System for predicting risk of metastasis of malignant tumor based on neutrophil polarization state

Abstract

The application provides a malignant tumor metastasis risk prediction system based on a neutrophil polarization state, relates to the technical field of biology, and comprises a neutrophil detection module, a patient information acquisition module, a tumor information acquisition module, a malignant degree scoring module, a risk prediction scoring module and a risk prediction report module. The neutrophil detection module is used for detecting neutrophil polarization state information in a tumor microenvironment. The patient information acquisition module is used for acquiring patient information. The tumor information acquisition module is used for acquiring tumor information. The malignant degree scoring module is used for determining a tumor malignant degree score according to the tumor information. The risk prediction scoring module is used for determining a malignant tumor metastasis risk prediction score according to the neutrophil polarization state information, the tumor malignant degree score and the patient information. The risk prediction report module is used for generating a risk prediction report according to the malignant tumor metastasis risk prediction score. According to the application, the accuracy of malignant tumor metastasis risk prediction can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biotechnology, and particularly relates to a malignant tumor metastasis risk prediction system based on a neutrophil polarization state. BACKGROUND

[0002] In the related art, CN117316426A discloses a tumor bone metastasis prediction system and method based on bone metastasis specific bone markers, relating to the technical field of machine learning, comprising: an index acquisition module for acquiring a plurality of bone marker indexes related to bone metastasis in the serum of a patient obtained by serum detection of the patient; a bone metastasis prediction module connected to the index acquisition module, for inputting each bone marker index into a pre-trained tumor bone metastasis prediction model to predict the early bone metastasis incidence of the patient for the doctor to refer to in clinical diagnosis. The beneficial effect is that by quantitatively detecting the contents of bone markers such as calcium and phosphorus metabolism regulation indexes, bone formation and bone resorption markers, bone metabolism related hormones and cytokines, and electrolytes, and by machine learning to predict the early bone metastasis incidence, it can be used for early auxiliary diagnosis of tumor bone metastasis, auxiliary evaluation of treatment effect during treatment and monitoring of metastasis recurrence after treatment, and the detection result can be earlier than the clinical symptoms.

[0003] CN115620903A discloses a pan-cancer malignant tumor bone metastasis risk prediction method and system, which comprises: classifying cancer species with bone metastasis risk according to bone metastasis prevalence; constructing a prediction model with the cancer species classification result as one of the risk factors; obtaining the clinical characteristics corresponding to the risk factors of the predicted sample, and predicting the bone metastasis risk by using the prediction model. The scheme first constructs a cancer classification system based on bone metastasis prevalence, and provides a pan-cancer bone metastasis risk prediction model based on cancer type, age, gender, insurance, tumor histological stage, TNM stage, and liver, lung and brain metastasis of cancer patients, to determine the current bone metastasis probability of tumor patients and accurately predict the bone metastasis incidence probability of malignant tumor patients of all cancer species, which is convenient to use and has high prediction accuracy.

[0004] Based on the above related technology, the bone metastasis incidence probability of malignant tumor patients can be accurately predicted, however, the related technology does not consider the influence of the neutrophil polarization state on the malignant tumor metastasis risk, that is, the malignant tumor metastasis risk cannot be predicted according to the neutrophil polarization state. SUMMARY

[0005] The present application provides a malignant tumor metastasis risk prediction system based on a neutrophil polarization state, which can solve the technical problem that the related technology cannot predict the malignant tumor metastasis risk according to the neutrophil polarization state.

[0006] According to a first aspect of the present application, a system for predicting the risk of metastasis of malignant tumors based on the polarization state of neutrophils is provided, comprising:

[0007] a neutrophil detection module for detecting neutrophil polarization state information in a tumor microenvironment, wherein the neutrophil polarization state information includes the proportion of N1-type neutrophils and the proportion of N2-type neutrophils;

[0008] a patient information acquisition module for acquiring patient information, wherein the patient information includes patient age and immune system parameters;

[0009] a tumor information acquisition module for acquiring tumor information, wherein the tumor information includes tumor location, cell differentiation degree, cell nuclear division frequency, and tumor infiltration range;

[0010] a malignancy score module for determining a tumor malignancy score based on the tumor information;

[0011] a risk prediction score module for determining a malignant tumor metastasis risk prediction score based on the neutrophil polarization state information, the tumor malignancy score, and the patient information,

[0012] a risk prediction report module for generating a risk prediction report based on the malignant tumor metastasis risk prediction score.

[0013] According to a second aspect of the present application, a method for predicting the risk of metastasis of malignant tumors based on the polarization state of neutrophils is provided, comprising:

[0014] detecting neutrophil polarization state information in a tumor microenvironment, wherein the neutrophil polarization state information includes the proportion of N1-type neutrophils and the proportion of N2-type neutrophils;

[0015] acquiring patient information, wherein the patient information includes patient age and immune system parameters;

[0016] acquiring tumor information, wherein the tumor information includes tumor location, cell differentiation degree, cell nuclear division frequency, and tumor infiltration range;

[0017] determining a tumor malignancy score based on the tumor information;

[0018] determining a malignant tumor metastasis risk prediction score based on the neutrophil polarization state information, the tumor malignancy score, and the patient information;

[0019] generating a risk prediction report based on the malignant tumor metastasis risk prediction score.

[0020] Technical effects: According to the present application, the polarization state information of neutrophils in the tumor microenvironment can be accurately detected, and the tumor malignancy degree of the patient can be accurately analyzed. Further, according to the neutrophil polarization state information, the tumor malignancy degree and the patient information, the risk of malignant tumor metastasis is predicted, and the accuracy of the risk prediction of malignant tumor metastasis is improved. In determining the tumor malignancy degree score, the tumor malignancy degree score can be determined according to the tumor location recognition result, the cell differentiation degree, the nuclear division frequency and the tumor infiltration range. In the calculation process, the malignancy degree of the tumor can be evaluated from four aspects of the location condition, the cell differentiation condition, the nuclear division condition and the infiltration range condition of the tumor, thereby improving the comprehensiveness and accuracy of the tumor malignancy degree score. In determining the first relationship function, the first relationship function can be determined according to the historical N1 type neutrophil proportion, the historical N2 type neutrophil proportion, the historical tumor malignancy degree score, the historical adaptive immune cell count, the historical immunoglobulin level and the historical innate immune cell count, thereby accurately describing the relationship between the patient's immune system condition, the tumor malignancy degree and the next cycle of tumor malignancy degree, and improving the accuracy and objectivity of the first relationship function. In determining the predicted tumor malignancy degree score, the tumor malignancy degree score of the current time cycle, the first relationship function, the adaptive immune cell count, the immunoglobulin level, the innate immune cell count and the neutrophil polarization state information are used to determine the predicted tumor malignancy degree score of the next time cycle, thereby improving the accuracy of the predicted tumor malignancy degree score and providing a data basis for calculating the malignant tumor metastasis risk prediction score. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 An exemplary block diagram of a malignant tumor metastasis risk prediction system based on neutrophil polarization state according to an embodiment of the present application is shown.

[0022] Figure 2 An exemplary flowchart of a malignant tumor metastasis risk prediction method based on neutrophil polarization state according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in some embodiments.

[0024] Figure 1 An exemplary block diagram of a malignant tumor metastasis risk prediction system based on neutrophil polarization state according to an embodiment of the present application is shown. The system comprises:

[0025] a neutrophil detection module configured to detect neutrophil polarization state information in a tumor microenvironment, wherein the neutrophil polarization state information comprises a proportion of N1-type neutrophils and a proportion of N2-type neutrophils;

[0026] a patient information acquisition module configured to acquire patient information, wherein the patient information comprises patient age and immune system parameters;

[0027] a tumor information acquisition module configured to acquire tumor information, wherein the tumor information comprises tumor location, cell differentiation degree, cell nuclear division frequency, and tumor infiltration range;

[0028] a malignancy degree scoring module configured to determine a tumor malignancy degree score according to the tumor information;

[0029] a risk prediction scoring module configured to determine a malignant tumor metastasis risk prediction score according to the neutrophil polarization state information, the tumor malignancy degree score, and the patient information,

[0030] a risk prediction report module configured to generate a risk prediction report according to the malignant tumor metastasis risk prediction score.

[0031] The malignant tumor metastasis risk prediction system based on neutrophil polarization state according to the embodiments of the present application can accurately detect neutrophil polarization state information in a tumor microenvironment and accurately analyze the tumor malignancy degree of a tumor of a patient. Furthermore, the malignant tumor metastasis risk is predicted according to the neutrophil polarization state information, the tumor malignancy degree, and the patient information, thereby improving the accuracy of malignant tumor metastasis risk prediction.

[0032] According to one embodiment of the present application, in the neutrophil detection module, neutrophil polarization state information in a tumor microenvironment is detected, wherein the neutrophil polarization state information comprises a proportion of N1-type neutrophils and a proportion of N2-type neutrophils.

[0033] For example, different markers on the surface of neutrophils, such as CD11b, CD15, CD66b, and polarization-related specific markers (for example, N1-type neutrophils may express certain pro-inflammatory cytokine receptors, while N2-type neutrophils may express other anti-inflammatory related molecules), are labeled with specific antibodies, and the fluorescence intensity of the cells is analyzed by flow cytometry to distinguish neutrophils in different polarization states,

[0034] According to one embodiment of the present application, in the patient information acquisition module, patient information is acquired, wherein the patient information comprises patient age and immune system parameters.

[0035] For example, the age of the patient is acquired, and the immune system condition of the patient is checked to acquire the immune system parameters.

[0036] According to one embodiment of the present application, in the tumor information acquisition module, the tumor information is acquired, wherein the tumor information includes: tumor location, cell differentiation degree, cell nucleus division frequency and tumor infiltration range.

[0037] For example, the differentiation degree of the tumor is determined by observing the cell morphology, tissue structure, cell nucleus size, nucleolus number and other characteristics of the tumor tissue by slicing the tumor tissue, the cell differentiation degree is generally divided into high differentiation, medium differentiation and low differentiation, the value corresponding to the high differentiation degree is set as 100, the value corresponding to the medium differentiation degree is set as 10, and the value corresponding to the low differentiation degree is set as 1; the cell nucleus division frequency is obtained by observing the nuclear division figure of the tumor cell using a microscope and calculating the number of nucleus divisions in a unit area or unit volume; the tumor infiltration range is determined by observing the size, shape, edge, internal structure of the tumor and the change of the surrounding tissue using B-ultrasound, CT and nuclear magnetic resonance imaging methods.

[0038] According to one embodiment of the present application, in the malignant degree scoring module, the tumor malignant degree score is determined according to the tumor information.

[0039] According to one embodiment of the present application, the tumor malignant degree score is determined according to the tumor information, including:

[0040] The tumor location recognition result is determined according to the tumor location;

[0041] The tumor malignant degree score is determined according to the tumor location recognition result, the cell differentiation degree, the cell nucleus division frequency and the tumor infiltration range.

[0042] For example, if the tumor is located in a position prone to metastasis, such as brain, lung, liver and bone marrow, etc., the tumor cells are more prone to metastasis, the value of the tumor location recognition result is 1, and vice versa, the value of the tumor location recognition result is 0; the malignant degree of the tumor is evaluated according to the tumor location recognition result, the cell differentiation degree, the cell nucleus division frequency and the tumor infiltration range, and the tumor malignant degree score is determined.

[0043] According to one embodiment of the present application, the tumor malignant degree score is determined according to the tumor location recognition result, the cell differentiation degree, the cell nucleus division frequency and the tumor infiltration range, including: the tumor malignant degree score Mdot i of the i th patient in the current time period is determined according to formula (1). i ,

[0044]

[0045] wherein, α1, α2, α3, α4 and are preset weight values, Tp i is a tumor location recognition result of the i-th patient, Cdd i is a cell differentiation degree of tumor cells of the i-th patient, Cdd T is a preset cell differentiation degree threshold, Ndf i is a nuclear division frequency of tumor cells of the i-th patient, Ndf T is a preset nuclear division frequency threshold, Tir i is a tumor infiltration range of the i-th patient, Tir T is a preset tumor infiltration range threshold.

[0046] According to an embodiment of the present application, Tp i is a tumor location recognition result of the i-th patient, if the tumor location of the i-th patient is in a metastasis-prone location, such as brain, lung, liver and bone marrow, etc., which have rich blood supply and lymphatic system, providing convenient conditions for tumor cell metastasis, representing that the tumor is prone to metastasis, and the tumor has high malignancy, Tp i is 1, otherwise, Tp i is 0.

[0047] According to an embodiment of the present application, a well-differentiated tumor indicates that the cells still retain more normal tissue structure characteristics, and has lower malignancy, while a poorly differentiated tumor is far away from normal cell morphology, and has significantly higher malignancy, is a relative difference between the cell differentiation degree of tumor cells of the i-th patient and the preset cell differentiation degree threshold, the greater the ratio, the lower the cell differentiation degree of tumor cells of the i-th patient, and the higher the malignancy of the tumor, the preset cell differentiation degree threshold can be set as the value corresponding to the high cell differentiation degree, i.e., 100; an increase in mitotic figures and activity in malignant tumors indicates rapid tumor cell proliferation and great malignant potential, is a relative difference between the nuclear division frequency of tumor cells of the i-th patient and the preset nuclear division frequency threshold, the greater the ratio, the higher the nuclear division frequency of tumor cells of the i-th patient, and the higher the malignancy of the tumor, the preset nuclear division frequency threshold is a threshold set according to the tumor type, such as when the tumor type is colorectal adenocarcinoma, the preset nuclear division frequency threshold can be set as 6 / 10 HPF; the greater the infiltration range of the tumor, the more invasive the tumor, the more likely the tumor to metastasize, and the higher the malignancy, A relative difference between the tumor infiltration range of the i-th patient and a preset tumor infiltration range threshold, the greater the ratio, the greater the tumor infiltration range of the i-th patient, the greater the malignancy degree of the tumor, and the preset tumor infiltration range threshold is a threshold set according to the tumor type, for example, when the tumor type is breast cancer, the preset tumor infiltration range threshold can be set to 5mm.

[0048] A tumor malignancy degree score of the i-th patient in the current time period is determined according to the position condition, cell differentiation condition, nuclear division condition and infiltration range condition of the tumor of the i-th patient.

[0049] In this way, the tumor malignancy degree score can be determined according to the tumor position recognition result, cell differentiation degree, cell nuclear division frequency and tumor infiltration range, and in the calculation process, the malignancy degree of the tumor can be evaluated according to the position condition, cell differentiation condition, nuclear division condition and infiltration range condition of the tumor, thereby improving the comprehensiveness and accuracy of the tumor malignancy degree score.

[0050] According to an embodiment of the present application, a risk prediction score module is configured to determine a malignant tumor metastasis risk prediction score according to the neutrophil polarization state information, the tumor malignancy degree score and the patient information.

[0051] For example, the metastasis risk of the malignant tumor is evaluated according to the neutrophil polarization state information, the tumor malignancy degree score and the patient information, and the malignant tumor metastasis risk prediction score is determined.

[0052] According to an embodiment of the present application, a risk prediction score module is configured to determine a malignant tumor metastasis risk prediction score according to the neutrophil polarization state information, the tumor malignancy degree score and the patient information.

[0053] According to the immune system parameters, the adaptive immune cell count, the immunoglobulin level and the innate immune cell count are determined;

[0054] The historical tumor information of a plurality of historical patients is obtained;

[0055] The historical tumor malignancy degree score of the i-th patient is determined according to the historical tumor information;

[0056] The similar historical patient is determined according to the patient age, the tumor malignancy degree score and the historical tumor malignancy degree score;

[0057] The historical tumor malignancy degree score, the historical immune system parameters and the historical neutrophil polarization state information of the similar historical patient are obtained;

[0058] determine a historical adaptive immune cell count, a historical immunoglobulin level, and a historical innate immune cell count according to the historical immune system parameter;

[0059] determine a historical N1 neutrophil proportion and a historical N2 neutrophil proportion according to the historical neutrophil polarization state information;

[0060] determine a first relationship function according to the historical N1 neutrophil proportion, the historical N2 neutrophil proportion, the historical tumor malignancy score, the historical adaptive immune cell count, the historical immunoglobulin level, and the historical innate immune cell count;

[0061] determine a malignant tumor metastasis risk prediction score according to the first relationship function, the tumor malignancy score, the adaptive immune cell count, the immunoglobulin level, and the innate immune cell count.

[0062] For example, the adaptive immune cell count, the immunoglobulin level, and the innate immune cell count of the patient are obtained; the tumor location, the cell differentiation degree, the cell nuclear division frequency, and the tumor infiltration range of a plurality of historical patients are obtained; the historical tumor malignancy score of the historical patients is determined according to the tumor location, the cell differentiation degree, the cell nuclear division frequency, and the tumor infiltration range of the historical patients, and the calculation method of the historical tumor malignancy score is similar to formula (1), which is not described herein; the historical patients with the same age and tumor malignancy score as the current patient are determined as similar historical patients; the historical tumor malignancy score, the historical immune system parameter, and the historical neutrophil polarization state information of the similar historical patients are obtained in the historical database; the historical adaptive immune cell count, the historical immunoglobulin level, and the historical innate immune cell count of the similar historical patients are determined according to the historical immune system parameter; the historical N1 neutrophil proportion and the historical N2 neutrophil proportion of the similar historical patients are determined according to the historical neutrophil polarization state information; the historical tumor malignancy score is related to the historical N1 neutrophil proportion, the historical N2 neutrophil proportion, the historical tumor malignancy score, the historical adaptive immune cell count, the historical immunoglobulin level, and the historical innate immune cell count to some extent, for example, when the immunity of the patient is strong, the treatment effect of the patient is relatively good, and the tumor malignancy score of the patient in the next cycle is also low, and the malignant tumor metastasis risk prediction score can be determined based on the correlation of the above data; the metastasis risk of the malignant tumor is evaluated according to the first relationship function, the tumor malignancy score, the adaptive immune cell count, the immunoglobulin level, and the innate immune cell count, and the malignant tumor metastasis risk prediction score is determined.

[0063] According to one embodiment of the present application, the first relationship function is determined according to the historical N1-type neutrophil ratio, the historical N2-type neutrophil ratio, the historical tumor malignancy score, the historical adaptive immune cell count, the historical immunoglobulin level and the historical innate immune cell count, comprising: determining a first undetermined coefficient equation of the first relationship function according to formula (2),

[0064]

[0065] wherein, Mdot h，k，j is the historical tumor malignancy score of the kth similar historical patient in the jth historical cycle, Mdot h，k，j+1 is the historical tumor malignancy score of the kth similar historical patient in the j+1th historical cycle, Aicc h，k，j is the historical adaptive immune cell count of the kth similar historical patient in the jth historical cycle, Hil h,k,j is the historical immunoglobulin level of the kth similar historical patient in the jth historical cycle, Wbcc h,k,j is the historical innate immune cell count of the kth similar historical patient in the jth historical cycle, N1 h,k,j is the historical N1-type neutrophil ratio of the kth similar historical patient in the jth historical cycle, N2 h,k,j is the historical N2-type neutrophil ratio of the kth similar historical patient in the jth historical cycle, β1, β2, β3, β4, β5, β6, β7, β8, β9, β 10 , β 11 and β 12 are first undetermined coefficients of the first undetermined coefficient equation;

[0066] According to the historical tumor malignancy score, the historical adaptive immune cell count, the historical immunoglobulin level and the historical innate immune cell count, the first undetermined coefficient is solved to obtain a solution value of the first undetermined coefficient.

[0067] According to the solution value of the first undetermined coefficient and the first undetermined coefficient equation, the first relationship function is determined.

[0068] According to one embodiment of the present application, represents a positive correlation between the historical tumor malignancy score of the kth similar historical patient at the j+1th historical period and the historical adaptive immune cell count of the kth similar historical patient at the jth historical period, e.g., adaptive immune cells release perforin, granzyme to induce apoptosis by recognizing tumor cell surface antigens (presented by MHC), reduce the malignancy of tumor, the more the historical adaptive immune cell count of the patient at the jth historical period, the relatively smaller the historical tumor malignancy score of the patient at the j+1th historical period, represents a negative correlation between the historical tumor malignancy score of the kth similar historical patient at the j+1th historical period and the historical immunoglobulin level of the kth similar historical patient at the jth historical period, e.g., recognizing tumor antigens by antibody-dependent cellular cytotoxicity (ADCC), marking malignant cells for clearance by NK cells and macrophages, reducing the malignancy of tumor, the higher the historical immunoglobulin level of the patient at the jth historical period, the relatively smaller the historical tumor malignancy score of the patient at the j+1th historical period, represents a negative correlation between the historical tumor malignancy score of the kth similar historical patient at the j+1th historical period and the historical innate immune cell count of the kth similar historical patient at the jth historical period, e.g., innate immune cells recognize tumor cell abnormal phenotypes by surface receptors (such as NKG2D, NKp30), release perforin and granzyme, reduce the malignancy of tumor, the more the historical innate immune cell count of the patient at the jth historical period, the relatively smaller the historical tumor malignancy score of the patient at the j+1th historical period, represents a negative correlation between the historical tumor malignancy score of the kth similar historical patient at the j+1th historical period and the historical N1 type neutrophil proportion of the kth similar historical patient at the jth historical period, e.g., N1 type neutrophils show enhanced antibacterial ability, increased ROS production, release of cytotoxic granules, and play an anti-tumor role, the higher the historical N1 type neutrophil proportion of the patient at the jth historical period, the relatively smaller the historical tumor malignancy score of the patient at the j+1th historical period, (β h，k，j +β 10 ) represents a positive correlation between the historical tumor malignancy score of the kth similar historical patient at the j+1th historical period and the historical tumor malignancy score of the kth similar historical patient at the jth historical period, e.g., when the historical tumor malignancy score of the kth similar historical patient at the jth historical period is larger, the malignancy of the tumor of the patient is larger, the treatment effect of the patient is relatively worse, the historical tumor malignancy score of the patient at the j+1th historical period is relatively larger, (β 11 N2 h，k，j +β 12) represents a positive correlation between the historical tumor malignancy score of the kth similar historical patient in the j+1th historical period and the historical N2-type neutrophil proportion of the kth similar historical patient in the jth historical period, for example, N2-type neutrophils promote tissue repair, angiogenesis, but can inhibit immune response, and even be used by tumors to support their growth, the higher the historical N2-type neutrophil proportion of the patient in the jth historical period, the relatively larger the historical tumor malignancy score of the patient in the j+1th historical period. Based on the above relationship, a first undetermined coefficient equation of the first relationship function can be obtained.

[0069] According to one embodiment of the present application, the above first undetermined coefficient involves multiple parameters, that is, based on historical tumor malignancy score, historical adaptive immune cell count, historical immunoglobulin level and historical innate immune cell count, the above multiple first undetermined coefficients are solved. There are 12 first undetermined coefficients, that is, β1, β2, β3, β4, β5, β6, β7, β8, β9, β 10 , β 11 and β 12 , the above 12 first undetermined coefficients are solved according to the historical tumor malignancy score, the historical adaptive immune cell count, the historical immunoglobulin level and the historical innate immune cell count of at least 12 similar historical patients, the solution values of the above 12 first undetermined coefficients are obtained, and the solution values of the above 12 first undetermined coefficients are substituted into the first undetermined coefficient equation to determine the first relationship function.

[0070] In this way, the first relationship function can be determined according to the historical N1-type neutrophil proportion, the historical N2-type neutrophil proportion, the historical tumor malignancy score, the historical adaptive immune cell count, the historical immunoglobulin level and the historical innate immune cell count, which accurately describes the relationship between the patient's immune system status, the tumor malignancy and the tumor malignancy of the next period, and improves the accuracy and objectivity of the first relationship function.

[0071] According to one embodiment of the present application, the malignant tumor metastasis risk prediction score is determined according to the neutrophil polarization state information, the tumor malignancy score and the patient information, comprising:

[0072] According to the tumor malignancy score of the current time period, the first relationship function, the adaptive immune cell count, the immunoglobulin level, the innate immune cell count and the neutrophil polarization state information, a predicted tumor malignancy score of the next time period is determined;

[0073] According to the tumor malignancy score of the current time period and the predicted tumor malignancy score, a first difference value is determined;

[0074] According to the first difference value and the tumor malignancy score of the current time period, a malignant tumor metastasis risk prediction score of the patient in the current time period is determined.

[0075] For example, the tumor malignancy score of the patient in the current time period and the adaptive immune cell count, the immunoglobulin level and the innate immune cell count of the patient are substituted into the first relationship function to determine a predicted tumor malignancy score of the patient in the next time period; if the predicted tumor malignancy score is less than or equal to the tumor malignancy score, it indicates that the patient's condition is predicted to improve in the next time period, and the risk of malignant tumor metastasis is low, and the first difference value is 0; if the predicted tumor malignancy score is greater than the tumor malignancy score, it indicates that the patient's condition is predicted to worsen in the next time period, and the risk of malignant tumor metastasis is greater, and the first difference value is determined according to the predicted tumor malignancy score minus the tumor malignancy score of the current time period; according to the ratio of the first difference value and the tumor malignancy score of the current time period, a malignant tumor metastasis risk prediction score of the patient in the current time period is determined, and the greater the malignant tumor metastasis risk prediction score, the greater the risk of malignant tumor metastasis.

[0076] According to one embodiment of the present application, according to the tumor malignancy score of the current time period, the first relationship function, the adaptive immune cell count, the immunoglobulin level, the innate immune cell count and the neutrophil polarization state information, the predicted tumor malignancy score of the next time period is determined, comprising: determining the predicted tumor malignancy score PMdot of the i-th patient in the next time period according to formula (3) i ,

[0077]

[0078] wherein Modt i is the tumor malignancy score of the i-th patient in the current time period, Aicc i is the adaptive immune cell count of the i-th patient in the current time period, Hil i is the historical immunoglobulin level of the i-th patient in the current time period, Wbcc i is the historical innate immune cell count of the i-th patient in the current time period, N1 i is the proportion of N1 type neutrophils of the i-th patient in the current time period, N2 i is the proportion of N2 type neutrophils of the i-th patient in the current time period, β 1，F is the solution value of β1, β 2，F is the solution value of β2, β 3，F is the solution value of β3, β4,F is a solution value of β4, β 5,F is a solution value of β5, β 6,F is a solution value of β6, β 7，F is a solution value of β7, β 8，F is a solution value of β8, β 9,F is a solution value of β9, β 10，F is a solution value of β 10 is a solution value of β 11，F is a solution value of β 11 is a solution value of β 12，F is a solution value of β 12 is a solution value of β.

[0079] According to one embodiment of the present application, the tumor malignancy score of the current time period, the adaptive immune cell count, the immunoglobulin level, the innate immune cell count, the neutrophil polarization state information and the first solution value of the first undetermined coefficient are substituted into the first relationship function to obtain formula (4), thereby obtaining the predicted tumor malignancy score of the next time period for the i th patient.

[0080] In this way, the predicted tumor malignancy score of the next time period can be determined by the tumor malignancy score of the current time period, the first relationship function, the adaptive immune cell count, the immunoglobulin level, the innate immune cell count and the neutrophil polarization state information, which can improve the accuracy of the predicted tumor malignancy score and provide a data basis for calculating the metastasis risk prediction score of the malignant tumor.

[0081] According to one embodiment of the present application, in the risk prediction report module, a risk prediction report is generated according to the metastasis risk prediction score of the malignant tumor.

[0082] For example, when the metastasis risk prediction score of the malignant tumor is 0, there is no risk of metastasis of the malignant tumor, and when the metastasis risk prediction score of the malignant tumor is greater than 0.1, there is a possibility of risk of metastasis of the malignant tumor.

[0083] The malignant tumor metastasis risk prediction system based on the neutrophil polarization state according to the embodiment of the present application can accurately detect the neutrophil polarization state information in the tumor microenvironment, and accurately analyze the tumor malignancy degree of the patient. Further, according to the neutrophil polarization state information, the tumor malignancy degree and the patient information, the malignant tumor metastasis risk is predicted, and the accuracy of the malignant tumor metastasis risk prediction is improved. When determining the tumor malignancy degree score, the tumor malignancy degree score can be determined according to the tumor location recognition result, the cell differentiation degree, the cell nuclear division frequency and the tumor infiltration range. In the calculation process, the malignancy degree of the tumor can be evaluated from four aspects of the location condition, the cell differentiation condition, the nuclear division condition and the infiltration range condition of the tumor, thereby improving the comprehensiveness and accuracy of the tumor malignancy degree score. When determining the first relationship function, the first relationship function can be determined according to the historical N1-type neutrophil proportion, the historical N2-type neutrophil proportion, the historical tumor malignancy degree score, the historical adaptive immune cell count, the historical immunoglobulin level and the historical innate immune cell count, thereby accurately describing the relationship between the immune system condition of the patient, the tumor malignancy degree and the tumor malignancy degree of the next cycle, and improving the accuracy and objectivity of the first relationship function. When determining the predicted tumor malignancy degree score, the tumor malignancy degree score of the current time cycle, the first relationship function, the adaptive immune cell count, the immunoglobulin level, the innate immune cell count and the neutrophil polarization state information can be used to determine the predicted tumor malignancy degree score of the next time cycle, thereby improving the accuracy of the predicted tumor malignancy degree score and providing a data basis for calculating the malignant tumor metastasis risk prediction score.

[0084] Figure 2 An exemplary flowchart of a malignant tumor metastasis risk prediction method based on a neutrophil polarization state according to an embodiment of the present application is shown. The method comprises:

[0085] In step S101, the neutrophil polarization state information in the tumor microenvironment is detected, wherein the neutrophil polarization state information comprises an N1-type neutrophil proportion and an N2-type neutrophil proportion.

[0086] In step S102, patient information is obtained, wherein the patient information comprises patient age and immune system parameters.

[0087] In step S103, tumor information is obtained, wherein the tumor information comprises tumor location, cell differentiation degree, cell nuclear division frequency and tumor infiltration range.

[0088] In step S104, a tumor malignancy degree score is determined according to the tumor information.

[0089] Step S105, determining a malignant tumor metastasis risk prediction score according to the neutrophil polarization state information, the tumor malignancy degree score and the patient information;

[0090] Step S106, generating a risk prediction report according to the malignant tumor metastasis risk prediction score.

[0091] The present application can be a method, device, system and / or computer program product. The computer program product can include a computer-readable storage medium having computer-readable program instructions loaded thereon for executing various aspects of the present application.

[0092] Those skilled in the art will understand that the above description and the embodiments of the present application shown in the drawings are only examples and do not limit the present application. The purpose of the present application has been fully and effectively achieved. The functional and structural principles of the present application have been demonstrated and described in the embodiments, and the embodiments of the present application can be modified or changed in any way without departing from the principles.

Claims

1. A malignant tumor metastasis risk prediction system based on a neutrophil polarization state, characterized by, The method comprises the following steps: a neutrophil detection module for detecting neutrophil polarization state information in a tumor microenvironment, wherein the neutrophil polarization state information comprises a proportion of N1-type neutrophils and a proportion of N2-type neutrophils; a patient information acquisition module for acquiring patient information, wherein the patient information comprises patient age and immune system parameters; a tumor information acquisition module for acquiring tumor information, wherein the tumor information comprises tumor location, cell differentiation degree, cell nuclear division frequency, and tumor infiltration range; a malignancy degree scoring module for determining a tumor malignancy degree score based on the tumor information; a risk prediction scoring module for determining a malignant tumor metastasis risk prediction score based on the neutrophil polarization state information, the tumor malignancy degree score, and the patient information; a risk prediction report module for generating a risk prediction report based on the malignant tumor metastasis risk prediction score; determining a malignant tumor metastasis risk prediction score based on the neutrophil polarization state information, the tumor malignancy degree score, and the patient information comprises: determining adaptive immune cell count, immunoglobulin level, and innate immune cell count based on the immune system parameters; acquiring historical tumor information of a plurality of historical patients; determining historical tumor malignancy degree scores based on the historical tumor information; determining similar historical patients based on the patient age, the tumor malignancy degree score, and the historical tumor malignancy degree scores; acquiring historical tumor malignancy degree scores, historical immune system parameters, and historical neutrophil polarization state information of the similar historical patients; determining historical adaptive immune cell count, historical immunoglobulin level, and historical innate immune cell count based on the historical immune system parameters; determining historical N1-type neutrophil proportion and historical N2-type neutrophil proportion based on the historical neutrophil polarization state information; determining a first relationship function based on the historical N1-type neutrophil proportion, the historical N2-type neutrophil proportion, the historical tumor malignancy degree score, the historical adaptive immune cell count, the historical immunoglobulin level, and the historical innate immune cell count; determining a malignant tumor metastasis risk prediction score based on the first relationship function, the tumor malignancy degree score, the adaptive immune cell count, the immunoglobulin level, and the innate immune cell count; determining a first relationship function based on the historical N1-type neutrophil proportion, the historical N2-type neutrophil proportion, the historical tumor malignancy degree score, the historical adaptive immune cell count, the historical immunoglobulin level, and the historical innate immune cell count comprises: based on the formula determining a first undetermined coefficient equation of a first relationship function, wherein Mdot h，k，j is a historical tumor malignancy score of the kth similar historical patient at the jth historical cycle, Mdot h,k,j+1 is a historical tumor malignancy score of the kth similar historical patient at the j+1th historical cycle, Aicc h，k，j is a historical adaptive immune cell count of the kth similar historical patient at the jth historical cycle, Hic h，k，j is a historical immunoglobulin level of the kth similar historical patient at the jth historical cycle, Wbcc h，k，j is a historical innate immune cell count of the kth similar historical patient at the jth historical cycle, N1 h，k，j is a historical N1 type neutrophil proportion of the kth similar historical patient at the jth historical cycle, N2 h，k，j is a historical N2 type neutrophil proportion of the kth similar historical patient at the jth historical cycle, β1, β2, β3, β4, β5, β6, β7, β8, β9, β 10 , β 11 , and β 12 are first undetermined coefficients of the first undetermined coefficient equation; solving the first undetermined coefficient based on the historical tumor malignancy degree score, the historical adaptive immune cell count, the historical immunoglobulin level, and the historical innate immune cell count to obtain a solution value of the first undetermined coefficient; determining the first relationship function based on the solution value of the first undetermined coefficient and the first undetermined coefficient equation.

2. The malignant tumor metastasis risk prediction system based on a polarization state of neutrophils according to claim 1, characterized by, According to the tumor information, a tumor malignancy score is determined, including: According to the tumor location, a tumor location identification result is determined; According to the tumor location identification result, the cell differentiation degree, the cell nuclear division frequency and the tumor infiltration range, a tumor malignancy score is determined.

3. The malignant tumor metastasis risk prediction system based on a polarization state of neutrophils according to claim 2, characterized by, According to the tumor location identification result, the cell differentiation degree, the cell nuclear division frequency and the tumor infiltration range, a tumor malignancy score is determined, including: According to the formula determining a tumor malignancy score Mdot of the i-th patient at a current time period i wherein a1, a2, a3, a4, and a5 are preset weights, Tp i is a tumor location recognition result of the i-th patient, Cdd i is a cell differentiation degree of tumor cells of the i-th patient, Cdd T is a preset cell differentiation degree threshold, Ndf i is a cell nuclear division frequency of tumor cells of the i-th patient, Ndf T is a preset cell nuclear division frequency threshold, Tir i is a tumor infiltration range of the i-th patient, Tir T is a preset tumor infiltration range threshold.

4. The malignant tumor metastasis risk prediction system based on a polarization state of neutrophils according to claim 1, characterized by, According to the neutrophil polarization state information, the tumor malignancy score and the patient information, a malignant tumor metastasis risk prediction score is determined, including: According to the tumor malignancy score of the current time period, the first relationship function, the adaptive immune cell count, the immunoglobulin level, the innate immune cell count and the neutrophil polarization state information, a predicted tumor malignancy score of the next time period is determined; According to the tumor malignancy score of the current time period and the predicted tumor malignancy score, a first difference value is determined; According to the first difference value and the tumor malignancy score of the current time period, a malignant tumor metastasis risk prediction score of the patient in the current time period is determined.

5. The malignant tumor metastasis risk prediction system based on a polarization state of neutrophils according to claim 4, characterized by, According to the tumor malignancy score of the current time period, the first relationship function, the adaptive immune cell count, the immunoglobulin level, the innate immune cell count and the neutrophil polarization state information, a predicted tumor malignancy score of the next time period is determined, including: According to the formula Determine the predicted tumor malignancy score PMdot of the i-th patient in the next time period i , among which, Mdot i Aicc is the malignancy score of the tumor of the i-th patient in the current time period, i is the adaptive immune cell count of the i-th patient in the current time period, Hil i is the historical immunoglobulin level of the i-th patient in the current time period, Wbcc i N1 is the historical innate immune cell count of the i-th patient in the current time period. i is the proportion of N1 neutrophils in the current time period of the i-th patient, N2 i is the proportion of N2 neutrophils in the i-th patient in the current time period, β 1，F is the solution value of β1, β 2，F is the solution value of β2, β 3，F is the solution value of β3, β 4，F is the solution value of β4, β 5，F is the solution value of β5, β 6，F is the solution value of β6, β 7，F is the solution value of β7, β 8，F is the solution value of β8, β 9，F is the solution value of β9, β 10,F β 10 The solution value, β 11，F β 11 The solution value, β 12，F β 12 The solution value of .

6. A method for predicting the risk of metastasis of a malignant tumor based on the polarization state of neutrophils, characterized by, The method is used for the system of any one of claims 1-5, including: Detecting neutrophil polarization state information in a tumor microenvironment, wherein the neutrophil polarization state information includes: N1 type neutrophil proportion and N2 type neutrophil proportion; Obtaining patient information, wherein the patient information includes: patient age and immune system parameters; Obtaining tumor information, wherein the tumor information includes: tumor location, cell differentiation degree, cell nuclear division frequency and tumor infiltration range; According to the tumor information, a tumor malignancy score is determined; According to the neutrophil polarization state information, the tumor malignancy score and the patient information, a malignant tumor metastasis risk prediction score is determined; According to the malignant tumor metastasis risk prediction score, a risk prediction report is generated.

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