Li Thomas Z, Hin Lee Ho, Xu Kaiwen, Gao Riqiang, Dawant Benoit M, Maldonado Fabien, Sandler Kim L, Landman Bennett A
Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States.
Vanderbilt University, School of Medicine, Nashville, Tennessee, United States.
J Med Imaging (Bellingham). 2023 Jul;10(4):044002. doi: 10.1117/1.JMI.10.4.044002. Epub 2023 Jul 18.
Anatomy-based quantification of emphysema in a lung screening cohort has the potential to improve lung cancer risk stratification and risk communication. Segmenting lung lobes is an essential step in this analysis, but leading lobe segmentation algorithms have not been validated for lung screening computed tomography (CT).
In this work, we develop an automated approach to lobar emphysema quantification and study its association with lung cancer incidence. We combine self-supervised training with level set regularization and finetuning with radiologist annotations on three datasets to develop a lobe segmentation algorithm that is robust for lung screening CT. Using this algorithm, we extract quantitative CT measures for a cohort () from the National Lung Screening Trial and analyze the multivariate association with lung cancer incidence.
Our lobe segmentation approach achieved an external validation Dice of 0.93, significantly outperforming a leading algorithm at 0.90 (). The percentage of low attenuation volume in the right upper lobe was associated with increased lung cancer incidence (odds ratio: 1.97; 95% CI: [1.06, 3.66]) independent of risk factors and diagnosis of whole lung emphysema. Quantitative lobar emphysema improved the goodness-of-fit to lung cancer incidence (, ).
We are the first to develop and validate an automated lobe segmentation algorithm that is robust to smoking-related pathology. We discover a quantitative risk factor, lending further evidence that regional emphysema is independently associated with increased lung cancer incidence. The algorithm is provided at https://github.com/MASILab/EmphysemaSeg.
对肺部筛查队列中的肺气肿进行基于解剖学的量化，有可能改善肺癌风险分层和风险沟通。分割肺叶是该分析中的关键步骤，但领先的肺叶分割算法尚未在肺部筛查计算机断层扫描（CT）中得到验证。
在这项工作中，我们开发了一种自动方法来量化肺叶肺气肿，并研究其与肺癌发病率的关联。我们将自监督训练与水平集正则化相结合，并在三个数据集上使用放射科医生的注释进行微调，以开发一种对肺部筛查CT具有鲁棒性的肺叶分割算法。使用该算法，我们从国家肺部筛查试验中提取了一个队列（）的定量CT测量值，并分析了与肺癌发病率的多变量关联。
我们的肺叶分割方法在外部验证中达到了0.93的Dice系数，显著优于领先算法的0.90（）。右上叶低衰减体积百分比与肺癌发病率增加相关（优势比：1.97；95%置信区间：[1.06, 3.66]），独立于 风险因素和全肺肺气肿的诊断。定量肺叶肺气肿改善了对肺癌发病率的拟合优度（，）。
我们首次开发并验证了一种对吸烟相关病理具有鲁棒性的自动肺叶分割算法。我们发现了一个定量风险因素，进一步证明区域肺气肿与肺癌发病率增加独立相关。该算法可在https://github.com/MASILab/EmphysemaSeg上获取。
