用全身自监督学习提升多种疾病早期风险预测能力
Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment
- 基于全身图像的自监督表征学习，无需人工标注特征
- 在心脑血管病、糖尿病等四类疾病上优于传统影像组学方法
- 可独立用于筛查或融合多模态数据，适合临床早期风险评估
可靠的前期疾病风险评估对推动公共医疗从被动治疗转向主动识别与预防至关重要。然而，现有的基于图像的风险预测算法通常只针对单一疾病，且依赖分割工具提取的手工特征。本文提出一种面向竞争风险建模的全身自监督表征学习方法，可在多种疾病（包括心血管疾病、2型糖尿病、慢性阻塞性肺病和慢性肾病）中超越传统全身影像组学表现。在模拟前期筛查场景下，结合心脏MRI进一步提升了对缺血性心脏病、高血压病和中风亚型的预测精度。结果表明，全身表征具有作为独立筛查工具或融入多模态临床流程的转化潜力，适用于早期个性化风险分层。代码已公开于 https://github.com/yayapa/WBRLforCR/
原文摘要 · Abstract (English)
Reliable preclinical disease risk assessment is essential to move public healthcare from reactive treatment to proactive identification and prevention. However, image-based risk prediction algorithms often consider one condition at a time and depend on hand-crafted features obtained through segmentation tools. We propose a whole-body self-supervised representation learning method for the preclinical disease risk assessment under a competing risk modeling. This approach outperforms whole-body radiomics in multiple diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D), chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD). Simulating a preclinical screening scenario and subsequently combining with cardiac MRI, it sharpens further the prediction for CVD subgroups: ischemic heart disease (IHD), hypertensive diseases (HD), and stroke. The results indicate the translational potential of whole-body representations as a standalone screening modality and as part of a multi-modal framework within clinical workflows for early personalized risk stratification. The code is available at https://github.com/yayapa/WBRLforCR/
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