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<p>Glioma is the most common primary brain tumor, with high-grade glioma (HGG) posing significant clinical challenges due to its poor survival outcomes. One-year tumor recurrence indicates a poor prognosis, making accurate progression risk prediction models critical for clinical decision-making. This study aimed to develop a novel combined model (DL_com) based on the MobileNet-based Hybrid Network (MobHy-Net), integrating clinical variables and deep learning features from both T2-FLAIR and extracellular volume images to predict 1-year progression risk. Preoperative multi-sequence MRI (T1WI, T1C, and T2-FLAIR) from 193 HGG patients across two centers was analyzed. DL_com demonstrated superior predictive performance, with area under the curve values of 0.954 (training), 0.911 (validation), and 0.919 (test), significantly outperforming other models (<i>P</i> &lt; 0.05). Furthermore, decision curve analysis confirmed its clinical utility, and Shapley Additive Explanations analysis enhanced its visualization and interpretability. DL_com effectively predicts 1-year progression risk in HGG, offering a valuable tool for risk stratification and clinical decision support.</p>

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Radiomics model integrating MRI and ECV enhances prediction accuracy for progression in high-grade glioma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月13日

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  • Gefei Jiang,
  • Xingjian Sun,
  • Yuchen Zhu,
  • Yinjiao Fei,
  • Weilin Xu,
  • Zhichao Jiang,
  • Tianchi Shao,
  • Yuandong Cao,
  • Liting Li,
  • Shu Zhou

摘要

Glioma is the most common primary brain tumor, with high-grade glioma (HGG) posing significant clinical challenges due to its poor survival outcomes. One-year tumor recurrence indicates a poor prognosis, making accurate progression risk prediction models critical for clinical decision-making. This study aimed to develop a novel combined model (DL_com) based on the MobileNet-based Hybrid Network (MobHy-Net), integrating clinical variables and deep learning features from both T2-FLAIR and extracellular volume images to predict 1-year progression risk. Preoperative multi-sequence MRI (T1WI, T1C, and T2-FLAIR) from 193 HGG patients across two centers was analyzed. DL_com demonstrated superior predictive performance, with area under the curve values of 0.954 (training), 0.911 (validation), and 0.919 (test), significantly outperforming other models (P < 0.05). Furthermore, decision curve analysis confirmed its clinical utility, and Shapley Additive Explanations analysis enhanced its visualization and interpretability. DL_com effectively predicts 1-year progression risk in HGG, offering a valuable tool for risk stratification and clinical decision support.