293 results about "Treatment response" patented technology

The invention discloses an intestinal cancer immune response graph neural network prediction method, a medium and equipment, and the method comprises the steps: collecting pathological image information, immunodetection information and basic clinical information, extracting a tissue space distribution characteristic spectrum through a deep convolutional network, and constructing a graph neural network model in combination with an immunomarker expression characteristic matrix; spatial interaction characteristics of a tumor microenvironment are modeled by adopting a graph attention mechanism, finally a treatment response probability, an optimal treatment opportunity and an adverse reaction risk are predicted through a multi-task learning framework, and a clinical decision report containing a prediction response curve, a risk early warning threshold and a treatment time window suggestion is output. According to the method, through multi-modal data fusion and spatial interaction modeling, accurate prediction of intestinal cancer immunotherapy response is realized, and a more comprehensive reference basis is provided for clinical decision making.

The invention provides a tumor treatment effect prediction method based on multi-modal data, and relates to the field of medical artificial intelligence. The method comprises the following steps: collecting magnetic resonance image data, histopathological section images and clinical baseline data of a tumor patient and a treatment response evaluation record after neoadjuvant chemotherapy; the collected image information is preprocessed; feature extraction is carried out based on the preprocessed image information; screening a feature subset with the highest prediction value from the extracted features; constructing a radiopathomics fusion model by using the feature subset with the highest prediction value; evaluating the radiopathomics fusion model; and predicting the tumor treatment effect by using the evaluated radiopathomics fusion model. The problems that an existing tumor neoadjuvant chemotherapy (NACT) curative effect prediction method is low in accuracy, poor in generalization ability, lack of multi-modal data fusion and insufficient in model interpretability are solved.

The invention discloses a sugar chain marker combination for predicting prognosis of liver cancer transarterial chemoembolization and application of the sugar chain marker combination. The invention discovers and verifies that the combination composed of NA3Fb, NA4Fb and NA4F2b can be used as a novel marker for evaluating TACE treatment reaction and prognosis by analyzing a serum sugar chain map of a liver cancer patient for the first time. Based on the marker combination, a TACE-GT prognosis scoring model and system are established, and the progression risk of a patient within 12 months after a TACE operation can be effectively distinguished by noninvasively detecting the expression levels of three carbohydrate chains in serum of the patient and calculating a specific score. The detection method provided by the invention is based on a blood sample, and has the advantages of noninvasiveness and convenience, and a risk assessment system can provide individualized curative effect prediction and decision support for clinicians before and after TACE treatment, and has important values for realizing precise treatment of liver cancer and improving prognosis of patients.

The invention relates to the technical field of medical decision and artificial intelligence, and discloses an artificial intelligence-driven new adjuvant therapy scheme optimization method and system for antral sinus cancer, and the method comprises the steps: S1, obtaining clinical data of a antral sinus cancer patient, and carrying out the preprocessing; s2, screening core features by adopting an iterative ReliefF algorithm, and constructing composite features; s3, constructing a prediction model based on a Bayesian optimization K-nearest neighbor algorithm, taking the composite features as input of the prediction model to predict treatment response, and outputting individual tumor recession grade probability distribution of the patient; s4, taking the tumor recession grade probability distribution as a core component of a state space, inputting the tumor recession grade probability distribution into a double-layer reinforcement learning framework based on a penalty mechanism to optimize a treatment decision, and outputting a personalized treatment strategy parameter set; and S5, performing real-time scanning and verification on the drug dosage parameters in the strategy parameter set, and outputting a final approved personalized treatment scheme. The precision and intelligent level of gastric antrum cancer treatment is improved.

The invention relates to an ultra-long-range medical record diagnosis and treatment question answering system and method based on a dynamic time sequence semantic map, belongs to the technical field of medical artificial intelligence and information processing, and solves the problems that in the prior art, the inference ability is insufficient when ultra-long-range medical records are processed. The system comprises a pre-processing module for pre-processing overlong-range medical record information of a patient to generate standardized data; the medical entity recognition module is used for recognizing event entities and descriptive entities in the standardized data; associating a time attribute for the event type entity to generate a time sequence entity tuple; outputting a medical entity recognition result; the dynamic semantic map construction module is used for constructing a specific dynamic semantic map of the corresponding patient; the text-map dual-mode fusion query engine is used for analyzing the medical problem proposed by the user and carrying out text-map dual-mode fusion query based on the specific dynamic semantic map; and the diagnosis and treatment reply module is used for generating a diagnosis and treatment reply result according to the text-map dual-mode fusion query result.

A system for personalized treatment, the system including an input device, at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to retrieve physiological data from at least the input device associated with a user, wherein the physiological data further includes a previous ailment, receive an ailment inquiry from the user generate a treatment response as a function of the physiological data and the ailment inquiry including inputting the ailment inquiry into a large language model and generating the treatment response as a function ailment, inquiry, the physiological data and the large language model and transmit the treatment response to at least the input device.

The invention discloses a CAR-T treatment response intelligent prediction system based on medical images, and relates to the field of medical image analysis and tumor immunotherapy, and the system comprises a data integration module, a multi-modal feature extraction module, a prediction model construction module, a model optimization module, a clinical analysis module and an adverse reaction risk early warning module. The system integrates PET-CT, MRI, ultrasound and other multi-mode medical image data, and extracts the form, texture and metabolic characteristics of the tumor; constructing a treatment response prediction model by adopting a three-dimensional convolutional neural network; innovatively applying a differential geometry theory, constructing a medical image manifold structure and generating a tumor adversarial sample maintaining topological characteristics, and executing an adaptive optimization strategy by using a Riemannian metric space; accurate prediction of CAR-T treatment response of a lymphoma patient is realized, and adverse reaction risk early warning is carried out through brain and visceral organ image analysis.

The invention provides an intelligent auxiliary diagnosis and treatment service system and method based on a large language model, and the system comprises a large language model engine and a medical knowledge dynamic updating device, and the medical knowledge dynamic updating device carries out the automatic and quantitative arbitration of the conflict between the new knowledge extracted from the literature and the existing knowledge of a knowledge graph, and carries out the automatic and quantitative arbitration of the conflict. A conflict detection unit identifies conflict knowledge pairs with mutually exclusive logic, an evidence intensity quantification unit calculates and synthesizes document timeliness, source authority and research type evidence grade multi-dimensional evidence intensity scores for two conflict parties, and a grading arbitration decision unit compares a score difference value with a dynamic threshold value. And making a grading decision of automatically adopting a high-score party, generating an artificial review task or marking dispute coexistence, and finally executing updating by a knowledge graph updating unit. The invention further provides a method corresponding to the system, double-chain collaboration of online diagnosis and treatment response and offline knowledge evolution is achieved, and the problems of automation and intelligentization of conflict processing in medical knowledge updating are solved.

The invention relates to the field of medical image processing and artificial intelligence, in particular to a pulmonary nodule treatment effect AI evaluation system which comprises an image acquisition module, an image registration module, a feature representation module, a multi-scale analysis module, a trajectory analysis module, a response prediction module and a decision support module. According to the system, accurate alignment of CT images before and after treatment is realized through a 4D registration technology, manifold representation of a pulmonary nodule state is constructed based on a differential geometry theory, and nodule features are mapped into points on a high-dimensional manifold; extracting features of different time and space scales by adopting multi-scale space-time analysis, and constructing a manifold trajectory representing a treatment response process; geodesic prediction is realized by using a Riemann geometric framework, and long-term curative effect is predicted from early treatment response; the system not only evaluates the current treatment effect, but also can provide personalized treatment suggestions and optimal follow-up visit plans.

The present invention provides a computer-implemented method for predicting the treatment response of a subject having a lung cancer to an immune checkpoint inhibitor (CPI) therapy, the method comprising: providing a mutation profile of the subject, said profile comprising the presence or absence of cancer-specific mutations at one or more locations in at least five genes selected from the group consisting of: NF1, STK11, TSC2, BRCA2, BRAF, STAG2, U2AF1, BRIP1, PDGFRA, CTNNA1, PDK1, FGF10, and FLT1; analysing the mutation profile to classify the profile as matching the mutation profile of a response signature or a resistance signature, wherein the subject is predicted to be likely to respond to the CPI therapy if the mutation profile for the subject is classified as matching the mutation profile of the response signature and is predicted to be likely not to respond to the CPI therapy if the mutation profile for the subject is classified as matching the mutation profile of the resistance signature. Also provided are related methods and systems for predicting the treatment response of a subject having a lung cancer to an immune checkpoint inhibitor (CPI) therapy.

The invention discloses a longitudinal CT image-assisted rectal cancer neoadjuvant radiotherapy and chemotherapy treatment reaction evaluation device, which comprises a focus area segmentation module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module and a rectum focus area detection module, obtaining a standardized three-dimensional tumor area; a feature fusion module extracts deep learning features of a focus area after segmentation at two time points before neoadjuvant radiotherapy and chemotherapy and before an operation from the three-dimensional tumor area, and performs weighted fusion on the deep learning features at the two time points through a dynamic attention weight layer to obtain longitudinal comprehensive features representing tumor treatment response changes; and the prediction module fuses the longitudinal comprehensive characteristics and clinical baseline data to construct a multi-modal prediction model, and outputs a treatment response prediction result of the rectal cancer patient on neoadjuvant chemoradiotherapy, so that the prediction accuracy of pathological complete remission of the rectal cancer patient can be improved, and the prognosis of the patient and the utilization efficiency of medical resources can be improved.

The invention relates to the technical field of medical image processing, and particularly discloses a lesion image generation method for ultrasonic diagnosis, which comprises the following steps of: 1, acquiring initial state data of a lesion; 2, obtaining intervention factor data; 3, performing feature coding on the initial state data and the intervention factor data to obtain an initial feature vector and an intervention feature vector; and 4, inputting the initial feature vector and the intervention feature vector into a pre-trained time sequence generation model, and generating a continuous image sequence of the focus changing along with time. By constructing the time sequence generation model fusing the multi-modal feature coding and the space-time attention mechanism, the continuous image sequence of the focus changing along with time can be accurately generated, the disease progress or treatment response process can be accurately simulated, and a powerful reference basis is provided for clinical prognosis evaluation.

Disclosed are approaches to non-invasively characterize a tumor or other lesion in a region of interest (ROI) based on various analyses of magnetic resonance imaging (MRI) data. The MRI data may correspond to ultrafast dynamic contrast enhanced MRI (DCE-MRI) and high spatial resolution DCE-MRI scans, and diffusion-weighted MRI (DW-MRI) scans of the ROI. Vasculature metrics may be determined, and tumor-associated blood flow velocity and / or tumor interstitial pressure may be obtained using the vasculature metrics as inputs to a computational fluid dynamics model. A combination of morphological vascular metrics and functional vascular metrics may be used to characterize the tumor. Malignancy, aggressiveness, treatment response, and other features of tumors or other lesions, in the breast or other regions of a patient, may be characterized through disclosed analyses of MRI data.

Disclosed herein are methods of using OX40 as a biomarker for predicting clinical sensitivity and therapeutic response of subjects having cancer to treatment with immunotherapeutic agents, and methods of using OX40 as a biomarker for selecting patient population for cancer treatment with immunotherapeutic agents. Disclosed herein are also methods of treating OX40-expressing cancers using immunotherapeutic agents. Further provided herein are kit for predicting the responsiveness of a subject having cancer to treatment with immunotherapeutic agents.

The present disclosure relates to oropharyngeal cancer and methods, kits and systems for providing a prognosis and / or an indication of treatment response in oropharyngeal cancer.

The invention relates to the technical field of medical data processing and prediction models, and discloses a cancer treatment response prediction system and a control method thereof, and the system comprises a data input and preprocessing module which is used for receiving static biological group data of a patient before treatment and dynamic molecule monitoring data in a treatment process; the personalized kinetic model initialization module is based on the static biological group data; the parameter self-adaptive correction and state estimation module is based on dynamic molecule monitoring data; the model structure self-adaptive evolution module is used for monitoring the statistical property of the prediction residual error of the state estimation algorithm; and the trajectory prediction and decision support output module is based on the personalized dynamic model after dynamic correction and structure reconstruction. By constructing a double-layer self-adaptive closed loop of parameter correction and model structure evolution, continuous high-precision tracking of a tumor evolution trajectory is realized, and the problem of prediction failure caused by the fact that a fixed model cannot correct a self structure when a new drug resistance mechanism occurs in a tumor is solved.

Biomarkers can be assessed for a variety of uses, including screening, detection, diagnosis, prognosis, risk prediction, disease progression, recurrence, selection of treatment, therapy response, to evaluate a subject's health status, whether the subject presents with no evidence of disease, or a benign or malignant condition such as cancer. Compositions (antibodies, polypeptide and polynucleotide markers) and methods are provided herein, which find application in the early detection of cancer, in the early detection of disease relapse and in monitoring therapy response.

The present invention relates to a method for predicting a solid tumor immune cell therapy treatment response. The method comprises acquiring a tumor tissue section from a patient, and detecting the presence, number, proportion or density of one or more cell types in the tumor tissue section. A solid tumor immune cell therapy response is predicted by determining cell markers, such as α-SMA, CD3, CD4 or CD8, associated with the cell types. In addition, the present invention also relates to a kit used for predicting a solid tumor treatment response, including a reagent which detects expression levels of one or more cell types and cell markers in tumor tissue sections, and software and / or a user manual used for analyzing detection results. The method and kit provide an effective tool for clinical treatment, so as to optimize dosages and clinical regimens of immune cell therapy, thereby improving treatment response rates.

Provided herein are methods for determining a subtype of a small cell lung cancer in a patient based on the express status of ASCL1, NEUROD1, and POU2F3, which are expressed in a mutually exclusive fashion. The subtype of the cancer can be used to determine the sensitivity of the cancer to certain anti-cancer therapies. As such, also provided are methods of treating patients having small cell lung cancer based on the subtyping results.

A rapid, ultrasensitive and inexpensive nanoplasmon-enhanced scattering (nPES) assay that directly quantifies tumor-derived EVs from as little as 1 μL of plasma is described herein. This assay uses binding of gold nanospheres and nanorods with EV- and tumor-derived EV-specificities to produce a local plasmon effect that enhances tumor-derived EV detection sensitivity and specificity. This nPES approach is also a non-invasive method for assessing pancreatic cancer stage and treatment response that can be easily refined for clinical use, and is readily adapted for diagnosis and monitoring of other conditions with disease-specific EV proteins.

Methods of treating a condition which is treatable with SSR-125543 or a pharmaceutically acceptable salt thereof in a subject in need thereof are provided. The methods of treatment include predicting a treatment response of a subject to a treatment with SSR-125543 and / or detecting a polymorphism genotype associated with a treatment response of a subject to treatment with SSR-125543. Sets of at least one polymorphism genotype useful in the predicting and / or detecting steps are also disclosed.

This invention belongs to the field of biomedical technology, specifically relating to the application of DNA damage response gene-related SNPs in the treatment and prognosis of acute myeloid leukemia (AML). This invention is the first to discover that single nucleotide polymorphisms (SNPs) in the DNA damage response pathway (focusing on three major functional modules: damage sensing, signal transduction, and DNA repair) are closely related to the clinical characteristics, treatment response, and survival prognosis of AML. This lays the foundation for constructing a multi-gene risk model based on the DNA damage response pathway and provides theoretical support for integrating germline SNP maps into precision medicine strategies for AML.

The present invention relates to a biomarker for predicting immunotherapeutic responsiveness based on spatial transcriptome analysis and uses thereof and, in particular, to: a marker composition for predicting the responsiveness of cancer patients to immunotherapy; a composition for predicting the responsiveness of cancer patients to immunotherapy; a kit for predicting the responsiveness of cancer patients to immunotherapy, comprising the composition; a method for providing information for predicting the responsiveness of cancer patients to immunotherapy; and a method for providing information for predicting the survival prognosis of cancer patients. The biomarker for predicting immunotherapeutic responsiveness, according to the present invention, was discovered by applying spatial transcriptome technology and analyzing cell group-specific gene expression values according to location information of cells in tissue sections, and can more precisely and accurately predict the responsiveness of cancer patients to immunotherapy and the survival prognosis of patients, thus enabling suitable treatments for patient groups, which may result in improved therapeutic effects and a reduction in pain and costs for patients.

The invention discloses a proton adaptive radiotherapy triggering decision method and system based on multi-modal data quantitative evaluation. The method comprises the following steps: acquiring image data, dose data and clinical data, respectively calculating a dosimetry scoring function, an anatomical structure scoring function and a proton specificity scoring function based on the image data and the dose data, obtaining an enhanced core quantitative score through weighted geometric average fusion, and obtaining the enhanced core quantitative score; determining parts, stages and prognosis adjustment parameters based on clinical data, and fusing a BMI dynamic evaluation factor, a treatment response prediction factor and a multi-factor interaction factor to obtain an enhanced clinical background adjustment coefficient; and calculating a final decision score in combination with the time modulation factor and the risk prediction index based on the LSTM, and outputting a grading decision result. According to the method, objective quantitative evaluation of deep fusion of multi-modal data is realized, specific risks of proton therapy are accurately captured, personalized intelligent decision is supported, and the method has good clinical operability.

The present application relates to the field of biomedical technology, and discloses an immune infiltration desert tumor benefit ICI treatment method based on a weighting strategy. The method obtains spatial transcriptome data of tumor tissue of a patient, divides the tumor microenvironment into functional regions through an adaptive grid division algorithm, and extracts a multi-scale feature vector; calculates dynamic weight coefficients of each functional region using a graph attention network, and generates a comprehensive response index after weighted integration; and finally outputs an individualized ICI treatment decision according to the comparison result of the index and a preset threshold. The present application realizes dynamic evaluation of tumor microenvironment heterogeneity based on spatial transcriptome data, and provides ICI treatment response prediction and clinical decision support for patients with immune infiltration desert tumors.

This disclosure generally relates to a molecular classification of cancer and particularly to molecular markers for predicting response to cancer therapy, including cancer immune therapy, and methods of use thereof.

The present invention relates to a method of determining the responsiveness of a cancer patient to treatment with a compound that inhibits a KRAS protein or a KRAS protein mutant or treatment with a compound that inhibits the interaction between MDM2 and p53, the method comprising measuring the survivin level in a first sample obtained from the patient prior to treatment with the compound, measuring the level of survivin in a second sample obtained from the patient during or after treatment with the compound, comparing the level of survivin in the second sample to the level of survivin in the first sample, and determining the level of survivin in the second sample when compared to the level of survivin in the first sample. When the survivin level in the second sample decreases, it is determined that the patient is responsive to treatment with the compound. The invention further relates to the use of survivin in compounds for determining the inhibition of KRAS protein or KRAS protein mutants, or in compounds for inhibiting the interaction between MDM2 and p53, or a pharmaceutical formulation comprising said compound that inhibits a KRAS protein or a KRAS protein mutant or said compound that inhibits an interaction between MDM2 and p53 in a method of treating cancer.

The present disclosure relates to a panel of ER regulated genes and method for using the panel for monitoring the response of an individual having cancer to treatment with amcenestrant, the method comprising determining a first ER activity score from a sample from the individual at a first time point before treatment with amcenestrant using the panel, then determining a second ER activity score from a sample from the individual at a second time point following administration of amcenestrant using the panel, and comparing the first ER activity score with the second ER activity score, wherein a decrease in the second ER activity score relative to the first ER activity score is predictive of target engagement of amcenestrant.

Methods of predicting whether a subject with chronic myelogenous leukaemia (CML) is at risk of relapse following cessation of a treatment for CML, whether a subject with CML will achieve TFR following cessation of a treatment for CML, and selecting a subject for treatment, wherein the subject has CML or has been diagnosed with CML are disclosed herein. The methods comprise determining the expression of one or more genes regulated by PRC1. Also provided are kits and antigen-binding molecules suitable for use in the disclosed methods.

Methods, systems, and kits are provided for selecting a subject for treatment with an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression based on the presence of one or more genotypes associated with a positive therapeutic response to an inhibitor of TL1A. Methods, systems, and kits are also provided for detecting one or more of the genotypes described herein.