Wang Jing, Leader Joseph K, Meng Xin, Yu Tong, Wang Renwei, Herman James, Yuan Jian-Min, Wilson David, Pu Jiantao
medRxiv. 2024 Oct 15:2024.10.14.24315477. doi: 10.1101/2024.10.14.24315477.
To investigate if body composition is a biomarker for assessing the risk of developing lung cancer.
Low-dose computed tomography (LDCT) scans from the Pittsburgh Lung Screening Study (PLuSS) (n=3,635, 22 follow-up years) and NLST-ACRIN (n=16,435, 8 follow-up years) cohorts were used in the study. Artificial intelligence (AI) algorithms were developed to automatically segment and quantify subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), intramuscular adipose tissue (IMAT), skeletal muscle (SM), and bone. Cause-specific Cox proportional hazards models were used to evaluate the hazard ratios (HRs). Standard time-dependent receiver operating characteristic (ROC) analysis was used to evaluate the prognostic ability of different models over time.
The final composite models were formed by seven variables: age (HR=1.20), current smoking status (HR=1.59), bone volume (HR=1.79), SM density (HR=0.29), IMAT ratio (HR=0.33), IMAT density (HR=0.56), and SAT volume (HR=0.56). The models trained on the PLuSS cohort achieved a mean AUC of 0.76 (95% CI: 0.74-0.79) over 21 follow-up years and 0.70 (95% CI: 0.66-0.74) over the first 7 follow-up years for predicting lung cancer development within the PLuSS cohort. In contrast, models trained on the PLuSS cohort alone, as well as in combination with the NLST cohorts, achieved an AUC ranging from 0.61 to 0.68 in the NLST cohort over a 7-year follow-up period.
Body composition assessed on LDCT is a significant predictor of lung cancer risk and could improve the effectiveness of LDCT lung cancer screening by optimizing the screening eligibility and frequency.
Body composition assessed on LDCT is a significant predictor of lung cancer risk and could improve the effectiveness of LDCT lung cancer screening by optimizing the screening eligibility and frequency.
This study unveils the significant associations between body tissues and lung cancer risk.The prediction models based on body composition alone, as well as the combination of demographics and body composition features can effectively identify patients at higher risk of developing lung cancer.
研究身体成分是否为评估肺癌发生风险的生物标志物。
本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 = 16435，随访8年）队列的低剂量计算机断层扫描（LDCT）数据。开发了人工智能（AI）算法以自动分割并量化皮下脂肪组织（SAT）、内脏脂肪组织（VAT）、肌内脂肪组织（IMAT）、骨骼肌（SM）和骨骼。采用特定病因的Cox比例风险模型来评估风险比（HRs）。使用标准的时间依赖性受试者工作特征（ROC）分析来评估不同模型随时间的预后能力。
最终的综合模型由七个变量构成：年龄（HR = 1.20）、当前吸烟状态（HR = 1.59）、骨体积（HR = 1.79）、SM密度（HR = 0.29）、IMAT比例（HR = 0.33）、IMAT密度（HR = 0.56）和SAT体积（HR = 0.56）。在PLuSS队列上训练的模型在21年的随访期内预测PLuSS队列中肺癌发生的平均AUC为0.76（95% CI：0.74 - 0.79），在前7年的随访期内为0.70（95% CI：0.66 - 0.74）。相比之下，仅在PLuSS队列上训练的模型以及与NLST队列联合训练的模型在NLST队列7年的随访期内AUC范围为0.61至0.68。
基于LDCT评估的身体成分是肺癌风险的重要预测指标，通过优化筛查资格和频率可提高LDCT肺癌筛查的有效性。
基于LDCT评估的身体成分是肺癌风险的重要预测指标，通过优化筛查资格和频率可提高LDCT肺癌筛查的有效性。
本研究揭示了身体组织与肺癌风险之间的显著关联。仅基于身体成分以及人口统计学和身体成分特征相结合的预测模型能够有效识别肺癌发生风险较高的患者。
