29 results about "Lymph node metastasis" patented technology

The invention relates to the field of medical image analysis, in particular to a deep learning prediction system and method based on multi-modal thyroid cancer lymph node metastasis, and the system comprises a data collection module, a preprocessing module, a nodule segmentation module, a feature extraction module, a feature fusion module, a metastasis prediction module, an interpretability analysis module and a result display module. An ultrasonic image, an elastic imaging image, an ultra-micro blood flow image and clinical index data of a patient are integrated, an improved U-Net algorithm is used for precise segmentation of a thyroid nodule region, a multi-branch deep network is used for extracting multi-modal features, a dynamic weight fusion algorithm is used for integrating the features, and the accuracy of the thyroid nodule region is improved. According to the method, the thyroid cancer lymph node metastasis state (non-metastasis, central region metastasis or lateral neck metastasis) is predicted, meanwhile, a two-dimensional interpretability framework of Grad-CAM activation diagram and SHAP value contribution degree analysis is introduced, an intuitive prediction basis is provided for doctors, and the thyroid cancer lymph node metastasis prediction accuracy is remarkably improved.

The invention discloses a breast cancer lymph node metastasis prediction system based on a gene spectrum, and relates to the technical field of breast cancer prediction systems. Comprising a data acquisition module which collects gene spectrum data of a breast cancer patient and collects detailed clinical information of the patient; the preprocessing module is used for carrying out data cleaning and data normalization processing on the collected data; and the feature extraction module is used for extracting principal component features by applying principal component analysis on the basis of the gene expression data. According to the method, gene expression data are considered, various gene spectrum data such as gene mutation and copy number variation and detailed clinical information are integrated, the biological characteristics of the breast cancer can be reflected more comprehensively, and the prediction accuracy is improved.

The invention relates to the field of image analysis, in particular to a colon cancer lymph node metastasis risk prediction method and system based on image analysis. The method comprises the following steps: acquiring a pathological section scanning image, carrying out layer-by-layer downsampling and feature reconstruction, and generating pathological section topological features; carrying out adaptive clustering analysis and multi-parameter fusion evaluation on the basis of the pathological section topological features to obtain independent migration evaluation values of individual tumor cells; performing tumor metastasis branch analysis according to the independent migration evaluation value, and constructing a metastasis branch path distribution diagram; extracting an immunohistochemical staining image of a patient to obtain immune cell space distribution data; and performing multi-region lymphatic metastasis simulation and metastasis risk situation prediction based on the immune cell spatial distribution data and the metastasis branch path distribution diagram to obtain a final metastasis risk assessment result. According to the method, the lymph node metastasis risk is accurately and efficiently analyzed, and the credibility of a pathological analysis result is 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 discloses an application of an LAMP1 inhibitor in preparation of a medicine for preventing and / or treating esophageal cancer. Specifically, the application value of the LAMP1 inhibitor Parishin C in esophageal cancer treatment is found and verified for the first time, invasion and metastasis of esophageal squamous carcinoma cells and in-vivo lymph node metastasis can be remarkably inhibited by targeting LAMP1 protein and downstream signal channels of the LAMP1 protein and Parishin C, and a new drug choice is provided for metastatic esophageal cancer lacking effective treatment means clinically.

The invention discloses application of LncRNA GSEC in preparation of a medicine for treating oral squamous cell carcinoma, and relates to the technical field of biological medicine. The nucleotide sequence of the LncRNA GSEC is shown as SEQ ID NO: 1, the medicine contains an active component capable of inhibiting expression or functions of the LncRNA GSEC, the active component is one or more of siRNA, shRNA or a small-molecule inhibitor, and the medicine is used for inhibiting proliferation, migration and / or invasion of OSCC cells and / or inducing cell apoptosis. The specific high expression of the LncRNA GSEC in oral squamous cell carcinoma tissues and cells is disclosed for the first time, and the expression level of the LncRNA GSEC is remarkably and positively correlated with adverse clinical parameters such as tumor T staging and lymph node metastasis, so that the LncRNA GSEC is proved to serve as a novel molecular target with clinical application potential.

The invention relates to application of a combination of STM2457 and gefitinib in preparation of a medicine for preventing and treating lung cancer, and belongs to the technical field of biological medicines. The invention provides an application of an METTL3 inhibitor and gefitinib in preparation of a medicine for preventing and treating lung cancer. In a cell experiment, the expression of METTL3 is inhibited and then Gefitinib is used, so that the killing effect of the Gefitinib on lung cancer cells is promoted, and the drug resistance of the lung cancer cells on the Gefitinib is reduced. The small molecule inhibitor STM2457 of the METTL3 and the Gefitinib are combined for medication, so that the killing effect on cancer cells can be synergistically improved. In a mouse experiment, compared with a single drug, the drug combination has the advantages that the growth of tumors is more effectively inhibited, the lymph node metastasis of lung cancer cells can be effectively prevented, no obvious side effect exists, and a good treatment effect is shown. Therefore, the drug combination of the METTL3 inhibitor and the Gefitinib has a remarkable treatment effect on the NSCLC.

The invention provides a multi-scale cross-regional lymph node metastasis pathological image recognition method, which comprises the following steps of: cutting a region of interest from a full-slice pathological image, dividing standard image blocks under an amplification factor, and extracting a feature sequence by utilizing a pre-training model; the multi-scale module performs intra-scale and inter-scale self-attention calculation and fusion on each amplification factor feature to construct unified multi-scale feature representation; the cross-region module recombines the features into a two-dimensional grid and divides regions, and local feature optimization and global semantic association are realized through multi-head self-attention in the regions and among the regions; and outputting the diagnosis result and probability of lymph node metastasis through the classifier, and generating a multi-scale attention heat map to integrate into a visual diagnosis report. According to the method, the accuracy and the interpretability of lymph node metastasis detection can be remarkably improved through collaborative modeling of the multi-scale information and the cross-regional dependency relationship of the pathological image.

The invention belongs to the field of medical contrast agents, and relates to an albumin-based near-infrared two-region fluorescence / magnetic resonance bimodal probe as well as a preparation method and application thereof. The bimodal probe disclosed by the invention is a protein compound formed by self-assembling a near-infrared two-region fluorescent dye (IR820), a nuclear magnetic contrast agent (GdL) and albumin. The near-infrared two-region fluorescent dye (IR820) and the nuclear magnetic contrast agent (GdL) respectively show the characteristics of near-infrared two-region fluorescence enhancement and relaxation rate increase in a serum albumin solution. The near-infrared two-region fluorescent dye and the nuclear magnetic contrast agent have the advantages of simple preparation process, high relaxation rate, good stability, good biocompatibility and the like. The near-infrared two-region fluorescence / magnetic resonance bimodal probe disclosed by the invention can be used for diagnosis, real-time monitoring and surgical navigation of tumor lymph node metastasis.

The invention discloses a tongue squamous cell carcinoma in-situ PDX model construction method based on SERINC3 high expression and application of SERINC3, and belongs to the technical field of tumor biology and animal model construction. The method comprises the following steps: segmenting small tissues from cancer tissues of a patient suffering from tongue squamous cell carcinoma, implanting the small tissues into the armpit of an NCG mouse to culture tumor tissues, and then subculturing the tumor tissues; the subcultured tumor tissue is taken and implanted into the lingual margin of an NCG mouse, and after the tumor is cultured, lentivirus carrying sh-NC / sh1-SERINC3 / sh2-SERINC3 plasmids is injected into the tumor at multiple points to obtain the tongue squamous cell carcinoma in-situ PDX model. The model constructed by the invention can better simulate the natural occurrence and development process of TSCC, reconstructs a tumor microenvironment closer to clinical practice, and particularly has remarkable advantages in the aspect of simulating head and neck lymph node metastasis of tongue squamous cell carcinoma.

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.

This invention relates to an animal model of mediastinal lymph node metastasis in lung cancer and its construction method. The core of this invention lies in the precise intrapulmonary in situ microinjection technique to directly implant human lung cancer cells into the lung parenchyma of immunodeficient mice. Utilizing the natural lymphatic drainage pathway of the lung tissue, tumor cells are induced to migrate to the mediastinal lymph nodes, thus successfully constructing a complete metastasis chain model from the primary tumor to regional lymph nodes. This model can more realistically simulate the microenvironment and process of lung cancer lymph node metastasis, and is suitable for metastasis mechanism research, drug evaluation, and the development of personalized treatment strategies, possessing high translational medicine value.

The invention discloses a thyroid cancer lymph node minimal metastatic focus detection model and a system thereof, the detection model takes a dual-scale Transform fusion model based on a meta-learning algorithm as a network architecture, cross-task generalization is realized, and the classification performance under the condition of few samples is remarkably improved; in addition, the detection model utilizes a Transform-based concentric dual-scale image block extraction and fusion mechanism to extract two Patches of 10 * high magnification (cellular morphology) and 4 * low magnification (tissue structure) from the same space coordinate, the features of the two scales are integrated through a Patch-Embedding high-dimensional feature fusion module, and finally the classification task of the thyroid cancer lymph node metastasis is completed. And obtaining a classification result and outputting an interpretable attention map. The accuracy and robustness of thyroid cancer lymph node micro-metastasis detection are improved, and more reliable decision support is provided for pathologists.

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 the field of tumor molecular diagnosis, in particular to a breast cancer lymph node metastasis prediction method and application thereof. Specifically, the invention provides a construction method of a breast cancer lymph node metastasis prediction model by taking the signal intensity of an LTR repetitive sequence region as one of extracted cfDNA features, and provides an application of the model in prediction of the breast cancer lymph node metastasis condition, so that non-invasive detection of breast cancer lymph node metastasis is realized, and the prediction accuracy of breast cancer lymph node metastasis is improved. The method is especially suitable for patient groups needing dynamic monitoring or difficult to obtain tumor tissues.

The application relates to the field of organoids, and discloses a culture medium and a construction method of an oral squamous carcinoma neck lymph node metastasis organoid. The method comprises the following steps: digesting tumor tissue in a digestive solution to obtain a digestive tissue suspension, performing cell filtration centrifugation on the digestive tissue suspension, retaining a cell precipitate, and obtaining the cell precipitate; resuspending the cell precipitate by using a buffer solution, and counting live cell numbers; inoculating organoids into a well plate according to the counted live cell numbers, adding a culture medium, and placing the well plate in a culture box for culture; after 3 days of culture, only 5% active cells of tumor infiltrating lymphocytes in the organoid culture system are left, the organoids are digested, the digestion is terminated, and centrifugation is performed. The application solves the problem that lymphocytes have an inhibitory effect on organoid formation in organoid culture, and affect the success rate of tumor organoid construction.

The application discloses application of LncRNA GSEC in preparation of a medicine for treating oral squamous cell carcinoma, and relates to the technical field of biological medicine. The nucleotide sequence of the LncRNA GSEC is shown as SEQ ID NO:1, the medicine comprises an active ingredient capable of inhibiting expression or function of the LncRNA GSEC, the active ingredient is one or more of siRNA, shRNA or a small molecule inhibitor, and the medicine is used for inhibiting proliferation, migration and / or invasion of OSCC cells and / or inducing cell apoptosis. The application firstly discloses that the LncRNA GSEC is specifically highly expressed in oral squamous cell carcinoma tissues and cells, and the expression level of the LncRNA GSEC is significantly positively correlated with adverse clinical parameters such as tumor T stage and lymph node metastasis, and it is confirmed that the LncRNA GSEC is a novel molecular target with clinical application potential.

The invention belongs to the field of biological medicine, and particularly discloses application of a CDKN3 gene as an endometrial cancer (UCEC) biomarker and a treatment target. Bioinformatics analysis and clinical sample verification show that the CDKN3 is remarkably and highly expressed in UCEC tissues, and the expression level of the CDKN3 is positively correlated with tumor malignancy degrees (such as low differentiation, deep muscular infiltration and lymph node metastasis) and serum markers CA125 and CEA, and is an independent risk factor influencing the progression-free lifetime. Functional experiments show that the CDKN3 promotes proliferation and invasion of tumor cells by regulating the level of reactive oxygen species (ROS), and silencing of the CDKN3 can induce apoptosis and inhibit tumor growth. The invention also provides a prognosis prediction model (AUCgt; AUCgt) of CDKN3 combined with CA125 and CEA; the invention further discloses application of siRNA, shRNA, an antibody or a small-molecule inhibitor of the targeted CDKN3 to treatment of the UCEC, and a new strategy is provided for accurate diagnosis and targeted treatment of the UCEC.

The application provides a thyroid cancer lateral neck lymph node metastasis prediction method based on multi-modal feature decoupling and fusion. The method comprises the following steps: collecting ultrasound images, text reports and clinical structured data of thyroid cancer patients, and preprocessing to improve the usability of the data; then, according to the distribution characteristics of different modal data, a model suitable for different data feature extraction is designed to obtain initial features of different modal data; further, by combining contrast learning and cross-modal feature reconstruction tasks, the multi-modal features are decoupled, the common representation and the specific representation of each modality are separated, so as to reduce information redundancy and noise interference; finally, based on the cross-attention mechanism, the common representation and the specific representation of each modality are fused, the deep correlation between multi-modal data is fully mined, and the fused representation is input into a classifier to realize the prediction of lateral neck lymph node metastasis.

The invention discloses an early gastric cancer lymphatic metastasis risk auxiliary inspection method based on collagen fiber morphological structure fusion characteristics. An extracellular matrix collagenous fiber image of a gastric tumor tissue is obtained in an unmarked second harmonic imaging mode, and automatic early gastric cancer lymphatic metastasis risk assessment can be realized by performing quantitative calculation and analysis of multivariate parameter characteristics on morphological structure characteristics of the collagenous fiber. According to the method, the morphological structure characteristics of the gastric tumor collagenous fibers can be intuitively displayed through a pixel-level morphological characterization method, and the information readability is enhanced. According to the method, the complementarity of the morphological structure characteristics of the collagenous fibers of the gastric tumor tissue is remarkably improved through multi-element parameter characteristic characterization, and the accuracy of lymphatic metastasis risk assessment is further improved. According to the method, an early gastric cancer lymphatic metastasis risk specificity index is formed based on the multi-element morphological characteristics of collagenous fibers, and a local focus area can be quantitatively and intuitively displayed.

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.

The application belongs to the field of medicine and biotechnology, and specifically discloses serum autoantibodies for identifying lymph node metastasis of cardiac gland cancer and application thereof. The biomarker for diagnosing cardiac gland cancer provided by the application is at least one of LIMCH1 autoantibody, RSPH1 autoantibody, ENAH autoantibody, COTL1 autoantibody and ASS1 autoantibody, and the expression level of the biomarker in cardiac gland cancer lymph node metastasis positive serum is higher than that in cardiac gland cancer lymph node metastasis negative serum, and the difference is statistically significant. The application further provides a kit for identifying lymph node metastasis of cardiac gland cancer, and the kit contains reagents for detecting the above biomarkers. By detecting the expression level of the above biomarkers in human serum, the application can effectively distinguish cardiac gland cancer lymph node metastasis positive and negative, and can be used for auxiliary diagnosis of lymph node metastasis of cardiac gland cancer.

The invention relates to the field of biological medicine, in particular to application of IL4i1 serving as a marker to prediction of neck lymph node metastasis of papillary thyroid cancer and application of IL4i1 serving as a treatment target to preparation of medicine for inhibiting thyroid cancer lymph node invasion and metastasis. The invention provides a biomarker IL4i1 for predicting thyroid cancer lymph node metastasis, and reveals that invasion and metastasis of tumors will be effectively inhibited by reducing expression of the IL4i1. The conclusion of the invention provides theoretical support and technical guarantee for accurate diagnosis of lymph node metastasis and disease progression of patients, and also provides strategy guidance for personalized medical schemes.

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 a pan-tumor lymph node metastasis detection method and device based on bidirectional reciprocity cross-scale attention fusion. The method comprises the following steps: acquiring a high-scale image of a region of interest and a low-scale image of the region of interest; obtaining a high-scale depth feature flow; obtaining a low-scale depth feature flow; performing bidirectional reciprocal attention processing on the high-scale depth feature flow and the low-scale depth feature flow to obtain enhanced high-scale features and enhanced low-scale features; fusing the enhanced high-scale features and the enhanced low-scale features to obtain multi-scale fusion features; and obtaining a final classification result according to the multi-scale fusion features. According to the universal tumor lymph node metastasis detection method based on bidirectional reciprocal cross-scale attention fusion, the multi-scale information of the pathological image is utilized, and an innovative bidirectional reciprocal cross-scale attention fusion mechanism is combined, so that the classification accuracy and credibility can be remarkably improved.

The invention discloses a pathological analysis method and device for colorectal cancer lymph node metastasis, and the method comprises the steps: firstly obtaining a to-be-diagnosed colorectal cancer pathological section scanning image, generating a plurality of candidate masks through region segmentation, and extracting fine-grained morphological features to obtain a category feature map; after feature vectors are extracted in combination with the candidate masks and the category feature maps, similarity calculation is conducted on the feature vectors and center prototype features of the reference feature library, category labels and confidence scores of the candidate masks are determined accordingly, and finally a pathological analysis result is obtained in combination with the scanned image and the confidence, label and position information corresponding to the candidate masks. Through region segmentation, fine-grained feature extraction and prototype comparative learning, colorectal cancer lymph node metastasis is accurately identified to improve diagnosis efficiency and accuracy, dependence on large-scale pixel-level labeling data is reduced, and applicability and deployment feasibility in different clinical scenes are enhanced.

This invention discloses the application of lncRNA MAGE-A10-encoded short peptide in regulating lymph node metastasis and targeted therapy in bladder cancer, belonging to the field of biomedical technology. This invention focuses on the coding ability of long non-coding RNAs, using high-throughput sequencing and bioinformatics algorithms to screen for lncRNA MAGE-A10, which is highly expressed in lymph node metastasis-positive bladder cancer tissues. The coding ability was verified by predicting open reading frames, constructing expression vectors, and synthesizing specific antibodies. The encoded short peptide MAGE-A10-44aa was positively correlated with lymph node metastasis in bladder cancer and poor patient prognosis, and also promoted lymphangiogenesis and lymph node metastasis in bladder cancer. This confirms the biological role of the MAGE-A10-44aa short peptide in bladder cancer lymph node metastasis, providing a potential target for bladder cancer treatment.

The present invention discloses the role of SEMA-123aa, encoded by circSEMA3C, in the targeted treatment of bladder cancer lymphatic metastasis, belonging to the field of biomedicine technology. The present invention uses the circRNA encoding product that drives bladder cancer lymphatic metastasis as the starting point, screens out circRNA-circSEMA3C that is upregulated in bladder cancer tissue through high-throughput sequencing, and confirms that SEMA3C-123aa, encoded by circSEMA3C, is positively correlated with the occurrence of bladder cancer lymphatic metastasis and can promote bladder cancer lymphangiogenesis and lymphatic metastasis, thereby revealing the biological role of SEMA3C-123aa in bladder cancer lymphatic metastasis and providing a theoretical basis and scientific basis for SEMA3C-123aa as an early diagnostic marker and new treatment target for bladder cancer lymphatic metastasis.