CN114093517A - Cancer screening method and system based on blood indexes and cfDNA - Google Patents
Cancer screening method and system based on blood indexes and cfDNA Download PDFInfo
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- CN114093517A CN114093517A CN202111429990.7A CN202111429990A CN114093517A CN 114093517 A CN114093517 A CN 114093517A CN 202111429990 A CN202111429990 A CN 202111429990A CN 114093517 A CN114093517 A CN 114093517A
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Abstract
The invention discloses a cancer screening method and a cancer screening system based on blood indexes and cfDNA, which belong to the field of medical data processing, and comprise a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring age, gender and blood routine, blood biochemistry, tumor marker detection indexes, cfDNA concentration and methylation data; the screening prediction module is used for predicting a malignant tumor disease prediction value of a person to be screened based on the malignant tumor screening model; wherein, the training process of the malignant tumor screening model comprises the following steps: constructing a large-scale population sample set, wherein the samples are divided into 2 types, namely a population without malignant tumor and a patient with malignant tumor; and training a machine learning algorithm model by using the crowd sample to obtain a malignant tumor screening model. The invention screens malignant tumors by using blood examination indexes, tumor markers and cfDNA, innovatively combines the omics inspection and the blood cell examination to screen tumors, and innovates the tumor screening mode.
Description
Technical Field
The invention belongs to the field of medical data processing, and relates to a cancer screening method and system based on blood indexes and cfDNA.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The conventional diagnosis of malignant tumor mainly depends on pathological morphological observation and clinical imaging detection, i.e., diagnosis is based on tumor morphology, and with the progress of various biological technologies, such as gene detection, proteomics and other technical means, applied to the auxiliary diagnosis of cancer, the diagnosis of cancer tends to be more functional diagnosis.
The blood routine, blood biochemistry and tumor markers contain a large amount of human health information, a plurality of specific indexes are common sensitive indexes, sensitive reflection is realized on a plurality of pathological changes in a body, and a patient can perform blood examination to perform auxiliary diagnosis when the cause of disease is unknown. Circulating free DNA or Cell free DNA (cfDNA), Circulating free DNA or Cell free DNA, is a degraded DNA fragment that is released into the plasma. cfDNA is present in various body fluids of the human body at concentrations that vary with tissue damage, cancer, inflammatory responses, and the like. At present, a large number of researches prove that the blood examination index, the tumor marker, the cfDNA and the malignant tumor have relevance. If the method can be combined with the comprehensive analysis of various test results, the accuracy of the malignant tumor screening is improved, and the method has high practical application value for the malignant tumor screening.
Disclosure of Invention
In order to solve the problems, the invention provides a cancer screening method and a cancer screening system based on blood indexes and cfDNA, which screen specific diseases by utilizing blood routine, blood biochemistry, tumor marker detection indexes, cfDNA and cfDNA methylation data, and innovate a disease screening and early warning mode.
In order to achieve the purpose, the invention adopts the following technical scheme:
the data acquisition module is used for acquiring age, gender and blood routine, blood biochemistry, tumor marker detection indexes, cfDNA concentration and methylation data in peripheral blood;
the screening prediction module is used for predicting a malignant tumor disease prediction value of a person to be screened based on the malignant tumor screening model;
wherein, the training process of the malignant tumor screening model comprises the following steps: constructing a large-scale population sample set, wherein the samples are divided into 2 types, namely a population without malignant tumor and a patient with malignant tumor; and (3) training a machine learning algorithm model by using the crowd sample, and distinguishing 2 types of crowds to obtain a malignant tumor screening model.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a schematic structural diagram of a malignant tumor screening method and system based on blood test indicators, tumor markers, and cfDNA according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
Fig. 1 shows a blood index, cfDNA-based cancer screening method and system of the present embodiment, which includes:
(1) and the data acquisition module is used for acquiring age, gender and blood routine, blood biochemistry, tumor marker detection indexes, cfDNA concentration and methylation data in peripheral blood.
Wherein the blood conventional index includes white blood cell count (WBC), red blood cell count (RBC), hemoglobin (Hb), hematocrit (Hct), Mean Corpuscular Volume (MCV), mean corpuscular hemoglobin content (MCH), Mean Corpuscular Hemoglobin Concentration (MCHC), platelet count (PLT), lymphocyte percentage (Lymph), monocyte percentage (Mono), neutrophil percentage (Neut), eosinophil percentage (Eos), basophil percentage (Baso), lymphocyte count (Lymph), monocyte count (Mono), neutrophil count (Neut), eosinophil count (Eos), basophil count (Baso), erythrocyte volume distribution width CV (RDW-CV), erythrocyte volume distribution width SD (RDW-SD), Platelet Distribution Width (PDW), mean Platelet Volume (MPV), percent large platelets (P-LCR%), hematocrit (PCT).
Wherein the biochemical blood indicators include glutamic-oxaloacetic transaminase (AST), glutamic-pyruvic transaminase (ALT), glutamic-oxaloacetic transaminase/glutamic-pyruvic transaminase (S/L), glutamyl transpeptidase (GGT), alkaline phosphatase (ALP), Total Protein (TP), Albumin (ALB), Globulin (GLO), albumin/globulin (A/G), Total Bilirubin (TBIL), Direct Bilirubin (DBIL), Indirect Bilirubin (IBIL), total Cholesterol (CHOL), high density lipoprotein (HDL-C), low density lipoprotein (LDL/C), Triglyceride (TG), Glucose (GLU), urea nitrogen (BUN), Creatinine (CREA), urea nitrogen/creatinine (BUN/CREA), URIC acid (URIC).
Wherein the tumor markers are AFP, CEA, Cyfra21-1, CA199, CA242, CA125, SCC, PSA.
(2) And the screening prediction module is used for predicting the malignant tumor disease prediction value of the person to be screened based on the malignant tumor screening model.
Step 1: the training process of the malignant tumor screening model comprises the following steps: constructing a large-scale population sample set, wherein the samples are divided into 2 types, namely a population without malignant tumor and a patient with malignant tumor; and (3) training a machine learning algorithm model by using the crowd sample, and distinguishing 2 types of crowds to obtain a malignant tumor screening model.
The machine learning algorithm model can be a preset algorithm, such as an SVM (support vector machine), a random forest algorithm, a LightGBM (LightGBM) algorithm or an XGboost algorithm. The machine learning algorithm model can also be an optimal machine learning algorithm model screened out after a plurality of algorithms are compared.
The selection of the calculation characteristics of the malignant tumor screening model can utilize partial indexes or all indexes of blood routine, blood biochemistry, tumor marker detection indexes, cfDNA concentration and methylation data.
Step 2: and calculating the malignant tumor risk of the testee by a malignant tumor screening model.
Various modifications and changes may be made by those skilled in the art without departing from the spirit and scope of the invention, and it is intended to cover in the appended claims all such modifications, equivalents, and improvements as fall within the true spirit and scope of the invention.
Claims (6)
1. A cancer screening method and system based on blood index and cfDNA are characterized by comprising the following steps:
the data acquisition module is used for acquiring age, gender and blood routine, blood biochemistry, tumor marker detection indexes, cfDNA concentration and methylation data in peripheral blood;
the screening prediction module is used for predicting a malignant tumor disease prediction value of a person to be screened based on the malignant tumor screening model;
wherein, the training process of the malignant tumor screening model comprises the following steps: constructing a large-scale population sample set, wherein the samples are divided into 2 types, namely a population without malignant tumor and a patient with malignant tumor; and (3) training a machine learning algorithm model by using the crowd sample, and distinguishing 2 types of crowds to obtain a malignant tumor screening model.
2. The method and system for screening cancer according to claim 1, wherein the cancer patient with malignant tumor comprises leukemia, lung cancer, liver cancer, stomach cancer, esophageal cancer, colorectal cancer, breast cancer, cervical cancer, kidney cancer, pancreatic cancer, thyroid cancer, prostate cancer, ovarian cancer, nasopharyngeal cancer.
3. The method and system for cancer screening based on blood indicators and cfDNA as claimed in claim 1, wherein training data for machine learning can be trained by comprehensively using some or all indicators of blood routine, blood biochemistry, tumor marker detection indicators, cfDNA concentration and methylation data, and can be used as a malignant tumor screening model as long as the requirements of model evaluation indicators are met.
4. A blood-marker, cfDNA-based cancer screening method and system as claimed in claim 1, wherein cfDNA methylation data can use whole genome methylation data or methylation data of selected partial genes.
5. A blood index, cfDNA based cancer screening method and system as claimed in claim 3 wherein model evaluation indices include prediction accuracy, AUC, sensitivity, specificity.
6. The method and system for cancer screening based on blood indicators and cfDNA as claimed in claim 5, wherein in the training process of the malignancy screening model, a plurality of machine learning algorithm models are trained by using a sample set; and comparing all the trained machine learning algorithm models by using the model evaluation indexes, and generating an optimal malignant tumor screening model by using the machine learning algorithm model with the highest accuracy.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115099355A (en) * | 2022-07-08 | 2022-09-23 | 中山大学孙逸仙纪念医院 | XGboost algorithm-based vertigo cause diagnosis model construction method and system |
| CN115472292A (en) * | 2022-09-14 | 2022-12-13 | 重庆大学附属肿瘤医院 | Method for constructing lung cancer risk prediction model based on peripheral blood markers |
| CN115662519A (en) * | 2022-09-29 | 2023-01-31 | 昂凯生命科技(苏州)有限公司 | cfDNA fragment feature combination and system for predicting cancer based on machine learning |
| CN116219012A (en) * | 2022-12-15 | 2023-06-06 | 华中科技大学同济医学院附属同济医院 | System and method for predicting cervical cancer neoadjuvant chemotherapy effect or recurrent high-risk classification based on plasma cfDNA fragment distribution characteristics |
-
2021
- 2021-11-29 CN CN202111429990.7A patent/CN114093517A/en not_active Withdrawn
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115099355A (en) * | 2022-07-08 | 2022-09-23 | 中山大学孙逸仙纪念医院 | XGboost algorithm-based vertigo cause diagnosis model construction method and system |
| CN115472292A (en) * | 2022-09-14 | 2022-12-13 | 重庆大学附属肿瘤医院 | Method for constructing lung cancer risk prediction model based on peripheral blood markers |
| CN115662519A (en) * | 2022-09-29 | 2023-01-31 | 昂凯生命科技(苏州)有限公司 | cfDNA fragment feature combination and system for predicting cancer based on machine learning |
| CN115662519B (en) * | 2022-09-29 | 2023-11-03 | 南京医科大学 | cfDNA fragment characteristic combination and system for predicting cancer based on machine learning |
| CN116219012A (en) * | 2022-12-15 | 2023-06-06 | 华中科技大学同济医学院附属同济医院 | System and method for predicting cervical cancer neoadjuvant chemotherapy effect or recurrent high-risk classification based on plasma cfDNA fragment distribution characteristics |
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