509 results about "Prognosis prediction" patented technology

The invention provides a multi-modal fusion method for breast cancer prognosis prediction based on deep learning. The multi-modal fusion method comprises the following steps: preprocessing a data set, marking a tissue region of a full-slice pathological image, performing slice processing, extracting multi-scale features, re-sampling a medical image, and extracting features of a tumor region; a two-dimensional region fusion module and a three-dimensional region fusion module are respectively selected to carry out multi-scale feature fusion on the preprocessed full-slice pathological image and the preprocessed medical image; performing multi-region feature fusion on the medical image by using a three-dimensional region fusion module; aligning the multi-modal data, performing cross-modal feature fusion by using self-attention pooling, and capturing cross-modal interaction information; and predicting the disease-free lifetime and the total survival of the patient through the loss function. The problems of limitation of single-mode analysis, unsatisfactory prognosis effect and the like are solved, and more accurate breast cancer prognosis prediction is realized.

The invention discloses a brain tumor multi-mode large model construction method, device and equipment and a storage medium, and is applied to the technical field of brain tumor imagines.The method comprises the steps that pixel-concept level alignment is conducted on a multi-mode MRI image and a pathological text; constructing a multi-modal feature fusion network for fusing image features and text features by adopting an attention mechanism of pathology perception and combining medical semantic information; training the multi-modal feature fusion network to generate an analysis report and a segmentation result; according to the technical scheme of multi-task cooperation, cross-modal pathological semantic accurate alignment, pathological knowledge graph injection and lightweight and continuous optimization parallelization, full-process coverage of brain tumor accurate segmentation, analysis report generation and prognosis prediction is achieved, the problems that a traditional model lacks pathological semantic support and is insufficient in clinical adaptability are solved, and the clinical adaptability of the traditional model is improved. And the deployment feasibility and the dynamic optimization capability are also considered.

The invention relates to an advanced gastric cancer survival prediction system based on Lasso regression, Cox regression and an interpretable machine learning technology, and belongs to the technical field of medical artificial intelligence and intelligent decision support. According to the system, by collecting multi-modal clinical data (including demographic information, TNM staging, treatment modes, tumor grading and the like) of a patient, survival-related variables are screened by adopting Lasso regression and a Cox proportional risk model, and an optimized feature set is constructed. Based on the feature set, the system integrates various mainstream machine learning algorithms (such as XGBoost, Random Forest, SVM, Logistic regression and the like) to construct a prediction model, compares the performance of each model, and selects a model with an optimal effect as a main model. And hyper-parameter tuning is performed on the model through grid search and cross validation, so that the precision and generalization ability of the model are improved. An SHAP interpretability analysis method is introduced into the system, transparent interpretation is carried out on a model output result from the global level and the individual level, and the importance and directional effect of all variables in survival prediction are determined. Finally, the model is deployed on a terminal device, a doctor is supported to automatically output the survival probability and an explanation result after inputting patient information, and a reference basis is provided for clinical treatment decision and personalized management. The system has the advantages of high prediction precision, high interpretability, convenience in use, sustainable optimization and the like, is suitable for clinical aid decision-making scenes, and has good application prospects and popularization values.

The invention discloses an effect evaluation method for a non-dialysis diabetic kidney disease, which comprises the following steps: monitoring the change trend of data by collecting renal function related data of a patient, and generating a basic data set; a dynamic nonlinear weighted regression algorithm is adopted to analyze the multi-dimensional renal function data, data weights are dynamically adjusted, and influence weights of different time periods are distributed according to the change mode of the illness state; the data influence is adjusted in real time through a time correlation mechanism, and a disease assessment curve is generated; according to the method, historical data and real-time data are combined, a patient condition trend line and a prediction result are generated, personalized treatment feedback is output, and intervention suggestions are provided for clinicians based on long-term prognosis prediction. The method is suitable for disease monitoring and treatment feedback of patients with non-dialysis diabetic kidney diseases.

The invention relates to the technical field of geological informatization, in particular to a time-space sequence prediction and risk assessment model for geological disaster monitoring data, which comprises a data acquisition module, an edge processing module, a cloud analysis module, a mobile terminal module and a data processing module, the server is connected with the cloud analysis module through a WebSocket protocol, and is used for receiving the prediction result and the visual data; and displaying the three-dimensional illness state evolution graph on augmented reality (AR) equipment. Through the innovative technical scheme, comprehensive breakthrough is achieved in the aspects of multi-modal data fusion, dynamic feature extraction, accurate prediction, robustness design, visual display and the like. The innovation points cooperate with one another and promote one another, and an efficient, reliable and intelligent MS remote monitoring and prognosis prediction system is jointly constructed.

The invention discloses a cerebral apoplexy risk and prognosis prediction system and method, and relates to the field of intelligent medical treatment, and the system comprises a data processing and knowledge construction layer which is used for extracting, cleaning and constructing a space-time multi-modal knowledge graph and structured clinical features from multi-source heterogeneous medical data; the feature engineering and fusion layer is used for deeply fusing dynamic semantic information in the space-time multi-modal knowledge graph and the structured clinical features through a graph embedding and attention mechanism to generate a fusion feature vector for a cerebral apoplexy prediction task; and the prediction model and output layer is used for performing cerebral apoplexy risk and prognosis prediction based on the fusion feature vector to obtain a prediction result, and generating a decision result for assisting a doctor in understanding the model through an interpretable mechanism. The method provided by the invention can improve the accuracy of stroke recurrence, bleeding transformation or function prognosis prediction, and provides a new way for accurate stroke management.

The invention discloses a multi-omics causal structure relation learning method based on comparative learning, which comprises the following steps: firstly, respectively constructing corresponding encoders for preprocessed gene mutation and gene expression data, and respectively carrying out feature extraction on two kinds of omics data; then, constructing a projection head with shared parameters to realize cross-modal feature alignment; then, using the aligned features as nodes, and constructing causal graph data through a learnable causal graph structure; constructing a graph neural network to learn causal graph representation, and constructing a contrast loss function; and finally, a model prediction result is obtained through a multi-layer perceptron, a survival prediction loss function is constructed, and a total loss function is obtained for multi-omics causal structure model training. Based on gene mutation and gene expression data, a cross-omics causal structure relationship is constructed and learned through comparative learning, more accurate prognosis prediction is provided for diseases such as acute myelogenous leukemia and the like, and potential biomarkers and key regulatory factors are helped to be found.

The invention discloses a stroke prognosis prediction method and system, and the method comprises the steps: S1, obtaining a CTP image of a whole cerebral vessel; and S2, the CTP image is segmented, and the segmented CTP image is obtained. And S3, carrying out feature extraction on the segmented CTP image to respectively obtain morphological features of whole cerebral vessels, perfusion features of an infarction core region and blood flow difference features of key blood vessel points. And S4, constructing a stroke prognosis prediction model based on machine learning according to the morphological characteristics of the whole brain blood vessels, the infarct core region perfusion characteristics and the blood flow difference characteristics of the key blood vessel points. According to the method, the blood flow and perfusion characteristics of the blood vessel are extracted more comprehensively, finely and quantitatively, and the whole method and system are full-automatic and do not need manual participation.

A juvenile depression cognitive impairment subtype classification and prognosis prediction method relates to the technical field of medical treatment, and mainly comprises the following steps: performing clinical evaluation and therapeutic response evaluation on a subject, performing MRI and magnetoencephalogram data acquisition, constructing a whole brain MSN of the subject, identifying MSN abnormal characteristics, obtaining functional connection change of a frequency band when magnetoencephalogram is abnormal, and determining the cognitive impairment subtype classification and prognosis prediction of the cognitive impairment subtype of the subject. A subtype classification model is established by fusing the MSN and cognitive function evaluation data, and a prognosis prediction model is established by analyzing MSN abnormal features, functional connection changes of frequency bands during abnormality, multi-dimensional treatment reactions and high-risk behaviors. According to the method, different levels of fusion measurement are carried out on the juvenile depression with cognitive function impairment brain mechanism through multi-modal brain images, a subtype classification model with diagnosis and treatment values is established, and a prognosis prediction model with clinical transformation potential is constructed; therefore, a theoretical basis and a technical means are provided for individualized precise diagnosis and treatment of the cognitive impairment of the juvenile depression.

The invention provides a laryngeal cancer multi-mode prognosis prediction method and a laryngeal cancer multi-mode prognosis prediction system fusing a CT (Computed Tomography) image and a ViT model. Relates to the technical field of biomedical images. The method comprises the following steps: acquiring and preprocessing multi-modal data of a laryngocarcinoma patient; carrying out lightweight compression, redundant information screening and robustness training on the ViT model to obtain an optimized ViT model; extracting depth features of the CT image data based on the optimized ViT model, and performing multi-stage fusion on the depth features and clinical and genome data to construct a prognosis prediction model; and performing risk stratification on the patient according to a prognosis prediction result predicted by the prognosis prediction model, and outputting treatment guidance suggestions based on the risk stratification. Through ViT model optimization, multi-modal data fusion and clinical adaptation design, precise prediction and personalized treatment guidance of laryngocarcinoma prognosis are realized, and the problems of insufficient image degradation processing, low model deployment efficiency and the like in existing laryngocarcinoma prognosis prediction are solved.

The invention relates to a lymphoma prognosis prediction method and device based on a PET-CT marker and a medium, and the method comprises the following steps: obtaining a PET / CT image of a patient, inputting the PET / CT image into a lymphoma prognosis prediction model based on image-clinical-molecular typing, and obtaining a prognosis risk category; the lymphoma prognosis prediction model based on image-clinical-molecular typing is constructed according to PET-CT markers, clinical indexes and genetic typing data, the PET-CT markers are obtained by calculating a segmentation result of a PET / CT image of a patient according to a bimodal lesion segmentation network, and prognosis significance verification is performed through a K-M survival curve; the bimodal lesion segmentation network comprises an encoder and a decoder, the encoder performs feature extraction through a plurality of convolutional layers, and a channel smoothing layer is introduced after the last convolutional layer to fuse the extracted features. Compared with the prior art, tumor information can be accurately captured from PET / CT images, and lymphoma prognosis prediction is carried out in combination with genomics and clinical medicine.

The invention discloses a tumor prognosis prediction method and a tumor prognosis prediction system, which are used for constructing a multi-modal fusion model based on image-pathology to improve the prognosis prediction efficiency of tumors, especially pancreatic cancer, and providing reference information for clinical decision-making. According to the technical scheme, the method comprises the following steps: S1, preprocessing an original tumor enhanced CT image, segmenting a tumor region, extracting radiomics features and depth image features of the tumor region, and establishing a CT image feature set; s2, after feature preprocessing is carried out on the tumor clinical data, clinical features with statistical significance are screened out, and a clinical feature set is established; s3, carrying out Hamp; e, preprocessing the pathological image, segmenting a tissue region, extracting spatial relation features, and generating a pathological spatial feature set; and S4, based on a feature interaction method, carrying out multi-modal fusion on the CT image features, the clinical features and the pathological spatial features, inputting a full-connection neural network, constructing a tumor survival risk prediction model, and outputting a tumor survival risk probability through the tumor survival risk prediction model.

The invention belongs to the field of liver cancer liver transplantation, and provides a liver cancer liver transplantation post-operation prognosis prediction system, which comprises a data acquisition module used for acquiring clinical data of a liver cancer patient from a plurality of data sources, including patient basic information, disease diagnosis information, drug use information and patient body function information; the data processing module is used for preprocessing the collected data, including data cleaning, missing value filling, data standardization and data integration, so as to ensure the quality and consistency of the data; by integrating multi-source data and combining advanced algorithms and models, accurate prediction of prognosis of patients after liver cancer liver transplantation is achieved, warning is automatically given out when a risk threshold value is reached, timely and accurate early warning information is provided for medical staff, and by collecting medicine use information and patient body function information, the patient prognosis is accurately predicted. And the physical condition of the patient can be obtained in real time through long-term monitoring, so that the prognosis prediction result is more accurate.

The invention relates to the technical field of rectal cancer prognosis prediction, in particular to a rectal cancer prognosis prediction method and device based on a multi-modal image and electronic equipment. The method comprises the following steps: firstly, acquiring an MRI image and a pathological image of a patient as original input data; respectively preprocessing the MRI image and the pathological image by adopting a double-flow heterogeneous feature extractor; then, by establishing a dynamic association mechanism, the change of the mapping relation between the two kinds of modal feature data is tracked in real time, the dynamic association mechanism can adjust the feature weight in a self-adaptive mode, and key changes in the disease progress process are effectively captured; and finally, based on the obtained dynamic association feature data, applying a pre-trained prediction model to obtain a prognosis prediction result. The MRI image and the pathological image are effectively integrated, and the accuracy of predicting the disease progress and the prediction repeatability are improved.

The invention discloses a lung cancer lifetime prediction system based on prognosis factor multi-data fusion, and belongs to the technical field of lung cancer prognosis prediction, and the system comprises a multi-source data collection module which is used for collecting prognosis multi-source data of a patient; the multi-source data processing module is used for carrying out preprocessing and feature extraction on the prognosis multi-source data of the patient; the multi-source data fusion module is used for carrying out cross-modal alignment and fine-grained fusion on the extracted multi-modal feature vectors, and capturing a dependency relationship between modals based on a hierarchical attention mechanism to form patient prognosis fusion data; and the survival analysis and prediction module is used for analyzing the prognosis fusion data of the patient according to the lung cancer lifetime prediction model, automatically predicting the lifetime of the patient and displaying the lifetime in a visual form. The problems that existing lung cancer lifetime prediction is low in accuracy and cannot provide support for personalized treatment are solved. The lung cancer lifetime prediction accuracy can be improved, and support can be provided for personalized treatment.

The invention discloses a disease prognosis evaluation and dynamic prediction method based on time sequence analysis, which comprises the following steps: acquiring disease prognosis medical data of a patient to construct a medical data time sequence set, extracting medical data time sequence characteristics and constructing a prognosis evaluation model, updating the prognosis evaluation model, and performing dynamic prediction. Inputting the to-be-evaluated patient disease prognosis medical data into the updated prognosis evaluation model to obtain a disease prognosis evaluation result, constructing a prognosis prediction model, inputting the to-be-evaluated patient disease prognosis medical data into the prognosis prediction model to obtain a disease prognosis prediction result, and updating the disease prognosis prediction result. And generating evaluation interpretation, prediction interpretation, a disease prognosis evaluation chart and a disease prediction trend chart. According to the method, the efficiency and accuracy of disease prognosis evaluation and prediction can be improved, timely and effective decision support is provided for clinic, the requirements of personalized medical treatment are met, and more accurate treatment schemes and rehabilitation guidance are provided for patients.

The invention relates to the technical field of pathological section image recognition, in particular to a system for achieving precise prognosis prediction of a gastric cancer patient based on deep learning, and aims at achieving precise prognosis prediction of the gastric cancer cell tumor patient on the pathological section level. The implementation package of the system comprises an information acquisition module, an image processing module, a wavelet parallel Mama model module, a result prediction module and an interpretability module, so that the efficiency of image feature extraction and the accuracy of model prediction are greatly improved. The core target of the invention is to solve the problems of insufficient accuracy, poorer result stability, limited model generalization ability, lack of interpretability and the like in the existing gastric cancer prognosis prediction method. Through strict verification and testing, the wavelet parallel Mama model of the research shows excellent performance in the aspect of predicting the total lifetime of gastric cancer patients in multiple centers, provides reliable support for clinical decision-making, and manifests the huge potential of the wavelet parallel Mama model in application in the field of precision medical treatment.

The invention provides a clustering method and system for transcriptome sequencing data. The method is applied to the technical field of data processing, and comprises the following steps: collecting cervical adenocarcinoma and para-carcinoma tissue specimens of a plurality of patients, and carrying out data preprocessing to obtain a standardized gene expression matrix; performing clustering operation on the standardized gene expression matrix based on a clustering analysis method combining a potential category model and sub-alliance division to obtain a clustered gene set; performing gene function annotation on the clustered gene set, and performing correlation analysis on a clustering result after function annotation and clinical characteristics of cervical adenocarcinoma to obtain correlation between gene expression and the clinical characteristics; and on the basis of clustering analysis and correlation analysis results, constructing a deep clustering prediction model based on a variational auto-encoder and a Gamma hybrid model so as to predict the prognosis risk or chemotherapy sensitivity of the patient. The problems that high-dimensional transcriptome data is poor in clustering stability and low in prognosis prediction precision are solved.

The invention aims to provide a multi-modal cancer prognosis evaluation method based on a graph. The method comprises the following steps: S1, pre-processing multi-modal data, segmenting a pathological tissue part into patches, extracting patch features, and performing surrounding type composition on the patches according to cell migration continuity; the omics data is subjected to GSEA enrichment classification, wherein five cancer development processes are included; s2, single-modal features are obtained, and modal corresponding features are obtained by using a modal enhanced and shared graph mask auto-encoder; s3, cross-modal interaction is carried out, and multi-modal data distribution mapping alignment is realized by using a cross-modal alignment module; s4, performing multi-modal fusion, and fusing the cross-modal interaction feature and each single-modal feature by using a Hyperdge-Mixer module to obtain a modal fusion feature; and S5, risk prediction: inputting the final multi-modal representation into a risk prediction module, and finally obtaining a risk prediction result. The cancer prognosis is realized by effectively fusing pathology, genes and clinical data, an end-to-end prognosis analysis framework is realized by utilizing a deep learning technology, the method has good performance and relatively high interpretability, and the clinical application practicability is enhanced.

The invention relates to the technical field of tumor prognosis prediction, in particular to a chest tumor prognosis prediction method and system based on data analysis, and the method comprises the following steps: S1, data collection: collecting multi-modal data of a chest tumor patient; s2, data preprocessing: preprocessing the collected multi-modal data; s3, prediction of tumor immune cell infiltration: predicting the infiltration degree of tumor immune cells, and evaluating the influence of the infiltration degree on patient prognosis; and S4, prognosis analysis and prediction: performing comprehensive analysis through a multi-layer perceptron algorithm to generate prognosis prediction results of the tumor patient, including tumor metastasis risk, recurrence probability and immunotherapy response. According to the invention, the complexity of the tumor immune microenvironment can be better captured, so that more accurate prognosis evaluation is provided, and clinical doctors are helped to understand the potential influence of immune response on tumor metastasis, recurrence and immunotherapy response.

The invention discloses a prediction model for tumor radiotherapy prognosis based on multi-modal information fusion, and relates to the technical field of radiotherapy prognosis prediction. By introducing multi-modal information fusion, not only is traditional image features considered, but also microscopic biological behaviors of tumors are deeply excavated. The key parameters are accurately extracted, corresponding score values are calculated, and finally a health index comprehensively reflecting the overall health condition of the tumor is obtained based on the score values. The dynamic evolution of the tumor in the treatment process can be monitored in real time. Dynamic monitoring enables the model to more accurately predict the future development trend of the tumor, and secondly, the health index serves as a comprehensive evaluation index, so that the current state of the tumor is reflected, and the future evolution trend of the tumor is predicted. By taking a plurality of key parameters into consideration, the health index can comprehensively quantify the overall health condition of the tumor, and a more comprehensive and accurate prognosis evaluation is provided.

The invention discloses a septicopyemia death risk prediction method based on artificial intelligence, and the method comprises the following steps: data collection and preprocessing: collecting clinical data and inspection indexes of a septicopyemia patient, and carrying out the cleaning and standardization processing of the data; feature selection and engineering: selecting features related to septicopyemia death risks based on clinical knowledge and statistical analysis, and performing feature engineering; model training and verification: training the preprocessed data by using a machine learning algorithm, and verifying the model by using a training set, a verification set and a test set; model evaluation and optimization: evaluating model performance by using indexes such as AUC, accuracy rate, recall rate, F1 score and the like, and optimizing model hyper-parameters; and model explanation and application: explaining a model prediction result by using an explanatory tool, and applying the model in a clinical environment to assist death risk assessment. The invention provides a method for improving the early diagnosis and prognosis prediction of septicopyemia, thereby reducing the death rate.

The invention belongs to the technical field of osteosarcoma prognosis prediction, and relates to a multi-view TSK deep learning model based on Dirichlet distribution and application thereof, a multi-view data module in the model transmits view features to a multi-view TSK fuzzy system module, and the multi-view TSK fuzzy system module maps the input view features into Dirichlet evidence parameters and transmits the Dirichlet evidence parameters to a Dirichlet distribution module; the Dirichlet distribution module establishes a classification probability representation space according to Di richlet evidence parameters and transmits the classification probability representation space to the uncertainty quantification module, the uncertainty quantification module quantifies the classification probability into view decision confidence through a dynamic evidence aggregation engine, self-adaptive weighted fusion is carried out on the view decision confidence through a combination rule, and the view decision confidence is obtained. And a multi-view TSK deep learning model is applied to osteosarcoma prognosis prediction, so that the prediction accuracy is improved.

The invention relates to the technical field of bioinformatics analysis, in particular to an enhanced CT image-based hepatocellular carcinoma postoperative early recurrence multi-modal prediction model and a construction method thereof, and the construction method comprises the following steps: S1, data acquisition: collecting clinical data and liver enhanced CT images of a hepatocellular carcinoma surgical patient, and evaluating the recurrence condition of the HCC patient; s2, image preprocessing: sketching a region of interest in the image, and extracting deep learning features; s3, establishing a model: establishing a plurality of image omics models, constructing a plurality of deep learning models, and constructing a multi-modal model; s4, evaluating and verifying the model: evaluating the prediction accuracy of the multi-modal model by adopting an ROC curve and an AUC value, and evaluating the prognosis prediction capability of the patient by using a Kaplan-Meier curve; according to the method, the characteristic expression ability and the recurrence risk discrimination performance are improved, a scientific basis can be provided for follow-up visit and treatment of the hepatocellular carcinoma postoperative patient, and the lifetime of the patient is prolonged.

The invention relates to the technical field of medical care, in particular to a tumor prognosis prediction method based on tumor database text recognition, which comprises the following steps: S1, data collection and preprocessing: collecting medical data related to tumors from a plurality of tumor databases; s2, text information extraction: performing information extraction on clinical text data; s3, predicting a tumor metastasis path: predicting the metastasis path by utilizing the graph neural network model, predicting a potential metastasis path of the tumor, and analyzing the influence of different metastasis paths on the prognosis of the patient; s4, prognosis evaluation and risk prediction: predicting the tumor prognosis risk in combination with the medical data of the patient, and comprehensively evaluating the five-year survival rate and the recurrence risk of the patient. According to the method, the medical institution can be assisted in optimizing resource allocation, a scientific personalized diagnosis and treatment scheme is formulated, and the life quality and the treatment effect of the patient are remarkably improved.

The invention discloses a multi-mode head and neck tumor segmentation and survival prognosis prediction method. The method comprises the following steps: constructing a DE-LS-UNet model; training the DE-LS-UNet model to obtain a trained DE-LS-UNet model, and inputting the new PET image and the CT image into the trained DE-LS-UNet model to obtain a predicted tumor segmentation mask; extracting multi-modal radiomics features according to the predicted tumor segmentation mask, and fusing clinical features through the multi-modal radiomics features to generate multi-source features of the patient; inputting the multi-source features of the patient into the survival analysis model for training to obtain a trained survival analysis model, and inputting the features of the patient into the trained survival analysis model to obtain a prognosis prediction result. According to the method, a feature extraction and fusion mechanism is provided, and a survival analysis model strategy is combined, so that the stability and prediction performance of survival prognosis modeling can be enhanced while the image segmentation precision is improved.

The invention relates to the technical field of liver cancer analysis, in particular to a liver cancer analysis method, system and equipment based on multi-modal deep learning and a medium. The method comprises the following steps: acquiring pathological data, transcriptome data, methylation data and clinical data of a target patient, and respectively preprocessing to obtain target pathological data, target transcriptome data, target methylation data and target clinical data; respectively carrying out feature extraction to obtain pathological features, transcriptome features, methylation features and clinical features; performing feature fusion on the pathological features, the transcriptome features, the methylation features and the clinical features to obtain liver cancer analysis features; extracting lipid metabolism related marker characteristics of the target patient, and performing liver cancer comprehensive analysis on the target patient according to the lipid metabolism related marker characteristics and the liver cancer analysis characteristics. According to the application, the tumor characteristics of the target patient can be comprehensively reflected, the accuracy of auxiliary diagnosis is improved, and accurate prognosis prediction is provided for the target patient.

The invention provides a glycolipid metabolism related gene-based endometrial cancer prognosis prediction model construction method, which comprises the following steps of 1, acquiring data containing gene expression and clinical information, and preprocessing the data; 2, differential expression and prognosis gene screening; 3, constructing a prognosis model; 4, analyzing model gene enrichment; 5, evaluating the immunocompetence of the two GLRG related dangerous groups; and step 6, statistical analysis. According to the technical scheme, more accurate and reliable prognosis evaluation is provided for endometrial cancer by comprehensively analyzing multi-dimensional information such as gene expression, immune characteristics, mutation characteristics and drug sensitivity.

A hybrid liver cancer prognosis model, prognosis system and medium, the model takes independent influence factors as input and outputs hybrid liver cancer postoperative risk scores, the hybrid liver cancer postoperative risk scores are used for dividing risk levels, and the risk levels are used for hybrid liver cancer prognosis evaluation. And / or is used for postoperative adjuvant chemotherapy decision-making of mixed liver cancer. The hybrid liver cancer prognosis model is jointly constructed by combining the traditional clinical pathological characteristics and immune microenvironment indexes, the key effect of the tumor immune microenvironment in CHC prognosis is indicated, and the constructed hybrid liver cancer prognosis model has good CHC prognosis prediction performance; in addition, decisions of postoperative adjuvant chemotherapy can be provided for patients with different risk levels, precise treatment of the CHC patients is achieved, and long-term survival prognosis of the CHC patients is improved.

The invention discloses a bladder cancer muscular layer infiltration and prognosis prediction system based on deep learning imageomics, relates to the technical field of medical treatment and belongs to auxiliary diagnosis, and adopts an imageomics method and a deep learning model to extract imageomics features and deep learning features of CT images respectively; compared with the prior art, potential meaningful features in the CT image are extracted more comprehensively, the prediction accuracy can be further improved, meanwhile, the features are screened more strictly by using technologies such as intra-class correlation coefficient (ICC) analysis, t inspection, Pearson correlation coefficient, minimum absolute contraction and selection operator (LASSO), PCA dimension reduction and Cox regression, and verification is performed in a plurality of external medical centers. On one hand, the over-fitting risk of the model can be greatly reduced, on the other hand, the excellent generalization of the model is also proved, the use of the model is not excessively influenced by the medical environment and the CT acquisition instrument, and the error generated by the multi-center effect can be effectively solved.