ArticleDiagnostics (Basel, Switzerland)2021
A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- Article
- Prediction of Survival in Surgically Treated Glioblastoma Multiforme Utilizing DTI and Contrast-Enhanced MRI.The Indian journal of radiology & imaging · 2026Article
- Correlation of contrast-enhancing tumor region on pre-radiotherapy MRI with overall survival in glioblastoma.European journal of medical research · 2025Article
- Radiogenomics and radiomics of skull base chordoma: Classification of novel radiomic subgroups and prediction of genetic signatures and clinical outcomes.Neuro-oncology · 2025Article
- A novel brain tumor magnetic resonance imaging dataset (Gazi Brains 2020): initial benchmark results and comprehensive analysis.PeerJ. Computer science · 2025Article
- Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction.Tomography (Ann Arbor, Mich.) · 2024Review
- Exploring the prognostic value and biological pathways of transcriptomics and radiomics patterns in glioblastoma multiforme.Heliyon · 2024Article
- Article
- Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI.Scientific reports · 2023Article
- Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme.Frontiers in medicine · 2023Article
- Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI.Diagnostics (Basel, Switzerland) · 2022Review
- Scavenger receptor class F member 2 (SCARF2) as a novel therapeutic target in glioblastoma.Toxicological research · 2022Article
- MRI radiomics predicts progression-free survival in prostate cancer.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
15 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adult patients with a median survival of around one year. Prediction of survival outcomes in GBM patients could represent a huge step in treatment personalization. The objective of this study was to develop machine learning (ML) algorithms for survival prediction of GBM patient. We identified a radiomic signature on a training-set composed of data from the 2019 BraTS challenge (210 patients) from MRI retrieved at diagnosis. Then, using this signature along with the age of the patients for training classification models, we obtained on test-sets AUCs of 0.85, 0.74 and 0.58 (0.92, 0.88 and 0.75 on the training-sets) for survival at 9-, 12- and 15-months, respectively. This signature was then validated on an independent cohort of 116 GBM patients with confirmed disease relapse for the prediction of patients surviving less or more than the median OS of 22 months. Our model insured an AUC of 0.71 (0.65 on train). The Kaplan-Meier method showed significant OS difference between groups (log-rank
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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.