35 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 biomarker for predicting the recurrence risk of papillary thyroid carcinoma and application of the biomarker, and a proteomics method is utilized to analyze proteins with significant abundance level difference in blood of two groups of people with papillary thyroid carcinoma recurrence and non-recurrence after papillary thyroid carcinoma patients are subjected to operative treatment, so that the papillary thyroid carcinoma recurrence risk can be predicted, and the papillary thyroid carcinoma recurrence risk can be predicted. According to the method, a biomarker capable of being used for predicting the papillary thyroid carcinoma recurrence risk is screened out, and a multi-marker joint detection model is further constructed, so that the papillary thyroid carcinoma recurrence risk can be accurately, noninvasively and efficiently predicted, and clinical requirements are met.

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 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.

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 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 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.

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 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 present invention provides a biomarker for predicting the risk of recurrence of papillary thyroid carcinoma and its application. By using proteomics methods, by analyzing proteins with significantly different abundance levels in the blood of two groups of people with recurrent and non-recurrent papillary thyroid carcinoma after surgical treatment of papillary thyroid carcinoma, biomarkers that can be used to predict the risk of recurrence of papillary thyroid carcinoma are screened out, and a multi-marker joint detection model is further constructed, which can achieve accurate, non-invasive and efficient prediction of the risk of recurrence of papillary thyroid carcinoma to meet clinical needs.

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 embodiment of the invention discloses a papillary thyroid carcinoma recurrence prediction method and device applied to postoperative patients. A specific embodiment of the method comprises the following steps: generating emotional state information and sleep state information for a postoperative patient according to physiological data corresponding to the postoperative patient; in response to emotional state information representing emotional abnormity and / or sleep state information representing sleep state abnormity, acquiring self-evaluation data corresponding to the postoperative patient through a dynamically generated data acquisition scale; generating a recurrence prediction result according to the multi-modal inspection data associated with the postoperative patient, the self-evaluation data and a pre-trained papillary thyroid carcinoma recurrence prediction model; and according to a trigger condition triggered by the recurrence prediction result, initiating a recurrence risk prompt to a trigger condition associated with the trigger condition. By means of the implementation mode, recurrence prediction of the thyroid papillary carcinoma postoperative patient is effectively achieved.

The invention relates to the technical field of biological medicine, in particular to an intestinal microbial marker for thyroid cancer and application of the intestinal microbial marker. According to the application, a metagenome sequencing method and a metabonomic sequencing method are used for finding out three types of specific bacteria (sBacteroidella CAG.47, sBacteroidella sp.3113 and sBacteroidella sp.3140A), and a combination of specific metabolites Deoxycholic acid (deoxycholic acid) is used for diagnosing papillary thyroid carcinoma, so that the papillary thyroid carcinoma can be diagnosed, and the papillary thyroid carcinoma can be diagnosed by using the combination of the three types of specific bacteria (sBacteroidella CAG.47, sBacteroidella sp.3140A) and the specific metabolites (Deoxycholic acid) of the specific bacteria (sBacteroidella CAG.47, sBacteroidella sp.3140A). According to the intestinal microbial marker for papillary thyroid carcinoma, high-risk groups of papillary thyroid carcinoma can be effectively and early screened or early patients can be found, so that further expansion of papillary thyroid carcinoma can be prevented as early as possible, the rupture risk can be reduced, and the treatment effect of papillary thyroid carcinoma can be monitored. Meanwhile, the intestinal microbial marker can be used for preparing diagnostic kits and therapeutic drugs. The invention overcomes the defects that the existing papillary thyroid carcinoma diagnosis cannot realize early screening, cannot predict the attack and development trend of papillary thyroid carcinoma and the like.

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 relates to a papillary thyroid carcinoma risk assessment system and device and a medium, and the system comprises an obtaining module which is used for obtaining a first significant predictive factor and a second significant predictive factor of a user; the diagnosis module is used for inputting the first significant predictive factor of the user into a papillary thyroid carcinoma diagnosis column diagram prediction model to obtain the papillary thyroid carcinoma suffering risk of the user; and the recurrence risk prediction module is used for inputting the second significant prediction factor of the user into a recurrence risk column diagram prediction model to obtain the initial recurrence risk of the papillary thyroid carcinoma of the user. According to the invention, PTC diagnosis and recurrence risk prediction can be carried out in a non-invasive, quantifiable and efficient manner.

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 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 invention relates to the technical field of terahertz near-field imaging, in particular to a thyroid papillary carcinoma cell terahertz near-field imaging quality distinguishing method. According to the technical scheme, the method comprises the following steps: placing an unstained papillary thyroid cancer cell sample under a terahertz near-field imaging system; a plurality of to-be-detected areas on the sample are selected to be scanned, terahertz imaging pictures of all the areas are obtained, and terahertz feedback voltage signals in the scanning process are synchronously recorded. The terahertz feedback voltage is used as an objective criterion, the subjectivity and blindness problems of detection position selection are solved, a high-quality imaging area can be quickly screened out, and therefore the detection efficiency and the instrument use efficiency are remarkably improved; the terahertz image obtained based on the method is higher in signal-to-noise ratio and feature significance, the accuracy and reliability of cancer cell recognition are enhanced, and the method does not need to modify hardware, is simple and convenient to operate, and is easy to integrate and popularize in an existing system.

The invention discloses a noninvasive prediction system and method for thyroid cancer gene mutation. The method comprises the following steps: collecting preoperative enhanced CT (Computed Tomography) image data, clinical data and BRAFV600E gene mutation detection results of papillary thyroid carcinoma patients; preprocessing the CT image, delineating a region of interest and extracting image omics features; optimal radiomics features are obtained through feature screening; constructing a radiomics prediction model by using a machine learning algorithm based on the optimal radiomics characteristics; constructing a clinical prediction model in combination with the clinical risk factors; and fusing the two models to construct a joint prediction model, and analyzing and evaluating the performance of the model through a subject working characteristic curve and a decision curve. The method realizes preoperative noninvasive prediction of papillary thyroid carcinoma BRAFV600E gene mutation state, has high accuracy and clinical application value, and can provide auxiliary support for individualized treatment decision.

The invention discloses an immune-related early screening marker for predicting and diagnosing radioiodine treatment effect response after total resection of papillary thyroid carcinoma based on Olink proteomics as well as a selection method and application of the immune-related early screening marker. A serum sample before radioiodine treatment after total resection of a papillary thyroid carcinoma patient is measured; up-regulated protein HGF in a group with poor iodine treatment effect is screened out, the reliability of the up-regulated protein HGF for predicting the radioactive iodine treatment effect is verified on the basis of an independent queue, and it is found that serum psTg and serum HGF are independent predictive factors of disease-free lifetime. The HGF protein screened by the invention can be used for evaluating the curative effect of radioactive iodine treatment and providing a target spot for mechanism research, and meanwhile, the protein can be used for preparing a kit which is poor in iodine treatment effect and is used for screening before radioactive iodine treatment.

The application belongs to the technical field of bioinformatics, and discloses a PTC risk stratification evaluation model and application. The model takes biomarker expression levels as input variables, and the biomarkers include BDKRB1, CXCL9, GP1BA, GPR132, ITGA5, KCNMB2, LPAR1, MEFV, MEP1A, MXD1, OPRK1, PDE4B, PROK2, RTP4, SERPINE1 and TNFSF15. The model integrates gene expression data, statistical models and clinical verification, combines gene expression with tumor immune microenvironment and drug sensitivity, realizes multi-dimensional risk evaluation, accurately evaluates the prognosis risk of patients with thyroid papillary carcinoma, and provides a basis for clinical individualized treatment.

The invention discloses a hypoxia-driven papillary thyroid carcinoma recurrence risk prediction system based on artificial intelligence image analysis, and the system receives thyroid ultrasound and CT image data through a Web interface, and carries out the preprocessing through a data preprocessing module, and then obtains a thyroid tumor image through segmentation; the feature extraction and selection module is used for extracting high-throughput radiomics features from the images and further screening out core features to construct radiomics tags; the risk prediction module is used for training a constructed prediction model based on radiomics tags, conventional clinical features, tumor image qualitative features and corresponding classification tags, analyzing key features influencing a prediction result through SHAP, and predicting new data by using the trained prediction model; the risk output module converts the prediction result into a chart for visual output; and the user interface module receives the information output by the risk output module and displays the information to a user in real time through a Web interface. According to the method, the multi-mode image data and the clinical data are integrated, the recurrence risk prediction model driven by hypoxia is constructed, and accurate prediction of the recurrence risk of the PTC patient is achieved.