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Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment. | LitMetric

AI Article Synopsis

  • - The study focuses on using Artificial Intelligence (AI) to improve the evaluation of glioblastoma (a type of brain tumor) through MRI imaging, which is crucial for clinical decisions and surgical planning.
  • - Researchers trained a segmentation algorithm on a dataset of 237 MRIs (71 preoperative and 166 postoperative) from patients with Grade IV Glioma to enhance the accuracy of tumor assessment before and after surgery.
  • - Results indicate that while the algorithm performed well in preoperative evaluation (DICE score of 91.09), its performance was lower in postoperative assessments (DICE score of 72.31), suggesting that AI can help mitigate issues related to low-quality MRI images.

Article Abstract

Objective: Clinical 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.

Methods: The 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).

Results: In 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.

Conclusions: The 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10558414PMC
http://dx.doi.org/10.1186/s40708-023-00207-6DOI Listing

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