7 results about "TNM Staging" patented technology

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 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 application belongs to the technical field of biological medicine, and particularly relates to application of a reagent specifically binding to OARD1 protein in preparation of a breast cancer diagnosis and / or prognosis evaluation product. The product detects the expression level of OARD1 protein in a breast tissue sample by the reagent specifically binding to OARD1 protein and carries out H-score scoring, takes a preset H-score score as a breast cancer positive diagnosis threshold, and judges whether the sample is breast cancer positive. Moreover, the product can distinguish high / low expression of OARD1 protein by a preset H-score score, constructs a prognosis prediction model in combination with TNM staging of a subject, and realizes evaluation of a breast cancer patient disease progression risk. The method is simple and easy to implement, the diagnosis process is safe and effective, is easy to be accepted by patients, diagnosis standards are unified, and is less affected by personal subjective factors, which shows that OARD1 protein has important significance for diagnosis or prognosis judgment of breast cancer.

This application relates to the field of data processing technology and discloses a method, system, and storage medium for intelligent recommendation of tumor reagents based on knowledge graphs. The method includes: constructing a temporal dynamic knowledge graph by adding timestamp attributes to relevant nodes; updating the node memory state when an event occurs, generating a time embedding vector; acquiring patient data to identify TNM staging and outputting a state vector; calculating time-weighted semantic similarity to screen candidate reagents; optimizing the path based on TNM staging constraints, and generating recommendation results. This application solves the problems of static knowledge representation's inability to adapt to temporal changes and lack of personalized path optimization by constructing a temporal dynamic knowledge graph and using a Q-learning optimization algorithm based on TNM staging constraints, thereby improving the timeliness and personalized accuracy of tumor reagent recommendations.

The invention discloses a multi-modal fusion and knowledge graph driven lung cancer intelligent staging method, which is realized through a lung cancer TNM staging prediction model, and comprises a feature processing module, a semantic alignment module and a TNM staging prediction module, and the TNM staging prediction module comprises a full connection layer and a discarding layer; collecting multi-modal data of a patient including chest CT images, full-slice digital pathological data, clinical data and gene data; inputting the data to a feature processing module to obtain a first feature; inputting the first feature into a semantic alignment module to obtain an aligned second feature; a loss function of the semantic alignment module is constructed on the basis of a preset knowledge graph, the second feature is input into a TNM staging prediction module, forward propagation processing is conducted on the second feature for multiple times through a full connection layer and a discarding layer, and an initial TNM staging prediction result of the lung cancer and the corresponding confidence coefficient are obtained; and when the confidence coefficient is low, obtaining a target TNM staging prediction result based on the knowledge graph and the initial TNM staging prediction result.

The invention relates to the technical field of medical diagnosis and artificial intelligence, in particular to a urinary tract disease prediction system based on multi-modal chromosome abnormality and clinical data, which integrates demographic statistics, clinical symptoms, laboratory detection, molecular biology of nine key chromosome sites and multi-dimensional data of images, and performs pre-processing and single-modal feature extraction to obtain a prediction result of the urinary tract disease. Fusion features are generated in a targeted mode through a task self-adaption fusion module, and then urinary tract epithelial cancer or neoplastic lesion positive prediction, positive sample TNM staging and pathological grading prediction and focus origin positioning are achieved through a multi-task model. According to the system, an edge cloud collaborative architecture is adopted, the computing power requirements of different medical institutions are met, the feature contribution degree is determined through an SHAP method, a visual clinical report is generated, the problems that in the prior art, multi-modal data integration is insufficient, and non-invasive staging and grading are lacked are solved, prediction reliability and clinical adaptability are improved, and support is provided for clinical auxiliary decision making.

The invention relates to an application of a testicular hormone orphan nucleus receptor 4 (TR4) in non-small cell lung cancer distant metastasis. The TR4 is highly expressed in NSCLC tissues and is closely related to lymph node metastasis, TNM staging and poor prognosis. In-vitro experiments and animal models prove that up-regulation of the TR4 can enhance cell invasion and migration ability, and inhibition of the TR4 can significantly reduce the metastasis potential. The invention further provides a kit and a determination method for predicting or diagnosing the NSCLC remote metastasis based on the TR4. The ratio of the TR4 expression quantity of a detection sample to the TR4 expression quantity of a control sample is adopted as a risk assessment index; meanwhile, the invention provides application of the TR4 inhibitor in preparation of a pharmaceutical composition for inhibiting NSCLC invasion or metastasis. According to the invention, a complete technical path of'detection-layering-intervention 'is constructed, early prompt of NSCLC transfer risk can be realized, and a new molecular target is provided for individualized treatment and drug research and development.