62 results about "Tumor type" patented technology

The invention relates to the field of medical resource scheduling, in particular to a multi-chamber particle therapy method, system and device, a storage medium and a computer storage medium, and the system comprises an irradiation source which is used for generating a particle beam; the plurality of particle treatment rooms are used for sending beam requests and receiving the particle beams; the beam transport device is used for transmitting the particle beam from the irradiation source to the particle treatment room; and the control device is used for receiving the beam requests of the particle treatment room, calculating the priority for each beam request according to the reserved irradiation time, the tumor type and the patient condition included in each beam request, and controlling the beam transport device to transport the particle beams according to the priorities. According to the method, the utilization efficiency of beam resources can be remarkably improved, the urgency degree and clinical importance of treatment are fully considered, and the demand intensity of different requests for the beam resources can be quantitatively reflected.

The invention relates to the field of medical artificial intelligence, in particular to a mucus-like brain tumor multi-source data auxiliary differential diagnosis system based on deep learning, and the system collects pathological images, medical images, immunohistochemical data and molecular biology data through a multi-source heterogeneous data collection and standardization module, and carries out standardization processing; a multi-scale feature extraction module extracts key features from the data to generate multi-modal feature data; a cross-modal correlation analysis module analyzes correlation among the feature data and generates attention mapping data and feature correlation data; the diagnosis reasoning and decision-making module identifies the tumor type by using the data, carries out differential diagnosis and calculates the uncertainty of a diagnosis result; the interpretability display module is used for generating a structured diagnosis report which comprises multi-level attention visualization data and positioning a key diagnosis area; and through multi-source data fusion and correlation analysis, the diagnosis accuracy is remarkably improved.

This platform is a privacy-preserving, federated learning system designed to integrate whole-slide digital pathology images with matched molecular profiling and clinical metadata for improved diagnosis, subtyping, and prognostic estimation of gynecologic tumors. The architecture comprises local institutional nodes that retain raw patient data while participating in distributed model training coordinated by a central orchestration server. Each local node preprocesses whole-slide images into patch-level tensors, extracts visual embeddings via convolutional backbones, and processes molecular vectors (e.g., somatic mutations, expression summaries, copy-number measures) via a molecular encoder. A multimodal fusion module - implemented as an attention- based transformer - integrates image and molecular embeddings into a unified representation used by multi-task heads for classification (histologic subtype, diagnostic label) and regression (risk score). The federated learning controller aggregates encrypted model updates (FedAvg) and returns improved global weights without exchanging raw data, enabling cross-site generalization while preserving patient privacy. Explainability components generate attention maps and tile-level saliency (Grad-CAM style) linked to molecular features, providing interpretable morpho- molecular correlations to pathologists. The platform supports API integration with PACS / LIMS, conforms to privacy standards via optional differential privacy and secure aggregation layers, and is extensible to additional omics modalities or transfer / fine-tuning workflows for related tumor types. By combining multimodal fusion, federated training, and clinician-facing interpretability, the system accelerates robust, generalizable AI for precision pathology in gynecologic oncology.

The invention belongs to the technical field of tumor immunity, and particularly relates to a tumor antigen and chemotactic factor co-coding system and application thereof. The invention provides a tumor antigen and chemotactic factor co-coding system. The tumor antigen and chemotactic factor co-coding system comprises a nucleotide sequence for coding a tumor antigen and a nucleotide sequence for coding a chemotactic factor. The tumor antigen and chemotactic factor co-coding system can flexibly replace an antigen sequence and chemotactic factor combination, is adaptive to different tumor types and various immunotherapy requirements, can be expanded to various solid tumor treatment scenes through a customized antigen-chemotactic factor combination in the future, and has a wide application prospect. The broad-spectrum application in tumor treatment means including tumor neoantigen vaccines, adoptive cell therapy and the like is realized.

The invention discloses a tumor tissue segmentation method based on an MR image, and relates to the technical field of image processing, and the method comprises the following steps: processing before segmentation, obtaining the MR image, quantifying texture features, calculating different-dimension feature values of each pixel of the MR image, obtaining a multi-scale texture feature vector of each pixel, and classifying the texture features. The method comprises the steps of dividing a pixel category and an image area of each pixel, enhancing boundary dynamic features and adjusting a threshold value, and has the advantages that double-weight boundary filtering is carried out by adopting confidence and spatial distance, similar texture features in a boundary transition area are amplified, boundary over-segmentation and under-segmentation probabilities are reduced, the judgment accuracy of the pixels in the boundary transition area is improved, and the judgment accuracy of the pixels in the boundary transition area is improved. According to the method, the threshold value of each image can be dynamically adjusted according to the tumor features of the image, manual parameter adjustment is not needed, multi-scene adaptation is achieved, the efficiency is improved, and through combination of multi-scale textures and dynamic double-feature fusion, the segmentation precision of cross-tumor types is guaranteed, and the heterogeneity tumor adaptability is improved.

The invention belongs to the field of medicinal chemistry, and particularly relates to a targeted drug coordinated metal complex as well as a preparation method and application thereof. The metal complex prepared by the invention is a ruthenium (II), iridium (III) and rhodium (III) complex coordinated with a targeted drug, the original biological activity of the ligand is remarkably optimized and improved by virtue of the synergistic effect between central metal ions and the ligand, the specific binding capacity of the ligand with a tumor cell target spot is enhanced, and the tumor cell target spot is further enhanced. The metal complex has a broad-spectrum and efficient anti-tumor activity, the action range of the metal complex can cover multiple common tumor types including but not limited to liver cancer, kidney cancer, breast cancer, ovarian cancer or colorectal cancer and the like, and an important technical support is provided for developing novel anti-tumor drugs with multiple target points and low toxic and side effects. The complex provided by the invention is novel in structure and good in anti-tumor activity, and has a good application prospect in the field of anti-tumor drugs.

The application discloses application of a pharmaceutical composition taking GL-V9 and a chemotherapeutic drug as active ingredients in treatment of tumors. When GL-V9 is combined with a classical chemotherapeutic drug to treat tumors, a drug synergistic effect can be obviously achieved, and the effect is obviously superior to combination of similar drugs and the chemotherapeutic drug. The chemotherapeutic drugs include 5-fluorouracil; and the tumor types include leukemia, colorectal cancer and lung cancer.

This invention discloses an intelligent nutrition recommendation method for cancer patients based on metabolic rhythm synchronization, belonging to the field of nutrition recommendation. This invention integrates temporal physiological data and clinical information, extracts features, and uses a bidirectional long short-term memory network to predict the host metabolic activity curve. Based on a tumor knowledge graph, a conditional generative adversarial network is constructed to infer and generate a tumor metabolic activity curve using the host curve, tumor type, and treatment stage as conditions. A temporal separation potential heatmap is generated by calculating the phase difference and overlap between the two curves. Using the heatmap and clinical labels as states, an integrated constraint proximal strategy optimization algorithm is used for decision-making, outputting a time-segmented nutrient delivery action sequence that satisfies temporal nutritional constraints. Using this sequence as the target, an optimization algorithm performs a multi-objective search on a recipe database to generate a personalized daily recipe that considers both nutritional and taste preferences, providing cancer patients with timely and intelligent decision-making.

This invention discloses an antibody-drug conjugate based on a p16-targeting single-chain antibody, comprising a p16 single-chain antibody, a cell-penetrating peptide, a linker, and a carrier drug. The p16 single-chain antibody and the cell-penetrating peptide are fused together to form a fusion protein, which is further conjugated to the carrier drug via a linker to form the antibody-drug conjugate. This invention provides a novel therapeutic strategy by targeting p16, particularly suitable for tumor types that highly express p16, for which effective treatments are currently lacking. By targeting the p16 site within the cell nucleus, this invention not only enriches the candidate target library for targeted therapy but also provides feasibility validation and technical reference for future drug development targeting nuclear targets.

The invention relates to a multi-modal ultrasonic image-based knowledge graph construction method and device, and a storage medium, and the method comprises the steps: obtaining a tumor gray-scale ultrasonic image, obtaining a tumor edge image and a tumor internal image through target segmentation, respectively extracting corresponding multi-modal candidate image omics features, and carrying out the target segmentation; forming a sample set comprising a plurality of samples in combination with a tumor type corresponding to the tumor gray-scale ultrasonic image; performing synthetic minority oversampling processing on a sample set, performing Z scoring processing on each candidate radiomics feature in each sample, screening radiomics features with strong correlation through principal component analysis, and performing recursive feature elimination to obtain finally reserved radiomics features corresponding to each tumor type; and establishing a knowledge graph and / or a machine learning model based on the finally reserved radiomics features corresponding to each tumor type. The method has the advantages that the association between the tumor type and the selected iconography features is fully established, and the abundant information of the tumor edge is fully considered.

Methods for enriching disseminated tumor cells (DTCs) or circulating tumor cells (CTCs) from a biological sample are provided. Methods for identifying disseminated tumor cells (DTCs) or circulating tumor cells (CTCs) or identifying a tumor type or tissue of origin of a tumor are also provided. Methods of identifying a druggable cancer target and a cancer drug against the target are provided.

The application belongs to the technical field of tumor typing, and discloses a tumor gene typing prediction method and system based on a multi-modal data graph structure, which comprises the following steps: acquiring sample pathological images and sample multi-omics characteristics of tumor samples; inputting the pathological images into a Hyper-RBAT-Net network to output corresponding structured mask images, and constructing a three-level pathological structure atlas based on the mask images; jointly embedding the three-level pathological structure atlas and the sample multi-omics characteristics to construct a fusion graph structure, learning a graph representation of the fusion graph structure, and obtaining a sample embedding representation; using the sample embedding representation to predict tumor typing, defining a loss function, training a tumor typing prediction model, and using the trained model to predict tumor gene typing. The application can more comprehensively mine the structural expression information of tumors by constructing a three-level pathological structure atlas, fusing multi-omics characteristics, and using an improved graph attention mechanism to learn a graph representation, thereby improving the accuracy and generalization ability of gene typing.

IT-139, sodium trans-[tetrachlorobis(1H-indazole)ruthenate(III)], is an intravenously administered small molecule compound. In preclinical anti-tumor and mechanism of action studies, IT-139 showed activity against a broad range of tumor types, including those which are resistant to standard anti-cancer agents (e.g., platinums, vinca alkaloids, taxanes, anthracyclines). This activity is believed to arise from IT-139's novel mechanism of action that targets the GRP78 pathway. It was found that up-regulation of GRP78 is a key cancer cell survival pathway. Downregulation of GRP78 using IT-139 removes this resistance pathway allowing for chemotherapy and immuno-oncology agents to be more effective in treating cancer.

The application discloses a 3'-deoxyadenosine compound and application thereof in preparation of an antitumor drug. The 3'-deoxyadenosine compound and the pharmaceutical composition thereof have good antitumor proliferation effect. Compared with a parent drug, the 3'-deoxyadenosine compound has better affinity to a cell membrane, so that the half-life of in-vivo metabolism of the drug is longer, and the drug stays in the body for a longer time. Compared with other nucleoside antitumor drugs, the cordycepin derivative and the pharmaceutical composition thereof have a wider range of tumor types and effects, including excellent inhibition effect on gastric cancer, pancreatic cancer, liver cancer, small cell lung cancer, colorectal cancer, melanoma, ovarian cancer and the like, and the side effect is lower and the curative effect is better.

This disclosure describes a machine learning (ML) framework, including a graph convolutional neural network (GCN), for identifying gene expression signatures associated with cancer driver events. The model is trained to identify the TP53 mutation status of cancer samples from gene expression, utilizing a comprehensive, curated graph structure of gene interactions. Quantitative scores are generated to rank the severity of driver events in each sample. Very high AUC results for unknown data across several tumor types are achieved in this method. A strong correlation with protein function exists. The Signature in Transcriptome Associated with Mutant Proteins (STAMP) model can also predict driver events in many combinations of key oncogenes / pathways and several tumor types, based on well-established annotations from the literature. Thus, the STAMP model can identify and quantify driver events, which may lead to improved targeted therapy selection and prioritization in cancer patients.

A combination therapeutic strategy employs multiple oncolytic viruses (OVs), grouped and administered based on their virological properties, tumor selectivity, and distinct antigen profiles, to treat malignant tumors. Representative groupings may include members of flaviviruses, each contributing distinct immune modulation and the same modes of tumor cell killing. A predefined treatment schedule involves sequential or concurrent administration of antigenically diverse OVs in multiple cycles, reducing cross-neutralization and sustaining cytolytic pressure on tumors. A pre-characterized OV panel, comprising members from a virus family, RNA and DNA viruses, both wild-type and genetically engineered, serves as a flexible resource for customizing treatment regimens by tumor type, immune landscape, and therapeutic goals. This platform establishes a rational framework for combination OV therapy with improved durability, safety, and clinical effectiveness.

A method (100) for visualizing heterogeneity of a patient's tumor, comprising: (i) receiving (120) information about a patient, comprising demographic and clinical information about the patient, an identification of the patient's tumor type, and a heterogeneity assessment of the patient's tumor; (ii) obtaining (130), based on the identification of the patient's tumor type and the heterogeneity assessment of the patient's tumor, therapy effectiveness information for each of the two or more subclones of the tumor: (iii) generating (140) a visual representation of the heterogeneity assessment of the patient's tumor and the obtained therapy effectiveness information for each of the two or more subclones of the tumor; and (iv) providing (150), via a user interface of the system, the generated visual representation, wherein the visual representation comprises a tumor heterogeneity graph and a therapy effectiveness graph.

The application discloses application of a pharmaceutical composition with GL-V9 and a chemotherapeutic drug as active ingredients in treatment of tumors, and the GL-V9 can obviously achieve a drug synergistic effect when the GL-V9 is combined with a classical chemotherapeutic drug to treat tumors, and the effect is obviously superior to combination of similar drugs and the chemotherapeutic drug. The chemotherapeutic drugs include PARP1 inhibitors, capecitabine, irinotecan, sorafenib, cisplatin or 5-fluorouracil; and the tumor types include leukemia, colorectal cancer and lung cancer.

The present application relates to a kind of distal anchoring bone cement type prosthesis stem, including stem body, several anchoring blades and piston.The bottom end of the stem body is fixed on prosthesis, top end is inserted into medullary cavity, bone cement channel is provided in the stem body along the axial direction, injection hole is formed on the side wall of the stem body, and the injection hole is communicated with the bone cement channel;The anchoring blade is arranged at the top of the stem body, the bottom of the anchoring blade is rotatably connected with the top end of the stem body, the piston is sleeved in the bone cement channel, and under the driving of bone cement, it slides upward, when the piston slides to the top of the stem body, several anchoring blades are opened, and the anchoring blade is converted from the first state to the second state.The distal anchoring bone cement type prosthesis stem can improve the long-term stability of tumor type prosthesis, reduce the aseptic loosening of prosthesis, and improve the long-term function of limb of patients with malignant bone tumor.

The application discloses a compound with anticancer effect based on 3'-deoxyadenosine modified by chemical modification, and a structure of the compound is shown as formula I. The cordycepin derivative and the pharmaceutical composition thereof have good antitumor proliferation effect. Compared with the parent drug, the cordycepin derivative has better affinity to the cell membrane, so that the half-life of the drug metabolism in the body is longer, and the drug stays in the body for a longer time. Compared with other nucleoside antitumor drugs, the cordycepin derivative and the pharmaceutical composition thereof have a wider range of tumor types and effects, including excellent inhibition effect on gastric cancer, pancreatic cancer, liver cancer, small cell lung cancer, colorectal cancer, melanoma, ovarian cancer and the like, and the side effect is lower and the curative effect is better.

The present invention includes compositions and methods for retrieving tumor-related antibodies and antigens. In one aspect, the invention includes a method for Sequential Tumor-related Antibody and antigen Retrieving (STAR) which directly and efficiently identifies potent antibodies that can specifically bind to tumor-related antigens on the tumor cell surface. In another aspect, the invention includes a CAR comprising a nanobody, a transmembrane domain, and an intracellular domain, wherein the nanobody is retrieved by a STAR method. In another aspect, the invention includes a CAR T system that targets CD13 and treats acute myeloid leukemia. In another aspect, the invention includes a CAR T system and ADC that targets CDH17 and treats NETs and other types of tumors expressing this antigen, with tolerable toxicities.

The invention belongs to the field of biological medicine, and relates to a recombinant VAR2CSA, which can be used for detecting specific types of tumor-type chondroitin sulfate (ofCS) and proteoglycan (ofCSPG) modified by the tumor-type chondroitin sulfate (ofCS). The recombinant VAR2CSA disclosed by the invention has relatively high binding affinity with chondroitin sulfate A (CSA), and the tumor type chondroitin sulfate constructed by the recombinant VAR2CSA disclosed by the invention and a detection method of proteoglycan modified by the tumor type chondroitin sulfate have a detection effect on all malignant tumors expressing the CSA. The tumor-type chondroitin sulfate based on the recombinant VAR2CSA and a detection method of proteoglycan modified by the tumor-type chondroitin sulfate can be used for early screening and diagnosis of tumors, tumor load monitoring and prognosis prediction.

The invention relates to the technical field of tumor tissue discrimination, and discloses a tumor tissue identification method and device and electronic equipment, and the method comprises the steps: obtaining a blood flow index time sequence of a target object; based on the blood flow index time sequence, blood flow characteristics used for reflecting the blood flow dynamic characteristics of the target object are determined; determining a judgment result of the target object based on the blood flow characteristics; and under the condition that the judgment result is the tumor tissue, determining the tumor type and confidence of the tumor tissue. On the basis of the determined target tissue blood flow index, the blood flow index is analyzed from multiple dimensions, the blood flow dynamic characteristics of the target object can be reflected more comprehensively, and the method does not only depend on a single index. The complexity of a blood flow mode can be captured through multi-dimensional feature extraction, so that the tumor can be distinguished more accurately and reliably.

The application discloses a tumor multidimensional evaluation and report generation system and method based on a unified workflow, relates to the technical field of clinical auxiliary decision and medical workflow optimization, and comprises a unified workflow entrance module, a tumor type identification module, a special evaluation engine scheduling module, a special evaluation engine library module, an evaluation result collection module, an intelligent report generation module, a decision support module and a workflow state visualization module, wherein the modules are connected in series. The unified workflow entrance module provides unified data management; the special evaluation engine library module is internally provided with multiple scoring engines for different tumor types and different diagnosis and treatment scenes; and the intelligent report generation module automatically generates a complete evaluation report. The application can effectively solve the problem of fragmentation of the tumor multidimensional evaluation workflow, effectively realize report integration, and enhance the clinical decision support capability.

The application belongs to the technical field of medicine, and particularly relates to a modularly constructed targeted-tumor-killing polypeptide-drug conjugate as well as a preparation method and application. The conjugate has a dendritic six-branch scaffold structure, and the scaffold can realize replaceable combination of a targeting polypeptide and a chemotherapeutic drug through chemical modification, so that precise drug delivery can be realized for different tumor types. Among them, a blood vessel binding peptide and an albumin binding peptide are selected as model polypeptides, and a common clinical drug doxorubicin is selected as a model drug. The polypeptide-drug conjugate has high drug loading and stability, can improve the pharmacokinetic characteristics of poorly soluble drugs, prolong in-vivo circulation, has small molecular weight and strong tissue permeability, can be precisely enriched in tumors, has good structural adjustability and functional expansion potential, and exhibits significant anti-tumor effect and safety.

A system and method for determining a cancerous condition based upon at least one DNA sample is provided. An interface provides data related to DNA methylation for the sample, the data including related information about the sample. A processor, responsive to the interface, identifies the data related to the DNA methylation of the sample and accesses a data store containing a library of DNA methylation information related to each of tumor, immune, and angiogenic microenvironment components. A deconvolution process, relative to the DNA sample and the DNA methylation information, then determines association with one or more components from the sample. Illustratively, the library can define a plurality of layers of information associated with aspects of the cancerous condition relative to microenvironment components thereof. One or more components can define a tumor-type-specific hierarchical model related to a plurality of immune cell types that are subject to the deconvolution process.

This invention teaches a tumor-targeting thiocolchicine nanoemulsion wherein the nanodroplets comprising the nanoemulsion are coated with an antinuclear antibody (ANA-nanoemulsion). The nanodroplets are less than 200 nm and will enter the tumor via the Enhanced Permeation and Retention Effect (EPR). Once inside the tumor the antinuclear antibody coating the nanodroplets will bind to extracellular nuclear material released from dead cells and anchor the nanodroplets within the tumor where the drug is released for maximum effect. As all solid tumors have areas of necrosis this means that the ANA-nanoemulsion can target and treat all solid tumors regardless of the tumor type. The ANA-nanoemulsion is formulated to have a phase transition temperature above 8 C. When cooled to below 8 C the ANA-nanoemulsion converts into a suspension of solid lipid nanospheres (ANA-nanospheres) suitable for storage at 4 C or below.

The invention belongs to the field of tumor classification, and particularly relates to a tumor fine classification system fusing multi-dimensional medical data. Comprising a medical data acquisition module, a medical data identification module, a multi-dimensional classification module, a data matching module, a tumor type confirmation module, an output display module, a database, a multi-dimensional data entry module, a data management module, a feedback module, a tumor type analysis platform, an opinion collection module, a comprehensive analysis and judgment module and an identity login module. The medical data acquisition module is connected with the medical data identification module, the medical data identification module is connected with the multi-dimensional classification module, the multi-dimensional classification module is connected with the data matching module, the data matching module is connected with the database, the feedback module and the tumor type confirmation module, and the tumor type confirmation module is connected with the output display module. According to the system, the tumors can be classified according to the medical data, and the management of the medical data is facilitated.

A brain tumor types distinguish system includes an image outputting device and a server computing device. The image outputting device outputs at least three brain images captured from the position of a brain tumor. The server computing device pre-stores a plurality of distinguish pathways corresponding to different types of brain tumors. The server computing device includes an image receiving module, an image pre-processing module, a data comparison module and a distinguish module. The image receiving module receives the brain images. The image pre-processing module pre-processes the brain images to obtain corresponding processed images thereof. The data comparison module compares the brain images and the processed images with the distinguish pathways to obtain at least three comparison results. The distinguish module statistically analyzes the comparison results to obtain a distinguish result.

Methods for characterizing aneuploidy based intratumor heterogeneity for a tumor of a tumor type for a subject. The methods may comprise, for example, obtaining sample aneuploidy data for the tumor, calling subclonal aneuploidy events in the sample aneuploidy data and generating an intratumor heterogeneity score for the tumor sample, and use of the aneuploidy based intratumor heterogeneity in cancer treatment and prognostics.