16 results about "TNM Staging" patented technology

The invention provides a gastric cancer TNM staging automation method and system based on a large language model. The method comprises the steps that a medical data set related to gastric cancer of a patient is acquired; preprocessing the medical data set; inputting the preprocessed medical data set into a trained large language model; extracting examination conclusions in the preprocessed medical data set, splicing all diagnosis conclusions into a long text for input, and inserting special separators between different sources for distinguishing; performing feature extraction on the long text input to generate a feature vector; probability inference is carried out after suspicious remote transfer labeling in the feature vectors; carrying out conflict statement resolution and credibility judgment on the feature vectors, and then carrying out TNM stage classification; and outputting the TNM staging result of the patient. The method has the beneficial effects that full-process automatic processing from medical data set acquisition, preprocessing and feature extraction to final TNM stage classification is realized.

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.

The present invention discloses the use of serum tRF in preparing a diagnostic or detection reagent for non-small cell lung cancer. The invention is characterized in that the serum tRF is tRF-32-897PVP941QKSJ, the nucleotide sequence of which is TCCTCGTTAGTATAGTGGTGAGTATCCCCGCC, the upstream primer for fluorescence quantitative PCR specific amplification is GAGAACACTGACAGCACGAG, and the downstream primer is CCTTCCTCGTT AGTATAGT. The invention also provides the use of the serum tRF in preparing a diagnostic or detection kit for non-small cell lung cancer, a target drug, tumor size, and a diagnostic or detection reagent for non-small cell lung cancer N0 and N1-3 stages, M0 and M1 stages, and I-II and III-IV stages in the TNM staging. The invention has the advantage of high specificity and sensitivity.

The present invention discloses a method for predicting the recurrence probability of a specific disease based on a clustering method. The method comprises collecting patient disease data from different time periods, preprocessing the patient disease data, dividing the patient disease data according to EBV DNA levels and tumor response to obtain subgroup data, characterizing the subgroup data according to relative risk to obtain time-series disease features, permuting and combining the time-series disease features to obtain disease manifestation data, risk stratifying the disease manifestation data using supervised clustering to obtain risk groups, assessing the prognostic performance of the risk groups using the Concordance Index, comparing the prognostic performance with the TNM staging to obtain recurrence data, constructing a recurrence probability prediction model based on the recurrence data, inputting the data to be predicted into the recurrence probability prediction model, and outputting a prediction result. This method can not only improve the accuracy of predicting the recurrence probability of a specific disease, but also has good interpretability.

The invention provides a gastric adenocarcinoma prognosis system based on a molecular marker and a use method, and relates to the technical field of biomedical detection.The system comprises an acquisition unit, an analysis unit, a prognosis unit, an analysis unit and an analysis unit, the acquisition unit is responsible for collecting key information of gastric adenocarcinoma patients, and the key information comprises molecular marker expression data, age and TNM staging information; according to the prognosis model, molecular marker risk scores are calculated through molecular marker expression data, and precise and individualized lifetime prediction is provided for each gastric adenocarcinoma patient in combination with the age and TNM staging information of the patient. According to the system, the prediction accuracy and individuation level are remarkably improved, the treatment decision is optimized, the patient management efficiency is improved, more accurate and effective treatment selection is brought to the patient, and therefore the overall prognosis of the gastric adenocarcinoma patient is improved.

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 present application belongs to the field of medical information technology, and relates to a method for constructing a lung cancer staging diagnosis model and a diagnostic model. The construction method comprises the following steps: S1: obtaining the lung cancer typing and TNM staging of the subject sample; S2: constructing a two-dimensional space based on the lung cancer typing and TNM staging of the subject sample, and integrating and correcting the subject samples in different TNM stages using the anchoring method; S3: forming a sample set with the subject samples uniformly distributed in the previous step and the corresponding feature variables, and performing machine learning using the random forest algorithm in the sample set to construct a lung cancer staging diagnosis model. The present application uses the two-dimensional space constructed by the lung cancer typing and TNM staging of the subject sample, and integrates and corrects the samples using the anchoring method, successfully making the samples in the sample set evenly distributed, so that the constructed data model is more reliable.

1. Name of the product of this design: Graphical user interface for electronic medical records for tumor staging used in electronic devices. 2. Purpose of this design product: for use in an electronic device. 3. The key design point of this appearance design product lies in the interface content of the graphical interface. 4. The picture or photo that best illustrates the key points of the design: main view. 5. The design for which protection is sought includes color. 6. Purpose of the graphical user interface: The interface of this design is used for the operation process of filling in the TNM staging electronic medical record. 7. Human-computer interaction mode of the graphical user interface: The main view is the main interface of the human-computer interaction; Interface change state Figure 1 is entered after entering "esophageal gastric" in the blank box after "Disease Name" in the main view; Interface change state Figure 2 is entered after clicking "Malignant tumor of esophagogastric junction (C16.002)" in the drop-down box of Interface change state Figure 1; Interface change state Figure 3 is entered after clicking "TNM stage" in the middle of the interface in Interface change state Figure 2; Interface change state Figure 4 is entered after clicking the drop-down box after "T stage" in the "cTNM stage" column in Interface change state Figure 3; Interface change state Figure 5 is entered after clicking the drop-down box after "N stage" in the "cTNM stage" column in Interface change state Figure 4; Interface change state Figure 6 is entered after clicking the drop-down box after "M stage" in the "cTNM stage" column in Interface change state Figure 5 Interface change status Figure 7 is entered after clicking the drop-down box after "T stage" in the "ypTNM stage" column in interface change status Figure 6; Interface change status Figure 8 is entered after clicking the drop-down box after "N stage" in the "ypTNM stage" column in interface change status Figure 7; Interface change status Figure 9 is entered after clicking the drop-down box after "M stage" in the "ypTNM stage" column in interface change status Figure 8; Interface change status Figure 10 is entered after clicking "Mx: distant metastasis cannot be evaluated" in the drop-down box after "M stage" in the "ypTNM stage" column in interface change status Figure 9; Interface change status Figure 11 is entered after clicking "Save" in interface change status Figure 10; Interface change status Figure 12 is entered after entering the doctor's name in the blank box after "Diagnosing Physician" in interface change status Figure 11.

The invention discloses a cancer TNM intelligent staging system and device and a storage medium, the cancer TNM intelligent staging system comprises an information acquisition module, an identification module and a typing output module, the information acquisition module is used for acquiring cancer diagnosis information; the identification module is used for identifying typing selection and staging selection corresponding to the diagnosis information; wherein the typing selection and the staging selection are both determined based on the diagnosis information and a preset rule base; and the typing output module is used for outputting a cancer TNM staging result corresponding to the diagnosis information according to the typing selection and the staging selection. The cancer TNM staging process is managed in a flow mode, staging errors caused by human factors such as mistaken and missed information are reduced, and therefore the staging accuracy is improved to a certain degree.

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.

The invention discloses a tumor TNM staging method and system based on a large model, and relates to the technical field of image analysis, and the method comprises the steps: obtaining a medical text report generated by a multi-modal large model for a medical image, and carrying out the text standardization preprocessing; carrying out deep semantic understanding on the medical text report by adopting a large language model so as to extract anatomical information; a time sequence analysis module is used for comparing and analyzing time sequence characteristics, and a TNM staging clinical reference report is selected; according to updating of a preset staging standard, a rule engine is adopted to synchronously update the staging rule base; and determining a TNM staging result by adopting the staging rule base based on the anatomical information and the TNM staging clinical reference report. Through the technical scheme of the invention, the recognition accuracy of medical terms is improved, the problem of staging misjudgment caused by dependence on newest examination data is effectively avoided, the method can quickly adapt to the update of staging guide, and the accuracy and efficiency of cancer tumor TNM staging are remarkably improved.

The invention relates to the technical field of medical diagnosis, in particular to an intrahepatic cholangiocarcinoma diagnosis, grading or prognosis marker, a detection reagent or kit, a system and application thereof. According to the present invention, the expression of the exosome source circUBR5 in the serum in the serum exosome of the intrahepatic cholangiocarcinoma patient is significantly up-regulated for the first time, the subject working characteristic curve analysis result shows that the under-curve area of the circUBR5 in the intrahepatic cholangiocarcinoma diagnosis reaches 0.833, and is significantly superior to the traditional tumor markers CA19-9 and CEA, and the sensitivity and the specificity of the circUBR5 have significant advantages; and when the circUBR5 and the CA19-9 are used in a combined manner, the diagnosis AUC is further improved to 0.858. In addition, the invention also finds that the expression level of circUBR5 is closely related to the TNM staging, vascular invasion, lymph node metastasis and distant metastasis of the patient, and the high expression of circUBR5 is positively related to the obvious shortening of the overall lifetime of the patient.