Wang Jing, Leader Joseph K, Meng Xin, Yu Tong, Wang Renwei, Yuan Jian-Min, Wilson David, Pu Jiantao
Department of Radiology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
Eur Radiol. 2025 Jun 6. doi: 10.1007/s00330-025-11735-6.
To investigate if body composition can serve as a biomarker for assessing the risk of developing lung cancer.
We conducted a retrospective study using low-dose computed tomography (LDCT) scans from the Pittsburgh lung screening study (PLuSS) (n = 3635, 22 follow-up years) and the NLST-ACRIN (n = 16,360, 8 follow-up years) cohort. Five types of body tissues, including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), intramuscular adipose tissue (IMAT), skeletal muscle (SM), and bone, were automatically segmented by our previously developed algorithms. Volume and density metrics were computed. Cause-specific Cox proportional hazards models were utilized to assess hazard ratios (HRs). Time-dependent area under the receiver operating characteristic curve (AUC-ROC) was used to evaluate model performance. The cumulative incidence function was estimated for different risk groups.
The final composite models were formed by age (HR = 1.30 (95% CI: 1.17-1.43)), current smoking status (HR = 1.85 (1.49-2.28)), bone volume (HR = 1.38 (1.25-1.52)), bone density (HR = 0.80 (0.71-0.89)), SM density (HR = 0.62 (0.58-0.66)), IMAT ratio (HR = 0.65 (0.58-0.73)), and SAT volume (HR = 0.76 (0.67-0.87)). The model trained on the PLuSS cohort achieved a mean AUC of 0.77 (0.75-0.79) over 21 years and 0.71 (0.68-0.74) over the first 7 years for lung cancer prediction. External validation on the NLST cohort yielded AUC values ranging from 0.63 to 0.66 over a 7-year follow-up period. The model trained on a combined dataset of PLuSS and NLST achieved a mean AUC of 0.71 (0.7-0.72) over 21 years.
Three-dimensional body composition metrics assessed through LDCT are a significant predictor of lung cancer risk.
Question Is body composition a biomarker for lung cancer risk assessment? Findings Body composition metrics derived from low-dose CT scans, including volumes and densities of fat, bone, and muscle, are strong predictors of lung cancer risk. Clinical relevance Lung cancer risk stratification can be improved by body composition features, providing guidance for personalized lung cancer screening strategies.
研究身体成分是否可作为评估患肺癌风险的生物标志物。
我们进行了一项回顾性研究，使用了来自匹兹堡肺癌筛查研究（PLuSS）（n = 3635，随访22年）和NLST - ACRIN（n = 16360，随访8年）队列的低剂量计算机断层扫描（LDCT）数据。通过我们先前开发的算法自动分割五种身体组织，包括皮下脂肪组织（SAT）、内脏脂肪组织（VAT）、肌内脂肪组织（IMAT）、骨骼肌（SM）和骨骼。计算体积和密度指标。使用特定病因的Cox比例风险模型评估风险比（HRs）。采用时间依赖性受试者操作特征曲线下面积（AUC - ROC）评估模型性能。估计不同风险组的累积发病率函数。
最终的综合模型由年龄（HR = 1.30（95%CI：1.17 - 1.43））、当前吸烟状态（HR = 1.85（1.49 - 2.28））、骨体积（HR = 1.38（1.25 - 1.52））、骨密度（HR = 0.80（0.71 - 0.89））、SM密度（HR = 0.62（0.58 - 0.66））、IMAT比例（HR = 0.65（0.58 - 0.73））和SAT体积（HR = 0.76（0.67 - 0.87））构成。在PLuSS队列上训练的模型在21年中预测肺癌的平均AUC为0.77（0.75 - 0.79），在前7年中为0.71（0.68 - 0.74）。在NLST队列上的外部验证在7年随访期内产生的AUC值范围为0.63至0.66。在PLuSS和NLST组合数据集上训练的模型在21年中的平均AUC为0.71（0.7 - 0.72）。
通过LDCT评估的三维身体成分指标是肺癌风险的重要预测指标。
问题身体成分是否是肺癌风险评估的生物标志物？发现从低剂量CT扫描得出的身体成分指标，包括脂肪、骨骼和肌肉的体积和密度，是肺癌风险的强有力预测指标。临床意义身体成分特征可改善肺癌风险分层，为个性化肺癌筛查策略提供指导。
