Pathomic Features Reveal Immune and Molecular Evolution from Lung Preneoplasia to Invasive Adenocarcinoma.

Recent statistics on lung cancer, including the steady decline of advanced diseases and the dramatically increasing detection of early-stage diseases and indeterminate pulmonary nodules (IPN), mark the significance of a comprehensive understanding of early lung carcinogenesis. Lung adenocarcinoma (ADC) is the most common histologic subtype of lung cancer, and atypical adenomatous hyperplasia (AAH) is the only recognized preneoplasia to ADC, which may progress to adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and eventually to invasive ADC. Although molecular evolution during early lung carcinogenesis has been explored in recent years, the progress has been significantly hindered, largely due to insufficient materials from ADC precursors. Here, we employed state-of-the-art deep learning and artificial intelligence (AI) techniques to robustly segment and recognize cells on routinely used H&E histopathology images and extracted nine biology-relevant pathomic features to decode lung preneoplasia evolution. We analyzed three distinct cohorts (Japan, China, and USA) covering 98 patients, 162 slides, and 669 regions of interest (ROI), including 143 Normal, 129 AAH, 94 AIS, 98 MIA, and 205 ADC. Extracted pathomic features revealed the progressive increase of atypical epithelial cells while the progressive decrease in lymphocytic cells from Normal to AAJ, AIS, MIA, and ADC, consistent with the results from tissue-consuming and expensive molecular/immune profiling. Furthermore, pathomics analysis manifested progressive increasing cellular intra-tumor heterogeneity (ITH) along with the evolution from normal lung to invasive ADC. These findings demonstrated the feasibility and substantial potential of pathomics in studying lung cancer carcinogenesis directly from the low-cost routine H&E staining.