Detection method of circulating tumor cells

Abstract

The invention relates to the technical field of cell detection, in particular to a circulating tumor cell detection method which comprises the following steps: clearly processing an imaging image to obtain a clear image and determining an edge characterization state and capillary density of a primary tumor; the tumor metastasis grade of a patient is determined according to a TNM staging report, an edge characterization state and the capillary density, and the coating thickness of the coated immunomagnetic beads is determined according to the tumor metastasis grade and the antibody affinity of the novel specific surface marker antibodies of the corresponding tumor cells so as to prepare a magnetic bead suspension. And incubating to form a pre-enrichment mixed solution, judging whether incubation is ended or not according to the color and turbidity of the pre-enrichment mixed solution, applying a magnetic field to form a post-enrichment mixed solution, separating supernatant liquid to obtain a magnetic bead-CTCs compound, and washing and detecting the magnetic bead-CTCs compound. The high-efficiency and high-precision circulating tumor cell detection method provided by the invention improves the evaluation accuracy of the tumor metastasis level and optimizes the detection efficiency of the circulating tumor cells.

Description

Technical Field

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

[0002] Background technologies of circulating tumor cell (CTC) detection technology involve multiple fields, including immune selection and physical property 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 tumor cell activity; 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; in order to overcome the limitations of a single method, researchers have tried to combine biological and physical properties for CTC separation, such as lateral filter array microfluidics technology (LFAM) combined with pore sieve principles of different sizes and filters modified with EpCAM antibodies to improve CTC capture rates.

[0003] Chinese patent announcement number CN110456034B discloses a method for detecting circulating tumor cells: collect human body fluid samples, fix them with formaldehyde to increase the permeability of the cell membrane, add the cells to several groups of PCR multiple tubes, each tube is added with a reverse transcription primer carrying ID1, after reverse transcription into cDNA molecules, collect the cells and mix them and add them to another group of PCR multiple tubes, each tube is added with an extension primer carrying ID2 and the 3' end can specifically recognize the 3' end of the cDNA molecule, after sequence complementation, an extension reaction is performed, the cells are collected and lysed, and the extension products are pre-amplified by PCR primers. High-throughput sequencing analyzes the combination of ID1:ID2. The invention simultaneously reverse transcribes and PCR amplifies different markers, which can accurately identify circulating tumor cells and circulating tumor cells that have undergone EMT, and can be used in combination with other technologies to detect very few circulating tumor cells and other non-body fluid rare cells in body fluid samples to the greatest extent. It can be seen that the invention has the following problems:

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

[0005] To this end, the present invention provides a method for detecting circulating tumor cells, which is used to overcome the problem in the prior art that the coating thickness of the coated immunomagnetic beads is not comprehensively optimized according to 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, comprising:

[0007] Step S1, perform imaging examination and regional lymph node biopsy on the patient to obtain imaging images and the patient's TNM staging report;

[0008] Step S2, performing clear processing on the imaging image to obtain a clear image, and determining the edge characterization state and microvessel density of the primary tumor according to the clear image;

[0009] Step S3, determining the patient's tumor metastasis grade according to the TNM staging report, the margin characterization status and the microvessel density;

[0010] Step S4, collecting 5ml to 10ml of peripheral venous blood samples from patients and pre-treating them;

[0011] Step S5, determining 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 of tumor cells, and preparing the coated immunomagnetic beads according to the coating thickness to prepare a magnetic bead suspension;

[0012] Step S6, adding a preset amount of magnetic bead suspension to the blood sample and then transferring the sample to a dynamic incubation device for incubation to form a pre-enrichment mixed solution for circulating tumor cells;

[0013] Step S7, judging whether to end the incubation according to the color and turbidity of the pre-enrichment mixed solution, and transferring the pre-enrichment mixed solution of circulating tumor cells to a separator and applying a magnetic field to enrich the circulating tumor cells to form a post-enrichment mixed solution according to the judgment result of the end of the incubation;

[0014] Step S8, separating the supernatant of the enriched mixed solution to obtain the magnetic bead-circulating tumor cell complex;

[0015] Step S9, automatically washing and detecting the magnetic bead-circulating tumor cell complex to analyze the circulating tumor cells.

[0016] Further, the step S2 includes:

[0017] Step S21, performing a clear processing on the imaging image to obtain a clear image, wherein the clear processing includes:

[0018] Step S211, performing denoising processing on the imaging image by using spatial domain filtering or frequency domain filtering to obtain a denoised image;

[0019] Step S212: adjusting the contrast of the denoised image by grayscale stretching or histogram equalization to obtain an enhanced image;

[0020] Step S213, using a gradient operator or a Laplace operator to perform edge enhancement and sharpening on the enhanced image to obtain a clear image;

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

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

[0023] Step S31, obtaining the TNM staging report, the margin characterization status and the microvascular density, and determining the T value, N value and M value;

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

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

[0026] If the M value is M0, the tumor metastasis grade is determined according to the edge characterization state, the microvascular density, the T value and the N value;

[0027] Step S33, determining the tumor metastasis tendency according to the edge characterization state and the microvessel density, including:

[0028] If the edge characterization state is an irregular edge and the microvessel density is greater than or equal to a preset density, the tumor metastasis tendency is determined to be overt metastasis;

[0029] If the edge representation state is a regular edge and the microvessel density is less than a preset density, the tumor metastasis tendency is determined to be occult metastasis;

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

[0031] If the T value is T2 and the N value is N0, the tendency of lymph node involvement is determined to be latent involvement;

[0032] If the T value is T2 and the N value is not N0, the lymph node involvement tendency is determined to be dominant involvement;

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

[0034] Furthermore, in the step S35, the tumor metastasis grade is determined according to the tumor metastasis tendency and the lymph node involvement tendency, including:

[0035] If the tumor metastasis tendency is dominant metastasis and the lymph node involvement tendency is dominant involvement, the tumor metastasis grade is determined to be high;

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

[0037] If the tumor metastasis tendency is occult metastasis and the lymph node involvement tendency is occult involvement, the tumor metastasis grade is determined to be low.

[0038] Further, step S4 includes,

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

[0040] Step S42, determining whether to store the blood sample in cold storage according to the time difference between the detection time and the blood drawing time, wherein:

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

[0042] If the time difference is less than or equal to the preset time length, 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] in,

[0045] If the blood sample is refrigerated, return it to room temperature and then shake it well at room temperature;

[0046] If the blood sample is not refrigerated, shake it directly at room temperature for later use.

[0047] Furthermore, in the step S5, the coating thickness of the coated immunomagnetic beads is determined according to the tumor metastasis grade and the antibody affinity, including:

[0048] If the tumor metastasis grade is high and the antibody affinity is greater than or equal to the preset antibody affinity, determining 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, the coating thickness is determined to be the second thickness;

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

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

[0052] Furthermore, in the step S5, color markers are added when preparing the coated immunomagnetic beads;

[0053] Wherein, the color of the color mark is a color that has a strong contrast with red.

[0054] Further, 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 plurality 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] Further, in the step S7, judging 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 is terminated;

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

[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 effect of the present invention lies in that the method for detecting circulating tumor cells provided by the present invention determines the coating thickness of the coated immunomagnetic beads according to the patient's tumor metastasis grade and the antibody affinity of the corresponding novel specific surface marker antibody of tumor cells, thereby preparing a magnetic bead suspension suitable for individual differences of patients, thereby improving the pertinence and accuracy of the detection. This method provides a complete and reliable method for detecting circulating tumor cells in clinic, which helps doctors to more accurately understand the patient's tumor metastasis situation, formulate personalized treatment plans / monitor treatment effects, and also provides an important reference basis for 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 clinicians.

[0064] Furthermore, the present invention can avoid the limitations of the determination results by comprehensively determining the tumor metastasis tendency through edge characterization status and microvascular density; in addition, the edge characterization status and microvascular density are determined by a deep learning model, which reduces the subjective influence of humans and can more objectively determine the tumor metastasis level.

[0065] Furthermore, the comprehensive evaluation method of 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 injection of contrast agent, further enhancing the accuracy and scientificity of the evaluation.

[0066] Furthermore, the present invention not only improves the assessment accuracy of tumor metastasis level by integrating multi-dimensional information, but also introduces antibody affinity to determine the coating thickness of coated immunomagnetic beads, thereby further optimizing the detection efficiency and purity of circulating tumor cells: for high-level metastasis level or high-affinity antibodies, a thinner coating layer is used to improve capture efficiency; for low-level metastasis level or low-affinity antibodies, 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 nonspecific 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 the comprehensive upgrade of circulating tumor cell detection technology and improves the accuracy and sensitivity of detection by accurately evaluating the tumor metastasis level, scientifically measuring the antibody affinity, and optimizing the coating thickness of immunomagnetic beads accordingly.

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

[0068] Figure 1 A step diagram of a method for detecting circulating tumor cells according to an embodiment of the present invention;

[0069] Figure 2 A diagram showing the steps of a method for determining a tumor metastasis grade according to an embodiment of the present invention;

[0070] Figure 3 1 is a method step diagram of the incubation process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0071] In order to make the objects and advantages of the present invention more clearly understood, the present invention is 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 are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0073] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely 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 cannot be understood as a limitation on the present invention.

[0074] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" 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 a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0075] See also Figure 1 As shown, it is a step diagram of a method for detecting circulating tumor cells according to an embodiment of the present invention. An embodiment of the present invention provides a method for detecting circulating tumor cells, comprising:

[0076] Step S1, performing imaging examination and regional lymph node biopsy on the patient to obtain imaging images and the patient's TNM staging report; in practice, the imaging examination includes CT enhanced scanning or MRI enhanced scanning, and MRI enhanced scanning is a scanning examination performed after intravenous injection of contrast agent on the basis of conventional magnetic resonance imaging (MRI), which can more clearly show the characteristics of the lesion tissue, including the scope, boundary, internal structure of the lesion, and the relationship with surrounding tissues and other detailed information;

[0077] Step S2, performing clear processing on the imaging image to obtain a clear image, and determining the edge characterization state and microvessel density of the primary tumor according to the clear image;

[0078] Step S3, determining the patient's tumor metastasis grade according to the TNM staging report, the margin characterization status and the microvessel density;

[0079] Step S4, collecting 5ml to 10ml of peripheral venous blood samples from patients and pre-treating them;

[0080] Step S5, determining 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 of tumor cells, and preparing the coated immunomagnetic beads according to the coating thickness to prepare a magnetic bead suspension;

[0081] Step S6, adding a preset amount of magnetic bead suspension to the blood sample and then transferring it to a dynamic incubation device for incubation to form a pre-enrichment mixed solution for circulating tumor cells; it is understandable that the amount of magnetic bead suspension added 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, judging whether to end the incubation according to the color and turbidity of the pre-enrichment mixed solution, and transferring the pre-enrichment mixed solution of circulating tumor cells to a separator and applying a magnetic field to enrich the circulating tumor cells to form a post-enrichment mixed solution according to the judgment result of the end of the incubation;

[0083] Step S8, using a pipette to separate the supernatant of the enriched mixed solution to obtain the magnetic bead-circulating tumor cell complex; in the implementation, it is also necessary to select a suitable pipette tip (its specifications are selected according to the volume of the supernatant and the size of the container, generally a small range and a thinner head pipette tip is used to more accurately absorb the supernatant and reduce the interference with the magnetic bead-circulating tumor cell CTCs complex; wherein, for a small volume (less than 1 ml) of supernatant, a pipette with a range of 10 to 1000 μl and a matching fine tip are selected); and the range of the pipette is adjusted according to the volume of the supernatant to be transferred;

[0084] Step S9, automatically washing and detecting the magnetic bead-circulating tumor cell complex to analyze the circulating tumor cells; in implementation, step S9 includes: placing a 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, and repeats the operation of adsorbing the complex through a magnetic field and discarding the washing solution 2 to 3 times to remove residual unbound blood components and non-specific adsorbed substances; if multifunctional immunomagnetic beads are used, the fluorescent probes they carry can be used for real-time monitoring, or combined with new detection technologies such as nanosensor technology and microfluidic chip technology to perform highly sensitive and rapid detection of circulating tumor cells.

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

[0086] Specifically, step S2 includes,

[0087] Step S21, performing a clear processing on the imaging image to obtain a clear image, wherein the clear processing includes:

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

[0089] Step S212: adjusting the contrast of the denoised image by grayscale stretching or histogram equalization to obtain an enhanced image;

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

[0091] Step S22, using a deep learning model to determine the edge representation state and microvessel density of the primary tumor based on the clear image; it can be understood that the edge representation state includes irregular edges and regular edges.

[0092] It can be understood that, firstly, the imaging image is clearly processed through spatial domain filtering, frequency domain filtering denoising, grayscale stretching or histogram equalization to enhance contrast, and gradient operator or Laplace operator to perform edge enhancement and sharpening, so as to obtain high-quality clear images, which provides a solid foundation for subsequent tumor feature analysis; at the same time, the clear images are analyzed by deep learning models to accurately determine the edge characterization state and microvascular density of the primary tumor, and can distinguish between irregular edges and regular edges, thereby 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 clinicians.

[0093] See also Figure 2 As shown, it is a step diagram of the method for determining the tumor metastasis level in an embodiment of the present invention. Specifically, the method for determining the tumor metastasis level in step S3 includes:

[0094] Step S31, obtaining the TNM staging report, the marginal characterization status and the microvascular density, and determining the T value, N value and M value; it is understandable that the TNM staging is determined by the doctor based on the three dimensions of the primary lesion (T), regional lymph node involvement (N) and distant metastasis (M) of the tumor; it is understandable that the T value only includes T1 and T2, and usually a tumor less than or equal to 2 cm is T1 stage; the N value includes N0, N1, N2 and N3, usually no lymph node metastasis is N0, and the more lymph node metastasis Ni (i=1, 2, 3) the greater i; the M value only includes M0 and M1, the stage when no signs of distant metastasis are found is M0, and the stage when signs of distant metastasis are found is M1;

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

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

[0097] If the M value is M0, the tumor metastasis grade is determined according to the edge characterization state, the microvascular density, the T value and the N value; it can be understood that the stage M1 represents that distant metastasis has occurred, and the corresponding tumor may have reached the middle or even late stage, at which time there must be a 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 is no sign of distant metastasis, the tumor metastasis grade is determined according to the consistency of the tumor metastasis grade and the tendency of lymph node involvement;

[0098] Step S33, determining the tumor metastasis tendency according to the edge characterization state and the microvessel density, including:

[0099] If the edge characterization state is an irregular edge and the microvessel density is greater than or equal to a preset density, the tumor metastasis tendency is determined to be overt metastasis;

[0100] If the edge representation state is a regular edge and the microvessel density is less than a preset density, the tumor metastasis tendency is determined to be occult metastasis;

[0101] It is understandable that tumors with irregular edges and invasive growth are more likely to metastasize. If pathological examinations show that tumor cells have broken through the capsule surrounding the tumor and infiltrated into the surrounding normal tissues, the probability of CTCs appearing and leading to metastasis will increase. In addition, tumor cells need to enter the blood circulation through new blood vessels, and a large number of microvessels with increased density have been found in tumor tissues, indicating that tumor cells are more likely to enter blood vessels, become CTCs, and metastasize.

[0102] In practice, the density of microvessels inside the tumor can be evaluated through enhanced CT or MRI examinations after injection of contrast agents. CT enhanced scanning is an examination performed after intravenous injection of contrast agents. Contrast agents (such as iodine contrast agents) flow in the blood circulation. When they flow through areas with rich blood vessels inside the tumor, they will accumulate in these areas due to the structural and functional characteristics of the tumor blood vessels. Since the principle of CT imaging is based on the difference in the penetration of X-rays into different tissues, contrast agents contain elements with high atomic numbers (such as iodine), which have a stronger ability to absorb X-rays than surrounding tissues. Therefore, during the scanning process, areas containing more contrast agents (i.e., areas with rich tumor blood vessels) will appear as higher densities on CT images.

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

[0104] It is understandable that by comprehensively determining the tendency of tumor metastasis through the edge characterization state and microvascular density, the limitations of the determination results can be avoided; in addition, the edge characterization state and microvascular density are determined by the deep learning model, which reduces the subjective influence of humans and can more objectively determine the grade of tumor metastasis.

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

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

[0107] If the T value is T2 and the N value is not N0, the lymph node involvement tendency is determined to be dominant involvement;

[0108] The tendency of the primary tumor to affect the surrounding lymph nodes is determined based on the doctor's subjective judgment of the T value and N value, which is biased towards the 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 this step comprehensively determines the tumor metastasis grade through the consistency of the objective tumor metastasis tendency and the subjective lymph node involvement tendency.

[0110] It can be understood that the present invention improves the accuracy and reliability of tumor metastasis grade assessment through a series of steps in step S3: first, it integrates multi-dimensional information such as TNM staging report, margin characterization status of primary tumor and microvascular density, and objectively analyzes through deep learning model, avoiding the limitation of single indicator evaluation; in particular, when determining the tumor metastasis grade, not only the key indicator of whether there is distant metastasis is considered, but also multiple factors such as margin characterization status, microvascular density, primary tumor size and regional lymph node involvement are combined to achieve a comprehensive assessment of tumor metastasis tendency and lymph node involvement tendency; this comprehensive assessment method not only improves the accuracy of the assessment, but also reduces the influence of human subjective judgment, making the assessment result more objective and reliable; in addition, the microvascular density inside the tumor is evaluated by enhanced CT or MRI examination after injection of contrast agent, which further enhances the accuracy and scientificity of the assessment.

[0111] Specifically, in step S35, the tumor metastasis grade is determined according to the tumor metastasis tendency and the lymph node involvement tendency, including:

[0112] If the tumor metastasis tendency is dominant metastasis and the lymph node involvement tendency is dominant involvement, the tumor metastasis grade is determined to be high; that is, when the subjective and objective judgments are consistent and both are dominant states, 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, the tumor metastasis grade is determined to be intermediate; that is, when the subjective and objective judgments are inconsistent, it means that the current tumor metastasis grade is intermediate;

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

[0115] It is understandable that by comprehensively considering the tumor metastasis tendency and lymph node involvement tendency, a more accurate and comprehensive evaluation method is provided for determining the tumor metastasis grade: first, the deep learning model is used to analyze the marginal characterization state and microvascular density of the primary tumor to objectively judge the tumor metastasis tendency, avoiding the limitations of a single indicator or human subjective judgment; at the same time, the T value and N value determined 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 based on the consistency of the tumor metastasis tendency and the lymph node involvement tendency, and when both are dominant, it is determined to be a high-level metastasis grade; when at least one of the two is dominant, it is determined to be an intermediate metastasis grade, which can deal with the inconsistency between subjective and objective judgments; when both are recessive, it is determined to be a low-level 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, which 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 from a patient using a vacuum blood collection tube containing an anticoagulant;

[0118] Step S42, determining whether to store the blood sample in cold storage according to the time difference between the detection time and the blood drawing time, wherein:

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

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

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

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

[0123] in,

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

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

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

[0127] If the tumor metastasis level is high and the antibody affinity is greater than or equal to the preset antibody affinity, the coating thickness is determined to be the first thickness; a thin coating layer can minimize the impact on the antibody activity, making the antibody's antigen binding site more easily exposed, thereby improving the binding efficiency of the antibody and the target antigen; a thin coating layer can allow the antibody to bind more quickly to low-expression antigens on the cell surface, and because the coating layer is thin, 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, the coating thickness is determined to be the second thickness;

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

[0130] Wherein, the first thickness ≤ the second thickness ≤ the third thickness.

[0131] In implementation, the antibody affinity is determined by determining the dissociation constant according to the equilibrium dialysis method or the surface plasmon resonance technique. The antibody affinity is negatively correlated with the dissociation constant, that is, the smaller the dissociation constant, the higher the affinity; that is, if the dissociation constant is less than or equal to the preset dissociation value, the antibody affinity is judged to be greater than or equal to the preset antibody affinity; if the dissociation constant is greater than the preset dissociation value, the antibody affinity is judged to be 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 is understandable that by integrating multi-dimensional information, not only the assessment accuracy of 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 high-level metastasis level or high-affinity antibodies, a thinner coating layer is used to improve the capture efficiency; for low-level metastasis level or low-affinity antibodies, 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 nonspecific 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 the comprehensive upgrade of circulating tumor cell detection technology and improves the accuracy and sensitivity of detection by accurately evaluating the tumor metastasis level, scientifically determining the antibody affinity, and optimizing the coating thickness of the immunomagnetic beads accordingly.

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

[0134] Specifically, in step S5, color markers are added when preparing the coated immunomagnetic beads;

[0135] The color of the color mark is a color that has a strong contrast with red. In practice, blue or green color marks are usually used;

[0136] See also Figure 3 As shown, it is a method step diagram of the incubation process of an embodiment of the present invention. Specifically, 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, obtaining several 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; by introducing the deep learning model, the image of the blood sample can be intelligently analyzed, and the initial turbidity and real-time color uniformity can be accurately determined, which provides scientific guidance for the subsequent incubation process, improves the degree of automation of the detection, reduces the interference of human factors, and makes the detection results more objective and reliable.

[0140] In implementation, the preset shooting frequency is 1 shot every 30 seconds to 1 minute, preferably 1 shot every minute.

[0141] It is understandable that the color of the blood sample mainly depends on the colors of the various components in the blood, which is usually dark red (venous blood). In the initial blood sample image, the dyed magnetic bead suspension has not yet merged with the blood, resulting in poor color uniformity. As the incubation proceeds, the magnetic beads are evenly distributed in the mixed liquid and become more and more evenly distributed (the uniformity becomes smaller).

[0142] It is understandable that during the incubation process, the turbidity of the mixed solution will further increase due to the combination of the immunomagnetic beads and CTCs to form a complex, especially when the combination of magnetic beads and CTCs causes some cells to aggregate, the change in turbidity will be more obvious; therefore, the mixed solution will become more turbid at the end of the incubation.

[0143] In implementation, the deep learning model determines turbidity based on the ratio of graininess (noise) in the initial blood sample image to the incubation image.

[0144] In implementation, the uniformity is determined according to the chromaticity value of each pixel 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 in the pixel to the average chromaticity value. The larger the real-time color uniformity, the more uneven it is, and the smaller the real-time color uniformity, the more uniform it is. The chromaticity value of a single pixel is the sum of the value of the pixel in the red channel, the value of the pixel in the green channel, and the value of the pixel in the blue channel.

[0145] Specifically, in step S7, judging 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 is terminated;

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

[0148] In implementation, the uniformity is preset to be [1%, 5%]. The smaller the preset uniformity is, the more uniform the mixing of the incubation image is. It is preferably set to 3%.

[0149] It is understandable that by real-time monitoring of the color uniformity and turbidity changes 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 with 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 solutions of the present invention have been described in conjunction with 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 description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, 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: include: Step S1, perform imaging examination and regional lymph node biopsy on the patient to obtain imaging images and the patient's TNM staging report; Step S2, performing clear processing on the imaging image to obtain a clear image, and determining the edge characterization state and microvessel density of the primary tumor according to the clear image; Step S3, determining the patient's tumor metastasis grade according to the TNM staging report, the margin characterization status and the microvessel density; Step S4, collecting 5ml to 10ml of peripheral venous blood samples from patients and pre-treating them; Step S5, determining 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 of tumor cells, and preparing the coated immunomagnetic beads according to the coating thickness to prepare a magnetic bead suspension; Step S6, adding a preset amount of magnetic bead suspension to the blood sample and then transferring the sample to a dynamic incubation device for incubation to form a pre-enrichment mixed solution for circulating tumor cells; Step S7, judging whether to end the incubation according to the color and turbidity of the pre-enrichment mixed solution, and transferring the pre-enrichment mixed solution of circulating tumor cells to a separator and applying a magnetic field to enrich the circulating tumor cells to form a post-enrichment mixed solution according to the judgment result of the end of the incubation; Step S8, separating the supernatant of the enriched mixed solution to obtain the magnetic bead-circulating tumor cell complex; Step S9, automatically washing and detecting the magnetic bead-circulating tumor cell complex to analyze the circulating tumor cells.

2. The method for detecting circulating tumor cells according to claim 1, characterized in that: The step S2 comprises: Step S21, performing a clear processing on the imaging image to obtain a clear image, wherein the clear processing includes: Step S211, performing denoising processing on the imaging image by using spatial domain filtering or frequency domain filtering to obtain a denoised image; Step S212: adjusting the contrast of the denoised image by grayscale stretching or histogram equalization to obtain an enhanced image; Step S213, using a gradient operator or a Laplace operator to perform edge enhancement and sharpening on the enhanced image to obtain a clear image; Step S22: using a deep learning model to determine the edge characterization state and microvessel density of the primary tumor based on the clear image.

3. The method for detecting circulating tumor cells according to claim 1, characterized in that: The method for determining the tumor metastasis grade in step S3 includes: Step S31, obtaining the TNM staging report, the margin characterization status and the microvascular density, and determining the T value, N value and M value; Step S32, determining the tumor metastasis grade according to the M value, include, If the M value is M1, the tumor metastasis grade is determined to be high; If the M value is M0, the tumor metastasis grade is determined according to the edge characterization state, the microvascular density, the T value and the N value; Step S33, determining the tumor metastasis tendency according to the edge characterization state and the microvessel density, include, If the edge characterization state is an irregular edge and the microvessel density is greater than or equal to a preset density, the tumor metastasis tendency is determined to be overt metastasis; If the edge representation state is a regular edge and the microvessel density is less than a preset density, the tumor metastasis tendency is determined to be occult metastasis; Step S34, determining the tendency of lymph node involvement according to the T value and the N value, include, If the T value is T2 and the N value is N0, the tendency of lymph node involvement is determined to be latent involvement; If the T value is T2 and the N value is not N0, the lymph node involvement tendency is determined to be dominant involvement; Step S35, determining the tumor metastasis grade according to the tumor metastasis tendency and the lymph node involvement tendency.

4. The method for detecting circulating tumor cells according to claim 3, characterized in that: In the step S35, the tumor metastasis grade is determined according to the tumor metastasis tendency and the lymph node involvement tendency, including: If the tumor metastasis tendency is dominant metastasis and the lymph node involvement tendency is dominant involvement, the tumor metastasis grade is determined to be high; If the tumor metastasis tendency is dominant metastasis or the lymph node involvement tendency is dominant involvement, the tumor metastasis grade is determined to be intermediate; If the tumor metastasis tendency is occult metastasis and the lymph node involvement tendency is occult involvement, the tumor metastasis grade is determined to be low.

5. The method for detecting circulating tumor cells according to claim 1, characterized in that: The step S4 comprises: Step S41, collecting a peripheral venous blood sample from a patient using a vacuum blood collection tube containing an anticoagulant; Step S42, determining whether to store the blood sample in cold storage according to the time difference between the detection time and the blood drawing time, wherein: If the time difference is greater than the preset time, the blood sample is placed in a refrigerator at 4°C for storage; If the time difference is less than or equal to the preset time length, the blood sample is not refrigerated; Step S43, shaking the blood sample at room temperature to evenly distribute the anticoagulant in the blood sample, wherein: If the blood sample is refrigerated, return it to room temperature and then shake it well at room temperature; If the blood sample is not refrigerated, shake it directly at room temperature for later use.

6. The method for detecting circulating tumor cells according to claim 5, characterized in that: In the step S5, the coating thickness of the immunomagnetic beads is determined 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, determining 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, the coating thickness is determined to be the second thickness; If the tumor metastasis grade is not high and the antibody affinity is less than the preset antibody affinity, determining that the coating thickness is the third thickness; Wherein, the first thickness ≤ the second thickness ≤ the third thickness.

7. The method for detecting circulating tumor cells according to claim 1, characterized in that: In the step S5, color markers are added when preparing the coated immunomagnetic beads; Wherein, the color of the color mark is a color that has a strong contrast with red.

8. The method for detecting circulating tumor cells according to claim 1, characterized in that: The step S6 comprises: 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; Step S62, transferring the blood sample added with the magnetic bead suspension to a dynamic incubation device; Step S63, obtaining a plurality 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.

9. The method for detecting circulating tumor cells according to claim 1, characterized in that: In the step S7, judging 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 is terminated; If the real-time color uniformity is less than a preset uniformity and / or the turbidity difference is less than a preset difference, it is determined that the incubation is not completed.

10. The method for detecting circulating tumor cells according to claim 9, characterized in that: The turbidity difference is the difference between the real-time turbidity and the initial turbidity.

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