323 results about "Individualized treatment" patented technology

The invention relates to the technical field of medical data processing systems, in particular to an artificial intelligence-driven medical diagnosis and treatment data processing method and system. The method comprises the steps that a multi-modal medical data acquisition module acquires and processes multi-source heterogeneous medical data of a patient, and a standardized data set is generated; a medical feature depth extraction module performs multi-dimensional feature extraction on the data set, and constructs a dynamic evolution feature matrix; a multi-dimensional health state space construction module constructs a patient health state multi-dimensional space according to the matrix and determines a key medical early warning index set; the real-time medical data fusion module maps real-time data to the space to generate real-time health risk factors; and the medical risk prediction and decision-making module establishes a personalized model, outputs a disease occurrence probability and generates personalized treatment suggestions. The system solves the problems that medical data processing is difficult, diagnosis analysis is not comprehensive, and a treatment scheme lacks personality, and diagnosis accuracy and treatment pertinence are improved.

The invention relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention relates to the technical field of treatment of chronic diseases of old people, and discloses a metabolic characteristic-based medicine dosage optimization method for chronic diseases of old people. The method comprises the following steps: acquiring drug metabolism parameters (including a plasma concentration peak value, a half-life period curve and the like) and organ function data (including a hepatocyte metabolism rate, a glomerular filtration rate and the like) of a plurality of monitoring nodes, arranging the drug metabolism parameters and the organ function data into a time sequence input vector, and extracting a dynamic feature vector by using a time convolution network and an adaptive filter network; performing feature crossing, pharmacokinetic constraint correction and feature enhancement processing, performing fusion to generate a joint feature vector, inputting the joint feature vector into a dose decision model to obtain an adjustment coefficient, and generating a drug dose interval with a safety threshold in combination with a historical drug use record. According to the method, multi-dimensional data integration and dynamic modeling are realized, the accuracy and safety of chronic disease medication of old people are improved, and the method is suitable for individualized treatment.

The invention discloses a cerebral hemorrhage postoperative gastrointestinal hemorrhage prediction method based on an LGBM model. The method comprises the steps of obtaining a multi-dimensional clinical feature sequence of a target patient, screening out a stable feature subset by adopting a Boruta feature selection algorithm, performing nonlinear relation fitting and integrated decision by utilizing a pre-trained LightGBM machine learning model, and generating an individualized ATH risk probability value; and when the risk probability value exceeds a dynamic risk threshold value, triggering a high-risk early warning signal, and based on a Kaplan-Meier survival analysis model, carrying out association mapping on a prognosis track of poor long-term neural function recovery, and finally generating a comprehensive prediction report. According to the invention, accurate quantitative evaluation of ATH risk is realized, clinical intervention timeliness is improved through a dynamic threshold early warning mechanism, short-term complication risk and long-term function prognosis are organically combined, and a comprehensive and reliable prognosis basis is provided for individualized treatment decision.

The invention discloses a chronic sinusitis CT (Computed Tomography) image automatic typing and severity assessment method and a chronic sinusitis CT image automatic typing and severity assessment system based on deep learning. The method comprises the following steps: acquiring paranasal sinus CT image data of a chronic sinusitis patient and an associated clinical tag; carrying out standardization processing on the CT image, wherein the standardization processing comprises gray level normalization, denoising and region-of-interest segmentation; a multi-task deep learning model is constructed, an improved 3D Re < s> Ne < t-50 > architecture is adopted by a typing sub-model to output an immune endotype classification result, and a severity evaluation sub-model segments a lesion area based on a U-Ne < t + + > structure and automatically calculates a severity level; integrating model output to generate individualized treatment suggestions; and superposing a labeling result through the thermodynamic diagram and embedding the labeling result into a hospital PACS system. Accurate typing and evaluation of chronic nasosinusitis are achieved, and diagnosis efficiency and clinical decision support capability are improved.

The invention relates to the technical field of biological medicine, in particular to a nano-vesicle for targeted brain glioma metabolic immune remodeling and a preparation method and application thereof. According to the nano-vesicle for targeted brain glioma metabolism immune remodeling, double-target metabolism and cascade responsive cleavage site modified biological vesicles are integrated, and effective penetration of a drug blood brain barrier and precise targeting of brain glioma can be achieved. The ROS responsive nanoparticles II are prepared by selecting BSA as a drug carrier, applying a nanoprecipitation method and utilizing SLC1A5, LDHA antagonists and responsive linkers, so that the metabolic double-target inhibitor only responds to specific ROS of tumor cells, and specific release in the tumor cells is realized. The cascade response polypeptide provides a simple and convenient delivery scheme with low invasiveness and application prospects for treatment of brain glioma, and is expected to realize precise and individualized treatment in the field of treatment of malignant glioma.

The invention discloses a rapid progression type nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and relates to the field of medical informatics crossing. The invention provides a rapid progressive nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and aims to solve the problem that a rapid progressive nasopharyngeal carcinoma patient is difficult to recognize in time by depending on TNM staging and experience judgment in the prior art. According to the method, historical case data collection, missing value filling and standardization preprocessing, core feature determination through feature screening, class imbalance correction, feature coding and feature matrix construction are sequentially carried out, an artificial neural network model is trained and optimized under a cross validation framework, and performance and threshold values are determined on a validation set. During clinical application, patient features are input, and the model outputs a rapid progress risk probability and a risk level. Compared with a conventional staging or linear model, the method can improve the prediction accuracy, and achieves the early recognition and individualized treatment of a high-risk patient.

The invention provides a heart failure risk prediction method and system based on multi-modal feature fusion, and the method comprises the steps: firstly carrying out the data feature analysis, carrying out the screening and extraction of data key features, and then building a multi-modal fusion model to provide more comprehensive and accurate medical data analysis. And finally, constructing a patient prognosis evaluation model so as to predict the heart failure risk of the heart failure patient. The heart failure key indexes are automatically screened through data feature analysis, then the multi-modal deep learning model is fused, the heart failure risk of the patient can be accurately predicted in advance, and especially in clinic, early intervention, individualized treatment and resource optimization can be achieved accordingly.

The invention relates to the technical field of medical treatment, and particularly discloses a compound prognosis model for cancer prognosis evaluation, a prognosis evaluation method and a calculator. The compound prognosis model is constructed based on inflammation burden related prognosis indexes ALNCR and TNM by stage combination; the creation method comprises the following steps: step 1, collecting albumin concentration, lymphocyte count, neutrophil count and C-reactive protein concentration data of a cancer patient; 2, calculating an ALNCR index according to a formula; 3, according to a clinical classic TNM staging system, the patients are divided into a TNM staging stage I, a TNM staging stage II, a TNM staging stage III or a TNM staging stage IV; 4, the ALNCR index obtained in the step 2 and the TNM staging result obtained in the step 3 are associated and integrated, and the individualized prognosis score of the ALNCR.TNM composite prognosis model is obtained through calculation. The method is suitable for clinical rapid application; high-risk patients can be accurately distinguished, a quantitative basis is provided for individualized treatment and follow-up visit strategy formulation, and excessive medical treatment or insufficient treatment is reduced.

The invention relates to the field of biomedical treatment, and particularly discloses application of CD146 in diagnosis and treatment of rhabdomyosarcoma, and the CD146 is specifically and highly expressed in tumor tissues of the rhabdomyosarcoma. The invention provides application of a biomarker for diagnosing rhabdomyosarcoma or / and a detection reagent thereof in preparation of a product for diagnosing rhabdomyosarcoma. Meanwhile, experiments prove that the CD146-targeted chimeric antigen receptor T cell has a relatively strong tumor cell killing effect and can effectively remove CD146 positive tumor cells. The CD146 biomarker provided by the invention provides a new target and theoretical basis for early diagnosis and individualized treatment of rhabdomyosarcoma, and has important clinical application value.

The invention relates to the technical field of biomedicine, in particular to a liver cancer molecular subtype typing and prognosis model construction method based on glycometabolism and lactic acid metabolism related genes and application thereof. According to the invention, the typing and prognosis model of liver cancer subtypes is constructed for the first time on the basis of a coordinated regulation network of a glycometabolism gene and a lactic acid metabolism gene, the constructed liver cancer subtype subtypes comprise Cluster1 and Cluster2, and the total survival rates of different subtypes have significant difference; the clinical characteristics T.stage and TNM.stage of different subtypes are obviously different from each other; 12 kinds of immune cells have significant differences among different subtypes; iPS immune scores of different subtypes are significantly different; the TIDE scores of different subtypes have significant score differences; the genes with the highest mutation frequency in different subtypes comprise TP53, CTNNB1 and TTN. CLM scores of the prognosis model constructed on the basis of the collaborative regulation network of the glycometabolism genes and the lactic acid metabolism genes have no significant difference in different patient states; the 17 immune cells have significant differences between high and low risk groups. And a new way is provided for individualized treatment strategy formulation and survival outcome improvement of HCC.

The invention discloses a topological mapping monitoring and correcting method for abnormal scapular brachial rhythm after breast surgery, and belongs to the field of medical rehabilitation and computer vision. The method comprises the following steps: acquiring a trunk dynamic three-dimensional point cloud and infrared thermal imaging data; constructing a multi-layer collaborative dynamic topology network fusing a skeleton structure, soft tissue deformation and physiological activeness; extracting an intrinsic motion field and comparing the intrinsic motion field with the ideal model to generate deviation measurement; and calculating a dynamic instability index, comparing the dynamic instability index with a threshold value, and triggering optimization energy correction path calculation and instruction output. According to the method, the multi-dimensional topological mapping and dynamics prediction technology is adopted, accurate recognition and feed-forward early warning of motion abnormity under the influence of deep tissue adhesion after the breast surgery can be achieved, rehabilitation of a patient is guided through an energy consumption minimization path, and the accuracy of shoulder joint function remodeling and the individualized treatment effect are improved.

The invention relates to the technical field of medical information, and discloses a CRRT treatment intelligent safety early warning system based on multi-modal fusion, and the system comprises a data collection and fusion module which is used for collecting multi-modal data from a plurality of heterogeneous data sources in real time, carrying out the standardization and fusion processing of the multi-modal data, and generating fusion data with a unified time sequence; the knowledge model library is used for storing medical knowledge, clinical guidelines and historical data related to CRRT treatment and trained early warning and decision models; according to the system, through multi-modal data fusion and machine learning, the crossing from passive alarm to active early warning is realized, and the safety of a patient is remarkably improved. The intelligent decision support can output an individualized treatment scheme, promote precise medical treatment and optimize resource utilization, the system integration and grading alarm mechanism effectively reduces the cognitive load of medical care, and the self-learning ability of the system ensures that an early warning model can be continuously evolved and promotes the continuous improvement of clinical treatment quality.

The invention relates to the technical field of biological medicine, in particular to an ulcerative colitis disease clearance evaluation molecular marker and application thereof. By detecting the expression level of the HMGCS2 gene in a sample, the ulcerative colitis disease clearance state and disease activity period can be evaluated, and the method is used for curative effect monitoring, recurrence prediction and individualized treatment strategy formulation of ulcerative colitis and has important clinical application value and market prospect.

The invention belongs to the technical field of medical data processing, and particularly relates to a bladder cancer vessel tumor embolus assessment method and system based on multi-modal feature fusion, and the optimization method comprises the steps: obtaining and preprocessing the pathological information and image data of a bladder cancer patient, extracting a CT image corresponding to a tumor, and determining a region of interest; image omics features are extracted from the region of interest of the CT image, and deep learning features are extracted from the maximum tumor cross section; carrying out feature selection processing on the obtained radiomics features and deep learning features, and inputting the processed features into a fusion model; and outputting a probability value of'lymphatic vessel invasion 'by the fusion model to obtain an evaluation result. A multi-modal machine learning model is constructed by fusing radiomics features and deep learning features extracted from a CT image, and the objective of the invention is to noninvasively and accurately evaluate the lymphatic vessel infringement state of bladder cancer before an operation, so that a key basis is provided for clinical individualized treatment decision-making.

The invention belongs to the technical field of tumor molecular diagnosis and biological information analysis, and relates to a high-throughput sequencing method and system for monitoring acute lymphocytic leukemia (MRD). Through targeted sequencing with a unique molecular identifier and / or a double-chain tag, error modeling based on a background noise spectrum and statistics / machine learning pseudo variation filtering, ultra-deep accurate detection of IG / TCR cloning and related gene low-frequency variation is realized. And an artificial intelligence recurrence risk prediction model is established by combining a time sequence MRD index, cloning diversity and clinical information, and a structured clinical report is output and docked with LIS / HIS. According to the method, the sensitivity and the specificity of ALL minimal residual disease detection can be remarkably improved, dynamic evaluation on leukemia cloning evolution and recurrence risks is realized, and a reliable basis is provided for individualized treatment decision and long-term follow-up visit.

The invention provides a dental implant data monitoring analysis method based on deep learning, and the method comprises the steps: collecting the multi-modal structural data of a patient, such as preoperative images, clinical examination and surgical parameters, carrying out the standardized preprocessing, and inputting the data into a deep neural network model with a double-branch structure, thereby achieving the extraction of the implant failure risk score and local causal association strength; on the basis of model output, a dynamic individualized causal map is constructed, key clinical intervention variables serve as regulation and control nodes, anti-fact disturbance simulation is carried out, variable intervention sensitivity is quantified, and a key causal path is analyzed; according to the method, a priority intervention suggestion list and an auxiliary decision report are generated, the causal atlas can be adaptively optimized according to actual follow-up feedback, long-term closed-loop evolution is realized, the accuracy of implant failure risk prediction and the pertinence and operability of an intervention scheme are improved, and clinical individualized treatment decision support is enhanced.

The invention belongs to the technical field of blood pressure management, and provides an intelligent closed-loop hypertension management method, system and equipment based on wearable equipment, and the method comprises the steps: applying controllable peripheral pressure to the body of a user through the wearable equipment, and inducing local blood flow change in a pressed area; according to the method, the peripheral pressure stimulation and blood pressure monitoring are combined, continuous blood pressure signals of a user are collected in real time, the instantaneous reference blood pressure value of the continuous blood pressure signals is calibrated, the system can dynamically adjust the peripheral pressure currently applied by the wearable device and regulation and control parameters after calibration, a self-adaptive closed loop is formed by peripheral pressure stimulation and blood pressure monitoring, and pressure stimulation can be applied again according to needs. According to the scheme, controllable peripheral pressure stimulation and undisturbed continuous blood pressure monitoring are combined, real-time self-adaptive hemodynamic adjustment and intelligent closed-loop control are achieved, a continuous and real-time intelligent closed-loop hypertension management mechanism is constructed, blood pressure management covering all time periods and all scenes can be achieved, and the blood pressure monitoring accuracy is improved. The composition is suitable for hypertension regulation and control, cardiovascular and cerebrovascular disease prevention and individualized treatment assistance.

The invention discloses a method and system for analyzing next-generation sequencing data and application. The specific detection of the transcriptional activity of the cccDNA is realized by utilizing the two characteristic sequences, a new analysis method is provided for evaluating the antiviral treatment effect and judging functional cure, and individualized treatment and new drug research and development can be guided. In addition, the method is also suitable for designing specific quantitative PCR probes and primers, the cccDNA transcriptional level is evaluated by detecting characteristic fragments, and a more convenient detection means is provided for clinical laboratories.

This invention discloses a breast cancer classification method based on Piezo1 expression. After obtaining tumor tissue samples from patients, the method detects the Piezo1 protein expression level using immunohistochemistry and classifies them into high-expression and low-expression types according to a validated scoring standard. This classification result guides individualized treatment, with high-expression patients recommended for treatment regimens including inhibitors of mechanotransmission pathways. By integrating tumor mechanotransmission characteristics into the clinical classification system, this invention can identify a subgroup of breast cancer driven by an abnormal mechanotransmission microenvironment that cannot be distinguished by traditional molecular classification. This not only improves and supplements the existing classification framework, making the understanding of breast cancer heterogeneity more comprehensive, but also more accurately predicts the invasiveness, metastasis potential, and poor prognostic risk of these patients.

The invention discloses a method for accurately identifying benign and malignant cells based on multi-dimensional characteristics of a single cell transcriptome, and belongs to the technical fields of bioinformatics, tumor molecular biology and cell identification. On the basis of single cell transcriptome sequencing data of tumor tissues and para-carcinoma tissues, three types of information including copy number variation, allele specific copy number variation and tumor-related transcriptional characteristics are synthesized, and final benign and malignant identification is performed on each EpCAM positive epithelial cell through a multi-evidence voting strategy. The method disclosed by the invention has relatively high stability and accuracy in a multi-patient, multi-sample and early tumor scene, particularly improves the recognition capability of early lesion and malignant cells in benign and malignant boundary transition state cells, and provides a reliable technical means for precise diagnosis and individualized treatment of tumors.

The invention relates to the technical field of tumor molecular imaging, nuclear medicine diagnosis and targeted therapy, and discloses a Trop2-targeted polypeptide, a Trop2-targeted molecular probe and application of the Trop2-targeted polypeptide and the Trop2-targeted molecular probe. The polypeptide can be coupled with different imaging elements to construct molecular probes or radiopharmaceuticals for tumor targeted imaging, so that noninvasive visual detection on the Trop2 expression level in tumor tissues is realized. The probe can specifically recognize Trop2 positive solid tumors, and a new technical means is provided for noninvasive diagnosis and dynamic monitoring of various Trop2 high-expression tumors such as pancreatic cancer, lung cancer, breast cancer and thyroid cancer. The probe disclosed by the invention has the advantages of simple preparation process, low cost, high specificity and stability, short imaging period, low radiation dosage, easiness in clinical transformation and the like, and has a wide application prospect in precise diagnosis and individualized treatment of tumors.

The invention relates to the technical field of biological medicine, in particular to application of a creatine related gene detection reagent in preparation of breast cancer prognosis products. The creatine related gene is an IGFBP1 gene and / or a TBC1D4 gene. According to the method, the expression levels of IGFBP1 and TBC1D4 genes in breast cancer patient samples are detected, a risk score is calculated by using a random survival forest (RSF) model, and the one-year, two-year and three-year survival rates of breast cancer patients are accurately predicted according to the risk score result. Meanwhile, the invention proves that IGFBP1 and TBC1D4 genes can be used as potential targets for breast cancer treatment, the IGFBP1 and fulvestrant as well as the TBC1D4 and vinblastine have stable binding capacity, and a new basis is provided for selection of individualized treatment medicines for breast cancer.

The invention provides a methylation marker for detecting liver cancer and application of the methylation marker, and relates to the technical field of biomedicine. The invention provides a methylation marker combination for detecting liver cancer of each stage, the methylation marker combination comprises one or any more of BEST4 sequences of BEST4 genes or genomes, SFMBT2 sequences of SFMBT2 genes or genomes and SPAG6 sequences of SPAG6 genes or genomes, and the combination can accurately distinguish healthy people or patients with liver benign diseases from liver cancer patients and can be used for detecting the liver cancer of each stage. Moreover, early discovery and early treatment of the liver cancer can be realized, and a new direction and possibility are provided for early diagnosis and individualized treatment of the liver cancer. According to the invention, modeling is carried out by using a method based on machine learning, three different liver cancer diagnosis models are constructed, and the models are rapid in diagnosis, can realize in-vitro noninvasive, high-sensitivity and high-specificity diagnosis, and are suitable for wide popularization.

The application discloses a joint cartilage stress dynamic monitoring system based on flexible sensing and AI and belongs to the technical field of medical health monitoring.The application solves the problem that the prior art can only measure single-point pressure or strain and cannot obtain full-field three-dimensional stress distribution of a cartilage contact surface, and the system function stops at monitoring and does not form a monitoring-to-warning closed loop, quantifies uncertainty through a Bayesian neural network, provides a reliable basis for clinical decision-making, combines multidimensional biomechanical characteristics and an attention mechanism, accurately focuses on a stress key area, significantly improves the precision and individualization level of joint cartilage health evaluation, helps early detection and prevention of injury, classifies stress abnormalities, implements accurate early warning, and formulates an individualized treatment scheme according to uncertainty results and cartilage health indexes, and customizes a rehabilitation training plan according to user conditions, and improves the scientific nature of joint injury prevention and rehabilitation.

This invention provides a combination of metabolic biomarkers for T-staging assessment of colorectal cancer, comprising four groups of metabolite combinations specifically distinguishing different T stages of colorectal cancer, each group containing 10 endogenous small molecule metabolites. This invention also provides detection methods and applications for the above-mentioned metabolic biomarker combinations. Furthermore, this invention provides a method for T-staging colorectal cancer for non-diagnostic and therapeutic purposes, as well as a combination of metabolic biomarkers related to the evolution of T-staging and tumor invasion depth in colorectal cancer. The advantages of this invention are: by combining iEESI-MS detection with multivariate statistics and machine learning, specific metabolic biomarkers related to T-staging of colorectal cancer are screened, a T-staging discrimination model is constructed, and objective, accurate, and rapid assessment of T-staging of colorectal cancer is achieved, providing molecular support for preoperative individualized treatment.

The invention provides a cancer subtype classification method, system, equipment and program product based on ferroptosis related miRNA, and belongs to the field of intelligent medical treatment. The breast cancer is divided into four subtypes on the basis of ferroptosis related miRNA, the breast cancer FAP + subtype is identified on the basis of ferroptosis activity, the molecular characteristics of the FAP + subtype of an individual breast cancer patient are measured by establishing FAPscore, and the higher the FAPscore is, the more remarkable the FAP + subtype characteristics of the breast cancer patient are. The invention also finds that FAPscore can be used for predicting response, prognosis and drug sensitivity of individual cancer patients to immunotherapy response, and the discovery has wide cross-cancer species applicability and is not limited to breast cancer. The invention provides a new insight for a more effective and individualized treatment strategy of cancer.

The invention discloses a colorectal cancer treatment effect evaluation method, system and equipment based on SPP1 + TAMs. According to the application, the high heterogeneity of the TAM in the colorectal cancer and the metastatic microenvironment thereof is systematically revealed from three dimensions of spatial distribution, metabolic characteristics and functional states. On the basis, the invention provides a method for evaluating the treatment effect of the multi-kinase inhibitor and / or the anti-PD-1 therapy medicine on the colorectal cancer based on SPP1 + TAMs, a medicine screening method, a system, equipment and the like, so that a doctor is assisted in evaluating the treatment effect of a patient suffering from the colorectal cancer, and a clinician is guided to formulate an individualized treatment scheme for the patient; meanwhile, a new thought is provided for treatment of colorectal cancer.

The invention provides a biomarker for predicting chemosensitivity of acute myelogenous leukemia and application of the biomarker, and belongs to the technical field of biological medicine. The leptin and fumaric acid provided by the invention can be used as biomarkers for predicting AML chemotherapy resistance. Wherein the leptin influences the chemotherapy sensitivity by regulating metabolic pathways such as mitochondrial OXPHOS and mitochondrial active oxygen mtROS; meanwhile, the invention also discloses a new mechanism that fumaric acid promotes BMSCs to transfer mitochondria to AML cells so as to enhance the OXPHOS level and induce Ara-C drug resistance. The levels of the two are obviously related to the chemotherapy reaction and prognosis stratification of the patient, so that early recognition of the patient with chemotherapy resistance or poor prognosis is facilitated. Leptin and fumaric acid can be detected in bone marrow plasma, the feasibility is high, and clinical transformation is facilitated. By detecting the level of leptin or fumaric acid in bone marrow of a patient, individualized treatment can be achieved, and the survival rate of the patient is increased.