40 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 relates to the field of molecular diagnosis and tumor risk assessment, in particular to a marker combination and a kit for assessing the lymphatic metastasis risk of cervical cancer and application of the marker combination and the kit. The marker comprises a group of gene markers and a group of microbial markers, the gene markers comprise CARD9, MNX1AS2, MRAS, OLFML2A and RPS28, preferably, the gene markers further comprise at least one of CFL1P1 and GRASLND, and the microbial markers comprise microorganisms of the genus Dialister, the genus Catonella and the genus Campylobacter. The marker provided by the invention can be used for constructing an accurate risk assessment model, and can be used for developing a corresponding PCR (Polymerase Chain Reaction) kit, a mass spectrum or a chromatographic analysis system to realize metastasis risk prediction, curative effect assessment and new drug screening of cervical cancer patients.

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 an application of a dendrimer modified perovskite nanocrystal probe in prediction of mammary gland lymph node metastasis. The probe is formed by coupling a biological material of a targeted EPN3 gene and a water-soluble perovskite nanocrystal through electrostatic interaction, the water-soluble perovskite nanocrystal is formed by packaging perovskite quantum dots and a dendritic macromolecular packaging material through the coordination bonding effect of chemical bonds. The EPN3 modified water-soluble perovskite nanocrystalline probe provided by the invention can be used for high-sensitivity rapid detection of tumor tissues or cells with breast cancer lymph node metastasis.

The invention belongs to the field of disease detection reagents, and particularly relates to application of a reagent for detecting a marker in preparation of an ovarian cancer diagnosis product or a prognosis evaluation product, the marker is piRNA as shown in SEQ ID NO: 1, an ovarian cancer diagnosis model established on the basis of the piRNA has an ROC curve lower area of 0.902, sensitivity of 0.844, specificity of 0.767 and accuracy of 0.804, and the marker is piRNA as shown in SEQ ID NO: 2. Ovarian malignant tumor patients, benign tumors and healthy individuals can be effectively distinguished, and the ovarian malignant tumor marker is remarkably superior to existing clinical common tumor markers CA-125, CEA, CA19-9, HE4, ROMA and the like. Meanwhile, the early diagnosis efficiency of the marker for ovarian tumor patients is remarkable (the area under the ROC curve is 0.854), and is superior to that of the existing clinical common tumor marker CA-125 (the area under the ROC curve is 0.815). And the expression level of the marker in ovarian cancer serum is related to the treatment state, lymph node metastasis state, clinical staging and survival prognosis time of an ovarian cancer patient, so that a reagent for detecting the marker can be used for preparing an ovarian cancer diagnosis product or a prognosis evaluation product.

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.

The invention provides a biomarker for diagnosing lymphatic metastasis of cervical cancer and application of the biomarker, and belongs to the field of medical diagnosis. According to the invention, three biomarkers of resting sulfydryl oxidase 1, paraoxonase 1 and keratin 6A for diagnosing lymphatic metastasis of cervical cancer are obtained through multivariate statistical analysis, difference screening, random forest, ROC analysis and other biosignal analysis methods. Based on the biomarker provided by the invention, a kit and the like for diagnosing lymphatic metastasis of cervical cancer are constructed, and the kit and the like are used for accurately judging whether lymphatic metastasis occurs in cervical cancer patients or not.

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 relates to application of a medicine for inhibiting ZIP1 gene expression in inhibiting pancreatic cancer lymph node metastasis. Pancreatic cancer cells can secrete S100A4, act on lymphatic endothelial cells (LEC) and up-regulate expression of ZIP1 molecules, glycolysis on the LEC is further up-regulated, lactic acid is generated, the lactic acid destroys expression of tight junction protein Claudin5 between the LEC, pancreatic cancer cells are promoted to enter lymphatic vessels and generate lymphatic metastasis, ZIP1 is used as a target spot, siRNA is constructed, or CRISPR / Cas9 technology is used, and the pancreatic cancer cells can be used for treating lymphatic metastasis. The expression of the lymphatic vessel ZIP1 gene is reduced, and the lymphatic metastasis of pancreatic cancer is blocked.

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 provides a gynecological tumor lymph node metastasis detection method based on edge feature extraction and fusion, and belongs to the technical field of medical image detection technologies. And preprocessing the pelvic cavity enhanced CT image, and inputting the processed image into a pre-trained gynecological tumor pelvic lymph node metastasis accurate detection model. According to the accurate detection model for the pelvic lymph node metastasis of the gynecological tumor, multi-scale features and edge features of lymph nodes are extracted by using a multi-scale feature and edge information extraction convolution technology by virtue of a newly designed backbone network, and multi-scale feature fusion is carried out through a feature channel selection aggregation module. The features of the neck network fusion are transmitted to a detection head for analysis, the detection head deeply analyzes the features, and finally the detection result of the gynecological tumor lymph node metastasis is obtained. According to the pelvic lymph node detection method, the edge details and the spatial position of the pelvic lymph node can be accurately captured, and the detection precision is greatly improved through accurate analysis of the lymph node form and the edge features when an enhanced CT image of a patient is processed.

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 relates to the field of medical diagnosis, in particular to a method and a model which are constructed based on transcriptome sequencing and are used for diagnosing lymph node metastasis of early-stage cervical cancer. The invention provides a method for constructing a cervical cancer lymph node metastasis diagnosis model, which comprises the following steps of: screening to obtain seven preferable cervical cancer lymph node metastasis biomarkers on the basis of gene expression differences of cervical tissue samples of cervical cancer lymph node metastasis patients and cervical cancer lymph node non-metastasis patients, and establishing a diagnosis model and a diagnosis line diagram. Corresponding scores are obtained according to the numerical value of the optimized cervical cancer lymph node metastasis biomarker, then the scores are added to obtain a total score, and the risk probability of cervical cancer lymph node metastasis is calculated according to the total score. According to the invention, a diagnosis and detection model is constructed for a plurality of genes, molecular detection is carried out on a plurality of genes of cervical cancer tissues, and valuable diagnosis is carried out on whether cervical cancer lymph node metastasis happens to cervical cancer patients whose lymph nodes are not swollen or not through image examination.

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 present application discloses a histopathological recognition method and device for prostate cancer lymph node metastasis. The method includes: performing pathological recognition on the pathological image of the prostate lymph node to obtain a first confidence level corresponding to the original pathological recognition result; performing masking processing on the pathological image to obtain an original pathological mask image; correcting the first confidence level according to a plurality of pathological example images in a preset pathological image library to obtain a target confidence level; when the original pathological mask image includes a target tissue mask part, performing pixel correction on the target tissue mask part according to the target confidence level, and outputting a target pathological mask image. By correcting the first confidence level according to a plurality of pathological example images in the preset pathological image library, the confidence level error caused by insufficient generalization performance in pathological recognition can be reduced, thereby improving the accuracy of the output target pathological mask image.

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 present invention provides biomarkers for diagnosing lymph node metastasis of cervical cancer and their applications, belonging to the field of bio-detection technology. Through bioinformatics analysis methods such as multivariate statistical analysis, differential screening, random forest, and ROC analysis, the present invention obtains three biomarkers, namely, quiescent thiol oxidase-1, paraoxonase 1, and keratin 6A, for diagnosing lymph node metastasis of cervical cancer. Based on the biomarkers of the present invention, kits for diagnosing lymph node metastasis of cervical cancer are constructed, etc., for accurately judging whether lymph node metastasis has occurred in cervical cancer patients.

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.