用基础模型将普通MRI转为可临床使用的量化生物标志物
Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes
- 用可提示的基础分割模型，自动处理多种骨骼肌影像数据
- 测量结果与专家标注高度一致，能预测48个月关节置换和骨关节炎
- 系统开源且模块化，适合医院部署与独立验证
肌肉骨骼影像的精准医疗需要可扩展的测量基础设施。我们开发了一个模块化系统，将常规MRI转化为适用于临床决策支持的标准化定量生物标志物。通过在异构肌肉骨骼数据集上微调可提示的基础分割模型（SAM、SAM2、MedSAM），并结合自动化检测实现完全自动化的提示。微调后的分割结果在软组织、骨和软骨生物标志物上与专家标注具有高一致性。基于相同测量，我们展示了两项应用：(i) 三阶段膝关节分诊流程，在保持敏感度的同时减少验证工作量；(ii) 48个月关键时间点模型，可在临床上相关阈值下良好校准并带来净收益，用于预测膝关节置换和新发骨关节炎。该模型无关、开源的架构支持独立验证与持续开发。本研究验证了从自动化测量到临床决策的路径：可靠生物标志物既可优化当前工作负荷，也可实现未来患者风险分层，证明了基础模型在精准医疗系统中的可落地性。
原文摘要 · Abstract (English)
Precision medicine in musculoskeletal imaging requires scalable measurement infrastructure. We developed a modular system that converts routine MRI into standardized quantitative biomarkers suitable for clinical decision support. Promptable foundation segmenters (SAM, SAM2, MedSAM) were fine-tuned across heterogeneous musculoskeletal datasets and coupled to automated detection for fully automatic prompting. Fine-tuned segmentations yielded clinically reliable measurements with high concordance to expert annotations across cartilage, bone, and soft tissue biomarkers. Using the same measurements, we demonstrate two applications: (i) a three-stage knee triage cascade that reduces verification workload while maintaining sensitivity, and (ii) 48-month landmark models that forecast knee replacement and incident osteoarthritis with favorable calibration and net benefit across clinically relevant thresholds. Our model-agnostic, open-source architecture enables independent validation and development. This work validates a pathway from automated measurement to clinical decision: reliable biomarkers drive both workload optimization today and patient risk stratification tomorrow, and the developed framework shows how foundation models can be operationalized within precision medicine systems.
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