ArticleScientific reports2015
Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
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- Uncertainty quantification in multi-parametric MRI-based meningioma radiotherapy target segmentation.Frontiers in oncology · 2025Article
- Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors.AJNR. American journal of neuroradiology · 2024Article
- Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas.Scientific reports · 2023Article
- Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms.Scientific reports · 2023Article
- Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Multiparametric Magnetic Resonance Imaging Correlates of Isocitrate Dehydrogenase Mutation in WHO high-Grade Astrocytomas.Journal of personalized medicine · 2022Article
- A Review of the Multi-Systemic Complications of a Ketogenic Diet in Children and Infants with Epilepsy.Children (Basel, Switzerland) · 2022Review
- Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework.Cancers · 2022Review
- Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities.Cancers · 2022Review
- Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging.Frontiers in radiology · 2022Review
- Segmentation of multicorrelated images with copula models and conditionally random fields.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Radiomics, machine learning, and artificial intelligence-what the neuroradiologist needs to know.Neuroradiology · 2021Review
- Conventional and advanced imaging throughout the cycle of care of gliomas.Neurosurgical review · 2021Review
- Impact of postoperative dexamethasone on survival, steroid dependency, and infections in newly diagnosed glioblastoma patients.Neuro-oncology practice · 2021Article
- Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals.Cancers · 2021Article
- Article
- Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.BMJ open · 2021Article
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9 authors.
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Abstract
Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. MRI sets of 109 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA). Spearman's correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Auto-segmented sub-volumes showed moderate to high agreement with manually delineated volumes (range (r): 0.4 - 0.86). Also, the auto and manual volumes showed similar correlation with VASARI features (auto r = 0.35, 0.43 and 0.36; manual r = 0.17, 0.67, 0.41, for contrast-enhancing, necrosis and edema, respectively). The auto-segmented contrast-enhancing volume and post-contrast abnormal volume showed the highest AUC (0.66, CI: 0.55-0.77 and 0.65, CI: 0.54-0.76), comparable to manually defined volumes (0.64, CI: 0.53-0.75 and 0.63, CI: 0.52-0.74, respectively). BraTumIA and manual tumor sub-compartments showed comparable performance in terms of prognosis and correlation with VASARI features. This method can enable more reproducible definition and quantification of imaging based biomarkers and has potential in high-throughput medical imaging research.
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