ArticleScientific reports2023
Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Spatial-temporal recurrence patterns of grade 4 glioma using deep learning integrated multiparametric MRI and molecular pathology.NPJ precision oncology · 2026Article
- Amsterdam IMAging and Clinical GliOma Dataset; IMAGO.Scientific data · 2026Article
- A deep learning-based model for postoperative resection assessment in glioblastoma: A comparative study.Neurosurgical review · 2026Article
- Assessing Impact of Data Quality in Early Post-Operative Glioblastoma Segmentation.Journal of imaging · 2026Article
- Cerebrospinal fluid cfDNA-based molecular assessment of resection extent and prognosis in glioma.Communications medicine · 2026Article
- NeuroFAIR curator: an AI-assisted framework for metadata enrichment, quality screening, curation prioritization, and FAIR readiness of brain tumor MRI data.Frontiers in neuroinformatics · 2026Article
- Deep Neural Network-Based Risk Prediction of Glioblastoma Multiforme Recurrence.Journal of molecular neuroscience : MN · 2025Article
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Clinical evaluation of two glioblastoma delineation methods based on neural networks.Technical innovations & patient support in radiation oncology · 2025Article
- Automatic and standardized reporting of perioperative MRIs in patients with central nervous system tumors.Frontiers in neurology · 2025Article
- A Bayesian deep segmentation framework for glioblastoma tumor segmentation using follow-up MRIs.Frontiers in neuroimaging · 2025Article
- The prognostic importance of glioblastoma size and shape.Acta neurochirurgica · 2024Article
- A Practical Guide to Manual and Semi-Automated Neurosurgical Brain Lesion Segmentation.NeuroSci · 2024Article
- Sensors, Signals, and Imaging Informatics: Best contributions from 2023.Yearbook of medical informatics · 2024Review
- The effect of editing clinical contours on deep-learning segmentation accuracy of the gross tumor volume in glioblastoma.Physics and imaging in radiation oncology · 2024Article
- Improving the Generalizability of Deep Learning for T2-Lesion Segmentation of Gliomas in the Post-Treatment Setting.Bioengineering (Basel, Switzerland) · 2024Article
- Standardized evaluation of the extent of resection in glioblastoma with automated early post-operative segmentation.Frontiers in radiology · 2024Article
- Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting.Scientific reports · 2023Article
- Deep learning-based postoperative glioblastoma segmentation and extent of resection evaluation: Development, external validation, and model comparison.Neuro-oncology advancesArticle
- Estimating overall survival of glioblastoma patients using clinical variables, tumor size, and location.Neuro-oncology advancesArticle
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Authors and funding
33 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.
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