnosis module is used to judge that the higher the proportion of platelets with "regular distribution" of alpha particles, the greater the possibility that the sample is a gynecological tumor. The gynecological tumors include benign gynecological tumors and malignant gynecological tumors.

[0011] Preferably, the gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure, its gynecological tumor diagnosis module is used to judge that the sample is at high risk of gynecological malignant tumors based on the fact that the proportion of platelets with "regular distribution" of α particles is greater than a preset threshold; the preset threshold is less than or equal to the ratio corresponding to the Youden maximum value of the ROC curve between healthy people and patients with gynecological malignant tumors.

[0012] Preferably, in the gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure, the preset threshold is the ratio of the Youden maximum value corresponding to the ROC curve between healthy people and patients with gynecological malignancies.

[0013] Preferably, in the gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure, the ratio of the Youden maximum value corresponding to the ROC curve between healthy people and patients with gynecological malignancies is 7.75%, that is, when the proportion of platelets with "regular distribution" of α particles is greater than 7.75%, it is judged to be a gynecological malignancy.

[0014] Preferably, in the gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure, if the sample to be diagnosed comes from a gynecological tumor patient, the gynecological tumor diagnosis module is further configured to compare the proportion of platelets with a "regular distribution" obtained with a threshold value to determine whether the gynecological tumor is benign or malignant. The specific determination method is as follows:

[0015] If the proportion of platelets with "regular distribution" of α particles in the patient is less than or equal to the threshold, the patient is judged to have a benign gynecological tumor;

[0016] If the proportion of platelets with "regular distribution" of α particles in the patient is greater than the threshold, the patient is diagnosed with gynecological malignancy;

[0017] The threshold is the ratio of the Youden maximum value corresponding to the ROC curve between patients with benign gynecological tumors and patients with malignant gynecological tumors.

[0018] Preferably, in the gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure, the ratio of the Youden maximum value corresponding to the ROC curve between patients with benign gynecological tumors and patients with malignant gynecological tumors is 5.66%.

[0019] Preferably, the gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure counts the total number of platelets by summing the number of four types of platelets: α particles with "regular distribution", α particles with "N<30", "N≥30" and α particles with "aggregated distribution".

[0020] Preferably, the gynecological tumor diagnosis system based on platelet subcellular structure super-resolution imaging further includes a gynecological tumor treatment evaluation module;

[0021] The platelet subcellular structure super-resolution image acquisition module is used to obtain platelet alpha granule super-resolution images composed of fluorescent signal pixels and non-fluorescent signal pixels of patients with gynecological malignancies before and after treatment, and submit them to the platelet classification and statistics module;

[0022] The platelet classification and statistics module classifies individual platelets according to the distribution of individual platelet α particles in the super-resolution images obtained before and after treatment, according to the α particle "regular distribution", α particle number "N < 30", "N ≥ 30", and α particle "aggregated distribution", and calculates the percentage of each type of platelet in the total number of platelets, and submits the result to the gynecological tumor treatment evaluation module;

[0023] The gynecological tumor treatment evaluation module is used to determine whether the treatment is effective based on the comparison of the proportion of platelets with "regular distribution" of α particles, the number of α particles "N < 30", and the proportion of platelets with "aggregated distribution" of α particles before and after treatment. The specific judgment method is as follows:

[0024] If the proportion of platelets with "regular distribution" of α particles decreases after treatment or the proportion of platelets with "N < 30" and / or "aggregated distribution" of α particles increases after treatment compared with before treatment, the treatment is considered to be effective.

[0025] Preferably, the gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure is evaluated as having a good treatment effect if the proportion of platelets with "regular distribution" of α particles in the patient decreases significantly after treatment compared with before treatment.

[0026] Preferably, the gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure is evaluated as having a good treatment effect if, compared with before treatment, the proportion of platelets with "regular distribution" of α particles in the patient after treatment is less than or equal to the proportion corresponding to the Youden maximum value of the ROC curve between the patient before and after treatment of the gynecological malignancy.

[0027] Preferably, in the gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure, the ratio of the Youden maximum value corresponding to the ROC curve between patients before and after treatment of gynecological malignant tumors is 17.71%.

[0028] In general, the above technical solutions conceived by the present invention, compared with the prior art, can achieve the following beneficial effects because the present invention provides a gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structures:

[0029] The gynecological tumor diagnostic system provided by the present invention is based on super-resolution imaging of α-granules, a subcellular structure of platelets. Platelets are classified according to α-granules showing a "regular distribution", α-granule number "N < 30", α-granule number "N ≥ 30", and α-granule "aggregated distribution". The total number of platelets in these four categories is counted, and the percentage of platelets with α-granules showing a "regular distribution" of the total number of platelets is calculated. The higher the proportion of platelets with α-granules showing a "regular distribution" in a patient, the higher the risk of gynecological tumors. In particular, if the proportion of platelets with α-granules showing a "regular distribution" is greater than the proportion corresponding to the Youden maximum, the sample is judged to be at high risk of gynecological malignancies. The gynecological tumor diagnostic system requires minimal invasiveness, high sensitivity, and good specificity. It can quickly and effectively distinguish between benign and malignant gynecological tumor patients, and can accurately and timely identify patients with gynecological malignancies, which is conducive to the early treatment of gynecological malignancies. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is the result of classifying platelets according to the distribution pattern of α particles;

[0031] Figure 2 This is the result of a differential analysis of the proportions of different platelet types between healthy individuals and patients with ovarian cancer, endometrial cancer, and cervical cancer based on α-particle distribution;

[0032] Figure 3 The ROC curve analysis is based on the proportion of platelets with "regular distribution" of α particles to identify healthy people and gynecological malignancies;

[0033] Figure 4 It is the result of classifying platelets according to their microtubule distribution;

[0034] Figure 5 It is based on the difference analysis results of the proportion of different types of platelets between healthy people and patients with ovarian cancer, endometrial cancer, and cervical cancer based on microtubule distribution;

[0035] Figure 6 This is the result of a differential analysis of the proportions of different platelet types between healthy individuals and patients with benign and malignant gynecological tumors based on α-particle distribution;

[0036] Figure 7 This is the result of a differential analysis of the proportion of different types of platelets between healthy individuals and patients with benign and malignant gynecological tumors based on microtubule distribution;

[0037] Figure 8 The ROC curve analysis is based on the proportion of platelets with "regular distribution" of α particles to identify benign and malignant gynecological tumors;

[0038] Figure 9 This is the result of a differential analysis of the proportion of different types of platelets in patients with gynecological cancer before and after treatment based on the distribution of α particles;

[0039] Figure 10 The ROC curve analysis was based on the proportion of platelets with "regular distribution" of α particles to identify gynecological tumors before and after treatment. DETAILED DESCRIPTION

[0040] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the following embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0041] Although some researchers have used electron microscopy to examine platelet subcellular structures (microtubules, mitochondria, etc.) and found that ovarian cancer can cause changes in the morphology and quantity of platelet subcellular structures, it remains unknown whether these changes can be used to differentiate between benign and malignant gynecological tumors. Furthermore, electron microscopy imaging, due to its time-consuming sample preparation and slow image acquisition and analysis speeds, greatly limits the clinical application of platelet subcellular structure detection. With the development of super-resolution fluorescence microscopy technology, precise detection of subcellular structures as small as tens of nanometers has become possible, but research on the application of platelet subcellular structure in the diagnosis of gynecological tumors has not yet been reported.

[0042] Based on the research of the previous patent CN115661074A on a platelet classification method and system based on super-resolution images of platelet α particles and the patent CN115908302A on a platelet microtubule classification method and classification system based on super-resolution images, the present invention further studies the differences in platelet subcellular structure (microtubules, α particles) between healthy people, patients with benign gynecological tumors (ovarian cysts, endometrial hyperplasia and endometrial polyps) and patients with gynecological malignancies (ovarian cancer, endometrial cancer and cervical cancer). The results show that the proportion of different types of platelets based on the distribution of α particles is significantly different between healthy people and patients with gynecological malignancies (ovarian cancer, endometrial cancer and cervical cancer). Compared with healthy people, the proportion of platelets with "regular distribution" of α particles is higher in patients with gynecological malignancies such as ovarian cancer, endometrial cancer and cervical cancer, while the proportion of platelets with α particles "N < 30" and the proportion of platelets with "aggregated distribution" of α particles are lower.

[0043] Furthermore, statistical analysis revealed that there are mainly the following situations between healthy people and patients with benign and malignant gynecological tumors (ovarian cancer, endometrial cancer, and cervical cancer):

[0044] ①The percentages of platelets with "regular distribution" of α particles and those with "N<30" α particles showed significant differences between healthy people and patients with gynecological malignancies (ovarian cancer, endometrial cancer, and cervical cancer). However, the percentages of platelets with "N≥30" α particles showed no significant difference between healthy people and patients with ovarian cancer, but showed significant differences between healthy people and patients with endometrial cancer and cervical cancer. The percentages of platelets with "aggregated distribution" of α particles also showed significant differences between healthy people and patients with ovarian cancer, endometrial cancer, and cervical cancer.

[0045] ②There was no significant difference in the percentage of platelets with "regular distribution" of α granules and platelets with α granule number "N<30" between healthy people and patients with benign gynecological tumors, while there was a significant difference in the percentage of platelets with α granule number "N≥30" and platelets with "aggregated distribution" of α granules between healthy people and patients with benign gynecological tumors.

[0046] Although there was no significant difference in the proportion of platelets with "regular distribution" of α particles and the proportion of platelets with α particles "N<30" between healthy people and benign gynecological tumors, but there was a significant difference between healthy people and patients with gynecological malignancies, further ROC curve analysis between patients with benign gynecological tumors and patients with gynecological malignancies found that the proportion of platelets with "regular distribution" of α particles can well distinguish patients with benign gynecological tumors and patients with gynecological malignancies, while the proportion of platelets with α particles "N<30" cannot distinguish patients with benign gynecological tumors and patients with gynecological malignancies.

[0047] Although the present invention also found that the proportions of different types of platelets based on microtubule distribution also differ between healthy people and patients with gynecological malignancies (endometrial cancer, cervical cancer), specifically as follows:

[0048] The proportion of platelets with "diffuse distribution" microtubules was significantly different between healthy people and endometrial cancer patients, but there was no significant difference between healthy people and ovarian cancer and cervical cancer patients.

[0049] The proportion of platelets with "aggregated distribution" of microtubules was significantly different between healthy people and cervical cancer patients, but there was no significant difference between healthy people and ovarian cancer and endometrial cancer patients.

[0050] There was no significant difference between "regular distribution" and "irregular distribution" of microtubules between healthy people and ovarian cancer, endometrial cancer, and cervical cancer.

[0051] Statistical analysis between healthy people, patients with benign gynecological tumors and patients with malignant gynecological tumors found that only the proportion of platelets with "aggregated distribution" of microtubules was significantly different between healthy people and patients with malignant gynecological tumors, but there was no significant difference between healthy people and patients with benign gynecological tumors. There was no significant difference in the proportion of other types of platelets between healthy people, patients with benign gynecological tumors and patients with malignant gynecological tumors.

[0052] However, ROC curve analysis between patients with benign gynecological tumors and patients with malignant gynecological tumors confirmed that the proportion of platelets with "aggregated distribution" of microtubules could not distinguish patients with benign gynecological tumors and patients with malignant gynecological tumors.

[0053] Based on the above findings, the present invention proposes a gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure, which includes a super-resolution image acquisition module for platelet subcellular structure, a platelet classification and statistics module, and a gynecological tumor diagnosis module;

[0054] The super-resolution image acquisition module of the platelet subcellular structure is used to perform binarization to obtain a super-resolution image of platelet α particles composed of fluorescent signal pixels and non-fluorescent signal pixels, and submit it to the platelet classification and statistics module;

[0055] The platelet classification and statistics module, based on the acquired multiple platelet α granule super-resolution images, counts the total number of platelets in all images and the number of platelets in which individual platelets are "regularly distributed" according to α granules, calculates the percentage of "regularly distributed" platelets in the total number of platelets in the sample, and submits the results to the gynecological tumor diagnosis module;

[0056] The platelets with "regular distribution" of α particles are searched for the ellipse with the smallest area that meets condition A in the α particle image of the single platelet, and the ellipse is used as the outer circle: if the area of ​​the outer circle is above the preset threshold of the area of ​​the regular distribution ellipse and the ratio of the minor axis to the major axis is within the preset range of the regular distribution ellipse, then the platelets involved in the single platelet α particle distribution image are judged to be "regularly distributed"; the threshold of the area of ​​the regular distribution ellipse is 0.15 μm. 2 -20.0μm 2 The range of the regular distribution ellipse is [0.2, 1];

[0057] The gynecological tumor diagnosis module is used to make a judgment based on the proportion of platelets with "regularly distributed" alpha particles obtained, as follows:

[0058] The higher the proportion of platelets with "regular distribution" of alpha particles, the greater the possibility of judging the sample as a gynecological tumor; the gynecological tumors include benign gynecological tumors and malignant gynecological tumors, among which benign gynecological tumors include ovarian cysts, endometrial hyperplasia, and endometrial polyps; malignant gynecological tumors include ovarian cancer, endometrial cancer, and cervical cancer.

[0059] Preferably, the proportion of platelets with "regular distribution" of α particles is greater than a preset threshold, and the sample is judged to be at high risk of gynecological malignancies; the preset threshold is less than or equal to the ratio corresponding to the Youden maximum value corresponding to the ROC curve between healthy people and patients with gynecological malignancies.

[0060] More preferably, the proportion of platelets with "regular distribution" of α particles is greater than the proportion corresponding to the Youden maximum corresponding to the ROC curve between healthy people and patients with gynecological malignancies, and the sample is judged to be from a high-risk person for gynecological malignancies; in some embodiments, the proportion corresponding to the Youden maximum corresponding to the ROC curve between healthy people and patients with gynecological malignancies is 7.75%. When the proportion of platelets with "regular distribution" of α particles is greater than 7.75%, it is judged to be a gynecological malignancy.

[0061] Furthermore, if the sample comes from a gynecological tumor patient, the gynecological tumor diagnosis module is used to perform a differential analysis based on the proportion of platelets with "regular distribution" of α particles obtained and a threshold value, and determine whether the gynecological tumor is benign or malignant based on the results of the differential analysis. The specific determination method is as follows:

[0062] If the proportion of platelets with "regular distribution" of α particles is significantly different from the threshold, it is judged that the patient is more likely to have a malignant gynecological tumor;

[0063] If the proportion of platelets with "regular distribution" of α particles is not significantly different from the threshold, it is judged that the patient is more likely to have a benign gynecological tumor;

[0064] The threshold value is the average value of the proportion of platelets with "regular distribution" of α particles in healthy people.

[0065] Preferably, the proportion of platelets with "regular distribution" of α particles obtained is compared with a threshold value to determine whether the gynecological tumor is benign or malignant, as follows:

[0066] If the proportion of platelets with "regular distribution" of α particles in the patient is less than or equal to the threshold, the patient is judged to have a benign gynecological tumor;

[0067] If the proportion of platelets with "regular distribution" of α particles in the patient is greater than the threshold, the patient is diagnosed with gynecological malignancy;

[0068] The threshold is the ratio of the Youden maximum value corresponding to the ROC curve between patients with benign gynecological tumors and patients with malignant gynecological tumors.

[0069] In some embodiments, the proportion of platelets with "regular distribution" of α particles in patients with benign gynecological tumors is less than 5%, while the proportion in patients with malignant gynecological tumors is greater than 40%; after ROC curve analysis between patients with benign gynecological tumors and patients with malignant gynecological tumors, the results show that the proportion corresponding to the maximum value of 5.66% can be used as the critical value for distinguishing patients with benign gynecological tumors and patients with malignant gynecological tumors, among which the proportion of platelets with "regular distribution" of α particles is greater than 5.66%, which is judged as gynecological malignant tumors, and the proportion of platelets with "regular distribution" of α particles is less than or equal to 5.66%, which is judged as benign gynecological tumors.

[0070] In some embodiments, the total platelet count is the sum of the numbers of four types of platelets: α particles with "regular distribution", α particles with "N<30", "N≥30", and α particles with "aggregated distribution".

[0071] Furthermore, the gynecological tumor diagnosis system based on platelet subcellular structure super-resolution imaging also includes a gynecological tumor treatment evaluation module;

[0072] The platelet subcellular structure super-resolution image acquisition module is used to obtain platelet alpha granule super-resolution images composed of fluorescent signal pixels and non-fluorescent signal pixels of patients with gynecological malignancies before and after treatment, and submit them to the platelet classification and statistics module;

[0073] The platelet classification and statistics module classifies individual platelets according to the distribution of individual platelet α particles in the super-resolution images obtained before and after treatment, according to the α particle "regular distribution", α particle number "N < 30", "N ≥ 30", and α particle "aggregated distribution", and calculates the percentage of each type of platelet in the total platelet population and submits the result to the gynecological tumor treatment evaluation module;

[0074] The gynecological tumor treatment evaluation module is used to determine whether the treatment is effective based on the comparison of the proportion of platelets with "regular distribution" of α particles, the number of α particles "N < 30", and the proportion of platelets with "aggregated distribution" of α particles before and after treatment. The specific judgment method is as follows:

[0075] If the proportion of platelets with "regular distribution" of α particles decreases after treatment or the proportion of platelets with "N < 30" and / or "aggregated distribution" of α particles increases after treatment compared with before treatment, the treatment is considered to be effective.

[0076] Preferably, if the proportion of platelets with "regular distribution" of α particles in the patient decreases significantly after treatment compared with before treatment, the treatment effect is evaluated to be good.

[0077] More preferably, if the proportion of platelets with "regular distribution" of alpha particles in patients after treatment is less than or equal to the proportion corresponding to the Youden maximum value of the ROC curve between patients before and after treatment of gynecological malignancies, the treatment effect is evaluated to be good.

[0078] In some embodiments, the ratio of the Youden maximum value corresponding to the ROC curve between the patients before and after the treatment of the gynecological malignancy is 17.71%.

[0079] The following are examples:

[0080] Example 1 Differentiation of healthy individuals and gynecological malignancies based on the distribution of α-granules in platelet subcellular structures

[0081] This example classifies platelets according to the distribution of α particles, and analyzes the differences in the proportions of different types of platelets between healthy individuals and patients with ovarian cancer, endometrial cancer, and cervical cancer.

[0082] Blood from healthy individuals, ovarian cancer patients, endometrial cancer patients, and cervical cancer patients was used as the test subjects. Super-resolution microscopy technology was used to obtain super-resolution imaging of α particles in the subcellular structure of platelets. The details are as follows:

[0083] (1) Super-resolution imaging of platelet subcellular α-granules

[0084] (1-1) Platelet extraction from whole blood: Place 2-4 mL of whole blood anticoagulated with EDTA-K2 in a medical centrifuge and centrifuge at 200g for 12 minutes at room temperature. Gently remove the supernatant and aspirate it into a centrifuge tube. Add ACDT solution to the supernatant and allow the tube to recover in a 37°C, 5% CO2 incubator for 2 hours.

[0085] (1-2) Platelet Fixation: Remove the centrifuge tube from the incubator and add an equal amount of fixative solution to the tube. Allow to fix for 30 minutes. Place the tube in a horizontal centrifuge and centrifuge at 1500g for 3 minutes at room temperature. Gently remove the tube, add 1 mL of PBS solution, pipette to mix, and centrifuge at 1500g for 3 minutes at room temperature. Repeat three times.

[0086] (1-3) Immunostaining of platelet subcellular structures:

[0087] ① Platelet plating: First dilute the platelet suspension with PBS, then draw a small amount of platelet suspension into the poly-lysine-treated culture dish. After standing for 1 hour, observe the platelet density under a microscope. If the density is too low, aspirate the solution in the dish and repeat this process. If it is too high, increase the dilution ratio and repeat this process.

[0088] ② 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 minutes, and then aspirate the solution.

[0089] ③ Blocking: Add 0.2 mL of blocking solution into the culture dish, let it stand at room temperature for 60 minutes, and then aspirate the solution.

[0090] ④ Primary antibody labeling: Dilute the primary antibody in blocking buffer at a dilution ratio of 1:500 for microtubule antibody and 1:1000 for α-granule antibody. Add a certain amount of primary antibody dilution to the culture dish and incubate at 4°C overnight.

[0091] ⑤ Primary antibody rinse: Aspirate the primary antibody dilution in the culture dish, add antibody washing solution, place it on a shaker and gently rinse at room temperature for 5 minutes, repeat 5 times.

[0092] ⑥ Secondary antibody labeling: Dilute the secondary antibody in blocking buffer at a 1:500 dilution ratio for microtubules and α-particles. Add a certain amount of secondary antibody dilution to the culture dish and incubate at room temperature for 1 hour.

[0093] ⑦ Secondary antibody rinsing: Aspirate the secondary antibody dilution solution in the culture dish, add antibody washing solution, place it on a shaker and gently rinse at room temperature for 5 minutes, repeat 5 times.

[0094] ⑧Re-fixation: Add a small amount of 4% PFA solution into the culture dish, let it stand at room temperature for 10 minutes, and then aspirate the solution.

[0095] ⑨ Rinse: Add a certain amount of PBS solution to the culture dish, place it on a shaker and rinse gently at room temperature for 5 minutes, repeat 3 times.

[0096] (1-4) Super-resolution imaging of platelet subcellular structures: Adjust the fluorescence intensity and exposure time of each sample to ensure that each reconstructed image has a high signal-to-noise ratio. Then, use super-resolution microscopy techniques (SIM, STED, STORM, etc.) to image a large number of platelets, obtaining 50 large super-resolution fluorescence images of platelet subcellular structures. Count the total number of platelets in all super-resolution images, ensuring that the total number of platelets is greater than or equal to 500.

[0097] (2) Platelet classification and statistical analysis: The super-resolution fluorescence image was input into the ResUNet and ResNet-50 algorithm models, and platelets were classified according to the α particle distribution in patent CN115661074A. Platelet classification is as follows: Figure 1 As shown in the figure, the percentage difference of each type of platelet between 28 healthy people and 34 ovarian cancer, 29 endometrial cancer and 31 cervical cancer patients was statistically analyzed. The results are shown in the figure. Figure 2 shown.

[0098] Figure 1 In order to classify platelets according to the distribution of α particles, they are divided into four categories: α particle number "N < 30", α particle number "N ≥ 30", "regular distribution" and "aggregated distribution".

[0099] Depend on Figure 2 The results show that the proportions of platelets with α-granules "N < 30," "regular distribution," and "aggregated distribution" differed significantly between healthy individuals and patients with ovarian, endometrial, and cervical cancer. Furthermore, the proportion of platelets with α-granules "N ≥ 30" also differed significantly between healthy individuals and patients with endometrial and cervical cancer, but did not differ significantly between healthy individuals and patients with ovarian cancer.

[0100] Furthermore, receiver operating characteristic curve analysis (ROC curve analysis) is mainly used to evaluate the effect of a certain indicator on the classification or diagnosis of two types of testers (such as patients and normal people), and to find the optimal indicator critical value, and then determine the critical value of this evaluation indicator.

[0101] The ROC curve analysis results between healthy people and patients with gynecological malignancies showed that the proportion of platelets with "regular distribution" of α particles can distinguish healthy people from patients with gynecological malignancies. Figure 3 As shown, the area under the receiver operating characteristic (ROC) curve was 0.915 (95% CI 0.866-0.964), with a significant p-value of <0.05. The Youden maximum for the proportion of platelets with "regularly distributed" α-granules that distinguishes healthy individuals from patients with gynecological malignancies was 7.75%, which can be used as the critical value for distinguishing healthy individuals from patients with gynecological malignancies. When the proportion of platelets with "regularly distributed" α-granules exceeds 7.75%, gynecological tumors can be diagnosed as malignant, with a corresponding sensitivity of 81.9% and a specificity of 92.9%. However, the proportion of other platelet types could not distinguish healthy individuals from patients with gynecological malignancies.

[0102] The true positive rate (TPR) is also called sensitivity: the ratio of the number of samples that are actually positive to the number of samples that are actually positive.

[0103] Specificity refers to the proportion of people who are actually disease-free that can be correctly identified as non-patients by the screening method, where specificity is equal to 1-false positive probability;

[0104] False positive rate (FPR): The ratio of the number of samples that are actually negative but are mistakenly judged as positive to the number of samples that are actually negative.

[0105] Comparative Example 1: Differentiation of healthy individuals and gynecological malignancies based on platelet subcellular structure microtubules

[0106] This study classified platelets according to their microtubule distribution and analyzed the differences in the proportions of different platelet types between healthy individuals and patients with ovarian cancer, endometrial cancer, and cervical cancer.

[0107] Blood from healthy individuals, ovarian cancer patients, endometrial cancer patients, and cervical cancer patients was used as the test subjects. Super-resolution microscopy technology was used to obtain super-resolution imaging of microtubules in the subcellular structure of platelets. The details are as follows:

[0108] (1) Super-resolution imaging of microtubules in platelet subcellular structures: In this example, the primary antibody required for microtubule detection was used, except for the different types of primary antibodies used for incubation, and the rest was the same as in Example 1;

[0109] (2) Platelet classification and statistical analysis: The super-resolution fluorescence image was input into the ResUNet and ResNet-50 algorithm models, and platelets were classified according to the microtubule distribution in patent CN115908302A. Platelet classification is as follows: Figure 4 As shown in the figure, the percentage differences of each type of platelets between 24 healthy people and 25 ovarian cancer, 29 endometrial cancer and 22 cervical cancer patients were statistically analyzed, as shown in the figure. Figure 5 shown.

[0110] Figure 4 In order to classify platelets according to their microtubule distribution, they are divided into four categories: “regular distribution”, “diffuse distribution”, “aggregated distribution” and “irregular distribution”.

[0111] like Figure 5 As shown, there was no significant difference in the proportion of platelets with "regular distribution" of microtubules and platelets with "irregular distribution" of microtubules between healthy people and patients with ovarian cancer, endometrial cancer, and cervical cancer, while there was a significant difference in the proportion of platelets with "diffuse distribution" of microtubules between healthy people and patients with endometrial cancer, but no significant difference between healthy people and patients with ovarian cancer and cervical cancer; there was a significant difference in the proportion of platelets with "aggregated distribution" of microtubules between healthy people and patients with cervical cancer, but no significant difference between healthy people and patients with ovarian cancer and endometrial cancer.

[0112] Furthermore, ROC curve analysis between healthy people and gynecological malignancies showed that the AUC of the proportion of platelets with "diffuse distribution" of microtubules for distinguishing healthy people from gynecological malignancies was 0.604 (95% Cl 0.489-0.720), and the significance p value was greater than 0.05. Therefore, the proportion of platelets with "diffuse distribution" of microtubules cannot distinguish healthy people from patients with gynecological malignancies.

[0113] At the same time, the results showed that the AUC of the proportion of platelets with microtubules in an "aggregated distribution" for distinguishing healthy people from gynecological malignancies was 0.615 (95% Cl 0.487-0.743), and the significant p value was greater than 0.05. Therefore, the proportion of platelets with microtubules in an "aggregated distribution" could not distinguish healthy people from patients with gynecological malignancies.

[0114] Example 2 Differentiation of healthy individuals, benign gynecological tumors, and malignant gynecological tumors based on the distribution of α-granules in platelet subcellular structures

[0115] This example classifies platelets according to the distribution of α particles, and analyzes the differences in the proportions of different types of platelets among 28 healthy individuals, 30 patients with benign gynecological tumors, and 94 patients with malignant gynecological tumors.

[0116] Among them, there were 30 patients with benign gynecological tumors, including 18 patients with ovarian cysts, 4 patients with endometrial hyperplasia, and 8 patients with endometrial polyps; there were 94 patients with malignant gynecological tumors, including 34 patients with ovarian cancer, 29 patients with endometrial cancer, and 31 patients with cervical cancer.

[0117] Blood from healthy individuals, patients with benign gynecological tumors, and patients with malignant gynecological tumors were used as test subjects. Super-resolution microscopy technology was used to obtain super-resolution imaging of α particles in the subcellular structure of platelets. The details are as follows:

[0118] (1) Super-resolution imaging of platelet subcellular structure α-granules: Same as Example 1.

[0119] (2) Platelet classification and statistical analysis: Platelet classification is the same as in Example 1. Statistical analysis of platelets after classification according to the distribution of α particles is shown in the following table. Figure 6 shown.

[0120] Depend on Figure 6 It can be seen that the proportion of platelets with α granules "N≥30" and the proportion of platelets with "aggregated" α granules decrease successively among healthy people, patients with benign gynecological tumors and patients with malignant gynecological tumors; while the proportion of platelets with "regular distribution" of α granules increases successively, indicating that the higher the proportion of platelets with "regular distribution" of α granules, the greater the possibility of suffering from gynecological tumors, especially the higher the risk of suffering from malignant gynecological tumors.

[0121] However, there was no significant difference in the proportion of platelets with α particles "N < 30" and "regular distribution" between healthy people and patients with benign gynecological tumors, but there was a significant difference between healthy people and patients with malignant gynecological tumors. It can be seen that the proportion of platelets with "regular distribution" of α particles and α particles "N < 30" may be a biomarker for quickly identifying benign and malignant gynecological tumors.

[0122] Comparative Example 2 Differentiation of healthy people, gynecological benign tumors and gynecological malignant tumors based on the distribution of platelet subcellular microtubules

[0123] This example classifies platelets according to their microtubule distribution, and analyzes the differences in the proportions of different types of platelets among 24 healthy individuals, 19 patients with benign gynecological tumors, and 76 patients with malignant gynecological tumors.

[0124] Among them, there were 19 patients with benign gynecological tumors, including 13 patients with ovarian cysts, 3 patients with endometrial hyperplasia, and 3 patients with endometrial polyps; there were 76 patients with malignant gynecological tumors, including 25 patients with ovarian cancer, 29 patients with endometrial cancer, and 22 patients with cervical cancer.

[0125] Blood samples from healthy individuals, patients with benign gynecological tumors, and patients with malignant gynecological tumors were used as test subjects. Super-resolution microscopy technology was used to obtain super-resolution imaging of microtubules in the subcellular structure of platelets. The details are as follows:

[0126] (1) Super-resolution imaging of platelet subcellular microtubules: same as comparative example 1.

[0127] (2) Platelet classification and statistical analysis: Platelet classification is the same as in Comparative Example 1. Statistical analysis of platelet classification according to microtubule distribution is shown in the following table. Figure 7 shown.

[0128] Depend on Figure 7 It can be seen that the proportion of the four types of platelets distributed according to microtubules is significantly different between healthy people and patients with benign gynecological tumors, and there is no significant difference in the other categories.

[0129] Further, receiver operating characteristic (ROC) curve analysis between healthy individuals and patients with benign gynecological tumors showed that the AUC for the proportion of platelets with microtubules in an aggregated distribution for differentiating between healthy individuals and patients with benign gynecological tumors was 0.712 (95% CI 0.556-0.868), with a significant p-value of <0.05, corresponding to a sensitivity of 84.2% and a specificity of 58.3%. When applied to differentiating between healthy individuals and patients with benign gynecological tumors, specificity is the primary consideration, with higher specificity being preferred. However, this low specificity suggests that the proportion of platelets with microtubules in an aggregated distribution is not a good predictor of healthy individuals from patients with benign gynecological tumors.

[0130] There was no significant difference between healthy people and patients with gynecological malignancies, indicating that the proportion of different types of platelets based on microtubule distribution cannot be used to distinguish healthy people from patients with gynecological malignancies.

[0131] Example 3 Specificity of Distinguishing Benign from Malignant Gynecological Tumors Based on Regular Distribution of Alpha Particles and the Ratio of Platelets with Alpha Particles N<30

[0132] In this embodiment, blood samples from patients with benign gynecological tumors and patients with malignant gynecological tumors are used as test objects, wherein sample group 1 is blood samples from patients with benign gynecological tumors, and sample group 2 is blood samples from patients with malignant gynecological tumors, as follows:

[0133] Similar to Example 1, super-resolution imaging technology was used to detect α particles in platelets, and super-resolution imaging of α particles of two groups of sample platelets was obtained;

[0134] Platelet classification and statistics were performed according to the platelet classification in Example 1. The results were as follows:

[0135] Sample group 1: In patients with benign gynecological tumors, the proportion of platelets with α-granules "N<30" was 89.77%±2.29%, the proportion of platelets with "N≥30" was 2.33%±1.46%, the proportion of platelets with "regular distribution" was 4.09%±1.31%, and the proportion of platelets with "aggregated distribution" was 3.81%±1.41%.

[0136] Sample group 2: In patients with gynecological malignancies, the proportion of platelets with α-granules "N<30" was 54.78%±2.99%, the proportion of platelets with "N≥30" was 1.84%±0.58%, the proportion of platelets with "regular distribution" was 42.20%±3.08%, and the proportion of platelets with "aggregated distribution" was 1.18%±0.27%.

[0137] The ROC curves were drawn for the different proportions of platelets with “regular distribution” of α particles and “N<30” of α particles in the two groups of samples, as shown in Figure 2. Figure 8 As shown in the results, the AUC area of ​​the ROC curve drawn with different proportions of platelets with "regular distribution" of α particles was 0.910 (95% Cl0.860-0.961), and the significant p value was <0.05, indicating that different proportions of platelets with "regular distribution" of α particles can better distinguish patients with benign gynecological tumors and gynecological malignancies, and the diagnostic results are highly reliable.

[0138] The ROC curve between benign gynecological tumors and malignant gynecological tumors was drawn using different proportions of platelets with α particles "N<30". The results showed that the proportion of platelets with α particles "N<30" was difficult to distinguish between patients with benign gynecological tumors and malignant gynecological tumors, and could not be used for the diagnosis of benign and malignant gynecological tumors.

[0139] The Youden Index, also known as the accuracy index, is a commonly used method that assumes that false negatives (missed diagnoses) and false positives (misdiagnoses) are equally harmful. It reflects the overall accuracy of the diagnosis between true patients and non-patients. The Youden Index is the sum of sensitivity and specificity minus 1. A higher Youden Index indicates greater accuracy. The value of the test variable corresponding to the maximum Youden Index is the diagnostic cutoff for this method: Youden Index = Sensitivity + Specificity - 1.

[0140] Further analysis showed that the proportion of platelets with "regular distribution" of α particles corresponding to the Youden maximum for distinguishing benign gynecological tumors from gynecological malignant tumors is 5.66%, which can be used as the critical value for distinguishing patients with benign gynecological tumors from gynecological malignant tumors. When the proportion of platelets with "regular distribution" of α particles exceeds 5.66%, the gynecological tumor can be diagnosed as malignant, with a corresponding sensitivity of 85.1% and a specificity of 83.3%.

[0141] Example 4: Evaluation of therapeutic efficacy in patients with malignant tumors based on the proportion of platelets with “regular distribution” of α particles

[0142] This example uses blood samples from patients with gynecological malignancies before and after treatment as test objects, where sample group 1 is blood samples from patients with gynecological malignancies before treatment, and sample group 2 is blood samples from patients with gynecological malignancies after treatment, as follows:

[0143] Similar to Example 1, super-resolution imaging technology was used to detect α particles in platelets, and super-resolution imaging of α particles of two groups of sample platelets was obtained;

[0144] Platelet classification and statistics, according to the platelet classification in Example 1, the statistical analysis results are as follows Figure 9 As shown:

[0145] Depend on Figure 9 It can be seen that the proportions of platelets with α granules "N<30", platelets with α granules "regular distribution" and platelets with α granules "aggregated distribution" were significantly different between patients with gynecological malignancies and healthy controls before treatment, but there were no significant differences between patients with gynecological cancer and healthy controls after treatment. Figure 9 It can be seen that after treatment, the proportion of platelets with α particles "N<30" and platelets with α particles showing "aggregated distribution" in patients with gynecological tumors increased significantly, while the proportion of platelets with α particles showing "regular distribution" decreased significantly, which can reflect that patients with gynecological tumors have improved after treatment, indirectly indicating that the treatment is effective.

[0146] The ROC curves between patients with gynecological malignancies before and after treatment were drawn for the different proportions of platelets with "regular distribution" of α particles in the two groups of samples, such as Figure 10 As shown:

[0147] Figure 10 The area under the receiver operating characteristic (ROC) curve was 0.891 (95% CI 0.731-1.000), with a significant p-value of <0.05, indicating that the proportion of platelets with "regularly distributed" α particles has the potential to distinguish patients with gynecological malignancies before and after treatment, and can be used to evaluate the effect of treatment in patients with gynecological malignancies.

[0148] Further analysis showed that the proportion of platelets with "regular distribution" of α particles distinguished the Youden maximum before and after treatment of gynecological malignancies, which corresponded to a ratio of 17.71%. This can be used as a critical value to judge whether the treatment of patients with gynecological malignancies is effective. When the proportion of platelets with "regular distribution" of α particles is lower than 17.71%, it can be judged that the treatment effect of patients with gynecological malignancies is good, and the corresponding sensitivity is 100%. When used to judge the treatment effect of patients with gynecological malignancies, the main reference is sensitivity, and the higher the sensitivity, the more accurate the assessment. Therefore, it can be seen that the use of the proportion of platelets with "regular distribution" of α particles to assess whether the treatment of patients with gynecological malignancies is effective has a high assessment accuracy.

[0149] It will be easily understood by those skilled in the art 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 in the scope of protection of the present invention.

Claims

1. A gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure, characterized in that: It includes a super-resolution image acquisition module for platelet subcellular structure, a platelet classification and statistics module, and a gynecological tumor diagnosis module; The super-resolution image acquisition module of the platelet subcellular structure is used to obtain multiple super-resolution images of platelet α particles composed of fluorescent signal pixels and non-fluorescent signal pixels of the sample to be diagnosed, and submit them to the platelet classification and statistics module; The platelet classification and statistics module counts the total number of platelets and the number of platelets with "regular distribution" of α particles based on all acquired α particle super-resolution images, calculates the proportion of "regularly distributed" platelets in the sample, and submits it to the gynecological tumor diagnosis module; the platelets with "regular distribution" of α particles are searched for the ellipse with the smallest area that meets condition A in the α particle image of the single platelet, as the outer circle: if the area of ​​the outer circle is above the preset threshold of the area of ​​the regular distribution ellipse and the ratio of the short axis to the long axis is within the range of the preset regular distribution ellipse, then the platelets involved in the single platelet α particle distribution image are judged to be "regularly distributed"; the threshold of the area of ​​the regular distribution ellipse is 0.15μm 2 -20.0μm 2 The range of the regular distribution ellipse is [0.2, 1]; The gynecological tumor diagnosis module is used to determine that the higher the proportion of platelets with "regular distribution" of alpha particles, the greater the possibility that the sample is a gynecological tumor. The gynecological tumors include benign gynecological tumors and malignant gynecological tumors.

2. The gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure according to claim 1, characterized in that: The gynecological tumor diagnosis module is used to judge that the sample is at high risk of gynecological malignancies based on the proportion of platelets with "regular distribution" of α particles being greater than a preset threshold; the preset threshold is less than or equal to the ratio corresponding to the Youden maximum value corresponding to the ROC curve between healthy people and patients with gynecological malignancies.

3. The gynecological tumor diagnosis system based on platelet subcellular structure super-resolution imaging according to claim 2, characterized in that: The preset threshold is the ratio of the Youden maximum value corresponding to the ROC curve between healthy people and patients with gynecological malignancies.

4. The gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure according to claim 3, characterized in that: The proportion corresponding to the Youden maximum is 7.75%, that is, when the proportion of platelets with "regularly distributed" α particles is greater than 7.75%, it is judged to be a gynecological malignant tumor.

5. The gynecological tumor diagnosis system based on platelet subcellular structure super-resolution imaging according to claim 1, characterized in that: If the sample to be diagnosed is from a gynecological tumor patient, the gynecological tumor diagnosis module is further used to compare the obtained proportion of "regularly distributed" platelets with a threshold value to determine whether the gynecological tumor is benign or malignant. The specific determination method is as follows: If the proportion of platelets with "regular distribution" of α particles in the patient is less than or equal to the threshold, the patient is judged to have a benign gynecological tumor; If the proportion of platelets with "regular distribution" of α particles in the patient is greater than the threshold, the patient is diagnosed with gynecological malignancy; The threshold is the ratio of the Youden maximum value corresponding to the ROC curve between patients with benign gynecological tumors and patients with malignant gynecological tumors.

6. The gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure according to claim 5, characterized in that: The Youden maximum corresponds to a ratio of 5.66%.

7. The gynecological tumor diagnosis system based on platelet subcellular structure super-resolution imaging according to any one of claims 1 to 6, characterized in that: Also included is a gynecologic oncology treatment assessment module; The platelet subcellular structure super-resolution image acquisition module is used to obtain platelet alpha granule super-resolution images composed of fluorescent signal pixels and non-fluorescent signal pixels of patients with gynecological malignancies before and after treatment, and submit them to the platelet classification and statistics module; The platelet classification and statistics module classifies individual platelets into categories of "regular distribution" of alpha particles, "N < 30" of alpha particles, "N ≥ 30" of alpha particles, and "aggregated distribution" of alpha particles based on the distribution of individual platelet alpha particles in super-resolution images acquired before and after treatment of patients with gynecological malignancies, and calculates the percentage of each type of platelet in the total number of platelets, and submits the results to the gynecological tumor treatment assessment module; The gynecological tumor treatment evaluation module is used to determine whether the treatment is effective based on the comparison of the proportion of platelets with "regular distribution" of α particles, the number of α particles "N < 30", and the proportion of platelets with "aggregated distribution" of α particles before and after treatment. The specific judgment method is as follows: If the proportion of platelets with "regular distribution" of α particles decreases after treatment, or the proportion of platelets with "N < 30" and / or "aggregated distribution" of α particles increases after treatment compared with before treatment, the treatment is considered to be effective.

8. The gynecological tumor diagnosis system based on platelet subcellular structure super-resolution imaging according to claim 7, characterized in that: If the proportion of platelets with "regular distribution" of α particles in patients after treatment is significantly reduced compared with before treatment, the treatment effect is considered to be good.

9. The gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure according to claim 8, characterized in that: If the proportion of platelets with "regular distribution" of α particles after treatment is less than or equal to the proportion corresponding to the Youden maximum value of the ROC curve between patients before and after treatment of gynecological malignancies, the treatment effect is evaluated as good.

10. The gynecological tumor diagnosis system based on super-resolution imaging of platelet subcellular structure according to claim 9, characterized in that: The proportion corresponding to the Youden maximum is 17.71%.

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