BACKGROUND Tobacco smoke exposure is associated with emphysema and pulmonary fibrosis, both of which are irreversible. We have developed a new objective computed tomography (CT) analysis tool that combines densitometry with machine learning to detect high attenuation changes in visually normal appearing lung (NormHA) that may precede these diseases. METHODS We trained the classification tool by placing 34528 training points in chest CT scans from 297 COPDGene participants. The tool was then used to classify lung tissue in 9038 participants as normal, emphysema, fibrotic/interstitial, or NormHA. Associations between the quartile of NormHA and plasma-based biomarkers, clinical severity and mortality were evaluated using Jonckheere-Terpstra, pairwise Wilcoxon Rank Sum tests, and multivariable linear and Cox regression. RESULTS A higher percentage of lung occupied by NormHA was associated with higher c-reactive protein and intercellular adhesion molecule 1 (p for trend for both < 0.001). In analyses adjusted for multiple covariates, including high and low attenuation area, compared to those in the lowest quartile of NormHA, those in the highest quartile had a 6.50 absolute percent lower percent predicted lower forced expiratory volume in one second (p<0.001), an 8.48 absolute percent lower percent predicted forced expiratory volume, a 10.78 meter shorter six-minute walk distance (p=0.011) and a 56% higher risk of death (p=0.003). These findings were present even in those individuals without visually defined interstitial lung abnormalities. CONCLUSIONS A new class of visually normal appearing tissue (NormHA) on CT may represent a unique tissue class associated with adverse outcomes, independent of emphysema and fibrosis.

中文翻译：

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胸部计算机断层扫描客观测量慢性肺损伤

背景烟草烟雾暴露与肺气肿和肺纤维化有关，两者都是不可逆的。我们开发了一种新的客观计算机断层扫描 (CT) 分析工具，该工具将密度计与机器学习相结合，以检测可能先于这些疾病的视觉正常外观肺 (NormHA) 的高衰减变化。方法 我们通过在来自 297 名 COPDGene 参与者的胸部 CT 扫描中放置 34528 个训练点来训练分类工具。然后使用该工具将 9038 名参与者的肺组织分类为正常、肺气肿、纤维化/间质或 NormHA。使用 Jonckheere-Terpstra、成对 Wilcoxon 秩和检验以及多变量线性和 Cox 回归评估 NormHA 四分位数与基于血浆的生物标志物、临床严重程度和死亡率之间的关联。结果 NormHA 占据的肺的百分比较高与较高的 c 反应蛋白和细胞间粘附分子 1 相关（p 趋势均 < 0.001）。在针对包括高衰减区和低衰减区在内的多个协变量进行调整的分析中，与 NormHA 最低四分位数的那些相比，最高四分位数的那些一秒内预测的较低用力呼气量的绝对百分比降低了 6.50%（p<0.001），预测用力呼气量的绝对百分比降低了 8.48%，六分钟步行距离缩短了 10.78 米 (p=0.011)，死亡风险提高了 56% (p=0.003)。这些发现甚至存在于那些没有视觉上定义的间质性肺异常的个体中。