A method for detecting circulating tumor cells

Abstract

The present invention relates to the technical field of cell detection, and particularly to a method for detecting circulating tumor cells, comprising: performing image enhancement on an imaging image to obtain a clear image and determining the marginal characterization status and microvessel density of the primary tumor, determining the tumor metastasis grade of the patient according to the TNM staging report, the marginal characterization status and the microvessel density, determining the coating thickness of the immunomagnetic beads for coating according to the tumor metastasis grade and the antibody affinity of the corresponding novel specific surface marker antibody of tumor cells to prepare a magnetic bead suspension, performing incubation to form a pre-enrichment mixture and judging whether to end the incubation according to its color and turbidity, and applying a magnetic field to form a post-enrichment mixture, separating the supernatant to obtain a magnetic bead-CTCs complex and washing and detecting it. The present invention provides a method for detecting circulating tumor cells with high efficiency and high precision, improving the accuracy of the evaluation of tumor metastasis grade and optimizing the detection efficiency of circulating tumor cells.

Description

Technical Field

[0001] The present invention relates to the technical field of cell detection, and particularly to a method for detecting circulating tumor cells. Background Art

[0002] The background art of circulating tumor cell (CTC) detection technology involves multiple fields, including immune selection and physical property-based CTC separation methods: Immune selection methods rely on specific biomarkers on the surface of tumor cells, such as EpCAM antibodies, which have high selection specificity but may affect the activity of tumor cells; Physical property separation methods are based on the physical differences between tumor cells and normal blood cells, such as size and density, to achieve high-throughput separation, but are prone to false positive results; To overcome the limitations of a single method, researchers have attempted to combine biological and physical properties for CTC separation, such as lateral filter array microfluidics technology (LFAM) combined with the principle of pore sieves of different sizes and filters modified with EpCAM antibodies to improve the CTC capture rate.

[0003] Chinese Patent Publication No. CN110456034B discloses a method for detecting circulating tumor cells: collecting a human body fluid sample, increasing cell membrane permeability after fixing with formaldehyde, adding the cells to several groups of PCR multi-tube reactions, each tube containing a reverse transcription primer carrying ID1, after reverse transcription into cDNA molecules, collecting the cells and mixing them into another group of PCR multi-tube reactions, each tube containing an extension primer carrying ID2 and capable of specifically recognizing the 3' end of the cDNA molecule at the 3' end, performing an extension reaction after sequence complementarity, collecting and lysing the cells, and pre-amplifying the extension products with PCR primers. High-throughput sequencing is used to analyze the combination mode of ID1:ID2. This invention simultaneously performs reverse transcription and PCR amplification on different markers, can accurately identify circulating tumor cells and circulating tumor cells undergoing EMT, and can be used in combination with other technologies to detect extremely few circulating tumor cells and other non-body fluid rare cells in body fluid samples to the greatest extent. Thus, the following problems exist in this invention:

[0004] This invention does not optimize the coating thickness of the coated immunomagnetic beads based on the comprehensive TNM report and imaging images to improve the detection accuracy and efficiency of circulating tumor cells. Summary of the Invention

[0005] Therefore, the present invention provides a method for detecting circulating tumor cells to overcome the problem in the prior art that the coating thickness of the coated immunomagnetic beads is not optimized based on the comprehensive TNM report and imaging images to improve the detection accuracy and efficiency of circulating tumor cells.

[0006] To achieve the above object, the present invention provides a method for detecting circulating tumor cells, including:

[0007] Step S1: Perform imaging examinations and regional lymph node biopsies on the patient to obtain imaging images and the patient's TNM staging report;

[0008] Step S2: Process the imaging images to obtain clear images, and determine the edge characterization status and microvessel density of the primary tumor based on the clear images;

[0009] Step S3: Determine the tumor metastasis grade of the patient based on the TNM staging report, the edge characterization status, and the microvessel density;

[0010] Step S4: Collect 5 ml to 10 ml of the patient's peripheral venous blood sample and perform pretreatment on it;

[0011] Step S5: Determine the coating thickness of the coated immunomagnetic beads based on the tumor metastasis grade and the antibody affinity of the corresponding novel specific surface marker antibody for tumor cells, and prepare the coated immunomagnetic beads according to the coating thickness to prepare a magnetic bead suspension;

[0012] Step S6: Add a preset amount of the magnetic bead suspension to the blood sample and then transfer it to a dynamic incubation device for incubation to form a pre-enrichment mixture of circulating tumor cells;

[0013] Step S7: Judge whether to end the incubation according to the color and turbidity of the pre-enrichment mixture, and transfer the pre-enrichment mixture of circulating tumor cells to a separator according to the judgment result of the end of incubation and apply a magnetic field to enrich it to form a post-enrichment mixture of circulating tumor cells;

[0014] Step S8: Separate the supernatant of the post-enrichment mixture to obtain a magnetic bead-circulating tumor cell complex;

[0015] Step S9: Automatically wash and detect the magnetic bead-circulating tumor cell complex to analyze the circulating tumor cells.

[0016] Further, the step S2 includes,

[0017] Step S21: Process the imaging images to obtain clear images, and the clear processing includes,

[0018] Step S211: Perform denoising processing on the imaging images using spatial domain filtering or frequency domain filtering to obtain a denoised image;

[0019] Step S212: Adjust the contrast of the denoised image using gray-scale stretching or histogram equalization to obtain an enhanced image;

[0020] Step S213: Perform edge enhancement and sharpening on the enhanced image using a gradient operator or a Laplacian operator to obtain a clear image;

[0021] Step S22: Use a deep learning model to determine the marginal characterization status and microvessel density of the primary tumor based on the clear image.

[0022] Further, the method for determining the tumor metastasis grade in step S3 includes

[0023] Step S31: Obtain the TNM staging report, the marginal characterization status, and the microvessel density, and determine the T value, N value, and M value;

[0024] Step S32: Determine the tumor metastasis grade according to the M value, including

[0025] If the M value is M1, it is determined that the tumor metastasis grade is high;

[0026] If the M value is M0, determine the tumor metastasis grade according to the marginal characterization status, the microvessel density, the T value, and the N value;

[0027] Step S33: Determine the tumor metastasis tendency according to the marginal characterization status and the microvessel density, including

[0028] If the marginal characterization status is an irregular margin and the microvessel density is greater than or equal to the preset density, it is determined that the tumor metastasis tendency is overt metastasis;

[0029] If the marginal characterization status is a regular margin and the microvessel density is less than the preset density, it is determined that the tumor metastasis tendency is occult metastasis;

[0030] Step S34: Determine the lymph node involvement tendency according to the T value and the N value, including

[0031] If the T value is T2 and the N value is N0, it is determined that the lymph node involvement tendency is occult involvement;

[0032] If the T value is T2 and the N value is not N0, it is determined that the lymph node involvement tendency is overt involvement;

[0033] Step S35: Determine the tumor metastasis grade according to the tumor metastasis tendency and the lymph node involvement tendency.

[0034] Further, in step S35, determining the tumor metastasis grade according to the tumor metastasis tendency and the lymph node involvement tendency includes

[0035] If the tumor metastasis tendency is overt metastasis and the lymph node involvement tendency is overt involvement, it is determined that the tumor metastasis grade is high;

[0036] If the tumor metastasis tendency is dominant metastasis or the lymph node involvement tendency is dominant involvement, it is determined that the tumor metastasis grade is intermediate;

[0037] If the tumor metastasis tendency is recessive metastasis and the lymph node involvement tendency is recessive involvement, it is determined that the tumor metastasis grade is low.

[0038] Furthermore, step S4 includes,

[0039] Step S41, collecting a peripheral venous blood sample of the patient using a vacuum blood collection tube containing an anticoagulant;

[0040] Step S42, determining whether to refrigerate the blood sample according to the time difference between the detection time and the blood drawing time of the blood sample, where,

[0041] If the time difference is greater than a preset duration, the blood sample is refrigerated in a refrigerator at 4°C;

[0042] If the time difference is less than or equal to the preset duration, the blood sample is not refrigerated;

[0043] Step S43, shaking the blood sample at room temperature to evenly distribute the anticoagulant in the blood sample,

[0044] where,

[0045] If the blood sample is refrigerated, it is restored to room temperature and then shaken at room temperature;

[0046] If the blood sample is not refrigerated, it is directly shaken at room temperature for standby.

[0047] Furthermore, in step S5, determining the coating thickness of the coated immunomagnetic beads according to the tumor metastasis grade and the antibody affinity includes,

[0048] If the tumor metastasis grade is high and the antibody affinity is greater than or equal to a preset antibody affinity, it is determined that the coating thickness is the first thickness;

[0049] If the tumor metastasis grade is high or the antibody affinity is greater than or equal to the preset antibody affinity, it is determined that the coating thickness is the second thickness;

[0050] If the tumor metastasis grade is not high and the antibody affinity is less than the preset antibody affinity, it is determined that the coating thickness is the third thickness;

[0051] where, the first thickness ≤ the second thickness ≤ the third thickness.

[0052] Furthermore, in step S5, a color marker is added when preparing the coated immunomagnetic beads;

[0053] Among them, the color of the color marking is a color with a strong contrast to red.

[0054] Furthermore, step S6 includes

[0055] Step S61, adding a preset amount of magnetic bead suspension to the blood sample and taking an initial blood sample image, and determining the initial turbidity according to the deep learning model;

[0056] Step S62, transferring the blood sample added with the magnetic bead suspension to a dynamic incubation device;

[0057] Step S63, obtaining a number of incubation images of the blood sample at a preset shooting frequency, and determining the real-time color uniformity and real-time turbidity of each of the incubation images according to the deep learning model.

[0058] Furthermore, in step S7, determining whether to end the incubation according to the incubation image and the initial blood sample image includes

[0059] If the real-time color uniformity is greater than or equal to the preset uniformity and the turbidity difference is greater than or equal to the preset difference, it is determined that the incubation ends;

[0060] If the real-time color uniformity is less than the preset uniformity and / or the turbidity difference is less than the preset difference, it is determined that the incubation has not ended.

[0061] Furthermore, the turbidity difference is the difference between the real-time turbidity and the initial turbidity.

[0062] Compared with the prior art, the beneficial effects of the present invention are that the detection method of circulating tumor cells provided by the present invention determines the coating thickness of the coated immunomagnetic beads according to the tumor metastasis grade of the patient and the antibody affinity of the corresponding novel specific surface marker antibody of the tumor cells, thereby preparing a magnetic bead suspension suitable for the individual differences of the patient, improving the pertinence and accuracy of the detection. This method provides a complete and reliable circulating tumor cell detection method for clinical use, helps doctors to more accurately understand the tumor metastasis situation of the patient, formulate personalized treatment plans / monitor the treatment effect, and also provides an important reference basis for the early detection, prognosis evaluation and recurrence monitoring of tumors.

[0063] Furthermore, step S2 of the present invention not only improves the accuracy of circulating tumor cell detection, but also provides more detailed and reliable tumor information for clinical use.

[0064] Furthermore, the present invention comprehensively determines the tumor metastasis tendency through the edge characterization state and the microvessel density, which can avoid the limitations of the determination results; in addition, the edge characterization state and the microvessel density are determined by the deep learning model, reducing the subjective influence of people and enabling a more objective judgment of the tumor metastasis grade.

[0065] Furthermore, the comprehensive evaluation method in step S3 of the present invention not only improves the accuracy of the evaluation, but also reduces the influence of human subjective judgment, making the evaluation result more objective and reliable. In addition, the microvessel density inside the tumor is evaluated by enhanced CT or MRI examination after injecting contrast agent, further enhancing the precision and scientific nature of the evaluation.

[0066] Furthermore, by integrating multi-dimensional information, the present invention not only improves the accuracy of evaluating the tumor metastasis grade, but also introduces antibody affinity to determine the coating thickness of the coated immunomagnetic beads, thereby further optimizing the detection efficiency and purity of circulating tumor cells: for high metastasis grade or high affinity antibody, a thinner coating layer is used to improve the capture efficiency; while for low metastasis grade or low affinity antibody, a thicker coating layer is selected to ensure sufficient stability and capture purity. This strategy of dynamically adjusting the coating thickness not only improves the detection sensitivity, but also effectively reduces non-specific binding, making the separation and enrichment of circulating tumor cells more efficient and pure. The detection method of circulating tumor cells provided by the present invention realizes a comprehensive upgrade of the circulating tumor cell detection technology by accurately evaluating the tumor metastasis grade, scientifically measuring the antibody affinity, and optimizing the coating thickness of the immunomagnetic beads accordingly, improving the accuracy and sensitivity of the detection.

[0067] Furthermore, the present invention can accurately judge whether the incubation is over by real-time monitoring of the change in the real-time uniformity and turbidity of the color of the blood sample during the incubation process, thereby ensuring the binding efficiency between the immunomagnetic beads and the circulating tumor cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a step diagram of the detection method of circulating tumor cells in the embodiment of the present invention;

[0069] Figure 2 It is a method step diagram for determining the tumor metastasis grade in the embodiment of the present invention;

[0070] Figure 3 It is a method step diagram of the incubation process in the embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0071] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0072] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0073] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention.

[0074] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and defined, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0075] Please refer to Figure 1 as shown, which is a step diagram of the detection method for circulating tumor cells in an embodiment of the present invention. The embodiment of the present invention provides a detection method for circulating tumor cells, including:

[0076] Step S1, perform imaging examinations and regional lymph node biopsies on the patient to obtain imaging images and the patient's TNM staging report; in practice, the imaging examinations include enhanced CT scans or enhanced MRI scans. The enhanced MRI scan is a scan examination performed after intravenous injection of a contrast agent on the basis of conventional magnetic resonance imaging (MRI), which can more clearly show the characteristics of the diseased tissue, including many detailed information such as the scope, boundary, internal structure of the lesion, and its relationship with the surrounding tissues.

[0077] Step S2, perform image enhancement processing on the imaging images to obtain clear images, and determine the edge characterization state and microvessel density of the primary tumor according to the clear images.

[0078] Step S3, determine the tumor metastasis grade of the patient according to the TNM staging report, the edge characterization state, and the microvessel density.

[0079] Step S4, collect 5 ml to 10 ml of the patient's peripheral venous blood sample and perform pretreatment on it.

[0080] Step S5, determine the coating thickness of the coated immunomagnetic beads according to the tumor metastasis grade and the antibody affinity of the corresponding novel specific surface marker antibody for tumor cells, and prepare the coated immunomagnetic beads according to the coating thickness to prepare a magnetic bead suspension.

[0081] Step S6: After adding a preset amount of magnetic bead suspension to the blood sample, transfer it to a dynamic incubation device for incubation to form a pre-enrichment mixture of circulating tumor cells. It can be understood that the addition amount of the magnetic bead suspension is usually 0.1 to 0.5 times the volume of the blood sample, and preferably the preset amount is 0.3 times the volume of the blood sample.

[0082] Step S7: Determine whether to end the incubation according to the color and turbidity of the pre-enrichment mixture, and transfer the pre-enrichment mixture of circulating tumor cells to a separator according to the judgment result of the end of incubation and apply a magnetic field to enrich it to form a post-enrichment mixture of circulating tumor cells.

[0083] Step S8: Separate the supernatant of the post-enrichment mixture using a pipette to obtain a magnetic bead-circulating tumor cell complex. In practice, it is also necessary to select a suitable pipette tip (its specification is selected according to the volume of the supernatant and the size of the container. Generally, a pipette with a small range and a thinner tip is used to accurately aspirate the supernatant and reduce interference with the magnetic bead-circulating tumor cell CTCs complex. Among them, for a small volume (less than 1 ml) of supernatant, a pipette with a range of 10 to 1000 μl and a matching thin tip are selected); and adjust the range of the pipette according to the volume of the supernatant to be transferred.

[0084] Step S9: Automatically wash and detect the magnetic bead-circulating tumor cell complex to analyze the circulating tumor cells. In practice, Step S9 includes: placing the container adsorbed with the magnetic bead-circulating tumor cell complex on an automated washing and detection platform; the platform automatically washes the complex with an appropriate amount of buffer solution, and repeats the operations of adsorbing the complex with a magnetic field and discarding the washing solution 2 to 3 times to remove residual unbound blood components and non-specific adsorption substances; if a multifunctional immunomagnetic bead is used, the fluorescence probe carried by it can be used for real-time monitoring, or new detection technologies such as nanosensor technology and microfluidic chip technology can be combined to perform highly sensitive and rapid detection of circulating tumor cells.

[0085] It can be understood that the detection method of circulating tumor cells provided by the present invention determines the coating thickness of the coated immunomagnetic beads according to the tumor metastasis grade of the patient and the antibody affinity of the corresponding novel specific surface marker antibody of the tumor cells, thereby preparing a magnetic bead suspension suitable for the individual differences of the patient, improving the pertinence and accuracy of the detection. This method provides a complete and reliable circulating tumor cell detection method for clinical practice, helps doctors more accurately understand the tumor metastasis situation of patients, formulate personalized treatment plans / monitor treatment effects, and also provides an important reference basis for the early detection, prognosis evaluation and recurrence monitoring of tumors.

[0086] Specifically, Step S2 includes

[0087] Step S21, perform clarity processing on the imaging image to obtain a clear image. The clarity processing includes:

[0088] Step S211, perform denoising processing on the imaging image using spatial domain filtering or frequency domain filtering to obtain a denoised image;

[0089] Step S212, adjust the contrast of the denoised image using gray-scale stretching or histogram equalization to obtain an enhanced image;

[0090] Step S213, perform edge enhancement and sharpening on the enhanced image using a gradient operator or a Laplacian operator to obtain a clear image;

[0091] Step S22, use a deep learning model to determine the edge characterization state and microvessel density of the primary tumor based on the clear image. It can be understood that the edge characterization state includes an irregular edge and a regular edge.

[0092] It can be understood that, first of all, through denoising using spatial domain filtering, frequency domain filtering, enhancing the contrast using gray-scale stretching or histogram equalization, and performing edge enhancement and sharpening using a gradient operator or a Laplacian operator, the imaging image is clearly processed to obtain a high-quality clear image, providing a solid foundation for subsequent tumor feature analysis. At the same time, using a deep learning model to analyze the clear image, the edge characterization state and microvessel density of the primary tumor are accurately determined, and the irregular edge and the regular edge can be distinguished, thus more comprehensively reflecting the biological characteristics of the tumor. Step S2 not only improves the accuracy of circulating tumor cell detection but also provides more detailed and reliable tumor information for clinical practice.

[0093] Please refer to Figure 2 as shown, which is a method step diagram for determining the tumor metastasis grade in an embodiment of the present invention. Specifically, the method for determining the tumor metastasis grade in the step S3 includes:

[0094] Step S31, obtain the TNM staging report, the edge characterization state, and the microvessel density, and determine the T value, the N value, and the M value. It can be understood that the TNM staging is determined by a doctor based on three dimensions: the primary tumor (T), the involvement of regional lymph nodes (N), and distant metastasis (M). It can be understood that the value of the T value only includes T1 and T2. Usually, when the tumor is less than or equal to 2 cm, it is in the T1 stage; the value of the N value includes N0, N1, N2, and N3. Usually, when there is no lymph node metastasis, it is N0, and the larger the i of Ni (i = 1, 2, 3) with the increasing number of lymph node metastases; the value of the M value only includes M0 and M1. The staging without signs of distant metastasis is M0, and the staging with signs of distant metastasis is M1;

[0095] Step S32: Determine the tumor metastasis grade according to the M value, including:

[0096] If the M value is M1, it is determined that the tumor metastasis grade is high;

[0097] If the M value is M0, determine the tumor metastasis grade according to the marginal characterization status, the microvessel density, the T value, and the N value; it can be understood that the staging of M1 indicates that distant metastasis has occurred, and the corresponding tumor may have reached the middle or late stage. At this time, there must be a relatively large number of circulating tumor cells in the blood, and the tumor metastasis grade is directly determined to be high; if the M value determines that there are no signs of distant metastasis, the tumor metastasis grade is determined according to the consistency between the tumor metastasis grade and the tendency of lymph node involvement;

[0098] Step S33: Determine the tumor metastasis tendency according to the marginal characterization status and the microvessel density, including:

[0099] If the marginal characterization status is an irregular margin and the microvessel density is greater than or equal to the preset density, it is determined that the tumor metastasis tendency is overt metastasis;

[0100] If the marginal characterization status is a regular margin and the microvessel density is less than the preset density, it is determined that the tumor metastasis tendency is occult metastasis;

[0101] It can be understood that tumors with irregular margins and infiltrative growth are more likely to metastasize. In pathological examinations, it is found that tumor cells break through the capsule around the tumor and infiltrate into the surrounding normal tissues, then the probability of the appearance of CTCs and the resulting metastasis will increase; in addition, tumor cells need to enter the blood circulation through new blood vessels. The discovery of a large increase in microvessel density in tumor tissues indicates that tumor cells are more likely to enter the blood vessels to become CTCs and metastasize.

[0102] In practice, the microvessel density inside the tumor can be evaluated by enhanced CT or MRI examinations after injecting contrast agents; CT enhanced scanning is an examination performed after intravenous injection of contrast agents. The contrast agent (such as iodine contrast agent) flows in the blood circulation. When it flows through the areas with rich blood vessels inside the tumor, due to the structural and functional characteristics of tumor blood vessels, the contrast agent will accumulate in these areas; since the principle of CT imaging is based on the penetration difference of X-rays for different tissues, the contrast agent contains elements with high atomic numbers (such as iodine), and its ability to absorb X-rays is stronger than that of surrounding tissues; therefore, the areas with more contrast agents (i.e., the areas with rich tumor blood vessels) will show higher density on CT images during the scanning process.

[0103] In practice, the microvessel density is the number of microvessels per high-power field; generally, the preset density value is 40 microvessels / high-power field to 70 microvessels / high-power field, and preferably, the preset density is set to 50 microvessels / high-power field.

[0104] It can be understood that by comprehensively judging the tumor metastasis tendency through the edge characterization state and the microvessel density, the limitation of the judgment result can be avoided; in addition, the edge characterization state and the microvessel density are determined by a deep learning model, reducing the subjective influence of people and enabling a more objective judgment of the tumor metastasis grade.

[0105] Step S34, determining the lymph node involvement tendency according to the T value and the N value, includes

[0106] If the T value is T2 and the N value is N0, it is determined that the lymph node involvement tendency is latent involvement;

[0107] If the T value is T2 and the N value is not N0, it is determined that the lymph node involvement tendency is overt involvement;

[0108] Determining the involvement tendency of the primary tumor in the surrounding lymph nodes according to the T value and the N value subjectively judged by the doctor is a relatively subjective judgment of the tumor metastasis grade;

[0109] Step S35, determining the tumor metastasis grade according to the tumor metastasis tendency and the lymph node involvement tendency. It can be understood that in this step, the tumor metastasis grade is comprehensively determined through the consistency of the objective tumor metastasis tendency and the subjective lymph node involvement tendency.

[0110] It can be understood that through a series of steps in step S3 of the present invention, the accuracy and reliability of the tumor metastasis grade evaluation are improved: First, multi-dimensional information such as the TNM staging report, the edge characterization state of the primary tumor, and the microvessel density is integrated, and objective analysis is performed through a deep learning model, avoiding the limitation of single-index evaluation; particularly when determining the tumor metastasis grade, not only the key index of whether there is distant metastasis is considered, but also multiple factors such as the edge characterization state, the microvessel density, the size of the primary tumor, and the involvement of regional lymph nodes are combined, achieving a comprehensive evaluation of the tumor metastasis tendency and the lymph node involvement tendency; this comprehensive evaluation method not only improves the accuracy of the evaluation, but also reduces the influence of subjective human judgment, making the evaluation result more objective and reliable; in addition, the microvessel density inside the tumor is evaluated by enhanced CT or MRI examination after injecting contrast agent, further enhancing the precision and scientific nature of the evaluation.

[0111] Specifically, in step S35, determining the tumor metastasis grade according to the tumor metastasis tendency and the lymph node involvement tendency includes

[0112] If the tumor metastasis tendency is dominant metastasis and the lymph node involvement tendency is dominant involvement, then the tumor metastasis grade is determined to be high; that is, when the subjective and objective judgments are consistent and both are in the dominant state, the current tumor metastasis grade can be accurately determined to be high;

[0113] If the tumor metastasis tendency is dominant metastasis or the lymph node involvement tendency is dominant involvement, then the tumor metastasis grade is determined to be medium; that is, when the subjective and objective judgments are inconsistent, it represents that the current tumor metastasis grade is medium;

[0114] If the tumor metastasis tendency is recessive metastasis and the lymph node involvement tendency is recessive involvement, then the tumor metastasis grade is determined to be low; that is, when the subjective and objective judgments are consistent and both are in the recessive state, the current tumor metastasis grade can be accurately determined to be low.

[0115] It can be understood that by comprehensively considering the tumor metastasis tendency and the lymph node involvement tendency, a more accurate and comprehensive evaluation method is provided for determining the tumor metastasis grade: First, a deep learning model is used to analyze the marginal characterization state and microvessel density of the primary tumor to objectively judge the tumor metastasis tendency, avoiding the limitations of single indicators or subjective judgments; at the same time, the T value and N value judged by the TNM staging report are combined to determine the lymph node involvement tendency, further improving the accuracy and reliability of the evaluation; when determining the tumor metastasis grade, a comprehensive judgment is made according to the consistency of the tumor metastasis tendency and the lymph node involvement tendency. When both are dominant, it is determined as a high metastasis grade; when at least one of them is dominant, it is determined as a medium metastasis grade, which can handle the situation where the subjective and objective judgments are inconsistent; when both are recessive, it is determined as a low metastasis grade; this comprehensive evaluation method not only improves the accuracy of the evaluation, but also provides a more reliable basis for doctors to formulate personalized treatment plans, helps patients obtain better treatment effects and quality of life, so that the detection method of circulating tumor cells has clinical application value and significance.

[0116] Specifically, step S4 includes,

[0117] Step S41, collecting a peripheral venous blood sample of the patient using a vacuum blood collection tube containing an anticoagulant;

[0118] Step S42, determining whether to refrigerate the blood sample according to the time difference between the detection time and the blood drawing time of the blood sample, where,

[0119] If the time difference is greater than the preset duration, the blood sample is refrigerated in a refrigerator at 4°C;

[0120] If the time difference is less than or equal to the preset duration, the blood sample is not refrigerated;

[0121] It is understandable that the preset duration is generally less than two hours, and preferably set to one hour;

[0122] Step S43, shake the blood sample at room temperature to evenly distribute the anticoagulant in the blood sample,

[0123] Among them,

[0124] If the blood sample is stored refrigerated, restore it to room temperature and then shake it at room temperature;

[0125] If the blood sample is not stored refrigerated, directly shake it at room temperature for standby.

[0126] Specifically, in the step S5, determine the coating thickness of the coated immunomagnetic beads according to the tumor metastasis grade and the antibody affinity, including,

[0127] If the tumor metastasis grade is high and the antibody affinity is greater than or equal to the preset antibody affinity, determine that the coating thickness is the first thickness; The thin coating layer can minimize the impact on antibody activity, making the antigen-binding site of the antibody more easily exposed, thus improving the binding efficiency between the antibody and the target antigen; The thin coating layer allows the antibody to bind more quickly to the low-expressed antigen on the cell surface. At the same time, because the coating layer is thinner, the magnetism of the magnetic beads is less affected, and the response speed in the magnetic field is faster, which is beneficial to subsequent cell separation operations;

[0128] If the tumor metastasis grade is high or the antibody affinity is greater than or equal to the preset antibody affinity, determine that the coating thickness is the second thickness;

[0129] If the tumor metastasis grade is not high and the antibody affinity is less than the preset antibody affinity, determine that the coating thickness is the third thickness; The thick coating layer can provide more antibody fixation sites, be able to load more antibodies, thereby increasing the chance of binding to the target substance; This may be helpful when dealing with situations where the antigen expression level is low or the number of target cells is small (the tumor metastasis grade is not high and the antibody affinity is less than the preset antibody affinity);

[0130] Among them, the first thickness ≤ the second thickness ≤ the third thickness.

[0131] In practice, determine the dissociation constant according to the equilibrium dialysis method or surface plasmon resonance technology to determine the antibody affinity. The antibody affinity is negatively correlated with the dissociation constant, that is, the smaller the dissociation constant, the higher its affinity; That is: if the dissociation constant is less than or equal to the preset dissociation value, it is determined that the antibody affinity is greater than or equal to the preset antibody affinity, and if the dissociation constant is greater than the preset dissociation value, it is determined that the antibody affinity is less than the preset antibody affinity; Usually, the preset dissociation value ∈ [10 -9 , 10 -7, preferably, the preset dissociation value is set to 10 -8 .

[0132] It can be understood that by integrating multi-dimensional information, not only the accuracy of the assessment of the tumor metastasis level is improved, but also the antibody affinity is introduced to determine the coating thickness of the coated immunomagnetic beads, thereby further optimizing the detection efficiency and purity of circulating tumor cells: for a high metastasis level or a high-affinity antibody, a thinner coating layer is used to improve the capture efficiency; while for a low metastasis level or a low-affinity antibody, a thicker coating layer is selected to ensure sufficient stability and capture purity; this strategy of dynamically adjusting the coating thickness not only improves the detection sensitivity, but also effectively reduces non-specific binding, making the separation and enrichment of circulating tumor cells more efficient and pure. The detection method of circulating tumor cells provided by the present invention realizes a comprehensive upgrade of the circulating tumor cell detection technology by accurately evaluating the tumor metastasis level, scientifically measuring the antibody affinity, and optimizing the coating thickness of the immunomagnetic beads accordingly, improving the accuracy and sensitivity of the detection.

[0133] In implementation, the first thickness ∈ [5nm, 10nm], and the first thickness is preferably set to 10nm; the second thickness ∈ (10nm, 30nm), and the second thickness is preferably set to 20nm; the third thickness ∈ [30nm, 50nm], however, an overly thick coating layer may affect the magnetism of the magnetic beads, so the third thickness is preferably set to 30nm.

[0134] Specifically, in the step S5, a color marker is added when preparing the coated immunomagnetic beads;

[0135] Among them, the color of the color marker is a color with a strong contrast to red. In implementation, blue or green color markers are usually used;

[0136] Please refer to Figure 3 as shown, which is a method step diagram of the incubation process of the embodiment of the present invention. Specifically, the step S6 includes,

[0137] Step S61, adding a preset amount of magnetic bead suspension to the blood sample and taking an initial blood sample image, and determining the initial turbidity according to the deep learning model;

[0138] Step S62, transferring the blood sample added with the magnetic bead suspension to a dynamic incubation device;

[0139] Step S63: Obtain a number of incubation images of the blood sample at a preset shooting frequency, and determine the real-time color uniformity and real-time turbidity of each incubation image according to the deep learning model. By introducing the deep learning model, the images of the blood sample can be intelligently analyzed to accurately determine the initial turbidity and real-time color uniformity, providing scientific guidance for the subsequent incubation process, improving the automation degree of detection and reducing the interference of human factors, making the detection results more objective and reliable.

[0140] In implementation, the preset shooting frequency is to take 1 image every 30s - 1min, and preferably set to take 1 image per minute.

[0141] It can be understood that the color of the blood sample mainly depends on the colors of various components in the blood, usually dark red (venous blood). In the initial blood sample image, the un-fused magnetic bead suspension and blood in the staining will result in very poor color uniformity. As the incubation progresses, the magnetic beads will be more and more evenly distributed in the mixture (the smaller the uniformity).

[0142] It can be understood that during the incubation process, since the immunomagnetic beads bind to CTCs to form a complex, it will further increase the turbidity of the mixture. Especially when the binding of magnetic beads and CTCs causes some cell aggregation, the change in turbidity will be more obvious; thus resulting in a more turbid mixture at the end of incubation.

[0143] In implementation, the deep learning model determines the turbidity according to the proportion of the granularity (noise) of the initial blood sample image and the incubation image.

[0144] In implementation, the uniformity is determined according to the chromaticity values of each pixel point in the incubation image. The real-time color uniformity is determined by the ratio of the difference between the maximum chromaticity value and the minimum chromaticity value of the pixel points to the average chromaticity value. The larger the real-time color uniformity, the more uneven it represents, and the smaller the real-time color uniformity, the more uniform it represents; the chromaticity value of a single pixel point is the sum of the value of the pixel point in the red channel, the value of the pixel point in the green channel, and the value of the pixel point in the blue channel.

[0145] Specifically, in the step S7, determining whether to end the incubation according to the incubation image and the initial blood sample image includes

[0146] If the real-time color uniformity is greater than or equal to the preset uniformity and the turbidity difference is greater than or equal to the preset difference, it is determined that the incubation ends;

[0147] If the real-time color uniformity is less than the preset uniformity and / or the turbidity difference is less than the preset difference, it is determined that the incubation has not ended.

[0148] In implementation, a preset uniformity ∈ [1%, 5%] is set. The smaller the preset uniformity, the more uniform the mixing of the incubation image. Preferably, it is set to 3%.

[0149] It can be understood that by real-time monitoring of the changes in the real-time uniformity and turbidity of the blood sample during the incubation process, it is possible to accurately determine whether the incubation is completed, thereby ensuring the binding efficiency of the immunomagnetic beads and circulating tumor cells.

[0150] Specifically, the turbidity difference is the difference between the real-time turbidity and the initial turbidity.

[0151] So far, the technical solution of the present invention has been described with reference to the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0152] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for detecting circulating tumor cells, characterized in that, Including: Step S1: Conduct imaging examinations and regional lymph node biopsies on the patient to obtain imaging images and the patient's TNM staging report; Step S2: Process the imaging images to obtain clear images, and determine the edge characterization status and microvessel density of the primary tumor based on the clear images; Step S3: Determine the tumor metastasis grade of the patient based on the TNM staging report, the edge characterization status, and the microvessel density; Step S4: Collect 5 ml to 10 ml of the patient's peripheral venous blood sample and preprocess it; Step S5: Determine the coating thickness of the coated immunomagnetic beads based on the tumor metastasis grade and the antibody affinity of the corresponding novel specific surface marker antibody of tumor cells, and prepare the coated immunomagnetic beads according to the coating thickness to prepare a magnetic bead suspension; Step S6: Add a preset amount of the magnetic bead suspension to the blood sample and transfer it to a dynamic incubation device for incubation to form a pre-enrichment mixture of circulating tumor cells; Step S7: Judge whether to end the incubation according to the color and turbidity of the pre-enrichment mixture, and transfer the pre-enrichment mixture of circulating tumor cells to a separator according to the judgment result of the end of incubation and apply a magnetic field to enrich it to form a post-enrichment mixture of circulating tumor cells; Step S8: Separate the supernatant of the post-enrichment mixture to obtain a magnetic bead-circulating tumor cell complex; Step S9: Automatically wash and detect the magnetic bead-circulating tumor cell complex to analyze the circulating tumor cells.

2. The detection method of circulating tumor cells according to claim 1, characterized in that, The said Step S2 includes, Step S21: Process the imaging images to obtain clear images, and the clear processing includes, Step S211: Denoise the imaging images using spatial domain filtering or frequency domain filtering to obtain a denoised image; Step S212: Adjust the contrast of the denoised image using gray scale stretching or histogram equalization to obtain an enhanced image; Step S213: Perform edge enhancement and sharpening on the enhanced image using a gradient operator or a Laplace operator to obtain a clear image; Step S22: Use a deep learning model to determine the edge characterization status and microvessel density of the primary tumor based on the clear image.

3. The detection method of circulating tumor cells according to claim 1, wherein The method for determining the tumor metastasis grade in the said Step S3 includes, Step S31: Obtain the TNM staging report, the edge characterization status, and the microvessel density, and determine the T value, N value, and M value; Step S32: Determine the tumor metastasis grade according to the M value, including, If the M value is M1, it is determined that the tumor metastasis grade is high; If the M value is M0, determine the tumor metastasis grade according to the edge characterization status, the microvessel density, the T value, and the N value; Step S33: Determine the tumor metastasis tendency according to the edge characterization status and the microvessel density, including, If the edge characterization status is an irregular edge and the microvessel density is greater than or equal to the preset density, it is determined that the tumor metastasis tendency is overt metastasis; If the edge characterization status is a regular edge and the microvessel density is less than the preset density, it is determined that the tumor metastasis tendency is covert metastasis; Step S34, determine the lymph node involvement tendency according to the T value and the N value, including, if the T value is T2 and the N value is N0, then determine that the lymph node involvement tendency is latent involvement; if the T value is T2 and the N value is not N0, then determine that the lymph node involvement tendency is overt involvement; Step S35, determine the tumor metastasis grade according to the tumor metastasis tendency and the lymph node involvement tendency.

4. The detection method of circulating tumor cells according to claim 3, characterized in that, In the step S35, determining the tumor metastasis grade according to the tumor metastasis tendency and the lymph node involvement tendency includes, if the tumor metastasis tendency is overt metastasis and the lymph node involvement tendency is overt involvement, then determine that the tumor metastasis grade is high; if the tumor metastasis tendency is overt metastasis or the lymph node involvement tendency is overt involvement, then determine that the tumor metastasis grade is medium; if the tumor metastasis tendency is latent metastasis and the lymph node involvement tendency is latent involvement, then determine that the tumor metastasis grade is low.

5. The detection method of circulating tumor cells according to claim 1, characterized in that, The step S4 includes, Step S41, collect the patient's peripheral venous blood sample using a vacuum blood collection tube containing an anticoagulant; Step S42, determine whether to refrigerate the blood sample according to the time difference between the detection time and the blood drawing time of the blood sample, where, if the time difference is greater than the preset duration, then place the blood sample in a refrigerator at 4°C for refrigerated storage; if the time difference is less than or equal to the preset duration, then do not refrigerate the blood sample; Step S43, shake the blood sample at room temperature to evenly distribute the anticoagulant in the blood sample, where, if the blood sample is refrigerated, then restore it to room temperature and shake it at room temperature; if the blood sample is not refrigerated, then directly shake it at room temperature for standby.

6. The detection method of circulating tumor cells according to claim 5, wherein In the step S5, determine the coating thickness of the coated immunomagnetic beads according to the tumor metastasis grade and the antibody affinity, including, if the tumor metastasis grade is high and the antibody affinity is greater than or equal to the preset antibody affinity, then determine that the coating thickness is the first thickness; if the tumor metastasis grade is high or the antibody affinity is greater than or equal to the preset antibody affinity, then determine that the coating thickness is the second thickness; if the tumor metastasis grade is not high and the antibody affinity is less than the preset antibody affinity, then determine that the coating thickness is the third thickness; wherein, the first thickness ≤ the second thickness ≤ the third thickness.

7. The detection method of circulating tumor cells according to claim 1, characterized in that In the step S5, add a color marker when preparing the coated immunomagnetic beads; wherein, the color of the color marker is a color with a strong contrast to red.

8. The detection method of circulating tumor cells according to claim 1, characterized in that, The step S6 includes, Step S61, add a preset amount of magnetic bead suspension to the blood sample and take an initial blood sample image, and determine the initial turbidity according to the deep learning model; Step S62, transfer the blood sample added with the magnetic bead suspension to a dynamic incubation device; Step S63, obtain a number of incubation images of the blood sample at a preset shooting frequency, and determine the real-time color uniformity and real-time turbidity of each incubation image according to the deep learning model.

9. The detection method of circulating tumor cells according to claim 8, characterized in that, In the step S7, determine whether to end the incubation according to the incubation image and the initial blood sample image includes, If the real-time color uniformity is greater than or equal to the preset uniformity and the turbidity difference is greater than or equal to the preset difference, it is determined that the incubation ends; If the real-time color uniformity is less than the preset uniformity and / or the turbidity difference is less than the preset difference, it is determined that the incubation has not ended.

10. The detection method of circulating tumor cells according to claim 9, wherein, The turbidity difference is the difference between the real-time turbidity and the initial turbidity.

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