RATIONALE AND OBJECTIVES: We have developed a technique to measure ventilation heterogeneity (VH) on low dose chest CT scan that we hypothesize may be associated with the development of lung nodules, and perhaps cancer. If true, such an analysis may improve screening by identifying regional areas of higher risk. MATERIALS AND METHODS: Using the National Lung Screening Trial database, we identified a small subset of those participants who were labeled as having a positive screening test at 1 year (T1) but not at baseline (T0). We isolated the region in which the nodule would form on the T0 scan ("target region") and measured VH as the standard deviation of the linear dimension of a virtual cubic airspace based on measurement of lung attenuation within the region. RESULTS: We analyzed 24 cases, 9 with lung cancer and 15 with a benign nodule. We found that the VH of the target region was nearly statistically greater than that of the corresponding contralateral control region (0.168 [0.110-0.226] vs. 0.112 [0.083-0.203], p = 0.051). The % emphysema within the target region was greater than that of the corresponding contralateral control region (1.339 [0.264-4.367] vs. 1.092 [0.375-4.748], p = 0.037). There was a significant correlation between the % emphysema and the VH of the target region (rho = +0.437, p = 0.026). CONCLUSION: Our study provides the first data in support of increased local VH being associated with subsequent lung nodule formation. Further work is necessary to determine whether this technique can enhance screening for lung cancer by low dose chest CT scan.

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease that can benefit from novel approaches to understanding its evolution and divergent trajectories. Artificial intelligence (AI) has revolutionized how we can use clinical, imaging, and molecular data to understand and model complex systems. AI has shown impressive results in areas related to automated clinical decision making, radiological interpretation and prognostication. The unique nature of COPD and the accessibility to well-phenotyped populations result in an ideal scenario for AI development. This review provides an introduction to AI and deep learning and presents some recent successes in applying AI in COPD. Finally, we will discuss some of the opportunities, challenges, and limitations for AI applications in the context of COPD.