Abstract Introduction: Lung adenocarcinoma (LUAD) is the most common non-small cell lung cancer, especially in never-smokers. Environmental pollution, particularly airborne particulates (PM2.5/PM5), is a critical factor driving lung cancer initiation 1. However, reliable individual-level pollution exposure quantification is challenging. We recently introduced the lung pollutant index (LPI), an AI-derived metric quantifying tissue-resident pollutant burden from pathology images. However, pathology-based LPI is invasive and unsuitable for large-scale application. We propose a machine-learning framework to predict CT-based LPI (CT-LPI) from chest CT, enabling non-invasive pollutant burden quantification for individuals undergoing chest imaging, including high-risk smokers and never-smokers with incidental nodules. Methods: We retrospectively investigated 153 LUAD patients who received preoperative lung CT at MD Anderson Cancer Center (IRB 2023-0114) with surgical pathology. LPI computed from digitalized H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2773.
Li et al. (Fri,) studied this question.