70 results about "Prognostic prediction" patented technology

The invention discloses a renal clear cell carcinoma prognosis prediction method based on multi-mode MRI (Magnetic Resonance Imaging) image and digital pathological omics fusion. The method comprises the following steps: S1, collecting a training data set based on an MR image and a pathological image; s2, feature extraction of MR radiomics; s3, deep learning feature extraction of the pathological image; s4, an MR-pathological feature fusion module; and S5, deploying the network. According to the method, depth features with prognosis information are obtained from two scales of pre-treatment images and post-operation pathology, effective features are extracted by adopting image omics and a convolutional neural network mode according to data characteristics of MR images and pathology images, and depth fusion of the two types of features is completed in a hidden space through a multi-task guiding mode, so that the accuracy of the MR image and the pathology image is improved. A precise prognosis model with multi-scale information is provided, and the method has a relatively strong clinical application prospect and is of great significance for realizing precise immunotherapy and improving prognosis of a patient.

The invention relates to a prognosis prediction method based on co-attention modal pre-fusion, and the method comprises the steps: respectively extracting cell interaction features and morphological features in a postoperative pathological image through a graph neural network and a pre-trained convolutional neural network, so as to construct a pathological omics expression; the method comprises the following steps of: extracting shape textures and morphological features in an image by utilizing an artificial predefined feature group and a pre-trained radiation large model to form a radiomics representation, capturing an interaction effect between pathological omics and radiomics by adopting a co-attention mechanism, and obtaining a cross-modal pathological omics representation based on image co-attention; and finally inputting two multi-instance learning modules to obtain a prognosis prediction result. Compared with the prior art, the method has the advantages that multi-omics and morphological information of pathology and images is comprehensively integrated, interaction between the pathology and the images is effectively captured through a common-attention multi-instance learning framework for prognosis prediction, and the accuracy of a traditional grading and staging index, a single-mode model and a multi-mode post-fusion method in prognosis prediction is remarkably improved.

Systems and methods for computer modeling in medicine. A sort of period table of medical models is described for personalized diagnostics, prognostics and therapeutics, including at least 80 major categories of medical models. Generative artificial intelligence and geometric deep learning techniques, and algorithms including 2D and 3D graph machine learning and GenAI algorithms, are described, tailored and applied to diagnostic disease description, prognostic prediction and therapeutic development and management, including generation of novel synthetic drugs. The AI and machine learning techniques and algorithms are applied to understand each individual's genetic, RNA and protein anomalies that represent the source of many unique patient diseases. AI-enabled software agents assist physicians and researchers in building patient medical models. Several personalized medicine applications of individualized medical modeling include cardiovascular disease, cancer, neurological disorders, immune system disorders and genetic diseases.

The invention provides a tumor mechanics multi-parameter prognosis prediction system and method based on elastography, and relates to the field of medical imaging, oncology and biomechanics, and the system comprises an elastography acquisition module which is used for collecting elastography data of a tumor of a patient; the image preprocessing module is used for preprocessing the elasticity imaging data; the parameter extraction module is used for extracting multi-dimensional tumor mechanical parameters and carrying out feature normalization processing; the feature selection module is used for screening key prognosis related parameters; the prognosis prediction model construction module is used for constructing a prognosis prediction model based on the screened key prognosis related parameters; and the result display module is used for displaying the tumor mechanics multi-parameter prognosis prediction result in a visual mode by applying the prognosis prediction model. The problem that a traditional tumor prediction method is insufficient in accuracy is solved.

The invention provides a hematologic tumor cord blood transplantation treatment prognosis index evaluation decision generation method and system based on prediction modeling, a medium and electronic equipment, and relates to the technical field of medical artificial intelligence. The method comprises the following steps: acquiring multi-source data of a key time point in a whole process of blood tumor umbilical cord blood transplantation treatment of a patient; integrating expert knowledge to form an expert knowledge network, taking the expert knowledge network as priori knowledge of multi-target prediction, and performing multi-dimensional prognosis prediction by using multi-source data; periodically acquiring time sequence multi-source data, capturing time evolution characteristics of patient states, and automatically triggering an updating mechanism at a key clinical time point to dynamically adjust a prediction result; generating a risk assessment grade, a feature importance analysis result and an individualized interpretation report based on the prediction result, and performing visualization; and generating a clinical decision using the large language model. According to the technology, accurate evaluation and individualized clinical decision support for hematologic tumor cord blood transplantation prognosis are achieved.

A system and method are provided for identifying a multimodal biomarker of a prognostic prediction, using a deep learning framework trained to analyze different modality data, including radiomic image data, pathology image data, and molecular image data to obtain unimodal embedding predictions from those modality data and generate multimodal embedding predictions, through application of a loss minimization and attention-based fusion processes.

The invention provides a lung adenocarcinoma prognostic marker and a model, constructs a lung adenocarcinoma prognostic model based on mRNA and lncRNA related to programmed cell death (PCD), and synthesizes bioinformatics analysis and experiments to explore the biological effect of the lncRNA model in lung adenocarcinoma. The prognosis model covers all PCD pathways and various molecules, has stable prognosis prediction accuracy, and can provide a potential molecular target for personalized treatment. According to the method disclosed by the invention, the PCD-related mRNA and lncRNA are integrated to construct a lung adenocarcinoma diagnosis model suitable for Chinese population, so that the identification capability of tumor patient classification is enhanced from multiple aspects, the accuracy and reliability of prognosis prediction are improved, powerful support can be provided for promoting personalized treatment strategies of lung adenocarcinoma patients, and the method is suitable for popularization and application.

The invention relates to a prognosis prediction method based on multi-modal intermediate fusion, and the method comprises the steps: extracting pathological features from a pathological image through a deep convolutional neural network and predefined omics, and carrying out the aggregation of the features through multi-instance learning, so as to form a pathological representation, the method comprises the following steps: extracting radioactive features by using a deep convolutional neural network and predefined omics to form a radiology representation, and then integrating a pathological representation, the radiology representation and clinical variables by using a deep survival network to generate a multi-modal prognosis prediction score. Compared with the prior art, information of three modals of pathology, radiology and clinic is integrated through the deep survival network, and traditional prognosis prediction indexes and prediction performance of a single-modal model are remarkably improved.

The embodiment of the invention provides a method and device for identifying lymph node metastasis and prognosis prediction under non-small cell lung cancer carina based on dual-region radiomics and a deep learning model, a storage medium and electronic equipment, and relates to the technical field of lung cancer diagnosis. Compared with a traditional scheme for judging lymph node metastasis of a non-small cell lung cancer patient based on physician experience, an artificial intelligence model is constructed by using dual-region CT radiomics, and a highly reliable prediction result is obtained.

This invention belongs to the field of biomedical technology, specifically relating to the discovery and application of biomarkers for predicting the efficacy of immunotherapy in nasopharyngeal carcinoma. This invention provides a reliable combination of 10-gene TLS biomarkers that can effectively predict the pathological TLS status in nasopharyngeal carcinoma tissue. This 10-gene TLS biomarker combination exhibits stable and significant prognostic predictive value: regardless of standard treatment or immunotherapy, a high TLS score can effectively identify patients with better survival outcomes (such as FFS, OS, and DMFS), providing important evidence for individualized risk assessment and treatment strategy selection.

The present invention relates to an artificial intelligence model and an implementation method therefor, which are capable of selecting and extracting, from CT images of patients with IgA nephropathy, image features significant for prognostic prediction and applying same to a machine learning model to thereby predict, with high accuracy, the likelihood of the patients with IgA nephropathy progressing to end-stage renal failure within five years. The present invention provides a prognostic prediction model that rapidly predicts the prognosis of a patient without an invasive kidney biopsy, overcomes the limitations of conventional pathology diagnosis relying on invasive methods, and enables periodic prognostic evaluation with significantly improved reliability. In addition, the present invention is capable of precisely reflecting characteristics of each item and accurately predicting a clinical course of a patient, by combining various feature selection methods and binary classifiers for each item of mesangial hypercellularity (M), endothelial hypercellularity (E), segmental glomerulosclerosis (S), and tubular atrophy / interstitial fibrosis (T), which constitute a MEST score.

The invention belongs to the technical field of biology, and particularly relates to a group of urine protein markers and application thereof in preparation of non-small cell lung cancer prognosis prediction products. A group of urine proteins including AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein and XPNPEP2 protein which are closely related to non-small cell lung cancer postoperative progress are screened out from urine of non-small cell lung cancer patients for the first time; a prognosis prediction model is constructed based on the six kinds of urine proteins, the constructed prognosis prediction model is verified to have high sensitivity and specificity on prognosis prediction of non-small cell lung cancer postoperative patients, the sensitivity and the specificity respectively reach 84.5% and 81.2%, and the prognosis prediction model has a good prognosis prediction effect on non-small cell lung cancer. Reliable guidance is provided for clinical targeted treatment, and important significance is achieved for improving prognosis survival conditions of non-small cell lung cancer patients.

The invention belongs to the technical field of tumor prognosis prediction, and relates to a colorectal cancer allotropic liver metastasis prediction method and system.The method comprises the steps that colorectal adenocarcinoma cases are collected, the volume of an intra-tumor region-of-interest is delineated and automatically expanded to generate the volume of a peritumor region-of-interest, image omics characteristics are extracted through a pyradiomics packet, core omics characteristics are screened out, and the colorectal cancer allotropic liver metastasis prediction result is obtained. The method comprises the following steps: respectively constructing an intratumoral model and a peritumoral model, analyzing and integrating intratumoral and peritumoral core omics characteristics through logistic regression to form a combined radiomics model, integrating the combined radiomics model and clinical risk factors, establishing a column diagram for predicting the non-hepatic metastasis lifetime, and dividing patients into a low-risk group and a high-risk group according to a column diagram score threshold value; according to the method, LMFS prediction results of 1-5 years can be quickly output, and 0.6911 is set as a standardized risk stratification cut-off value so as to support risk stratification and personalized treatment decision of a patient.

A computer-based method and system for predicting future geographic atrophy (GA) lesion growth. An optical coherence tomography (OCT) volume for a retina of a subject associated with a first point in time is received by a process over a network. A segmentation model is used to generate a segmented volume corresponding to the OCT volume, the segmented volume comprising a plurality of 3D segments. For each 3D segment of the plurality of 3D segments, a map output is generated. A set of biomarker values is computed for a corresponding set of biomarker features using the map output. A prediction system is used to generate a prediction output that predicts a progression of a GA lesion in the retina with respect to a future point in time after the first point in time using at least one of the set of biomarker values or the map output.

The invention relates to a tumor prognosis prediction model training method, a tumor prognosis prediction model using method, a tumor prognosis prediction model training device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a first sample image and a second sample image of different sample users for a target organ, and a tumor prognosis tag corresponding to the second sample image; pre-training a diffusion model according to the first sample image so as to enable the diffusion model to learn the feature extraction capability of the target organ; the diffusion model comprises a coding network for feature extraction; generating a tumor prognosis prediction model comprising a prognosis classification network and a pre-trained coding network; and according to the second sample image and the corresponding tumor prognosis label, performing fine adjustment on the tumor prognosis prediction model to obtain a trained tumor prognosis prediction model. According to the invention, the feature extraction capability of the model can be improved, and the prediction performance of the tumor prognosis prediction model is further improved.

The present invention discloses a prognostic marker for gastric adenocarcinoma, which consists of 8 genes: F5, SLC5A1, PHYHD1, FNDC1, NFE2L3, SCUBE2, CBS, and CTHRC1. The risk assessment model constructed based on these 8 genes helps to better predict the prognosis of gastric adenocarcinoma patients, effectively evaluate the prognostic risks of different patients, intervene early, bring great help to guiding the clinical treatment of this disease, and thus improve the 5-year survival rate of gastric adenocarcinoma patients. The risk assessment model of the present invention has high prediction accuracy, is universal, and is significantly superior to other gastric adenocarcinoma prognosis methods in the prior art and is also significantly superior to other models constructed during the research process.

The invention discloses a biliary duct cancer PD-1 monoclonal antibody treatment prognosis prediction system based on blood magnesium level, and relates to the technical field of biliary duct cancer immunotherapy prognosis, the biliary duct cancer PD-1 monoclonal antibody treatment prognosis prediction system comprises four core modules, a blood magnesium detection module uses a full-automatic biochemical analyzer to detect serum magnesium, the serum magnesium is divided into a low magnesium group and a normal blood magnesium group, and synchronous quality control is performed; the clinical data acquisition module acquires multi-dimensional information, and two persons check and complement data; the prognosis analysis module integrates data, and evaluates prognosis through statistical test, survival analysis and subgroup verification; and the result output module generates an encrypted report, visually presents and synchronizes the encrypted report to the electronic medical record system. The serum magnesium is used as a prediction index, the cost is low, the serum magnesium is easy to obtain, the serum magnesium is adaptive to hospital equipment at all levels, the independent prediction value and subgroup consistency are confirmed through rigorous analysis, prognosis can be dynamically updated, individualized suggestions are generated, prediction accuracy and clinical practicability are improved, and biliary duct cancer immunotherapy precision is promoted.

The invention relates to application of HLA-DR < + > tumor cells in evaluation of liver cancer prognosis or immunotherapy response, and belongs to the technical field of tumor biomarkers and prognosis evaluation. The technical problem to be solved by the invention is to provide a biomarker for evaluating liver cancer prognosis or immunotherapy response, namely HLA-DR < + > tumor cells. Through scRNA-seq and accurate differential expression analysis, gene expression characteristics of HLA-DR < + > tumor cells in HBV-HCC are disclosed for the first time, and a high-specificity molecular marker is provided for prognosis evaluation or immunotherapy response prediction. A prognosis scoring model is constructed by using the molecular marker, so that the prognosis prediction accuracy of HBV-HCC patients can be improved, and a scientific basis is provided for clinical precision medical treatment. An immune response scoring model is constructed by using the molecular marker, and is used for predicting the treatment response of a patient to a PD-1 / PD-L1 inhibitor and guiding clinical formulation of an individualized treatment scheme. In addition, the invention also helps to deeply reveal an immune escape mechanism of HBV liver cancer, and lays a foundation for subsequent immunotherapy strategy development of targeted HLA-DR + tumor cells.

The present disclosure relates to a method of determining a prognostic outlook for patients having metastatic breast cancer. The method includes receiving imaging data from an image of a patient that is receiving or that is to receive cycline dependent kinase 4 and 6 (CDK 4 / 6) inhibitor therapy for hormone receptor-positive (HR+) metastatic breast cancer. Radiomic heterogeneity features are extracted from imaging data associated with a metastasis within the imaging. A prognostic marker is determined from the radiomic heterogeneity features. The prognostic marker is indicative of a response of the patient to CDK 4 / 6 inhibitor therapy for HR+ metastatic breast cancer.

According to the technical scheme, the pancreatic cancer prognosis prediction system based on deep learning is characterized by comprising a data input module, a preprocessing module, a feature extraction module, a cross sequence attention fusion module and a multi-task prediction module. The system disclosed by the invention can accurately predict the overall lifetime (OS) and the disease-free lifetime (DFS) of the patient by analyzing the preoperative multi-sequence magnetic resonance imaging (MRI) image of the patient, and carries out risk stratification. More importantly, clinical doctors can be guided to formulate personalized adjuvant therapy schemes for patients with different risk levels, for example, whether the patients can benefit from postoperative adjuvant chemotherapy or immunotherapy or not is judged, and therefore precise medical treatment is achieved, the treatment effect is improved, and invalid treatment is avoided.

System and methods for computer modeling in medicine. A sort of period table of medical models is described for personalized diagnostics, prognostics and therapeutics, including at least 80 major categories of medical models. Generative artificial intelligence and geometric deep learning techniques, and algorithms including 2D and 3D graph machine learning and GenAI algorithms, are described, tailored and applied to diagnostic disease description, prognostic prediction and therapeutic development and management, including generation of novel synthetic drugs. The AI and machine learning techniques and algorithms are applied to understand each individual's genetic, RNA and protein anomalies that represent the source of many unique patient diseases. AI-enabled software agents assist physicians and researchers in building patient medical models. Several personalized medicine applications of individualized medical modeling include cardiovascular

The invention discloses a spinal cord electrical stimulation prognosis prediction system for a patient with disturbance of consciousness based on neurovascular coupling, and belongs to the technical field of brain-computer interfaces. The system synchronously acquires electroencephalogram and cerebral blood flow signals of the patient through electroencephalogram acquisition equipment and transcranial Doppler ultrasound; brain network connections are calculated by using weighted pairwise phase congruency indexes, neurovascular coupling parameters are calculated based on a phase-amplitude coupling method, and finally results are presented through a display module to predict prognosis. The system can solve the problem of clinical lack of prognosis evaluation indexes of SCS treatment consciousness disorder patients, has the advantages of simple acquisition equipment, reusability and the like, and can assist doctors in providing personalized clinical schemes and reducing the treatment cycle; the system relates to a data acquisition, processing and result display module, a key parameter calculation process is clear, and the system is suitable for prognosis evaluation after SCS implantation of a consciousness disorder patient.

This invention provides a method and system for processing hepatocellular carcinoma data. The method utilizes the CIBERSORT tool combined with weighted gene co-expression network analysis to screen a gene set positively correlated with M2 macrophage infiltration. Through differential expression analysis and prognostic correlation analysis, target genes with prognostic value are identified from the gene set. A dataset containing the target genes is acquired, and a comprehensive machine learning algorithm is used to generate several corresponding prognostic prediction models for each dataset. The C-index of the dataset in the corresponding prognostic prediction model is calculated, and target prognostic prediction models are selected based on the C-index and the complexity of the prognostic prediction model. Compared to the traditional method of selecting an algorithm for modeling at the beginning of the study, the final selected target prognostic prediction model reflects more objective and realistic prediction results. Furthermore, the prediction accuracy of this target prognostic prediction model is improved.

The invention provides application of DUSP26 as a therapeutic target for transformation from acute kidney injury to chronic kidney disease. Specifically, the invention relates to application of a preparation for inhibiting DUSP26 enzyme activity in preparation of a medicine for preventing and / or treating conversion from acute kidney injury to chronic kidney disease. The reagent for detecting the expression quantity of the DUSP26 is applied to preparation of a product for predicting the prognosis of the acute kidney injury. It is found for the first time that after injury, DUSP26 with continuous high expression of near-end renal tubules can inhibit fatty acid oxidation, trigger lipid metabolism disorder and cause abnormal lipid deposition of cells, then renal tubule injury and fibrosis are aggravated, and AKI-CKD conversion is promoted; through intervention of a DUSP26 enzyme activity inhibitor NSC87877, chronic renal function deterioration and fibrosis progression can be significantly slowed down in the early stage of AKI, and the discovery provides a new treatment strategy for early intervention of AKI-CKD progression.

The invention relates to the technical field of biology, in particular to a colorectal cancer prognosis marker, a prognosis prediction model and a construction method of the colorectal cancer prognosis marker. Gene expression data of a colorectal cancer patient and single-cell RNA sequencing data of a tumor immune single-cell center are obtained from a cancer genome map and a gene expression comprehensive database, and 224 hypoxia and fructose metabolism related genes are selected; a hypoxia and fructose metabolism related gene scoring system is constructed through regression analysis and various machine learning algorithms, a risk model is constructed, eight genes significantly related to poor prognosis are preliminarily selected from the risk model, and further verification is performed in combination with TCGA transcriptome data and a protein expression profile of a human protein map database. Kaplan-Meier survival curve, multi-factor regression analysis, column diagram construction and ROC curve evaluation are carried out, key prognosis genes of P4HA1, ANKZF1 and STC2 are determined, it is found that sabutok can be combined with ANKZF1, STC2 and P4HA1 in a high-affinity mode, and sabutok is revealed to serve as a promising treatment choice for high-risk patients.

A Computer Aided Diagnosis, CADx, system and method for predicting an outcome score for a patient is described. The system comprising: an input circuit configured to receive input data comprising at least one input medical image for a patient; an outcome prediction circuit operably coupled to the input circuit configured to receive input data from the input circuit for outcome prediction analysis; wherein the outcome prediction circuit is further configured to receive details of a suggested future intervention for the patient; and the input data to the outcome prediction circuit is analysed to generate the outcome score for the patient accounting for the details of the future intervention.

The present invention discloses a method for assessing the risk of lung cancer recurrence, including data preprocessing, data quality control, data re-quality control, sample data correction, constructing a protein data matrix, constructing a recurrence model through the protein data matrix and clinical information, screening and testing to select the best prediction model, and finally determining the positive judgment criteria and result interpretation. Compared with the prior art, the method of the present invention has a high detection rate, which is the basis for postoperative risk stratification of early-stage lung cancer patients. Existing research data show that the present invention can have a good prognostic prediction effect on patients with stage I lung cancer. Compared with using only NCCN stratification or traditional TNM staging alone, the combined use of the risk stratification of this model can more accurately identify the prognostic risk, help clinicians recognize the tumor status earlier, perform clinical intervention earlier, and bring better prognosis for patients.

The invention discloses an auxiliary method for prognosis prediction of renal clear cell carcinoma, which comprises the following steps of: extracting four encoders by multi-modal representation and acquiring multi-modal knowledge; extracting three types of knowledge components from the CT image and the pathological image: individual knowledge only existing in a single mode, common knowledge existing among multiple modes and derivative knowledge obtained through interaction among the modes; optimal weights are dynamically allocated to different types of knowledge through a self-adaptive fusion strategy, so that optimal fusion features are obtained; and the extreme learning machine is used to predict each center feature so as to obtain a prediction result of the ccRCC patient. In the training process, through the guiding loss of the knowledge level and the sample level, different knowledge is more discriminative and distinctive. The ccRCC prognosis evaluation performance is effectively improved, so that the auxiliary diagnosis effect is achieved.

This invention relates to a multi-task learning-based integrated nutritional decision-making method and system for examination, diagnosis, treatment, and evaluation, belonging to the interdisciplinary field of clinical medicine and artificial intelligence. This method aims to address the problems of isolated tasks, limited data utilization, and lack of quantitative recommendation and prognostic prediction capabilities in existing nutritional decision-making technologies. The technical solution involves constructing a serial multi-task learning neural network model, sequentially including a feature extraction module, a nutritional diagnosis module, a nutritional treatment recommendation module, and a prognostic and efficacy evaluation module. A joint loss function that dynamically balances the weights of each task and reuses prognostic labels is designed for training. This invention achieves integrated decision-making throughout the entire process of examination, diagnosis, treatment, and prognosis. It can integrate multimodal data to achieve accurate feature extraction, provide personalized quantitative nutritional recommendations, and prospectively predict efficacy to guide clinical intervention, significantly improving the efficiency and accuracy of nutritional decision-making.