医学大模型首次在真实临床中验证，可精准识别前列腺癌。
ProstNFound+: A Prospective Study using Medical Foundation Models for Prostate Cancer Detection
- 用医疗大模型+临床指标定制提示编码器，生成癌症热图与风险评分。
- 前瞻性测试显示性能稳定，与临床评分高度一致，热图与活检结果吻合。
- 适合希望提升诊断效率、实现可解释性辅助的医疗机构使用。
目的：医学基础模型（FMs）为构建高性能诊断系统提供了可能，但其在微超声（μUS）图像中用于前列腺癌（PCa）检测的临床应用尚未经过验证。本文提出ProstNFound+，对医学大模型进行适配，以实现μUS图像中的PCa检测，并完成首次前瞻性验证。方法：ProstNFound+结合医学基础模型、适配器微调和自定义提示编码器，嵌入前列腺癌特异性临床生物标志物；模型输出癌症热图及显著性前列腺癌风险评分。在多中心回顾性数据上训练后，模型在五年后新临床站点采集的前瞻性数据上进行评估。预测结果与标准临床评分体系（PRI-MUS 和 PI-RADS）进行对比。结果：ProstNFound+在前瞻性数据上表现出强泛化能力，性能未下降，与临床评分高度一致，生成的热图与活检确诊病灶位置吻合。结论：研究结果表明该模型具备临床部署潜力，可作为专家驱动协议的可扩展、可解释替代方案。
原文摘要 · Abstract (English)
Purpose: Medical foundation models (FMs) offer a path to build high-performance diagnostic systems. However, their application to prostate cancer (PCa) detection from micro-ultrasound (μUS) remains untested in clinical settings. We present ProstNFound+, an adaptation of FMs for PCa detection from μUS, along with its first prospective validation. Methods: ProstNFound+ incorporates a medical FM, adapter tuning, and a custom prompt encoder that embeds PCa-specific clinical biomarkers. The model generates a cancer heatmap and a risk score for clinically significant PCa. Following training on multi-center retrospective data, the model is prospectively evaluated on data acquired five years later from a new clinical site. Model predictions are benchmarked against standard clinical scoring protocols (PRI-MUS and PI-RADS). Results: ProstNFound+ shows strong generalization to the prospective data, with no performance degradation compared to retrospective evaluation. It aligns closely with clinical scores and produces interpretable heatmaps consistent with biopsy-confirmed lesions. Conclusion: The results highlight its potential for clinical deployment, offering a scalable and interpretable alternative to expert-driven protocols.
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