14 results about "Thyroid papillary carcinoma" patented technology

The invention discloses a disease diagnosis method and system based on multi-modal space-frequency domain adaptive fusion, and relates to the field of artificial intelligence and biomedical engineering.The method comprises the steps that multi-modal data are standardized, the unified and standardized multi-modal data are coded, and multi-modal initial feature representation is obtained; after projection and gating alignment and cross-modal interactive attention alignment are carried out on the initial feature representation of each modal, enhanced representations of each modal are obtained, and then the enhanced representations of each modal are fused into a shared feature representation; performing deep feature extraction on the enhanced representation of each mode to obtain deep features of each mode, and performing adaptive multi-domain feature enhancement processing to obtain multi-domain enhanced features of each mode; performing semantic alignment on the multi-domain enhanced features of each mode, and then performing fusion through a hierarchical attention mechanism to obtain fusion features; and the fusion features are input into a diagnosis network for prediction, a disease diagnosis result is obtained, and the intelligent diagnosis precision and robustness of papillary thyroid carcinoma are improved.

The invention provides a papillary thyroid carcinoma neck lymph node metastasis risk prediction method and system based on blood indexes and TI-RADS grading, and relates to the technical field of medical data analysis. According to the method, an original data set is constructed by obtaining TI-RADS classification, the maximum diameter of nodules, the number of nodules, gender, age and conventional hematology indexes including apolipoprotein B and carcino-embryonic antigen of a patient, characteristic variables are screened by adopting LASSO regression, and an independent prediction risk factor model is established in combination with multi-factor Logistic regression. And further constructing a column graph model, carrying out performance verification through multiple statistical indexes, and deploying the model to a webpage calculator based on a shiyapp to realize convenient output of the individualized risk probability. According to the method, efficient and accurate prediction of the preoperative lymph node metastasis risk is realized, and reliable decision support can be provided for clinic.

The present application relates to the technical field of biological medicine, and particularly relates to application of a circPSD3 inhibitor in PTC related drugs. Knocking down of circPSD3 can significantly inhibit the proliferation, migration and invasion of PTC cells, and the circPSD3 inhibitor can prevent or treat thyroid papillary carcinoma by inhibiting circPSD3. Therefore, the circPSD3 inhibitor can be used for preparing a drug for preventing or treating thyroid papillary carcinoma.

The application relates to the technical field of cardiovascular internal medicine disease automation, in particular to a cardiovascular internal medicine disease onset risk prediction method and system. The application can effectively evaluate and predict the cardiovascular internal medicine disease, especially the coronary heart disease, by jointly using multiple groups of clinical indexes and multiple image group feature data, the score data is more intuitive to determine the onset degree of the thyroid papillary carcinoma, so that the coronary heart disease onset risk can be accurately judged. Compared with the blood vessel CT angiography technology in the prior art, the application embodiment combines the image group feature data in the ultrasonic image with the clinical indexes, so that the discrimination result is more accurate.

This application provides a YOLOv11-based multimodal detection system and method for papillary thyroid carcinoma cells, relating to the field of image detection technology. It addresses the problem in existing technologies of achieving both high accuracy and robustness in papillary thyroid carcinoma cell detection under limited computing power. The system specifically includes: an improved backbone network that extracts first-modal features from images at different scales by introducing C3k2_Faster and C2MSLA modules; a priori pyramid module for STEM processing and progressive downsampling of the image, and outputs second-modal features aligned with the first-modal features at different scales through convolutional projection; a gated fusion module for channel-level weighted fusion of the first and second-modal features at corresponding scales, outputting fused features; a neck module for transferring and aggregating the fused features; and a head module for outputting the target detection results. This application is used for detecting papillary thyroid carcinoma cells.

The invention discloses a protein marker combination for predicting papillary thyroid carcinoma lateral cervical lymph node metastasis and a screening method and application thereof, and belongs to the technical field of biological medicine. The marker combination comprises 31 proteins, namely VWA8, PDHX, DENR, ATP5MG, SFTPB, MAOA, PHB1, CHI3L1, ACAA2, PTPN9, AFM, ACADVL, HSPE1, PPP2R2A, REEP5, FABP5, NME3, AUH, TMEM214, NDUFAF2, PHB2, FUCA2, PAXX, APOO, TMEM109, CHID1, MAGT1, ARL8B, BCAP29, AFG3L2 and MTCH2, in total. According to the method, multiple independent proteomics queues are integrated, direction consistency and performance screening are combined, a high-stability candidate marker library is constructed, a prediction model is constructed based on machine learning, and excellent prediction performance is shown in multiple verification sets. The method can be used for evaluating the preoperative lateral cervical lymph node metastasis risk of papillary thyroid carcinoma, and has an important clinical transformation value.

The application discloses a thyroid papillary carcinoma pathological image classification method based on multi-task learning, realizes multi-task learning on high cell type tumor cell rotation target detection, cell nucleus target detection and cell nucleus classification, and further takes the abnormal evaluation of the high cell type tumor cell detection and the abnormal evaluation of the cell nucleus detection as two auxiliary tasks. The application effectively overcomes the problem of insufficient single task information utilization, improves the accuracy and efficiency of the classification and / or statistics of the thyroid papillary carcinoma pathological image through the information mutual assistance among the multi-tasks.

This invention relates to the field of medical auxiliary diagnosis, specifically disclosing an interpretable machine learning model for predicting papillary thyroid carcinoma. The model includes collecting clinical data and related examination data of patients with papillary thyroid carcinoma to construct an initial feature dataset; performing initial feature screening using clinical significance testing, and determining key features based on Lasso regression and 10-fold cross-validation; constructing a diagnostic model for papillary thyroid carcinoma based on machine learning algorithms, inputting the key features into the diagnostic model for training, and selecting the optimal model; and using the SHAP method to perform interpretability analysis on the optimal model to determine the most critical features affecting the model. The predictive model constructed in this invention can integrate a large amount of diverse clinical data, significantly improving the accuracy of the predictive model, thereby providing accurate and reliable auxiliary diagnosis for the treatment of patients with papillary thyroid carcinoma.

The application discloses application of an alkaloid compound in dog tooth flower and belongs to the technical field of antitumor. The alkaloid compound has a structural formula as shown in the specification, can be extracted and separated from medicinal dog tooth flower of a plant of the dog tooth flower genus in the Apocynaceae family, can induce tumor cell apoptosis by inhibiting cell activity and cell proliferation, and has certain medical uses in resisting tumors such as human renal clear cell carcinoma and papillary thyroid carcinoma.

The invention discloses a methylation marker for identifying papillary thyroid carcinoma invasion subtype, a diagnosis model and application. According to the application, methylation sequencing data analysis is carried out on the basis of measured tissue samples of papillary thyroid carcinoma invasion subtypes and papillary thyroid carcinoma non-invasion subtypes, markers for identifying the papillary thyroid carcinoma invasion subtypes are identified, and a corresponding diagnosis model is constructed on the basis of the markers; based on the marker and / or the diagnosis model, whether papillary thyroid carcinoma invasion subtypes exist or not can be effectively identified, the defect that the papillary thyroid carcinoma invasion subtypes cannot be distinguished through an existing identification method is overcome, and a new method is provided for identification of the papillary thyroid carcinoma invasion subtypes; the detection process based on the marker or the diagnosis model is high in safety, and large-scale clinical application is facilitated.

The invention provides a method and system for evaluating the papillary thyroid carcinoma risk of a thyroid nodule patient, and relates to the technical field of medical information processing and tumor risk evaluation. According to the method, a postoperative pathological result, TI-RADS classification and multiple laboratory inspection indexes are collected to construct a data set, variables are screened through minimum absolute contraction and selection operator regression, TI-RADS 4b, TI-RADS 4c, GLU, ALB, FN and CEA are determined as independent risk factors based on Logistic regression, and a joint prediction model is established. And further converting a model regression coefficient into a total integral and constructing a column graph model, and finally deploying a visual risk prediction calculator to realize quantitative evaluation of the individual PTC risk. According to the invention, the risk discrimination accuracy of the intermediate graded thyroid nodules is improved.

This application relates to the field of otolaryngology technology, specifically a method and system for predicting the risk of papillary thyroid carcinoma. By combining multiple sets of clinical indicators and multiple imaging feature data, this application can effectively assess and predict the risk of papillary thyroid carcinoma. The scoring data provides a more intuitive determination of the severity of papillary thyroid carcinoma, thereby enabling the differentiation between atypical subacute thyroiditis and papillary thyroid carcinoma. Compared to existing technologies, the discrimination results of the embodiments in this application are more accurate.

The present application belongs to the technical field of medical image analysis, and particularly relates to a thyroid cell pathology image auxiliary analysis system. The system of the present application comprises an input module for inputting a thyroid cell pathology section image; a cell detection module for detecting a target cell through a neural network model; a semantic classification module for performing semantic classification on the target cell to obtain a classification label; and a knowledge graph search module for searching in a knowledge graph according to the classification label and performing textual interpretation on an obtained diagnosis result. The system of the present application can perform high-accuracy auxiliary diagnosis on thyroid papillary carcinoma. Meanwhile, the diagnosis result can provide a diagnosis basis and reference literature according to the knowledge graph, so that the AI auxiliary pathology diagnosis has public credibility. Therefore, the present application has a good application prospect.

The present application relates to the technical field of diagnosis and risk assessment of thyroid papillary carcinoma, and particularly relates to a composition, application and system for preoperative diagnosis and / or risk assessment of thyroid papillary carcinoma. The composition for preoperative diagnosis and / or risk assessment of thyroid papillary carcinoma comprises reagents for measuring the expression level of CLDN16 protein and / or the mRNA expression level of the gene encoding CLDN16 protein. Through the composition, the mRNA expression level of CLDN16 in thyroid tissue can be detected preoperatively, which is used for accurate diagnosis of the primary lesion and metastatic lesion of thyroid papillary carcinoma, and simultaneously, the disease risk of thyroid papillary carcinoma is accurately predicted to guide clinical decision-making.