ArticleFrontiers in oncology2021
Efficacy of Location-Based Features for Survival Prediction of Patients With Glioblastoma Depending on Resection Status.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers, 1 of them a synthesis 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- From Anatomy to Outcome: Linking Glioma Location Patterns to Survival Using Non-Negative Matrix Factorization.Clinical neuroradiology · 2026Article
- Machine learning-based integration of dosiomics and pre-radiotherapy multimodal MRI radiomics for survival stratification in patients with glioblastoma multiforme.Radiation oncology (London, England) · 2025Article
- Correlation of contrast-enhancing tumor region on pre-radiotherapy MRI with overall survival in glioblastoma.European journal of medical research · 2025Article
- GlioSurvQNet: A DuelContextAttn DQN Framework for Brain Tumor Prognosis with Metaheuristic Optimization.Diagnostics (Basel, Switzerland) · 2025Article
- A Radiomic Model for Gliomas Grade and Patient Survival Prediction.Bioengineering (Basel, Switzerland) · 2025Article
- Predicting survival in malignant glioma using artificial intelligence.European journal of medical research · 2025Review
- Biparametric MRI of the prostate radiomics model for prediction of pelvic lymph node metastasis in prostate cancers : a two-centre study.BMC medical imaging · 2024Article
- Article
- Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review.Cancers · 2023Review
- Radiomics for precision medicine in glioblastoma.Journal of neuro-oncology · 2022Review
- Towards survival prediction of cancer patients using medical images.PeerJ. Computer science · 2022Article
- Radiomics nomogram based on multi-parametric magnetic resonance imaging for predicting early recurrence in small hepatocellular carcinoma after radiofrequency ablation.Frontiers in oncology · 2022Article
- Radiomics Analysis Based on Magnetic Resonance Imaging for Preoperative Overall Survival Prediction in Isocitrate Dehydrogenase Wild-Type Glioblastoma.Frontiers in neuroscience · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer stands out as one of the fatal diseases people are facing all the time. Each year, a countless number of people die because of the late diagnosis of cancer or wrong treatments. Glioma, one of the most common primary brain tumors, has different aggressiveness and sub-regions, which can affect the risk of disease. Although prediction of overall survival based on multimodal magnetic resonance imaging (MRI) is challenging, in this study, we assess if and how location-based features of tumors can affect overall survival prediction. This approach is evaluated independently and in combination with radiomic features. The process is carried out on a data set entailing MRI images of patients with glioblastoma. To assess the impact of resection status, the data set is divided into two groups, patients were reported as gross total resection and unknown resection status. Then, different machine learning algorithms were used to evaluate how location features are linked with overall survival. Results from regression models indicate that location-based features have considerable effects on the patients' overall survival independently. Additionally, classifier models show an improvement in prediction accuracy by the addition of location-based features to radiomic features.
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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.