ArticleScientific reports2025
Implication of tumor morphology and MRI characteristics on the accuracy of automated versus human segmentation of GBM areas.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: CT vs MRI.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Decoding the Glioblastoma Microenvironment: AI-Driven Analysis of Cellular MRI Signatures for Targeted Therapy.Cellular and molecular neurobiology · 2026Review
- Prediction of MGMT promoter methylation in glioblastoma and grade 4 astrocytoma using fluid-suppressed chemical exchange saturation transfer MRI and machine learning-based segmentation.Frontiers in oncology · 2026Article
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8 authors.
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Abstract
An assessment scheme is proposed to evaluate GBM gross tumor core and T2-FLAIR hyper-intensity segmentations on preoperative multicentric MR images as a function of tumor morphology and MRI characteristics. 74 gross tumor core and T2-FLAIR hyper-intensity BraTS-Toolkit and DeepBraTumIA automatic segmentations, and 42 gross tumor core neurosurgeon manual segmentations were accordingly evaluated. Brats-Toolkit and DeepBraTumIA generally provide accurate segmentations, particularly for the most common round-shaped or well-demarked tumors, where: (1) gross tumor segmentation correctly includes necrosis and contrast enhanced tumor in 100% and 97.06% of cases (vs. 73.68% for manual segmentation) and wrongly includes healthy or non-tumor related tissues in 2.94% and 20.59% of cases (vs. 10.53% for manual segmentations); (2) T2-FLAIR hyper-intensity segmentations completely includes edema in 88.24% of cases for both software. MR image quality has little impact on the segmentation performance on these tumors. Conversely, on less common tumors with more complex tissue distribution and infiltrative behavior, manual segmentation works better than BraTS-Toolkit and DeepBraTumIA, and image quality has a larger impact on automatic segmentation performance. BraTS-Toolkit and DeepBraTumIA gross tumor segmentation properly includes necrosis and contrast enhanced areas in 50% and 37.50% of cases (vs. 66.67% for manual segmentation), all corresponding to higher image quality; T2-FLAIR hyper-intensity segmentation wrongly includes necrosis and contrast enhanced areas in 37.50% and 50% of cases.
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