24 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.

The invention belongs to the technical field of big data processing, and provides a papillary thyroid carcinoma Ki-67 expression level prediction method based on deep learning, and the method comprises the steps: obtaining the processed clinical features of a target object, and obtaining the target radiomics features; performing feature combination processing on the processed clinical features and the target radiomics features to obtain corresponding combined features; and inputting the joint feature into a classifier of a support vector machine for prediction, and outputting a prediction result which can predict the papillary thyroid carcinoma Ki-67 expression level of the target object. Through the prediction method, the papillary thyroid carcinoma Ki-67 expression level of the target object can be rapidly and accurately predicted.

The invention relates to the technical field of medical diagnosis, in particular to a papillary thyroid carcinoma diagnosis method based on hsacirc0076710 and a kit. The invention provides a papillary thyroid carcinoma detection kit based on hsacirc0076710, the expression condition of circular RNA hsacirc0076710 in a living body can be accurately and rapidly detected, a new gene detection means can be provided for early diagnosis and prognosis of papillary thyroid carcinoma, meanwhile, the invention provides a papillary thyroid carcinoma diagnosis method based on hsacirc0076710, and the papillary thyroid carcinoma detection kit can be applied to diagnosis of papillary thyroid carcinoma. According to the method, diagnosis is carried out by detecting the expression level of hsacirc0076710 in a sample, the areas (AUC) under an ROC curve in tissue, a fine needle biopsy eluent and a serum sample are 0.851, 0.838 and 0.769 respectively, and the sensitivity reaches 83.7%-95.1%. Further analysis shows that the hsacirc0076710 has high expression and high expression of lymph node metastasis (OR = 3.21, plt; 0.001) and advanced TNM (stage III / IV), and can be used for early diagnosis and prognosis risk assessment of papillary thyroid carcinoma.

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 present invention is directed to a method for diagnosing a thyroid cancer in a subject in need thereof, the method including determining presence, amount, or both, of at least one species of Zinc, Magnesium or both, wherein the species is in a microscopic calcification (MC) derived from a thyroid biopsy obtained from the subject, wherein: (a) the presence of the species; (b) alteration in the amount of the species compared to a healthy control; or (c) both, in the MC is indicative of the subject being afflicted with the thyroid cancer. Further provided are a method for treating a subject diagnosed with thyroid cancer, as well as preparing a sample including a thyroid biopsy for thyroid cancer diagnosis.

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 invention belongs to the technical field of bioinformatics, and discloses a PTC risk hierarchical evaluation model and application. According to the model disclosed by the invention, the expression level of a biomarker is taken as an input variable, and the biomarker comprises BDKRB1, CXCL9, GP1BA, GPR132, ITGA5, KCNMB2, LPAR1, MEFV, MEP1A, MXD1, OPRK1, PDE4B, PROK2, RTP4, SERPINE1 and TNFSF15. According to the model disclosed by the invention, gene expression is combined with tumor immune microenvironment and drug sensitivity by integrating gene expression data, a statistical model and clinical verification, so that multi-dimensional risk assessment is realized, accurate assessment of prognosis risk of papillary thyroid carcinoma patients is realized, and a basis is provided for clinical individualized treatment.

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.

The invention relates to deep learning, in particular to a cancer cell detection method for papillary thyroid carcinoma based on YOLOv8, and the method comprises the steps: collecting a related medical image, and carrying out the preprocessing of the medical image; cancer cell labeling is carried out on the preprocessed medical images, and multi-modal data fusion is carried out on various labeled medical images; constructing a training data set according to the fused data; performing model training on the YOLOv8 target detection model by using the training data set to obtain a trained YOLOv8 target detection model; acquiring a real-time medical image, inputting the real-time medical image into the trained YOLOv8 target detection model, and outputting a cancer cell detection result; according to the technical scheme provided by the invention, the defects that cancer cells of papillary thyroid carcinoma are difficult to accurately and efficiently detect and the sensitivity to minimal cancer focus is relatively low in the prior art can be effectively overcome.

The invention relates to an in vitro method for the diagnosis and / or prognosis of thyroid carcinoma, discriminating between benign and malignant nodules, by comparing the methylation level of the cytosine residue of various CpG sites, particularly the CpG sites at position 97,858,492-97,858,493 of the human chromosome 13, position 74,957,673-74,957,674 of the human chromosome 14 and / or position 5,672,830-5,672,831 of the human chromosome 20. Further, the present invention relates to the use of the methylation levels of said CpG sites and the kit for in vitro determination of the methylation levels.

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 present invention discloses a screening method for cell slide smear specimens for thyroid fine needle examination, the method comprising a diagnostic model generation step (S110); a specimen smearing step (S120), a tomographic image acquisition step (S130), a classification step (S140), and a diagnostic stage determination step (S150), in which first image data (A), which provides optimal information suitable for diagnosing papillary thyroid carcinoma from the same specimen image, and second image data (B), which is unsuitable for diagnosis, and a dataset on results of papillary thyroid carcinoma diagnosis stage determination based on feature analysis between the first image data (A) and the second image data (B) are pre-trained, whereby both clear and blurred tomographic images can be utilized in the diagnostic determination.

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 application relates to the technical field of thyroid papillary carcinoma classification, and discloses a multi-source thyroid papillary carcinoma classification system based on federal learning, which comprises a central server and a plurality of local devices connected with the central server; the local device is used for acquiring a thyroid ultrasound image and classifying the thyroid ultrasound image by adopting a classification model; the central server and the plurality of local devices are used for updating the classification model by federal learning based on a training set; wherein the global model in the federal learning process is updated based on the data points in the training set adopted by each local device and the local models uploaded by each local device. The accuracy of thyroid papillary carcinoma classification is improved.

The present invention relates to quinoline compounds of Formula (I); pharmaceutically acceptable salts, pharmaceutically acceptable stereoisomers, N-oxides or combination thereof, wherein 'X' ring A, ring B, R1, R2, R3, R4, R5, R6, R7, R8, 'm' and 'n' are as defined herein. The present invention also relates to quinoline compounds that modulate cellular activities like proliferation, differentiation, adhesion, migration and apoptosis by modulating protein kinase enzymatic activity. In particular the invention relates to compounds which inhibit, regulate, and / or modulate tyrosine kinases such as FLT1 (VEGFR1), FLT4 (VEGFR3), KDR (VEGFR2), MET (C-Met), MERTK (c-Mer), RET, AXL, TEK (TIE 2) and EGFR. The present disclosure relates to quinoline compounds for use in modulating kinase enzymatic activity and accordingly modulating kinase-dependent associated diseases and conditions such as cancers like Thyroid carcinoma, Ovarian carcinoma, Pancreatic carcinoma, Prostatic carcinoma, Renal cell carcinoma, Hepatocellular carcinoma, Breast carcinoma, Colorectal carcinoma, Oral squamous cell carcinoma, Colorectal, Lung adenocarcinoma or Endometrial cancer.

The invention relates to a thyroid cancer side neck lymph node metastasis prediction model construction method and application. The thyroid cancer side neck lymph node metastasis prediction model comprises a Logistic regression model and a decision process module. The Logistic regression model is used for calculating an actual risk value based on a value of a primary tumor maximum diameter, a value of a tumor boundary, a value of a BRAF V600E mutation state, a value of micro-calcification existence, a value of an aspect ratio and a value of single / double-side cancerous lesion; the decision process module is used for giving a decision suggestion according to the calculated actual risk value and a set decision process; the model provides a new quantitative tool for preoperative evaluation of papillary thyroid carcinoma patients, can provide individualized and precise treatment schemes for clinic, reduces unnecessary treatment and missed diagnosis, and finally improves the treatment effect and life quality of the patients.

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.