130 results about "Tumor heterogeneity" patented technology

The invention relates to the technical field of medical image analysis, in particular to a tumor partition recognition method and system based on multi-scale image analysis, and the method comprises the following steps: based on an input medical tumor partition image, defining the gray value distribution range of pixel blocks, dividing pixel block groups according to a fixed size, and calculating the neighborhood gray values of the pixel blocks, and determining a distribution change set through accumulation of gray difference values of the pixel block groups, and generating a multi-scale gray distribution diagram. According to the invention, by calculating the gray value distribution and the gray difference value of the pixel block group, enhancing the image detail identification of the tumor area, improving the tumor boundary analysis capability, analyzing the texture feature difference of the boundary area, quantifying the gray gradient difference value and the connection characteristic, improving the geometric characteristic analysis precision, and combining the space geometric expansion data and the texture characteristic, the tumor boundary analysis precision is improved. Partitioning precision and image analysis accuracy are improved, misrecognition of a boundary fuzzy region is effectively avoided, and a finer tumor heterogeneity research basis is provided.

The embodiment of the invention discloses a medical image tumor heterogeneity detection method and device. A specific embodiment of the method comprises the following steps: acquiring a brain glioma multi-modal medical image set from medical imaging equipment; performing image preprocessing on the brain glioma multi-modal medical images in the brain glioma multi-modal medical image set to obtain a processed medical image set; performing brain glioma region segmentation on the processed medical image set to obtain a brain glioma segmentation region set; performing high-order feature extraction and subregion division on the brain glioma segmentation region set to generate a high-order statistic feature map group and a tumor subregion image group; boundary optimization and topological repair are carried out on tumor sub-region images in the tumor sub-region image group, and a processed tumor sub-region image group is generated; and generating a tumor heterogeneity assessment report by using the processed tumor subregion image group. According to the embodiment, the automation level and precision of medical image tumor heterogeneity evaluation can be improved.

The invention relates to the technical field of medical image processing, in particular to a lung tumor CT image 3D segmentation method and system based on multi-modal image fusion. The method comprises the following steps: acquiring a lung tumor CT image; determining a first texture feature based on the lung tumor CT image; identifying a lung tumor boundary by using the first texture feature; detecting a nodule protrusion area in the boundary of the lung tumor; obtaining tissue infiltration data from the nodule protrusion area; determining a second texture feature according to the tissue infiltration data; determining a tumor heterogeneity feature according to the first texture feature and the second texture feature; evaluating the potential malignancy degree by utilizing tumor heterogeneity characteristics; and dividing a tumor risk area of the lung tumor CT image based on the potential malignancy degree. According to the invention, accurate heterogeneity identification and risk region division of the lung tumor CT image are realized based on a medical image processing technology, and the accuracy of lung tumor 3D segmentation is improved.

The invention discloses a multi-scale brain tumor segmentation method and system based on adaptive KAN, and a storage medium. The method comprises the following steps: preprocessing three-dimensional brain magnetic resonance imaging data; the preprocessed data are input into an encoder, the encoder comprises a plurality of levels, and each level executes convolution operation to extract local features, executes spatial KAN processing to extract spatial features and downsamples a feature map; the output of the encoder is input into a bottleneck layer, and the bottleneck layer captures a multi-scale global context by using a plurality of parallel expansion convolution branches; the output of the bottleneck layer is input into a decoder, the decoder comprises a plurality of stages, and each stage executes transposing a convolution up-sampling feature map, executes cross-scale gating processing to fuse encoder jump connection features and decoder features, and executes spatial KAN processing to optimize features; and the output of the decoder is input into the output module. According to the method, the problems of low calculation efficiency, poor tumor heterogeneity adaptation, insufficient multi-scale context capture and the like in the existing brain tumor segmentation can be effectively solved.

The invention provides a breast cancer recurrence risk prediction method, system and device based on an ultrasonic image, and relates to the field of intelligent medical treatment, the method uses a deep convolutional neural network to perform deep network feature extraction on a breast ultrasonic image, and uses a deep learning semantic segmentation algorithm to perform accurate positioning and automatic segmentation on a breast tumor region of interest, thereby improving the accuracy of breast cancer recurrence risk prediction. Meanwhile, habitat analysis is carried out on the ultrasonic images to extract tumor heterogeneity features, multi-level and multi-mode features such as deep learning features, radiomics features and habitat analysis features are fused, a breast cancer recurrence risk prediction model is constructed, breast cancer recurrence risk prediction is carried out, and a breast cancer recurrence risk assessment result is output. And a quantitative basis is provided for clinical treatment decisions. The accuracy and robustness of recurrence risk prediction are remarkably improved through multi-feature fusion, and standardization and objectification of breast cancer prognosis evaluation are achieved. In addition, the method further has the advantages of being easy and convenient to operate, low in cost, noninvasive, nonradiative, good in repeatability and the like.

The invention discloses an intelligent tumor microenvironment analysis and drug screening method based on multi-modal data, and relates to the field of tumor research. The method comprises the following steps: obtaining a plurality of prognosis image features according to preoperative magnetic resonance images of the brain of a plurality of glioblastoma patients; identifying a key image genomics gene set for each prognostic image feature; analyzing biological reasons of prognosis differences of high-risk patients and low-risk patients by using a transcriptome method; analyzing biological functions of prognosis image features; utilizing Fisher to accurately test and determine a cell population associated with the prognostic image features; determining a functional state of the cell population based on the scRNA-seq data of the cell population; constructing an image-gene-cell population-functional state association; potential therapeutic drugs for cell populations associated with prognostic image features are parsed using chi-square test. According to the invention, the tumor heterogeneity of glioblastoma can be depicted from the perspective of images, and potential therapeutic drugs can be identified.

The invention discloses an anti-TP53 mutation T cell receptor and application thereof, the T cell receptor comprises specific alpha chain and beta chain variable domains, and the complementary determining region (CDR) sequence is shown as SEQ ID NO: 9-14. The TCR has the core advantage that the TCR has excellent broad-spectrum recognition capability, can target six different amino acid substitutions (A, G, I, N, S and T) at the R249 site, and effectively deals with tumor heterogeneity and mutation difference between patients. Aiming at high-frequency HLA-B * 07: 02 alleles in people, the TCR lays a foundation for developing TCR-T cell therapy covering a wide range of people, and has great clinical application value and market potential in treatment of various solid tumors carrying TP53 R249 hotspot mutation, such as liver cancer.

The invention provides an ecDNA structure prediction method based on scATAC-seq, a method for identifying cells carrying ecDNA and a medium, and relates to the technical field of biological information. According to the ecDNA structure prediction method based on scATAC-seq, inconsistent readings of ecDNA breakpoint sources are extracted through comparison with a reference genome, enriched peak regions are called in sequence and converted into a graph, a breakpoint graph is combined with a Bayesian model, and accurate prediction of structural information such as ecDNA breakpoints, ecDNA regions and connection modes is achieved. The invention also provides a method for identifying cells carrying the ecDNA, and the method is used for accurately acquiring the cells carrying the ecDNA through standardization treatment, formation of a gene activity matrix and non-single-peak test on the ecDNA structure based on the scATAC-seq. According to the present invention, the ecDNA structure can be predicted, the cells carrying the ecDNA can be accurately identified, and the method can be used for the accurate analysis and research of the tumor heterogeneity, evolution and drug resistance process of the ecDNA.

The invention provides an ultrasonic activated blood coagulation targeting tumor selective prodrug as well as a preparation method and application thereof. The blood coagulation targeting tumor selective prodrug has a structure as shown in a formula I. The blood coagulation targeting tumor selective prodrug can trigger a blood coagulation cascade reaction by utilizing tumor vascular injury generated by ultrasonic induction so as to realize targeted anchoring of the drug and inhibit off-target diffusion of an active drug; meanwhile, the prodrug is activated by ultrasound, so that the limitation of tumor heterogeneity on the activation of the prodrug can be overcome. According to the technical scheme, selective enrichment of active drugs in tumors can be effectively improved, and toxicity caused by drug off-target is reduced while tumor growth is remarkably inhibited.

The invention provides a screening platform for chemotherapy combined anti-tumor natural products and application of the screening platform. PTC has tumor cells, interstitial cells, fibroblasts and immune cells from primary epithelium, is rich in cell components, and has advantages in the aspect of tumor microenvironment simulation. Meanwhile, PTC can reproduce tumor heterogeneity, the culture success rate and clinical consistency can reach 90% or above, and the detection period is within two weeks. And the detection result can be read through AI intelligent software, so that the accuracy can be further improved. The screening period can be shortened, the cost is reduced, and the accuracy is improved; according to the platform, high-throughput screening can be carried out in a short time, and the treatment effect of a chemotherapy drug combined anti-tumor natural product drug scheme can be predicted.

The invention provides a full-slice image classification method and system based on multi-branch attention and random instance masks and a medium, and the method comprises the steps: obtaining a full-slice digital pathological image, carrying out the image segmentation, and obtaining a plurality of instances; performing feature extraction based on a feature extraction network to obtain an instance feature sequence; carrying out parallel analysis on the instance feature sequence based on a multi-branch attention mechanism, executing a random Top-K instance mask operation, and carrying out weighted summation on the instance feature sequence based on the attention distribution of each attention branch; aggregating the packet level feature representations of all the attention branches, generating a comprehensive feature representation of the full-slice digital pathological image, and obtaining a classification prediction result; by setting a plurality of parallel attention branches, different branches are promoted to actively learn and capture a plurality of different visual modes existing in the full-slice digital pathological image, so that tumor heterogeneity can be effectively represented, and the generalization ability of full-slice digital pathological image classification is improved.

The invention discloses a tumor heterogeneity analysis method and equipment based on super-resolution microscopic imaging, and relates to the field of medical image analysis. The method comprises the steps that a tumor imaging result is obtained, the tumor imaging result is generated through a super-resolution microscopic imaging technology, and the tumor imaging result comprises a tumor image; determining a target area of the tumor image; based on the tumor imaging result, quantitative analysis parameters are determined, and the quantitative analysis parameters are used for quantifying capillary features of at least two dimensions of tumor capillaries in the target area; on the basis of the quantitative analysis parameters, tumor heterogeneity analysis is conducted on different target areas, heterogeneity analysis results are obtained, and different target areas belong to the same tumor or different tumors. By adopting the scheme provided by the invention, the quantitative analysis parameters are generated based on the tumor imaging result, and quantitative analysis of tumor microvessel characteristics is realized, so that the accuracy of tumor heterogeneity analysis is improved.

The invention discloses a method and a device for constructing a cancer early screening model based on cfDNA difference statistics. The construction method of the cancer early screening model comprises the following steps: performing difference statistics on tumor-derived cfDNA and healthy individual-derived cfDNA in a low-depth whole-genome sequencing mode, and establishing the cancer early screening model to realize non-invasive early screening of cancers; the difference statistics comprises cfDNA fragment size characteristic difference statistics, cfDNA upstream and downstream continuous three groups of 3bp base combination category distribution frequency change trend difference statistics at a fracture position and sequencing data coverage depth mode difference statistics of the cfDNA near TSS. The method has the advantages of being minimally invasive, high in early diagnosis sensitivity, capable of achieving dynamic monitoring and the like, the limitation of cfDNA gene mutation detection in the early stage of the tumor is overcome, and information lag or deletion caused by tumor heterogeneity is avoided.

The invention is suitable for the field of medical image analysis, and provides a tumor pathological image segmentation method based on a U-Net neural network, and the method comprises the steps: extracting the gray, texture and shape multi-dimensional features of tumor cells through a U-Net encoder, constructing a feature cluster, building an invasion direction probability model based on the cluster, analyzing the morphological features of a necrotic region, and speculating the growth speed. And dynamically adjusting the segmentation sensitivity threshold of the decoder, and finally outputting a three-channel segmentation map containing a tumor core region, a false envelope invasion region and a capillary invasion region. According to the scheme, through multi-dimensional feature fusion and biological behavior modeling, precise characterization of tumor heterogeneity is achieved, the adaptability of a segmentation model to different invasion active areas is improved through a dynamic sensitivity regulation mechanism, a segmentation result with anatomical positioning and biological evaluation values is provided for clinic, and the segmentation accuracy is improved. And diagnosis and treatment decision of tumors are effectively assisted. The method is easy and convenient to operate and high in automation degree and has remarkable clinical application value.

The invention discloses a rectal cancer postoperative recurrence risk prediction system and method based on multi-modal time sequence data, and relates to the technical field of medical artificial intelligence. The system comprises a data acquisition and preprocessing module, a feature extraction module and a multi-modal feature fusion and modeling module. The method comprises the following steps: constructing a cross-modal data set containing time sequence clinical data, a time sequence MR image and a biopsy digital pathological image; respectively extracting clinical features, radiomics and deep learning features of the MR image, and nucleus morphology and spatial distribution features of the pathological image; and fusing all the features by using a Transform network, and constructing a prediction model. According to the method, macroscopic images, micropathology and dynamic time sequence information are integrated, tumor heterogeneity is comprehensively quantified, the problem that prediction of a single-mode static model is not accurate is solved, the postoperative recurrence risk of the stage III rectal cancer patient can be evaluated more accurately, and clinical treatment decision making is assisted.

The invention relates to the technical field of biomedicine, in particular to a method for in-situ detection of multiple lung cancer related markers in living circulating malignant cells and tumor heterogeneity. The lung cancer targeted nanoprobe loaded with multiple molecular beacons is used for accurately detecting at least one nucleic acid marker related to the lung cancer in living lung cancer cells, and the levels of different markers in the lung cancer cells are reflected from the single cell level, so that the tumor heterogeneity state of a specific patient is reflected. The nano-probe is a nano-particle self-assembled by a high polymer material, electropositive protein, functional polypeptide and / or functional aptamer, and a molecular beacon of a lung cancer related marker. The nano-probe can target circulating malignant cells with different phenotypes in whole blood, simultaneously detect multiple nucleic acid markers in living cells, and reflect the levels of different markers in the malignant cells from the single cell level, so that the tumor heterogeneity state of a specific patient is reflected, and early accurate diagnosis of lung cancer is realized.

The invention discloses an angle-adjustable interventional biopsy needle suitable for deep tumors and a sampling method of the angle-adjustable interventional biopsy needle, and relates to the field of tumor interventional biopsy sampling, the angle-adjustable interventional biopsy needle comprises a guide sheathing canal, the guide sheathing canal is composed of an outer sheath layer on the outermost layer, a braid layer in the middle and a first lining layer on the inner layer, and a bending mechanism is arranged at the bottom of the guide sheathing canal; the bending mechanism comprises an active bending tube, the active bending tube is fixedly connected with the bottom of the guiding sheath tube, a protection pad is arranged at the bottom of the active bending tube, and a second lining layer is arranged on the inner side of the active bending tube. On the premise of single puncture, multi-point and multi-angle sampling of different quadrants and different depths in a tumor can be easily achieved, representative tissue samples can be obtained to the maximum extent through the design, sampling errors caused by tumor heterogeneity are effectively avoided, the occurrence rate of false negative results is remarkably reduced, and the sampling efficiency is improved. And a more reliable pathological basis is provided for clinical diagnosis.

Disclosed is a single cell analysis device for analyzing a tumor using artificial intelligence, the device including, a memory, and a processor, and the processor is configured to acquire single cell data on a tumor of a particular patient through the communication part; pre-process the single cell data on the tumor through a data pre-processing module; classify a type of the single cell based on the data pre-processed by a cell type defining module; predict a phenotype derivable from RNA expression of the tumor of the particular patient through a single-cell state predicting module materialize heterogeneity of the tumor of the particular patient through the tumor analyzing module; and report and provide an analysis result of the tumor of the particular patient through an analysis result generating module.

The invention discloses a marker for predicting the curative effect of tumor immunotherapy as well as application and a detection method of the marker, and belongs to the technical field of biomedicine. The marker is glycerophosphorylcholine (GPC) in peripheral blood, and under the condition that the GPC level of peripheral plasma is low, it is judged that ICIs immunotherapy is poor in curative effect response. Peripheral blood samples are simple and noninvasive to obtain, longitudinal curative effect monitoring can be realized, the method is independent of tissue biopsy and is not influenced by tumor heterogeneity, the cost is reduced, and application is facilitated; meanwhile, peripheral blood not only objectively reflects the systematic immune state of a host, but also is closely related to tumor immunity as an important ring in a tumor immune cycle, and in the aspect of accuracy, the efficiency of a prediction model constructed by GPC reaches 0.886 and is remarkably higher than that of PDL1.

The invention provides a model for predicting lung adenocarcinoma prognosis and immunotherapy response based on copper death related LncRNAs, and belongs to the technical field of bioinformatics. According to the prediction model provided by the invention, the accuracy and the stability of prognosis judgment of a lung adenocarcinoma patient can be remarkably improved, and the defects of a traditional staging system in the aspects of reflecting tumor heterogeneity and individual treatment reaction difference are effectively overcome; the model not only can realize reliable individual risk grading, but also can further evaluate the tumor immune microenvironment state and predict the potential reaction of a patient to immunotherapy, so that a powerful auxiliary tool is provided for clinically formulating an accurate treatment strategy, in particular to the application decision of an immune checkpoint inhibitor; and finally, the method has important practical application value for improving treatment selection and life quality of patients.

The invention discloses an interactive lattice radiotherapy planning method and system based on a functional image, and relates to the technical field of radiotherapy, and the method comprises the steps: obtaining a radiotherapy positioning CT image of a patient and a functional metabolism image after the space registration with the radiotherapy positioning CT image; based on the radiotherapy positioning CT image and a preset safety boundary parameter, determining a candidate area of lattice layout in the tumor target area; for each candidate position in the candidate region, adaptively calculating lattice parameters for the position based on the corresponding SUV value of the position in the functional metabolism image; determining a final lattice center set from the candidate positions through an iterative screening algorithm on the basis of the calculated lattice parameters of the candidate positions and preset geometric constraints and biological constraints, and generating a lattice target region set on the basis of the final lattice centers and the lattice diameters corresponding to the final lattice centers; and outputting the lattice target region set for dose calculation of a radiotherapy planning system. Individualized dosage improvement really based on tumor internal heterogeneity is achieved.

The application belongs to the technical field of biological medicine, and discloses an in-situ vaccine type mRNA-tLNP preparation for solid tumor treatment and preparation and application thereof. The preparation is designed by double targets, so that CAR-T kills more tumor cells to overcome tumor heterogeneity, changes the tumor immune microenvironment through autocrine fusion proteins (anti-PD-1 scFv and TGFbetaRII, IL-15 and Flt3L, CD40L), promotes T cell proliferation, and recruits and activates dendritic cells, cross-presents new antigens in the tumor local part, and produces in-situ vaccination effects; circular RNA encoding the above proteins is prepared, and the circular RNA is wrapped by lipid nanoparticles modified by CD3 antibodies, so as to be directly delivered to the body, and a significant solid tumor treatment effect is produced.

The present application relates to medical image analysis and brain network research technical field, specifically to glioma induced brain network remodeling visualization and quantitative analysis method based on fMRI. The method comprises obtaining patient fMRI and structural MRI data and preprocessing, excluding tumor area by lesion mask registration strategy, reducing quality effect interference; dividing tumor core area, peritumoral abnormal area and normal brain area; registering Yeo-17 network template to individual brain area to realize mapping; defining tumor core area as independent network unit, and 17 normal networks to form a new set; calculating whole brain voxel and network functional connection strength, and determining functional connection voxel according to threshold; quantifying intratumoral function proportion RIFR and peritumoral connection proportion RPTR, and generating visualization atlas. The present application accurately maps individual brain function network, overcomes tumor heterogeneity interference, provides repeatable quantitative index, and provides reliable imaging analysis tool for brain glioma function protection and clinical research.

The invention relates to a method and device for predicting the curative effect of an image heterogeneity region fusion technology based on a graph network, and belongs to the technical field of intelligent medical treatment and medical image analysis. The method comprises the following steps: firstly, dividing a tumor and a region around the tumor in a medical image into a plurality of non-connected sub-regions as graph nodes, and generating a comprehensive feature representation fusing a local image feature and a node type embedding vector for each node; then, defining edges between nodes based on a spatial distance threshold value, and constructing a graph structure including intra-tumor, peritumor and cross-boundary connection; and finally, inputting the graph structure into a graph neural network model for training to obtain a curative effect prediction model. According to the method, the complex spatial topological relation between the tumor heterogeneity areas is explicitly modeled through the graph structure, the core problems of spatial information loss and insufficient heterogeneity modeling in the prior art are solved, and the accuracy and robustness of treatment effect prediction are remarkably improved.

The invention relates to the technical field of medical image analysis and brain network research, in particular to a glioma induced brain network remodeling visualization and quantitative analysis method based on fMRI. The method comprises the steps of obtaining and preprocessing fMRI and structural MRI data of a patient, eliminating a tumor area through a focus shielding registration strategy, and reducing mass effect interference; dividing a tumor core region, a peritumoral abnormal region and a normal brain region; a Yeo-17 network template is registered to an individual brain region to realize mapping; defining a tumor core area as an independent network unit, and forming a new set with 17 normal networks; calculating the connection strength of whole brain voxels and network functions, and judging functional connection voxels according to a threshold value; and quantifying the intratumoral function ratio RIFR and the peritumoral ligation ratio RPTR, and generating a visual map. According to the method, an individual brain function network is accurately mapped, tumor heterogeneity interference is overcome, repeatable quantitative indexes are provided, and a reliable imaging analysis tool is provided for brain glioma function protection and clinical research.

Disclosed is a tiered, multipart method for tracking tumor heterogeneity across samples obtained from a subject at different timepoints. Each sample undergoes at least an intra-individual analysis to generate background-corrected methylation information. The change in the background-corrected methylation information across the different samples is informative for tracking a change in the tumor heterogeneity. The change in tumor heterogeneity is useful e.g., for providing a guided therapy.

The invention discloses a chimeric antigen receptor T cell (CAR-T) of modular artificial antigen-natural antigen logic gating (MANAGE) and application of the chimeric antigen receptor T cell in treatment of solid tumors. At present, solid tumors still lack efficient targets capable of covering tumor cells of the same focus and crossing tumor species, and most solid tumor targets are also expressed on normal tissue cells. Therefore, the application of single-target CAR-T therapy in solid tumors is limited by tumor heterogeneity and non-tumor targeted toxicity (OTOT). According to the invention, proteins and antibodies of targeted tumor cells modified by the artificial antigen FITC are constructed, and CAR-T double-gated by the FITC and the natural tumor antigen is researched and developed. When the two compounds are combined for use, the toxicity of OTOT can be reduced, the range of applicable cells and tumor species can be expanded, the injection time of FITC can be regulated and controlled, the effect of CAR-T can be'switched ', cytokine syndromes can be reduced, and a new method is provided for solid tumor treatment.

The invention discloses a breast cancer liver metastasis evaluation method and system based on iconomics feature analysis, and the method comprises the steps: extracting an original iconomics feature set through collecting the multi-modal image data of a liver region, obtaining a calibration feature subset through screening, constructing a feature time sequence evolution track, generating a high-dimensional fusion feature matrix through feature fusion, and carrying out the recognition of the high-dimensional fusion feature matrix. A specialized platform is combined to complete tumor heterogeneity quantitative grading, and finally comprehensive evaluation of the liver metastasis state and the progress trend is achieved; the system comprises a plurality of function units which are sequentially connected in series and interact in two directions, and full-process connection from feature collection, screening, modeling and fusion to quantitative evaluation and result output is achieved. According to the method, multi-dimensional static and dynamic characteristic information is comprehensively absorbed, invasive trauma is avoided by utilizing a non-invasive technology path, scientific support is provided for clinical treatment scheme formulation, curative effect monitoring and prognosis judgment, and the accuracy, systematicness and clinical practicability of breast cancer liver metastasis evaluation are remarkably enhanced.

Disclosed is a tiered, multipart method for tracking tumor heterogeneity across samples obtained from a subject at different timepoints. Each sample undergoes at least an intra-individual analysis to generate background-corrected methylation information. The change in the background-corrected methylation information across the different samples is informative for tracking a change in the tumor heterogeneity. The change in tumor heterogeneity is useful e.g., for providing a guided therapy.

The present invention discloses a constrained multi-objective optimization method for detecting drug targets in individual cancer patients, comprising: constructing a personalized gene interaction network (PGIN) from the genomic data of the individual patient; forming a structural network control model based on multi-objective optimization; and using a constrained multi-objective evolutionary algorithm to search for a personalized drug target set, i.e., a driver gene set. The present invention can effectively identify drug targets in individual cancer patients, explore cancer heterogeneity, and provide a new perspective for understanding tumor heterogeneity in precision medicine.