ArticleNeuroradiology2019
Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging.
Article in Neuroradiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- The Three Pillars of Glioblastoma: A Systematic Review and Novel Analysis of Multi-Omics and Clinical Data.Cells · 2024Pooled it
- Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction.AJNR. American journal of neuroradiology · 2023Pooled it
- Artificial Intelligence for the Preoperative Molecular Characterization of Adult-Type Diffuse High-Grade Gliomas: A Systematic Review and Meta-Analysis.Journal of imaging informatics in medicine · 2026Review
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Review
- Explainable MRI radiomics identifies a PTEN-associated imaging phenotype in adult gliomas.Frontiers in oncology · 2026Article
- Decision Support Systems in Neurosurgery: Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- Molecular Biomarkers of Glioma.Biomedicines · 2025Review
- Radiomics in glioma: emerging trends and challenges.Annals of clinical and translational neurology · 2025Review
- Anticancer effect of the oncolytic Newcastle disease virus harboring the PTEN gene on glioblastoma.Oncology letters · 2025Article
- COL22A1 Activates the PI3K/AKT Signaling Pathway to Sustain the Malignancy of Glioblastoma.International journal of genomics · 2025Article
- Predicting survival in glioblastoma with multimodal neuroimaging and machine learning.Journal of neuro-oncology · 2023Article
- Deep-learning and conventional radiomics to predictFrontiers in oncology · 2023Article
- Article
- Radiogenomic Predictors of Recurrence in Glioblastoma-A Systematic Review.Journal of personalized medicine · 2022Review
- Reverse Engineering Glioma Radiomics to Conventional Neuroimaging.Neurologia medico-chirurgica · 2021Review
- Review
- Current Advances and Challenges in Radiomics of Brain Tumors.Frontiers in oncology · 2021Review
- Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma.Frontiers in oncology · 2021Article
- Non-Invasive Radiomics Approach Predict Invasiveness of Adamantinomatous Craniopharyngioma Before Surgery.Frontiers in oncology · 2020Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
Abstract
purposePTEN mutation status is a pivotal biomarker for glioblastoma. This study aimed to establish a radiomic signature to predict PTEN mutation status in patients with glioblastoma, and to investigate the genetic background behind this radiomic signature.
methodsIn this study, a total of 862 radiomic features were extracted from each patient. The training (n = 69) and validation (n = 40) sets were retrospectively collected from the Cancer Genome Atlas and the Chinese Glioma Genome Atlas, respectively. The minimum redundancy maximum relevance (mRMR) algorithm was used to select the best predictive features of PTEN status. A machine learning model was then built with the selected features using a support vector machine classifier. The predictive performance of each selected feature and the complete model were evaluated via the area under the curve from receiver operating characteristic analysis in both the training and validation sets. The genetic background underlying the radiomic signature was determined using radiogenomic analysis.
resultsSix features were selected using the mRMR algorithm, including two features derived from contrast-enhanced images and four features derived from T2-weighted images. The predictive performance of the machine learning model for the training and validation sets were 0.925 and 0.787, respectively, which were better than the individual features. Radiogenomics analysis revealed that the PTEN-associated biological processes could be described using the radiomic signature.
conclusionThese results show that radiomic features derived from preoperative MRI can predict PTEN mutation status in glioblastoma patients, thus providing a novel noninvasive imaging biomarker.
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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.