13 results about "Lymph node metastasis" patented technology

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 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 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 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 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.

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.