547 results about "Cancers diagnosis" patented technology

The invention discloses a multi-omics pathological analysis system and method for colorectal cancer liver metastasis risk prediction, and relates to the technical field of biomedicine and cancer diagnosis, the system comprises a data acquisition module, a data preprocessing module, a cell analysis module, a score construction module, a model construction and training module and a prediction and evaluation module; according to the method, multiple omics data including the single cell data set, the space transcriptome data, the batch RNA-seq data, the clinical data and the full slice image WSIs are integrated, comprehensive and accurate prediction of the colorectal cancer liver metastasis risk is achieved, cross-dimensional data fusion and analysis improve the prediction accuracy and reliability, and the risk of the colorectal cancer liver metastasis is reduced. The invention also provides a powerful tool for deeply exploring the molecular mechanism of tumorigenesis, development and metastasis, and particularly in a cell analysis module, cell subtypes and molecular characteristics closely related to liver metastasis can be disclosed by identifying and analyzing malignant cell subgroups LMTMCs triggering liver metastasis, so that the liver metastasis can be discovered. And a scientific basis is provided for formulating a personalized treatment scheme.

Presented herein are systems and methods that provide for automated analysis of medical images to determine a predicted disease status (e.g., prostate cancer status) and / or a value corresponding to predicted risk of the disease status for a subject. The approaches described herein leverage artificial intelligence (AI) to analyze intensities of voxels in a functional image, such as a PET image, and determine a risk and / or likelihood that a subject's disease, e.g., cancer, is aggressive. The approaches described herein can provide predictions of whether a subject that presents a localized disease has and / or will develop aggressive disease, such as metastatic cancer. These predictions are generated in a fully automated fashion and can be used alone, or in combination with other cancer diagnostic metrics (e.g., to corroborate predictions and assessments or highlight potential errors). As such, they represent a valuable tool in support of improved cancer diagnosis and treatment.

The invention discloses a CTC cell detection method based on a micro-fluidic chip, and belongs to the technical field of cell treatment. In order to solve the problems of high false positive and false negative rates, time consumption and low efficiency caused by difficulty in accurately distinguishing CTC from other cell types, the invention can efficiently and specifically capture CTC cells, reduce the possibility of false positive and false negative and improve the detection accuracy by constructing a specific capture chamber and filling specific antibodies and immunomagnetic beads. The multi-channel design allows a plurality of samples to be processed at the same time, the detection efficiency is improved, the micro-fluidic chip technology integrates a plurality of steps on one platform, automation and standardization of operation are achieved, personal errors are reduced, the stability and repeatability of detection are improved, the uniformity and stability of the detection process are ensured, and the detection efficiency is improved. The fluorescence imaging technology clearly displays CTC and obtains key parameters such as quantity, form and fluorescence intensity of CTC, thereby providing powerful support for cancer diagnosis and research.

The invention discloses a deep learning-based lung cancer early screening system, and particularly relates to the technical field of medical assistance, which comprises an intelligent data ecological module, an intelligent analysis and diagnosis module, an intelligent decision support module and a user interaction management module. According to the invention, cross-mechanism data sharing and cooperative training are realized by using data semantic understanding and federated sharing technologies, transparency and traceability of data are ensured through a block chain technology, and in an intelligent analysis and diagnosis module, a deep learning model comprehensively analyzes a multi-modal image and identifies molecular features of cancer cells, so that the accuracy of cancer diagnosis is improved. Potential early-stage lung cancer signs are found through an unsupervised learning algorithm, individual risks are dynamically evaluated, focus development is tracked, and an intelligent decision support module combines personalized treatment suggestions, optimizes clinical pathways and multidisciplinary collaboration, provides accurate decision suggestions and assists doctors in making scientific and effective treatment schemes.

The invention relates to the technical field of medical images, and discloses a CT automatic lung cancer diagnosis and classification system and method based on deep learning. According to the system and the method, a lung CT scanning image is acquired from medical imaging equipment through a data acquisition module, and image denoising, translation and cutting operation is performed on the acquired image through a data preprocessing module; the feature extraction module extracts lung contour features, lung boundary features, contrast features, gray features and focus structure features based on the processed image through a convolutional neural network, and the classification model module inputs the extracted features into a full-connection neural network for classification and judges whether the image is a normal lung or has lung cancer. The cancer diagnosis module is used for further classifying the specific types and stages of cancers and generating classified diagnosis results, the output and feedback module is used for displaying the classified diagnosis results in a report form and providing the classified diagnosis results to doctors for further analysis, and the model is continuously iterated and optimized by collecting feedback of the doctors and new case data.

The invention provides a cancer patient gene methylation feature screening method based on DQN reinforcement learning, which converts a gene methylation feature screening problem into a reinforcement learning problem by constructing a deep Q network model, and dynamically selects the most representative methylation feature by taking maximization of prediction accuracy as a target. The method comprises the following steps: firstly, preprocessing gene methylation data of a cancer patient, then continuously optimizing a feature selection strategy in a multi-round training process through a DQN model, and finally screening out key methylation features closely related to cancer diagnosis and prognosis. The method can effectively reduce the feature dimension, improves the generalization ability of the model, and has wide application prospects in early diagnosis, personalized treatment and prognosis evaluation of cancers.

The present invention relates to a molecular imaging probe for tumor diagnosis, and provides a sugar nucleic acid molecular imaging probe, the probe comprises a DNA aptamer sequence and more than one glycosyl nucleoside module, and is labeled by radionuclide, the DNA aptamer sequence can specifically target tumor cells after functional folding; the glycosyl nucleoside module is a glycosyl modified nucleoside derivative, is accurately modified at the 5'end or / and the 3 'end of the aptamer through DNA solid-phase synthesis, has the imaging characteristics of biological safety, high sensitivity and high resolution, and has an excellent diagnosis effect on part of cancers which are difficult to diagnose by a traditional method. The invention also provides a preparation method and a use method of the sugar nucleic acid molecular imaging probe, so that sugar modification of the aptamer is convenient, rapid, accurate, controllable and modularized, and the sugar nucleic acid molecular imaging probe has huge clinical application potential and transformation value in cancer diagnosis.

A dyeing-free imaging method for a plasmon tissue section based on a generative adversarial network relates to the technical field of biomedical detection, and comprises the following steps: 1) recording the tissue section placed on a plasmon glass slide by using a high-resolution bright field microscope and passing through a Hamp; e, dividing the dyed tissue slices into a training set and a verification set in proportion; 2) amplifying the number of paired images in the training set to a plurality of times through data enhancement; 3) constructing a GAN model, and training the GAN model by using the paired image after data enhancement as a data set; and 4) based on the trained GAN model, performing virtual staining of the staining-free imaging image of the plasmon tissue section. According to the invention, the generated virtual dyeing image is visually and statistically compared with the real Hamp; the E-dyed image has high similarity, and provides pathological interpretable images and more effective discrimination information for pathologists. The time is remarkably shortened, the cancer diagnosis efficiency is improved, and a rapid and effective tissue slice analysis means is provided for clinic.

In alternative embodiments, provided are chimeric, synthetic or recombinant antibodies (Ab) against the human homologous box NKX-2. 1 (also known as NK2 homologous box 1, or thyroid transcription factor 1 (TTF-1)) protein, including articles of manufacture and kits comprising them, and methods of making and using them, including, for example, their use in the detection or diagnosis and treatment of cancer. In alternative embodiments, an anti-NKX-2. 1 (TTF-1) antibody provided herein is used with an agent for determining whether NKX-2. 1 (TTF-1) expression or activity is present, reduced or deleted; or the sensitivity of the specific binding of the Ab or the antigen (Ag) binding fragment or protein (ABP) or the monomer or dimer ABP provided by the invention with the squamous cell carcinoma cells is lower than the sensitivity of the specific binding with the adenocarcinoma cells (or binding with lower affinity), so that false positives of the squamous cell carcinoma are less, and the positive recognition of the lung adenocarcinoma is better.

The invention discloses an aggregation-induced emission fluorescent compound with near infrared (NIR) luminescence. The fluorescent compound is wrapped by a polymer matrix to form a nano aggregate, which proves that the fluorescent compound shows excellent photothermal conversion efficiency while maintaining high fluorescence quantum yield. Therefore, compared with the mode of relying on high permeability and retention (EPR) effect, the nanometer aggregate accumulation guided by the photo-induced thermoacoustic effect (PTA) can further improve the photo-thermal treatment effect. In-vitro and in-vivo verification further confirms that the nano-aggregate shows excellent fluorescence-guided optical diagnosis and treatment capability in the aspect of tumor elimination.

The invention belongs to the field of molecular imaging probes for cancer diagnosis, and discloses a targeted nucleic acid aptamer aiming at tumor-associated calcium channel sensing protein 2, a molecular imaging probe for diagnosing lung cancer and a preparation method of the molecular imaging probe. The nucleic acid aptamer contains 46 nucleotides, can specifically target the surface Trop-2 of a lung cancer tumor cell, has a specific targeting recognition effect with Trop-2 overexpressed lung cancer cells such as non-small cell lung cancer cells H1650 and small cell lung cancer cells SBC-2, and can be used for molecular imaging of Trop-2 positive lung cancer patients. The molecular imaging probe can be used for noninvasive visual molecular imaging of lung cancer, and has the advantages of high specificity, high sensitivity, good imaging effect and the like. The preparation method of the molecular imaging probe is simple in reaction condition, low in cost and easy for clinical transformation.

The invention relates to a multi-modal medical image analysis method and device for cancer diagnosis. The method comprises the following steps: converting a two-dimensional projection sequence of an intraoperative gamma camera into an initial three-dimensional radioactive distribution voxel field through Radon inverse transformation; performing spatial registration by combining a tumor segmentation mask in the preoperative image to obtain a registered image; positioning a hole defect area based on the registration image and the initial three-dimensional radioactive distribution voxel field; generating a multi-modal constraint matrix and a coherence parameter optimization value through anatomical weighted continuous coherence modeling, and then correcting an initial distribution voxel field; fusing the corrected voxel field and the registered image to generate an initial boundary map and confidence; and judging whether to adjust the coherence parameter and regenerate the boundary diagram according to the confidence coefficient. By adopting the method, the problem of projection data missing caused by limitation of the rotation angle of the gamma camera in the operation can be effectively solved, topological structure distortion of a traditional reconstruction algorithm is avoided, and the accuracy and reliability of the cancer infiltration boundary diagram are improved.

Methods, devices and systems are described that enable identification of cancerous or pre-cancerous colon tissue. The described methods and systems rely on non-imaging. randomly sampled second harmonic generation (SHG) intensity measurements that are sufficient for differentiating between tumor and normal tissue. The disclosed methods and system further provide for angle-resolved SHG measurements associated with forward and backward scattered light that eliminate the need to obtain paired normal and cancer-suspicious measurements in each patient using the described techniques.

Provided are: a peptide targeting Fusobacteria, comprising a peptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 77 to 83; a composition for diagnosing cancer, comprising same; and a drug delivery composition. The peptide targeting Fusobacteria accurately targets Fusobacteria, which are harmful bacteria in various diseases, including cancer, and thus can be effectively used in cancer diagnosis and drug delivery.

Provided is a method of providing a cancer assessment for a subject, comprising (i) measuring a value of one or more cancer traits of at least one tumor associated immune cell (TAIC) or TAIC-derived biomarker in a fluid sample obtained from the subject to obtain a test profile; (ii) comparing the test profile to one or more reference profiles; (iii) wherein similarity or a differential in the test profile to the one or more reference profiles is indicative of one or more cancer characteristics; and (iv) based on the one or more cancer characteristics, providing the cancer assessment of the subject.

The present invention relates to an isolated cellular targeted delivery system comprising a CD45+ leukocyte cell comprising within said cell a complex of one or more iron binding proteins and / or a label as well as methods for producing such isolated cellular targeted delivery system and uses of such system for therapy diagnosis and in particular for diagnosis of cancer, particularly metastatic cancer, in particular for therapy of cancer.

The invention provides a self-supervised cancer detection system based on subspace clustering. The system comprises a model training module, an image processing module and a result output module. According to the invention, a classification model for cancer discrimination is established and is applied to intelligent diagnosis of clinical pathology; when a to-be-detected pathological image is received, preprocessing operation is firstly carried out, and the preprocessing operation aims at extracting key features in the pathological image so as to better carry out cancer discrimination; inputting the preprocessed pathological image into a trained cancer discrimination classification model for discrimination to obtain a prediction result of the to-be-detected pathological image; the prediction result is displayed on a diagnosis and treatment interface of a pathologist, meanwhile, a diagnosis report is generated, and the pathologist makes more accurate cancer diagnosis and treatment decisions according to the prediction result and the diagnosis report, so that the accuracy and efficiency of pathological diagnosis are improved; therefore, a more accurate cancer judgment result is provided, and doctors are assisted in diagnosis and treatment decision making.

A method for cancer diagnosis and treatment. In particular, in identifying associations between biomedical markers which is indicative of having a diagnostic, prognostic or predictive value for a medical condition, in particular nasopharyngeal cancer (NPC) metastasis after radiation therapy. The biomedical marker or group of biomedical markers associated with a high likelihood of responsiveness of a subject to a cancer therapy, preferably radiation therapy, wherein said biomedical marker or group of biomedical markers comprises at least 1, 2, 3, 4, or all markers selected from CXCR6, ASPHD2, DDX39B and AGAP9. Furthermore, an assay for detecting, diagnosing, graduating, monitoring or prognosticating a medical condition, or for detecting, diagnosing, monitoring or prognosticating the responsiveness of a subject to a therapy against said medical condition, in particular nasopharyngeal cancer, is provided, as well as a corresponding method for classifying a subject and a medical decision support system.

The present invention relates to compositions, kits, and methods for molecular profiling and cancer diagnostics, including but not limited to genomic DNA markers associated with cancer. In particular, the present invention provides molecular profiles associated with thyroid cancer, methods of determining molecular profiles, and methods of analyzing results to provide a diagnosis.

The invention provides a benzene azepine derivative, a radioactive probe and application thereof, and belongs to the technical field of biological medicine. According to the present invention, the benzazepine derivative is modified, and the hydrophilic or lipophilic side chain is introduced, such that the prepared benzazepine derivative can be used as the V2R targeting precursor for application, the specific V2R targeting can be achieved, and the new molecular tool is provided for the subsequent V2R positive tumor diagnosis and treatment. The radioactive probe combined with the 68Ga marker shows a good tumor imaging effect, can realize PET imaging of V2R positive tumors, is helpful for improving the accuracy of tumor diagnosis, can also be used for evaluating the treatment effect, has a relatively high target / non-target ratio, can better display the distribution of whole body V2R receptors, and has a good application prospect. The invention has various applications in cancer diagnosis and treatment.

Segmentation or other classification of digital pathology images with a deep learning model allows for sophisticated spatial features for cancer diagnosis to be extracted in an automated, fast, and accurate manner. A tiered analysis of tissue structure based in part on deep learning methods is provided. First, tissues depicted in a digital pathology image are segmented into cellular compartments (e.g., epithelial and stromal compartments). Second, the heterogeneity in the different cellular compartments are examined based on a clustering algorithm. Tissue can then be characterized in terms of inertia (or other spatial measures or features), which can be used to recognize disease. In some instances, multidimensional inertia (i.e., inertia computed in different cellular compartments or clustered components) can be used as an indicator of disease and its outcome.

The invention discloses a gastric cancer diagnosis model construction method, system and device and a medium. The method comprises the following steps: acquiring a volunteer clinical data set; extracting a single index and a composite index of each volunteer from the volunteer clinical data set; calculating the importance of each index on gastric cancer diagnosis, and determining final feature combination data based on the importance corresponding to each index; and constructing a plurality of gastric cancer diagnosis models based on a machine learning model, training each gastric cancer diagnosis model based on the final feature combination data, performing performance evaluation on each trained gastric cancer diagnosis model by using verification set data, and determining an optimal gastric cancer diagnosis model based on a Marius correlation coefficient and an F1 score evaluation result. The model is evaluated again by using test set data; and performing gastric cancer diagnosis of the to-be-diagnosed person based on the optimal gastric cancer diagnosis model. According to the technical scheme, clinical data and routine examination indexes of a clinical laboratory are integrated, and the efficiency of gastric cancer diagnosis is improved.

According to an embodiment of the present disclosure, there is provided a marker composition for diagnosing cancer or predicting prognosis, comprising exosomes overexpressing a Tubulin alpha-1C chain (TUBA1C) protein.

The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel for diagnosis of cancer.

The invention discloses a sugar chain marker combination for predicting the recurrence risk of a gastric cancer patient after treatment and application of the sugar chain marker combination. The invention discovers that the levels of six specific N-sugar chains (NGA2F, NG1A2F, NA3, NA3Fb, NA4 and NA4Fb) in blood are obviously related to relapse during definite diagnosis of gastric cancer for the first time. On the basis, a gastric cancer recurrence risk prediction model GC-GPSS is constructed through Cox regression. According to the model, individual risk scores are calculated by using a quantification formula containing a fixed regression coefficient (beta), and patients are divided into a high-risk group and a low-risk group according to a preset threshold. A verification result shows that the model can effectively distinguish patient groups with different recurrence risks. The marker combination, the prediction model and the system provided by the invention provide a brand new tool for realizing earlier and more objective recurrence risk assessment in the early stage of gastric cancer diagnosis and treatment, and have important clinical application value.

A precursor compound and a radioligand for cancer diagnosis and treatment comprises a high-affinity ligand PL for a cancer- associated protein conjugated to a covalent warhead CW for strong or covalent binding to an amino acid sidechain of the cancer-associated protein and a chelator Ch for complexation of a radioisotope or a leaving group LR for substitution with a radioisotope, wherein the covalent warhead CW is configured for quasi-covalent ionic binding using a sulfonic acid group or for covalent binding using a sulfur fluoride exchange (SuFEx) group, a benzotriazole or N-methyl-N- arylmethanesulfonamide leaving group or a malolactone group for strain-release alkylation.

The circulating snoRNA biomarker comprises the following six specific snoRNAs: SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94, the differential expression of the circulating snoRNA biomarker in plasma is remarkable, and the circulating snoRNA biomarker is suitable for early screening, diagnosis and prognosis evaluation of gastric cancer. The circulating snoRNA is applied to a detection reagent for gastric cancer diagnosis, and has the advantages of non-invasiveness and convenience in detection.

The invention belongs to the technical field of medical image processing, and discloses an OCT-SERS combined multi-mode gastric cancer tissue diagnosis system and method. A set of OCT-SERS combined system light path which shares a sample arm and is high in registration precision realizes high-precision registration of an OCT imaging range and an SERS detection position; the surface of a biological tissue is covered with a hydrogel film doped with silver nanoparticles, then SERS signal detection is carried out, and non-destructive SERS enhancement of the tissue is achieved; multi-modal gastric cancer diagnosis is carried out by adopting an artificial intelligence algorithm through collection of normal and cancerous gastric wall tissue OCT images and SERS spectral data and establishment of a database. According to the invention, by establishing the normal and cancer tissue OCT image and SERS spectrum database and carrying out artificial intelligence learning, real-time accurate diagnosis of pathological tissues can be realized.

Methods for cancer source of origin (CSO) prediction are disclosed to predict CSO characteristics. The CSO prediction may include the affected organ or organ group and tumor biology. The method for training parallel CSO classifiers includes obtaining training samples derived from subjects with known cancer diagnosis, each training sample comprising methylation sequence reads corresponding to nucleic acid fragments in a biological sample collected from each subject and each known cancer signal origin including a known affected organ or organ group a plurality of organs or organ groups and a known tumor biology from a plurality of tumor biology classes. The method includes generating, for each training sample, a feature vector based on the methylation sequence reads. The method includes generating a first training data set comprising the feature vectors for the training samples and the known organs or organ groups, and training an organ or organ group classifier with the first training data set to predict organ or organ group from the plurality of organs or organ groups based on an input feature vector. The method includes generating a second training data set comprising the feature vectors for the training samples and the known tumor biology classes, and training a tumor biology classifier with the second training data set to predict tumor biology from the plurality of tumor biology classes based on input feature vector.

The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein are methods of designing, making, and using the cancer assay panel for the diagnosis of cancer.