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<p>Predicting isocitrate dehydrogenase (IDH) mutations in gliomas using magnetic resonance imaging (MRI) is clinically important for treatment planning. This study compared two artificial intelligence (AI) models, GliomaDepth-IDH (ResNet34-based) and GliomaVista-IDH (Vision Transformer-based), with 18 physicians (eight neuroradiologists, five neurosurgeons, and five neurosurgery residents) in predicting IDH mutation status. On the Brain Tumor Segmentation Challenge dataset, the GliomaVista-IDH AI model achieved an area under the curve (AUC) value of 0.97, significantly outperforming all physician groups. However, external validation on a Japanese cohort revealed performance degradation: GliomaDepth-IDH declined to an AUC of 0.75 and GliomaVista-IDH to 0.82, with GliomaVista-IDH showing significant calibration issues (Brier score = 0.32). High-performing physicians achieved comparable results (AUC = 0.88) with superior calibration (Brier score = 0.19). Inter-rater reliability analysis revealed substantial variability across physician groups. These findings suggest that AI models can assist many physicians, while experienced practitioners remain competitive with better-calibrated predictions in challenging domains.</p>

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Comparing artificial intelligence and physician performance in predicting IDH mutation status in glioma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月5日

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  • Satoshi Takahashi,
  • Masamichi Takahashi,
  • Manabu Kinoshita,
  • Mototaka Miyake,
  • Risa Kawaguchi,
  • Naoki Shinojima,
  • Akitake Mukasa,
  • Kuniaki Saito,
  • Motoo Nagane,
  • Ryohei Otani,
  • Fumi Higuchi,
  • Shota Tanaka,
  • Nobuhiro Hata,
  • Kaoru Tamura,
  • Kensuke Tateishi,
  • Ryo Nishikawa,
  • Hideyuki Arita,
  • Masahiro Nonaka,
  • Takehiro Uda,
  • Junya Fukai,
  • Yoshiko Okita,
  • Naohiro Tsuyuguchi,
  • Yonehiro Kanemura,
  • Fumiyasu Tsushima,
  • Shingo Kakeda,
  • Toshiaki Akashi,
  • Toshiaki Taoka,
  • Yoshiyuki Watanabe,
  • Kei Yamada,
  • Toshinori Hirai,
  • Minako Azuma,
  • Takashi Yoshiura,
  • Jun Sese,
  • Koichi Ichimura,
  • Yoshitaka Narita,
  • Ryuji Hamamoto

摘要

Predicting isocitrate dehydrogenase (IDH) mutations in gliomas using magnetic resonance imaging (MRI) is clinically important for treatment planning. This study compared two artificial intelligence (AI) models, GliomaDepth-IDH (ResNet34-based) and GliomaVista-IDH (Vision Transformer-based), with 18 physicians (eight neuroradiologists, five neurosurgeons, and five neurosurgery residents) in predicting IDH mutation status. On the Brain Tumor Segmentation Challenge dataset, the GliomaVista-IDH AI model achieved an area under the curve (AUC) value of 0.97, significantly outperforming all physician groups. However, external validation on a Japanese cohort revealed performance degradation: GliomaDepth-IDH declined to an AUC of 0.75 and GliomaVista-IDH to 0.82, with GliomaVista-IDH showing significant calibration issues (Brier score = 0.32). High-performing physicians achieved comparable results (AUC = 0.88) with superior calibration (Brier score = 0.19). Inter-rater reliability analysis revealed substantial variability across physician groups. These findings suggest that AI models can assist many physicians, while experienced practitioners remain competitive with better-calibrated predictions in challenging domains.