ArticleNeuroImage. Clinical2018
MRI features predict p53 status in lower-grade gliomas via a machine-learning approach.
Article in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 3 of them syntheses that pooled it.
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Who cites it
68 citing papers in PubMed, 3 syntheses or guidelines pooled it, 112 citations in OpenAlex.
- Radiomics and Machine Learning in Diagnostics of Glial Brain Tumors: a Systematic Review and Meta-Analysis.Sovremennye tekhnologii v meditsine · 2025Pooled it
- Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Prognostic prediction of hypertensive intracerebral hemorrhage using CT radiomics and machine learning.Brain and behavior · 2021Trial
- Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images.Scientific reports · 2021Trial
- Radiomics-guided multimodal MRI fusion framework for non-enhanced glioblastoma noninvasive identification.PLOS digital health · 2026Article
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- The role of radiomics in predicting the response to neoadjuvant chemotherapy for breast cancer.Cancer biology & medicine · 2026Review
- Radiomics of T2-weighted MRI for pretreatment prediction of prognosis and temozolomide chemosensitivity in glioma.BMC cancer · 2026Article
- Review
- Advanced MRI, Radiomics and Radiogenomics in Unravelling Incidental Glioma Grading and Genetic Status: Where Are We?Medicina (Kaunas, Lithuania) · 2025Review
- Predicting p53 Status in IDH-Mutant Gliomas Using MRI-Based Radiomic Model.Cancer medicine · 2025Article
- Hybrid classical and quantum computing for enhanced glioma tumor classification using TCGA data.Scientific reports · 2025Article
- Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Role of Amide Proton Transfer Weighted MRI in Predicting MGMTp Methylation Status, p53-Status, Ki-67 Index, IDH-Status, and ATRX Expression in WHO Grade 4 High Grade Glioma.Tomography (Ann Arbor, Mich.) · 2025Article
- Radiomics in glioma: emerging trends and challenges.Annals of clinical and translational neurology · 2025Review
- Predicting podoplanin expression and prognostic significance in high-grade glioma based on TCGA TCIA radiomics.PloS one · 2025Article
- Voxel-based mapping of postoperative epilepsy risk in glioma patients: supplementary motor area and limbic system correlations.Frontiers in neurology · 2025Article
- Role of artificial intelligence in cancer detection using protein p53: A Review.Molecular biology reports · 2024Review
- Whole brain morphologic features improve the predictive accuracy of IDH status and VEGF expression levels in gliomas.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
8 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images. Methods: Preoperative MR images were retrospectively obtained from 272 patients with primary grade II/III gliomas. The patients were randomly allocated in a 2:1 ratio to a training ( Results: The p53-related radiomic signature was built using the LASSO algorithm; this procedure consisted of four first-order statistics or related wavelet features (including Maximum, Median, Minimum, and Uniformity), a shape and size-based feature (Spherical Disproportion), and ten textural features or related wavelet features (including Correlation, Run Percentage, and Sum Entropy). The prediction accuracies based on the area under the curve were 89.6% in the training set and 76.3% in the validation set, which were better than individual features. Conclusions: These results demonstrate that MR image texture features are predictive of p53 mutation status in lower-grade gliomas. Thus, our procedure can be conveniently used to facilitate presurgical molecular pathological diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.