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<p>Accurate and rapid delineation of diffuse gliomas is essential in emergency neuro-oncology, yet MRI is often unavailable. We present a deep-learning segmentation approach for routine non-contrast CT and evaluate its technical feasibility and WHO grade–stratified performance, demonstrating its potential as a decision-support tool in time-sensitive, resource-limited clinical settings. CT scans from 206 adults with histopathologically confirmed diffuse gliomas were retrospectively collected from a single center and were divided into a development cohort (<i>n</i> = 177) and an independent validation cohort (<i>n</i> = 29). The glioma segmentation network (GSN) consists of a ResNet-18 encoder and five-stage U-Net decoder, trained with inverse-frequency weighted cross-entropy to address class imbalance. Model development involved five-fold cross-validation on the development cohort (results reported in the main text). Primary outcome measures were Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95). Reference segmentations were generated by a board-certified neuroradiologist with over a decade of clinical experience using MITK. In the independent validation cohort, DSCs were 0.846 for grade 2 (95% CI 0.835–0.856), 0.806 for grade 3 (95% CI 0.800–0.811), and 0.802 for grade 4 (95% CI 0.796–0.809). Corresponding HD95 values were 13.677&#xa0;mm (95% CI 12.70–14.65), 16.193&#xa0;mm (95% CI 14.73–17.65), and 18.776&#xa0;mm (95% CI 17.95–19.90). Inference throughput was 20 slices per second (95% CI 16.1–26.3). The proposed GSN demonstrates robust internal performance and clinically meaningful segmentation accuracy on independent CT data, supporting the technical feasibility of CT-based automated glioma delineation for emergency and resource-constrained settings. Evaluation was performed at the 2D slice level. Limitations include the single-center design and reliance on reference segmentations performed by a single neuroradiologist. Prospective multi-center validation is warranted before routine clinical implementation.</p>

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CT-Based Glioma Segmentation Using Deep Learning: Validation for Emergency Neuro-oncological Care

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月13日

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  • Zohal Alnour Ahmed Emam,
  • Emel Ada,
  • Berrin Çavuşoğlu,
  • Burçin Pehlivanoğlu,
  • M. Alper Selver,
  • Kadir Akgüngör

摘要

Accurate and rapid delineation of diffuse gliomas is essential in emergency neuro-oncology, yet MRI is often unavailable. We present a deep-learning segmentation approach for routine non-contrast CT and evaluate its technical feasibility and WHO grade–stratified performance, demonstrating its potential as a decision-support tool in time-sensitive, resource-limited clinical settings. CT scans from 206 adults with histopathologically confirmed diffuse gliomas were retrospectively collected from a single center and were divided into a development cohort (n = 177) and an independent validation cohort (n = 29). The glioma segmentation network (GSN) consists of a ResNet-18 encoder and five-stage U-Net decoder, trained with inverse-frequency weighted cross-entropy to address class imbalance. Model development involved five-fold cross-validation on the development cohort (results reported in the main text). Primary outcome measures were Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95). Reference segmentations were generated by a board-certified neuroradiologist with over a decade of clinical experience using MITK. In the independent validation cohort, DSCs were 0.846 for grade 2 (95% CI 0.835–0.856), 0.806 for grade 3 (95% CI 0.800–0.811), and 0.802 for grade 4 (95% CI 0.796–0.809). Corresponding HD95 values were 13.677 mm (95% CI 12.70–14.65), 16.193 mm (95% CI 14.73–17.65), and 18.776 mm (95% CI 17.95–19.90). Inference throughput was 20 slices per second (95% CI 16.1–26.3). The proposed GSN demonstrates robust internal performance and clinically meaningful segmentation accuracy on independent CT data, supporting the technical feasibility of CT-based automated glioma delineation for emergency and resource-constrained settings. Evaluation was performed at the 2D slice level. Limitations include the single-center design and reliance on reference segmentations performed by a single neuroradiologist. Prospective multi-center validation is warranted before routine clinical implementation.