ArticleFrontiers in radiology2024
Standardized evaluation of the extent of resection in glioblastoma with automated early post-operative segmentation.
Article in Frontiers in radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic revalidation of RANO categories for extent of resection in glioblastoma: a reconstruction of individual patient data.Journal of neuro-oncology · 2025Pooled it
- Automated delineation of putative non-contrast-enhancing tumor in glioblastoma: Prognostic insights.Neuro-oncology · 2026Article
- A deep learning-based model for postoperative resection assessment in glioblastoma: A comparative study.Neurosurgical review · 2026Article
- Decoding the Glioblastoma Microenvironment: AI-Driven Analysis of Cellular MRI Signatures for Targeted Therapy.Cellular and molecular neurobiology · 2026Review
- 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
- Automatic and standardized reporting of perioperative MRIs in patients with central nervous system tumors.Frontiers in neurology · 2025Article
- BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers.Frontiers in oncology · 2025Article
- Deep learning-based postoperative glioblastoma segmentation and extent of resection evaluation: Development, external validation, and model comparison.Neuro-oncology advancesArticle
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Standard treatment of patients with glioblastoma includes surgical resection of the tumor. The extent of resection (EOR) achieved during surgery significantly impacts prognosis and is used to stratify patients in clinical trials. In this study, we developed a U-Net-based deep-learning model to segment contrast-enhancing tumor on post-operative MRI exams taken within 72 h of resection surgery and used these segmentations to classify the EOR as either maximal or submaximal. The model was trained on 122 multiparametric MRI scans from our institution and achieved a mean Dice score of 0.52 ± 0.03 on an external dataset (
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