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/

翻译：可靠的临床前疾病风险评估对于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，基于图像的风险预测算法通常一次只考虑一种病症，并且依赖于通过分割工具获得的手工特征。我们提出了一种全身自监督表征学习方法，用于竞争风险建模下的临床前疾病风险评估。该方法在多种疾病（包括心血管疾病、2型糖尿病、慢性阻塞性肺疾病和慢性肾脏病）上的表现优于全身影像组学。通过模拟临床前筛查场景并随后与心脏磁共振成像结合，该方法进一步提升了针对心血管疾病亚组（缺血性心脏病、高血压性疾病和卒中）的预测能力。结果表明，全身表征作为一种独立的筛查方式，或作为临床工作流程中多模态框架的一部分，在早期个性化风险分层方面具有转化潜力。代码可在 https://github.com/yayapa/WBRLforCR/ 获取。