Publications by authors named "O P Solheim"
Background: Radiologically presumed diffuse lower-grade glioma (dLGG) are typically non or minimal enhancing tumors, with hyperintensity in T2w-images. The aim of this study was to test the clinical usefulness of deep learning (DL) in mutation prediction in patients with radiologically presumed dLGG.
Methods: Three hundred and fourteen patients were retrospectively recruited from 6 neurosurgical departments in Sweden, Norway, France, Austria, and the United States.
Article Synopsis
- - The research aimed to create a deep learning model for accurately assessing the extent of resection (EOR) in glioblastoma patients using postoperative MRI scans, addressing limitations of existing algorithms that only focus on preoperative images.
- - Utilizing data from multiple sources, the model was trained to segment tumor features like contrast-enhancing tumor, edema, and surgical cavity, and was compared with other segmentation models, showing high performance in classifying resection categories.
- - The study found that the nnU-Net framework outperformed other algorithms, achieving high accuracy in both segmentation (with median Dice scores up to 0.81) and EOR classification (96% accuracy in comparisons), making it a valuable tool for clinical use.
Purpose: Extent of resection, MGMT promoter methylation status, age, functional level, and residual tumor volume are established prognostic factors for overall survival in glioblastoma patients. Preoperative tumor volume has also been investigated, but the results have been inconclusive. We hypothesized that the surface area and the shape were more representative of the tumor's infiltrative capacities, and thus, the purpose of this study was to assess the prognostic value of tumor size and shape in patients with glioblastoma.
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