Xu Kaiwen, Khan Mirza S, Li Thomas, Gao Riqiang, Antic Sanja L, Huo Yuankai, Sandler Kim L, Maldonado Fabien, Landman Bennett A
Vanderbilt University, 2201 West End Ave, Nashville, TN, USA 37235.
Vanderbilt University Medical Center, 1211 Medical Center Dr, Nashville, TN, USA 37232.
Proc SPIE Int Soc Opt Eng. 2023 Feb;12464. doi: 10.1117/12.2654018. Epub 2023 Apr 11.
In lung cancer screening, estimation of future lung cancer risk is usually guided by demographics and smoking status. The role of constitutional profiles of human body, a.k.a. body habitus, is increasingly understood to be important, but has not been integrated into risk models. Chest low dose computed tomography (LDCT) is the standard imaging study in lung cancer screening, with the capability to discriminate differences in body composition and organ arrangement in the thorax. We hypothesize that the primary phenotypes identified using lung screening chest LDCT can form a representation of body habitus and add predictive power for lung cancer risk stratification. In this pilot study, we evaluated the feasibility of body habitus image-based phenotyping on a large lung screening LDCT dataset. A thoracic imaging manifold was estimated based on an intensity-based pairwise (dis)similarity metric for pairs of spatial normalized chest LDCT images. We applied the hierarchical clustering method on this manifold to identify the primary phenotypes. Body habitus features of each identified phenotype were evaluated and associated with future lung cancer risk using time-to-event analysis. We evaluated the method on the baseline LDCT scans of 1,200 male subjects sampled from National Lung Screening Trial. Five primary phenotypes were identified, which were associated with highly distinguishable clinical and body habitus features. Time-to-event analysis against future lung cancer incidences showed two of the five identified phenotypes were associated with elevated future lung cancer risks (HR=1.61, 95% CI = [1.08, 2.38], p=0.019; HR=1.67, 95% CI = [0.98, 2.86], p=0.057). These results indicated that it is feasible to capture the body habitus by image-base phenotyping using lung screening LDCT and the learned body habitus representation can potentially add value for future lung cancer risk stratification.
在肺癌筛查中，未来肺癌风险的评估通常由人口统计学特征和吸烟状况来指导。人体的体质特征，即体型，其作用日益被认为很重要，但尚未被纳入风险模型。胸部低剂量计算机断层扫描（LDCT）是肺癌筛查的标准影像学检查，能够区分胸部的身体成分和器官排列差异。我们假设，通过肺部筛查胸部LDCT识别出的主要表型可以形成体型的一种表征，并为肺癌风险分层增加预测能力。在这项初步研究中，我们评估了基于体型图像的表型分析在大型肺部筛查LDCT数据集上的可行性。基于空间归一化胸部LDCT图像对的基于强度的成对（不）相似性度量，估计了一个胸部成像流形。我们对这个流形应用层次聚类方法来识别主要表型。使用事件发生时间分析评估每个识别出的表型的体型特征，并将其与未来肺癌风险相关联。我们在从国家肺癌筛查试验中抽取的1200名男性受试者的基线LDCT扫描上评估了该方法。识别出了五种主要表型，它们与高度可区分的临床和体型特征相关。针对未来肺癌发病率的事件发生时间分析表明，五种识别出的表型中有两种与未来肺癌风险升高相关（风险比=1.61，95%置信区间=[1.08, 2.38]，p=0.019；风险比=1.67，95%置信区间=[0.98, 2.86]，p=0.057）。这些结果表明，使用肺部筛查LDCT通过基于图像的表型分析来捕捉体型是可行的，并且所获得的体型表征可能会为未来肺癌风险分层增加价值。
