Xu et al., 2022 - Google Patents
Radiomics features based on MRI predict BRAF V600E mutation in pediatric low-grade gliomas: A non-invasive method for molecular diagnosisXu et al., 2022
- Document ID
- 5594154065600749921
- Author
- Xu J
- Lai M
- Li S
- Ye K
- Li L
- Hu Q
- Ai R
- Zhou J
- Li J
- Zhen J
- Cai L
- Shi C
- Publication year
- Publication venue
- Clinical Neurology and Neurosurgery
External Links
Snippet
Objective To investigate the clinical application value of radiomics features based on preoperative magnetic resonance imaging for predicting B-Raf proto-oncogene serine/threonine-protein (BRAF) V600E mutation in pediatric low-grade gliomas. Materials …
- 230000035772 mutation 0 title abstract description 39
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay
- G01N33/574—Immunoassay; Biospecific binding assay for cancer
- G01N33/57407—Specifically defined cancers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
- G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Tan et al. | A radiomics nomogram may improve the prediction of IDH genotype for astrocytoma before surgery | |
Han et al. | Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas | |
Permuth et al. | Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms | |
Calabrese et al. | A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas | |
US20230360217A1 (en) | Assessing treatment response with estimated number of tumor cells | |
Xu et al. | Radiomics features based on MRI predict BRAF V600E mutation in pediatric low-grade gliomas: A non-invasive method for molecular diagnosis | |
Castellano et al. | Progress in neuro-imaging of brain tumors | |
McDonald et al. | Restriction spectrum imaging predicts response to bevacizumab in patients with high-grade glioma | |
Li et al. | A meta-analysis of MRI-based radiomic features for predicting lymph node metastasis in patients with cervical cancer | |
Dong et al. | Differential diagnosis of solitary fibrous tumor/hemangiopericytoma and angiomatous meningioma using three‐dimensional magnetic resonance imaging texture feature model | |
Zhu et al. | Value of the application of CE-MRI radiomics and machine learning in preoperative prediction of sentinel lymph node metastasis in breast cancer | |
Xiao et al. | Diagnosis of invasive meningioma based on brain-tumor interface radiomics features on brain MR images: a multicenter study | |
Mahajan et al. | Glioma radiogenomics and artificial intelligence: road to precision cancer medicine | |
Lu et al. | A radiomics feature-based nomogram to predict telomerase reverse transcriptase promoter mutation status and the prognosis of lower-grade gliomas | |
Wang et al. | Comparison of diffusion kurtosis imaging and dynamic contrast enhanced MRI in prediction of prognostic factors and molecular subtypes in patients with breast cancer | |
Hagiwara et al. | Visualization of tumor heterogeneity and prediction of isocitrate dehydrogenase mutation status for human gliomas using multiparametric physiologic and metabolic MRI | |
Xing et al. | Non-invasive prediction of p53 and Ki-67 labelling indices and O-6-methylguanine-DNA methyltransferase promoter methylation status in adult patients with isocitrate dehydrogenase wild-type glioblastomas using diffusion-weighted imaging and dynamic susceptibility contrast-enhanced perfusion-weighted imaging combined with conventional MRI | |
Ma et al. | MRI radiomics for the preoperative evaluation of lymphovascular invasion in breast cancer: A meta-analysis | |
Li et al. | The histogram analysis of intravoxel incoherent Motion-Kurtosis model in the diagnosis and grading of prostate cancer—a preliminary study | |
Lin et al. | A predictive nomogram for atypical meningioma based on preoperative magnetic resonance imaging and routine blood tests | |
Li et al. | Development and Validation of a Nomogram Based on DCE-MRI Radiomics for Predicting Hypoxia-Inducible Factor 1α Expression in Locally Advanced Rectal Cancer | |
Zhou et al. | Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma | |
Yang et al. | The utility of texture analysis based on quantitative synthetic magnetic resonance imaging in nasopharyngeal carcinoma: a preliminary study | |
Gelezhe et al. | Magnetic resonance imaging radiomics in prostate cancer radiology: what is currently known? | |
Guo et al. | Ultra high b-value diffusion weighted imaging enables better molecular grading stratification over histological grading in adult-type diffuse glioma |