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<p>Despite prior success in classifying recurrent glioma noninvasively with multi-parametric MRI and AI, clinical applicability has yet to be demonstrated due to a lack of robust model evaluation and spatial preservation of tumor characteristics. This study develops, robustly evaluates, and clinically validates an interpretable model for predicting recurrent tumors from spatially varying, histopathologically-confirmed tissue samples. Machine learning models were developed using 254 pre-surgical multi-parametric MRI patches surrounding coordinates of tissue samples taken during recurrent surgery. A test AUROC of 0.74 ± 0.08 for distinguishing recurrent tumors, and 0.99 ± 0.01 for normal-appearing brain, demonstrated the feasibility of spatially mapping heterogeneity. Important features were consistent with current literature, and uncertainty was correlated with model failures (<i>p</i> ≤ 0.05). Volumetrics derived from prediction maps of recurrent tumors generated using a separate cohort of 56 patients with recurrent high-grade gliomas were significantly associated with survival. These results demonstrate a step towards clinical applicability of spatially mapping glioma recurrence.</p>

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Spatially identifying regions of tumor recurrence in patients with suspected recurrent glioma using physiologic MRI and machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月25日

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  • Jacob Ellison,
  • Nate Tran,
  • Tracy L. Luks,
  • Paramjot Singh,
  • Angela Jakary,
  • Tiffany Ngan,
  • Julia Cluceru,
  • Joanna J. Phillips,
  • Yan Li,
  • Annette M. Molinaro,
  • Valentina Pedoia,
  • Anny Shai,
  • Devika Nair,
  • Javier E. Villanueva-Meyer,
  • Mitchel S. Berger,
  • Shawn L. Hervey-Jumper,
  • Manish Aghi,
  • Susan M. Chang,
  • Janine M. Lupo

摘要

Despite prior success in classifying recurrent glioma noninvasively with multi-parametric MRI and AI, clinical applicability has yet to be demonstrated due to a lack of robust model evaluation and spatial preservation of tumor characteristics. This study develops, robustly evaluates, and clinically validates an interpretable model for predicting recurrent tumors from spatially varying, histopathologically-confirmed tissue samples. Machine learning models were developed using 254 pre-surgical multi-parametric MRI patches surrounding coordinates of tissue samples taken during recurrent surgery. A test AUROC of 0.74 ± 0.08 for distinguishing recurrent tumors, and 0.99 ± 0.01 for normal-appearing brain, demonstrated the feasibility of spatially mapping heterogeneity. Important features were consistent with current literature, and uncertainty was correlated with model failures (p ≤ 0.05). Volumetrics derived from prediction maps of recurrent tumors generated using a separate cohort of 56 patients with recurrent high-grade gliomas were significantly associated with survival. These results demonstrate a step towards clinical applicability of spatially mapping glioma recurrence.