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<p>Accurate prostate cancer (PCa) diagnosis remains difficult because of tumor heterogeneity and the challenge of integrating multimodal clinical information. We developed Prost-LM, a multimodal large language model that jointly embeds MRI-derived features, numerical PSA values, and free-text clinical reports into a unified semantic space to enable deep cross-modal reasoning. Trained and validated on a large multi-center cohort of 3940 patients, Prost-LM achieved strong diagnostic performance, with an internal validation AUC of 0.954 for distinguishing PCa from benign conditions, outperforming MRI-only models (AUC = 0.868, <i>P</i> &lt; 0.001). For detecting clinically significant PCa (Gleason score ≥ 7), Prost-LM reached an AUC of 0.955. Additionally, the model provides interpretable diagnostic decisions to support clinical verification. These results suggest Prost-LM can improve automated PCa diagnosis and support precision oncology through multimodal AI.</p>

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Integrating multimodal clinical data with a large model for prostate cancer diagnosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月25日

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  • Chengbang Wang,
  • Yuan Tian,
  • Shaojie Yin,
  • Xuhong Zhang,
  • Xuedong Wei,
  • Lingfeng Wu,
  • Zhengdong Zhou,
  • Guijian Pang,
  • Yan Wang,
  • Wangjian Wu,
  • Shukai Zhao,
  • Ziwei Wang,
  • Jiangnan Xu,
  • Hao He,
  • Minglun Li,
  • Zhankui Jia,
  • Xu Gao,
  • Fubo Wang,
  • Guangtao Zhai,
  • Bin Xu

摘要

Accurate prostate cancer (PCa) diagnosis remains difficult because of tumor heterogeneity and the challenge of integrating multimodal clinical information. We developed Prost-LM, a multimodal large language model that jointly embeds MRI-derived features, numerical PSA values, and free-text clinical reports into a unified semantic space to enable deep cross-modal reasoning. Trained and validated on a large multi-center cohort of 3940 patients, Prost-LM achieved strong diagnostic performance, with an internal validation AUC of 0.954 for distinguishing PCa from benign conditions, outperforming MRI-only models (AUC = 0.868, P < 0.001). For detecting clinically significant PCa (Gleason score ≥ 7), Prost-LM reached an AUC of 0.955. Additionally, the model provides interpretable diagnostic decisions to support clinical verification. These results suggest Prost-LM can improve automated PCa diagnosis and support precision oncology through multimodal AI.