ArticleBrain informatics2023
Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment.
Article in Brain informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties.Frontiers in neurology · 2025Pooled it
- Knowledge-guided brain tumor segmentation via synchronized visual-semantic-topological prior fusion.BMC medical imaging · 2026Article
- BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection.Brain informatics · 2026Article
- A deep learning-based model for postoperative resection assessment in glioblastoma: A comparative study.Neurosurgical review · 2026Article
- U-Net-based transfer learning for automated tumour segmentation enabling fully automated [Brain informatics · 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
- Editorial: Artificial intelligence in neurosurgical practices: current trends and future opportunities.Frontiers in neurology · 2026Article
- A Review on Deep Learning Methods for Glioma Segmentation, Limitations, and Future Perspectives.Journal of imaging · 2025Review
- Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.AJNR. American journal of neuroradiology · 2025Article
- Automatic and standardized reporting of perioperative MRIs in patients with central nervous system tumors.Frontiers in neurology · 2025Article
- Development and validation of a deep learning algorithm for discriminating glioma recurrence from radiation necrosis on MRI.Frontiers in oncology · 2025Article
- BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers.Frontiers in oncology · 2025Article
- Integrating quantitative DCE-MRI parameters and radiomic features for improved IDH mutation prediction in gliomas.Frontiers in oncology · 2025Article
- Modality redundancy for MRI-based glioblastoma segmentation.International journal of computer assisted radiology and surgery · 2024Article
- Repurposing the Public BraTS Dataset for Postoperative Brain Tumour Treatment Response Monitoring.Tomography (Ann Arbor, Mich.) · 2024Article
- Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field.Biomedicines · 2024Review
- The Impact of Lateral Ventricular Opening in the Resection of Newly Diagnosed High-Grade Gliomas: A Single Center Experience.Cancers · 2024Article
- Super-resolution reconstruction improves multishell diffusion: using radiomics to predict adult-type diffuse glioma IDH and grade.Frontiers in oncology · 2024Article
- Augmented surgical decision-making for glioblastoma: integrating AI tools into education and practice.Frontiers in neurology · 2024Article
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16 authors.
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Abstract
objectiveClinical and surgical decisions for glioblastoma patients depend on a tumor imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance imaging (MRI) assessment to support clinical practice, surgery planning and prognostic predictions. In a real-world context, the current obstacles for AI are low-quality imaging and postoperative reliability. The aim of this study is to train an automatic algorithm for glioblastoma segmentation on a clinical MRI dataset and to obtain reliable results both pre- and post-operatively.
methodsThe dataset used for this study comprises 237 (71 preoperative and 166 postoperative) MRIs from 71 patients affected by a histologically confirmed Grade IV Glioma. The implemented U-Net architecture was trained by transfer learning to perform the segmentation task on postoperative MRIs. The training was carried out first on BraTS2021 dataset for preoperative segmentation. Performance is evaluated using DICE score (DS) and Hausdorff 95% (H95).
resultsIn preoperative scenario, overall DS is 91.09 (± 0.60) and H95 is 8.35 (± 1.12), considering tumor core, enhancing tumor and whole tumor (ET and edema). In postoperative context, overall DS is 72.31 (± 2.88) and H95 is 23.43 (± 7.24), considering resection cavity (RC), gross tumor volume (GTV) and whole tumor (WT). Remarkably, the RC segmentation obtained a mean DS of 63.52 (± 8.90) in postoperative MRIs.
conclusionsThe performances achieved by the algorithm are consistent with previous literature for both pre-operative and post-operative glioblastoma's MRI evaluation. Through the proposed algorithm, it is possible to reduce the impact of low-quality images and missing sequences.
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