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<p>Gliomas and brain metastases (BMs) on MRI pose significant diagnostic challenges for radiologists. This study aims to develop a multi-task model and a computer-aided diagnosis (CAD) system for the detection and diagnosis of gliomas and BMs. This study enrolled 3909 participants from seven centers, and developed a brain tumor segmentation and classification network (BTSC-Net) and BTSC-CAD with visualization of tumor masks. For detection, BTSC-Net achieved a Dice coefficient of 0.888 and 0.872 on the internal and external test sets, respectively. For diagnosis, BTSC-Net achieved AUCs of 0.941 and 0.933 on the internal and external test sets, respectively. With BTSC-CAD assistance, junior radiologists achieved mean AUC improvements of 4.8% (<i>P</i> &lt; 0.05) for detection and 17.3% (<i>P</i> &lt; 0.001) for diagnosis, along with an average reduction of 64.75 s in reading time. BTSC-CAD significantly improved radiologists’ diagnostic accuracy and efficiency.</p>

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Multi-task deep learning assists detection and diagnosis of gliomas and brain metastases

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月16日

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  • Xiao Liu,
  • Kun Lv,
  • Peng Du,
  • Kun Zhou,
  • Qianru Zhao,
  • Sen Jiao,
  • Hongyi Chen,
  • Danni Zhang,
  • Hui Fang,
  • Qiuyue Han,
  • Yanwei Zeng,
  • Xin Cao,
  • Haiqing Li,
  • Jian Dai,
  • Zhiji Zheng,
  • Hao Wu,
  • Xin Wang,
  • Yuxin Li,
  • Daoying Geng

摘要

Gliomas and brain metastases (BMs) on MRI pose significant diagnostic challenges for radiologists. This study aims to develop a multi-task model and a computer-aided diagnosis (CAD) system for the detection and diagnosis of gliomas and BMs. This study enrolled 3909 participants from seven centers, and developed a brain tumor segmentation and classification network (BTSC-Net) and BTSC-CAD with visualization of tumor masks. For detection, BTSC-Net achieved a Dice coefficient of 0.888 and 0.872 on the internal and external test sets, respectively. For diagnosis, BTSC-Net achieved AUCs of 0.941 and 0.933 on the internal and external test sets, respectively. With BTSC-CAD assistance, junior radiologists achieved mean AUC improvements of 4.8% (P < 0.05) for detection and 17.3% (P < 0.001) for diagnosis, along with an average reduction of 64.75 s in reading time. BTSC-CAD significantly improved radiologists’ diagnostic accuracy and efficiency.