14 results about "Thyroid 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.

This invention provides a method for analyzing the radiomics characteristics of medullary thyroid carcinoma based on ultrasound, relating to the field of image or video recognition or understanding technology. The method includes: obtaining data from thyroid surgeries at various target hospitals to form a backup dataset; filtering the backup dataset to obtain a target dataset; labeling the data in the target dataset with Regions of Interest (ROIs); performing feature extraction and feature filtering on the data in the target dataset to obtain training and validation sets; constructing a target radiomics feature model; calculating radiomics feature scores based on the target radiomics feature model; obtaining a clinical feature model, an ultrasound feature model, a comprehensive model, and a scoring model based on logistic regression analysis; and performing radiomics feature analysis based on the clinical feature model, ultrasound feature model, comprehensive model, and scoring model. This invention solves the problems of low diagnostic accuracy and high requirements for experience of image interpreters in existing technologies for thyroid nodules.

The invention relates to the technical field of near-field imaging, in particular to a papillary thyroid carcinoma identification method based on terahertz imaging. According to the technical scheme, the method comprises the following steps that two sets of sections at the same position of the thyroid tissue of a patient are obtained, one set is stained sections, and the other set is unstained sections; and comparing the two groups of slices through an optical microscope, observing the position corresponding relation of the two groups of slices, and selecting an observation area based on cell morphological characteristics. According to the invention, synchronous acquisition of tissue apparent morphology and internal structure under nanoscale resolution is realized through the terahertz near-field imaging system, automatic analysis of an undyed thyroid section can be completed within 30 minutes by combining multi-modal feature fusion and a target detection algorithm, objective positioning and diagnosis scores of a cancerous region are directly output, and the accuracy and accuracy of diagnosis of the cancerous region are improved. And rapid, lossless and quantitative papillary thyroid carcinoma identification is realized.

The application discloses a method for predicting the lymph node metastasis risk of thyroid papillary carcinoma based on an integrated machine learning model. Specifically, the application provides a training method for an integrated risk prediction model for the lymph node metastasis of thyroid papillary carcinoma, which comprises the following steps: (s1) providing a data set comprising the expression data of marker genes of thyroid cancer patients with and without lymph node metastasis; (s2) training a basic model: training a basic prediction model based on the data set; (s3) training a fusion layer: training a stacked ensemble model and a bagging ensemble model based on the output results of the basic prediction model; (s4) training a decision layer: training a weighted fusion model based on the output results of the stacked ensemble model and the bagging ensemble model, thereby obtaining the integrated risk prediction model. The model can realize accurate prediction of the lymph node metastasis risk of thyroid papillary carcinoma.

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.

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 present application relates to a protein combination for prognosis stratification of patients with medullary thyroid carcinoma, a related kit and a prediction system. The prediction system is based on a combination of 18 proteins and 2 clinical information, or based on a combination of 29 proteins. The prognosis stratification method of the present application is more objective than the grading method of the prior art, has stronger prediction ability, and has been proven effective in patient cohorts of multiple hospitals in China.

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.

The invention discloses a marker, a kit and a method for thyroid papillary carcinoma lymphatic metastasis assessment, and belongs to the technical field of carbohydrate chemistry. The marker comprises a miRNA (micro Ribonucleic Acid) biomarker and an IgG (Immunoglobulin G) N-glycan, wherein the miRNA biomarkers are miR-616-3p and miR-1285-5p, and the miRNA biomarkers are miR-616-3p and miR-1285- And the IgG N-glycans are GP4, GP10, GP13 and GP24. A biomarker model is constructed by jointly detecting six key indexes of miR-616-3p, miR-1285-5p, GP4, GP10, GP13 and GP24, the problem of multicollinearity is effectively solved, the AUC value reaches 0.930, the general efficiency level of a clinical conventional diagnosis method is remarkably exceeded, and a high-precision judgment tool is provided for PTC LNM clinical decision. The numerical value shows extremely high discrimination capability; and the constructed model is based on internal correlation analysis of miRNA and initial glycan which are remarkably related to PTC LNM, and has good specificity and reliability.

The invention discloses application of a NOD1 gene and an MYBBP1A gene as targets in screening candidate drugs for inhibiting or relieving papillary thyroid carcinoma (PTC) lymph node metastasis, application of the NOD1 gene and the MYBBP1A gene as markers in preparing products for evaluating the risk of PTC lymph node metastasis, a method for screening the candidate drugs and a kit for screening. Belongs to the technical field of biological medicine. Aiming at the problem that at present, the PTC lymph node metastasis is lack of effective predictive biomarkers and therapeutic targets, the invention reveals for the first time that the NOD1 gene can regulate and control the BCL2 / BAX pathway by up-regulating the MYBBP1A gene expression so as to enhance the anoikal apoptosis sensitivity of PTC cells, thereby inhibiting the lymph node metastasis. On the basis, the gene can be used as a target for drug screening and a marker for risk assessment, the screening method and the kit can be used for discovering potential treatment drugs, and a new tool is provided for accurate diagnosis and treatment of PTC.

The invention discloses a bipolar electrode-electrochemical luminescence biosensor for detecting a myeloid thyroid carcinoma M918T mutant gene. The preparation method comprises the following steps: preparing a g-C3N4-coated FeMOFs nano-enzyme compound from graphite phase carbon nitride (g-C3N4) and an iron metal organic framework (FeMOFs), and constructing the dual-mode BPE-ECL biosensor based on the nano-enzyme and a 2 '-FN monomer modified probe. When a target gene exists, the g-C3N4atFeMOFs catalyzes H2O2 reduction, so that an ECL signal of Ru (bpy) 32 + / TPrA at a BPE anode is increased, and the ECL signal is quantitatively related to a cathode H2O2 reduction process. Meanwhile, 3, 3 ', 5, 5'-tetramethyl benzidine is converted into a blue oxidation product by the nano-enzyme under the action of H2O2, and colorimetric analysis is carried out through an RGB mode of a smart phone. The biosensor is simple and convenient to operate, low in cost and high in accuracy and sensitivity, and can be an auxiliary tool for accurate diagnosis and treatment of the myeloid thyroid carcinoma.