163 results about "Glioma" patented technology

The invention discloses a glioma radiotherapy postoperative risk assessment method based on a magnetic resonance image, particularly relates to the field of glioma radiotherapy patient health risk assessment, and is used for solving the problem that an existing assessment mode depends on manual interpretation and is difficult to predict bad clinical outcomes in advance. The method comprises the following steps: performing clinical data gridding reconstruction on a corticoid use cycle of a patient and tumor molecular typing, and combining morphological characteristics of an edema region in a magnetic resonance image to generate time-aligned clinical comprehensive characteristic vectors; mining a frequent association item set between the comprehensive feature vector and the pathological process to construct a mapping relation model; establishing a probability graph reasoning model of the bad outcome based on the pathological process vector sequence and the probability weight; and integrating the two types of models to form a causal reasoning network, and inputting a target patient feature vector to calculate an accumulated risk value of reaching a bad outcome. According to the method, an interpretable individual risk assessment result can be output, and a basis is provided for postoperative follow-up visit and intervention.

The invention belongs to the technical field of glioma image recognition, and discloses a glioma area rapid recognition method and system based on a medical image. The method comprises the following steps: acquiring a multi-modal image sequence of a brain to be detected; performing weighted fusion on the multi-modal image sequence to generate a fused image; performing gray asymmetry analysis on the fused image, and identifying an asymmetry salient region of the fused image; performing texture difference analysis on the fused image, and extracting a high-difference region of the fused image; performing spatial intersection operation on the asymmetric salient region and the high-difference region, and taking a spatial intersection operation result as a glioma suspicious region; and carrying out dynamic region growth segmentation on the glioma suspicious region, and rapidly outputting a glioma region boundary. According to the method, dual feature screening of gray and texture is combined with dynamic region growth segmentation, the tumor infiltration boundary is accurately captured, the positioning precision is improved, and the false positive rate of recognition is reduced.

The invention discloses application of a CXCL14 combined immune checkpoint inhibitor in treatment of spinal glioma. The immune checkpoint inhibitor is selected from a PD-1 inhibitor or a combination of the PD-1 inhibitor and a CTLA4 inhibitor. In-vivo and in-vitro experiments prove that the CXCL14 combined with the PD-1 inhibitor, the CXCL14 combined with the PD-1 inhibitor and the CTLA4 inhibitor can be used for remarkably inhibiting the growth of mutant spinal glioma and have a remarkable synergistic effect. The invention provides a new strategy for treatment of spinal glioma, and is widely applied clinically.

The invention belongs to the technical field of medical artificial intelligence, and discloses a glioma feature prediction method and system based on multi-modal magnetic resonance and multi-task deep learning, and the method comprises the steps: processing input data through a modal completion module which combines knowledge distillation and modal existence coding, so as to deal with modal deficiency; after deep features are extracted by using a shared encoder, the deep features are sent to a hierarchical task gating prediction network, and the network performs gating modulation on post-stage task features through a pre-stage task result to realize task collaboration; and carrying out model training by adopting a self-adaptive loss function based on sample difficulty. Through the design, the accuracy and robustness of glioma segmentation, molecular marker prediction and lifetime prediction are improved, and the uncertainty score of the prediction result can be provided during reasoning.

The invention provides methods and compositions for treatment of a subject with a glioma. The methods comprise administering a composition comprising Coenzyme Q10 to the subject by continuous intravenous infusion; and administering radiation therapy to the subject. The composition comprising Coenzyme Q10 may be administered to the subject by continuous intravenous infusion for at least 24 hours before the radiation therapy is initiated.

Disclosed are methods of treating cancer in a subject comprising (i) administering a pharmaceutical composition comprising a therapeutically effective amount of a mutagenized IL 13 moiety (mIL 13); and then (ii) delivering a radiation therapy.

The invention provides an innovative model based on the correlation between the PLEKHA4 gene expression level and the low-grade glioma radiotherapy sensitivity and a prediction method. According to the model, a multi-factor Logistic regression analysis framework is constructed, and the PLEKHA4 gene expression level and key clinical pathological parameters are organically combined, so that a radiotherapy sensitivity prediction model is established. The method comprises a series of steps of sample collection, gene expression detection, clinical information collection, model calculation, result interpretation and the like, and can realize accurate prediction of radiotherapy response of low-grade glioma patients. Compared with the prior art, the method has the remarkable advantages of simplicity and convenience in operation, high prediction accuracy, high clinical transformability and the like. The method has great potential in the aspect of guiding individualized radiotherapy scheme formulation, can significantly improve the treatment effect, and has important value for medical application. Besides, the model can be optimized through further clinical verification, so that clinical practice can be better served, and the life quality and prognosis effect of patients are improved.

The invention belongs to the technical field of medical image processing, and relates to a brain tumor segmentation method based on three-axis context coding, which comprises the following steps: jointly inputting an FLAIR image, a T2 image, a T1 image and a T1ce image of the same glioma into a trained image segmentation model, and outputting a prediction segmentation image with a non-enhanced necrosis region, an edema region and an enhanced tumor region by the trained image segmentation model; the image segmentation model comprises an encoder and a decoder; the encoder comprises a TCE module, and the TCE module adaptively learns relative importance among an axial plane branch, a coronal plane branch and a sagittal plane branch to realize dynamic weighted fusion of multi-plane features. By pertinently solving the problems of insufficient anisotropic feature capture and poor multi-modal fusion adaptability, high-precision segmentation of a non-enhanced necrosis region, an edema region and an enhanced tumor region is realized, and reliable technical support is provided for clinical diagnosis and treatment planning of brain tumors.

The invention discloses an MRI (Magnetic Resonance Imaging) image segmentation method based on symmetric multidirectional Mama and double attention, which comprises the following steps: firstly, establishing an image segmentation model based on a ResUNet backbone network, taking a standard image as input, mapping the standard image to a multi-scale semantic feature space through down-sampling, enhancing attention features of the image by adopting multi-scale self-adaptive attention, and obtaining an image segmentation result; and features are weighted and dynamically fused, collaborative optimization of channel-space dimensions and dynamic balance of local-global information are realized, multi-scale heterogeneity challenges of glioma are effectively handled, the perceptual performance of the network on tumor features of different scales is enhanced, four groups of symmetric scanning strategies are adopted to scan enhanced images, and the accuracy of the network is improved. The MRI image segmentation method comprises the following steps of: firstly, extracting a tumor image, enhancing feature expression of the image, improving the understanding ability of a model on a tumor global structure, and finally, sequentially adopting multi-operator edge detection, edge feature coding, adaptive intensity adjustment and gating residual fusion, fusing image features through symmetric up-sampling, outputting a segmented image, completing MRI image segmentation, and improving segmentation precision.

The invention belongs to the technical field of medical image processing and artificial intelligence, and particularly relates to a weakly supervised brain tumor segmentation method which comprises the following steps: acquiring a low-level glioma segmentation data set of a cancer genome map, and randomly dividing the data set into a training set and a test set according to patient division; based on the image data in the data set, carrying out transfer learning by using a pre-trained ResNet50 as a backbone network; a global optimization mechanism of an SZIO algorithm is utilized to perform adaptive optimization and intelligent screening on features extracted based on a traditional class activation mapping technology, and fine fusion and semantic enhancement of multi-level features are realized; and performing fine adjustment on the ResNet50 backbone network at a preset initial learning rate. According to the method, the performance breakthrough of weakly supervised brain tumor segmentation can be realized, the practicability, robustness and clinical transformation potential are relatively high, and an efficient and reliable intelligent segmentation solution is provided for the field of medical image analysis.

The invention discloses a positioning and delimiting method and system for a cortex pathological focus based on an intracranial electroencephalogram signal. The method comprises the following steps: constructing an individualized three-dimensional navigation model; obtaining an original intracranial electroencephalogram signal; generating a plurality of representative segments according to the original intracranial electroencephalogram signals; analyzing the original intracranial electroencephalogram signal and the plurality of representative segments so as to obtain a network analysis core index; generating a fusion type three-dimensional brain model according to the network analysis core index and the individualized three-dimensional navigation model; generating a space-time three-dimensional electroencephalogram model and an electrophysiology functional area distribution diagram according to the fused three-dimensional brain model and the network analysis core indexes; and according to the space-time three-dimensional electroencephalogram model and the electrophysiology functional area distribution map, generating accurate electrophysiology positioning and delimiting information of the cortical pathological focus. According to the invention, accurate electrophysiological delimitation of intra-operative cortical pathological lesions (glioma, hemangioma, focal cortical dysplasia and the like) is realized.

The application discloses a glioma IDH mutation state noninvasive prediction system based on a machine learning algorithm, relates to the technical field of medical image processing, and comprises a data acquisition module, a data processing module, a feature extraction module, a single nuclear magnetic sequence image classifier training module, a multi-nuclear magnetic sequence image classifier training module, a model evaluation module and a prediction module; the system acts on conventional nuclear magnetic sequence image data and FW sequence image data, FA sequence image data and MD sequence image data obtained by processing DTI sequence image data, and combines a machine learning algorithm to construct a prediction model, so that the extraction and analysis of features are more reliable and accurate, the limitation of DWI sequence image data in analyzing an IDH mutation state is solved, and the accuracy of the established prediction model is ensured.

The invention belongs to the field of preparation processes of porous nano materials, and relates to a preparation method of ZnFe2O4 aerogel for glioma photothermal therapy. The preparation method comprises the following steps: firstly, preparing a ZnFe2O4 precursor by adopting a sol-gel method in combination with a supercritical drying process, and carrying out supercritical drying; then polyethylene glycol (PEG), EDC, NHS and FA are introduced for functional modification, and the ZnFe2O4 aerogel is prepared through high-temperature heat treatment. The method is simple in process, raw materials are environmentally friendly and easy to obtain, and the prepared ZnFe2O4 aerogel is high in specific surface area, low in density and good in photo-thermal performance. The research provides a new thought for targeted treatment of glioma, and promotes the development of nano medicine and material science.

The invention relates to the technical field of biology, and provides a LanCL1 gene as a target for typing and prognosis of glioma. According to the application of the reagent for detecting LanCL1 gene expression in preparation of products for glioma molecular typing and prognosis prediction, molecular typing of glioma patients can be achieved, LanCL1 high expression is highly related to low malignancy and good prognosis, and clinical risk stratification, treatment decision and treatment assistance are facilitated.

The application discloses a method for detecting glioma chromosome abnormalities based on targeted sequencing, and belongs to the technical field of biological medicine. The method first acquires the allele frequency of a to-be-detected sample at preset SNP sites (covering 1p, 1q, 19p, 19q, chromosome 7 and chromosome 10), and then calculates and determines whether specific chromosome arms or chromosomes have loss of heterozygosity. Meanwhile, the copy number of the region where each SNP site is located is calculated based on the sequencing depth, and the total copy number of the above-mentioned chromosomes is obtained by integration. Finally, the loss of heterozygosity determination result and the chromosome copy number information are comprehensively combined, so that the simultaneous identification of 1p / 19q co-deletion, gain of chromosome 7 (+7) and deletion of chromosome 10 (-10) is realized. The method does not require paired samples, can accurately quantify the copy number, avoid false positives, and only needs to detect part of the SNP sites, that is, can be combined with hot spot mutation detection, thereby saving cost and improving detection efficiency.

To provide information on glioma of a subject.SOLUTION: A method comprising making a determination about the detection and / or staging of glioma in a subject using an indicator for D-amino acids in the subject, and providing information about the glioma in the subject based on the result of the determination.SELECTED DRAWING: Figure 1

This invention proposes a three-dimensional glioma image segmentation method, electronic device, and storage medium. The method includes: acquiring three-dimensional volume data from multimodal magnetic resonance imaging; fusing the three-dimensional volume data with prior information on orientation boundaries to obtain orientation-boundary-enhanced three-dimensional volume data; decomposing the orientation-boundary-enhanced three-dimensional volume data into a two-dimensional slice sequence and performing inter-slice context modeling to aggregate information from adjacent slices to generate a target feature volume; dynamically inferring discrete token combinations based on the target feature volume using a discrete token vocabulary and an attribute predictor to generate high-level semantic cue features; inputting the target feature volume into an orientation-aware dual-domain enhancement branch to obtain enhanced features; and inputting the enhanced features and high-level semantic cue features together into a sparse hybrid expert decoder to output segmentation probability maps of three nested tumor regions. This invention significantly improves the boundary accuracy and small target recognition capability of glioma subregion segmentation, thereby enhancing the accuracy of image segmentation.

The invention discloses a method for screening interferon pathways activated by intervention of glioblastoma, and relates to the technical field of biological medicines. The invention provides a new application of Atopaxar in treatment of glioma, and the clinical application range of the medicine is widened; meanwhile, clear molecular mechanism evidence is provided, and it is proved that the cGAS-STING-1 type interferon pathway is activated to achieve the anti-tumor effect; the invention proposes that Atopaxar has good blood brain barrier penetrating power, solves the bottleneck problem of a traditional immune agonist in central nervous system tumor treatment, and provides a new target spot and strategy for precise immunotherapy of glioblastoma.

A method and apparatus for detecting MGMT promoter methylation in glioblastoma, relating to the field of Raman spectroscopic detection. The method comprises: performing Raman spectral scanning on a glioma tissue sample to be detected to obtain Raman spectral data; preprocessing the Raman spectral data to obtain normalized Raman spectral data; extracting intensity values at characteristic shifts from the normalized Raman spectral data; and inputting the intensity values at the characteristic shifts into a classification model to calculate a classification prediction value, the classification prediction value being used for assisting in determining the MGMT promoter methylation state of said glioma tissue sample. The effect of the present application is that low-cost, non-destructive and accurate detection of the MGMT promoter methylation state of glioblastoma can be achieved.

The invention relates to the field of biological detection, and discloses a method for analyzing prognosis gene characteristics of glioma based on machine learning. The method comprises the following steps: acquiring prognosis gene expression data and pathological variable data corresponding to a glioma evaluation sample; determining a prognostic gene score of the prognostic gene expression data based on a pre-constructed prognostic scoring model; and analyzing the prognosis risk of the neuroglioma evaluation sample according to the prognosis gene score and the pathological variable data to obtain a prognosis risk analysis result. The problems that an existing neuroglioma prognosis evaluation method is insufficient in accuracy and lacks a quantitative analysis model are solved.

The invention discloses a 2-amino-4-aromatic heterocyclic-1, 3, 5-triazine compound and application thereof, and the 2-amino-4-aromatic heterocyclic-1, 3, 5-triazine compound has the following structural general formula as shown in the specification, wherein W, X, Y and Z are independently selected from carbon or nitrogen. The 2-amino-4-aromatic heterocyclic-1, 3, 5-triazine compound designed and synthesized by the invention is a novel NF-kappa B induced kinase (NIK) inhibitor and is suitable for drug development taking INIK as a target spot, and the obtained drug can be used for treating malignant tumors such as leukemia, multiple myeloma glioma and the like.

This invention relates to animal models and their applications, specifically to a method for constructing a spontaneous mouse cerebral edema model and its application. This invention utilizes PEN2... fl / + Mice and PEN2 fl / + Mice were mated to obtain PEN2. fl / fl Homozygous mice; then the obtained PEN2 fl / fl Mice and hGFAP Cre / + Mice were mated to obtain PEN2. fl / + hGFAP Cre / + Mice; the final PEN2 obtained fl / + hGFAP Cre / + Mice and PEN2 fl / fl Mice were mated to obtain PEN2. fl / fl hGFAP Cre / + The mouse model is a spontaneous mouse cerebral edema model. In this model, PEN2 protein levels are significantly downregulated, and the mouse exhibits a severe cerebral edema phenotype. This model can be used to establish a cerebral edema drug screening platform. It can also be used for research on cerebral edema-related diseases, such as the pathogenesis of cerebral edema caused by infection, glioma, and traumatic brain injury, as well as for the development of technologies and products for the diagnosis and treatment of related diseases.

Disclosed herein are methods of using narazaciclib or a pharmaceutically acceptable salt thereof in the treatment of glioma (e.g., glioblastoma), either alone or in combination with an additional therapy.

Methods of treating glioma using a WEE1 inhibitor are disclosed.

The invention discloses an application of a compound as a CYP2E1 inhibitor, which is characterized in that the compound or a salt thereof as an inhibitor takes CYP2E1 as a target spot and is combined with the CYP2E1, and the CYP2E1 inhibitor has an inhibition effect on CYP2E1. The inhibitor can be used for preventing and treating tumors such as liver cancer, glioma, ovarian cancer, bladder cancer and gallbladder cancer. Similarly, the inhibitor can also be used for preventing and treating other inflammation-related diseases such as liver injury, fatty liver, hepatitis, hepatic fibrosis, rheumatic and rheumatoid arthritis, sepsis, Alzheimer's disease, ischemic cerebral apoplexy, Parkinson's disease, hyperlipidemia, atherosclerosis, coronary heart disease, diabetes mellitus and the like.

The present invention relates to the compound 2-hydroxy-octadecene-9-cis-oate for use in the treatment of oncological pathologies including grade IV glioblastoma with native IDH, grade 3 glioma, embryonal tumour, pituitary tumour, meningioma, haemangiopericytoma, haemangioblastoma, mesothelioma, bile duct adenocarcinoma, exocrine pancreatic cancer, neuroendocrine pancreatic cancer, rectal cancer, urachal cancer, adenocarcinoma of the bladder and oesophageal cancer, and for the treatment of neuropathic pain caused by diabetic peripheral neuropathy, spinal cord injury, post-herpetic neuralgia, chemotherapeutic agent, antitumour agent, cancer, fibromyalgia or hepatitis. The present invention also relates to the pharmaceutical composition comprising 2-hydroxy-octadecene-9-cis-oate.

This invention relates to the field of medical control technology, specifically to a glioma ECoG electrode localization system based on the fusion of multimodal MRI and intraoperative photographs. It includes a brain imaging data acquisition module for preoperative acquisition of multimodal brain imaging data and an acquisition module for acquiring intraoperative craniotomy photographs for ECoG electrode placement. The system further aligns the multimodal brain imaging data to the same three-dimensional spatial coordinate system; then segments and corrects the multimodal brain imaging data into different regions; and establishes an individualized brain region atlas based on a healthy brain tissue texture model and segmented brain regions. Vascular structures are extracted, and a three-dimensional spatial coordinate system for vascular feature points and electrode contacts is established and transformed. The minimum Euclidean distance is calculated based on the electrode contacts, and the individualized brain region atlas is labeled. This invention addresses the problems of insufficient spatial positioning accuracy of electrode contacts and difficulty in quantifying tumor-electrode distance during surgery for glioma patients, facilitating intraoperative determination of the anatomical location of the epileptogenic focus by surgeons.

The present application relates to the field of Raman spectroscopic detection. Disclosed are a method and apparatus for detecting chromosome 1p / 19q co-deletion in glioma. The method comprises: performing Raman spectroscopic scanning on a glioma tissue sample to be subjected to detection, in order to obtain Raman spectral data; preprocessing the Raman spectral data, in order to obtain normalized Raman spectral data; extracting intensity values at characteristic shifts from the normalized Raman spectral data; and inputting the intensity values at the characteristic shifts into a classification model to calculate a classification prediction value, which is used for assisting with the determination of the chromosome 1p / 19q co-deletion state of said glioma tissue sample. The present application can realize the low-cost and rapid detection of the chromosome 1p / 19q co-deletion state in gliomas.

Imipridones selectively modulate Class A G protein-coupled receptors (GPCRs), such as the D2-like subfamily of dopamine receptors, and are useful for treating conditions and disorders in need of such modulation, such as cancers. Specifically, the cancer involves a midline structure of the brain, a cancer having a histone H3 mutation, or both. In addition, methods of identifying whether a subject having these conditions, is likely to be responsive to a treatment regimen, such as imipridone administration, are provided. Furthermore, methods of assessing the effectiveness of a treatment regimen, such as imipridone administration, monitoring, or providing a prognosis for a subject with these condition are also provided.