Pickhardt Perry J, Kattan Michael W, Lee Matthew H, Pooler B Dustin, Pyrros Ayis, Liu Daniel, Zea Ryan, Summers Ronald M, Garrett John W
The Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA.
The Department of Medical Physics, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA.
Nat Commun. 2025 Feb 7;16(1):1432. doi: 10.1038/s41467-025-56741-w.
We derive and test a CT-based biological age model for predicting longevity, using an automated pipeline of explainable AI algorithms that quantifies skeletal muscle, abdominal fat, aortic calcification, bone density, and solid abdominal organs. We apply these AI tools to abdominal CT scans from 123,281 adults (mean age, 53.6 years; 47% women; median follow-up, 5.3 years). The final weighted CT biomarker selection was based on the index of prediction accuracy. The CT model significantly outperforms standard demographic data for predicting longevity (IPA = 29.2 vs. 21.7; 10-year AUC = 0.880 vs. 0.779; p < 0.001). Age- and sex-corrected survival hazard ratio for the highest-vs-lowest risk quartile was 8.73 (95% CI,8.14-9.36) for the CT biological age model, and increased to 24.79 after excluding cancer diagnoses within 5 years of CT. Muscle density, aortic plaque burden, visceral fat density, and bone density contributed the most. Here we show a personalized phenotypic CT biological age model that can be opportunistically-derived, regardless of clinical indication, to better inform risk assessment.
我们推导并测试了一种基于CT的生物年龄模型来预测寿命，该模型使用了一套可解释人工智能算法的自动化流程，对骨骼肌、腹部脂肪、主动脉钙化、骨密度和腹部实性器官进行量化。我们将这些人工智能工具应用于123281名成年人（平均年龄53.6岁；47%为女性；中位随访时间5.3年）的腹部CT扫描。最终的加权CT生物标志物选择基于预测准确性指数。在预测寿命方面，CT模型显著优于标准人口统计学数据（IPA = 29.2对21.7；10年AUC = 0.880对0.779；p < 0.001）。CT生物年龄模型中，最高风险四分位数与最低风险四分位数的年龄和性别校正生存风险比为8.73（95%CI，8.14 - 9.36），在排除CT检查后5年内的癌症诊断后，该比值增至24.79。肌肉密度、主动脉斑块负荷、内脏脂肪密度和骨密度的贡献最大。在此，我们展示了一种个性化的表型CT生物年龄模型，该模型可以在不考虑临床指征的情况下机会性地推导出来，以更好地为风险评估提供信息。
