Multi-regional CT-based radiomics fusion predicts pathological pollutant index associated with lung adenocarcinoma

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&E images [2] categorized cohorts into LPI-high (n=61) and LPI-low (n=92). Region-wise radiomics features (793 per region) were extracted from 5-mm peritumoral ring, normal lung, and whole lung, including first-order, texture, shape, Laplacian-of-Gaussian, wavelet, and habitat features. We incorporated COPD-associated radiomic markers to enhance biological interpretability. We built a multi-regional ensemble framework with features selected using mutual information and Elastic-Net. Region-specific classifiers (Ridge Logistic, Gradient Boosting, CatBoost) were trained, with final predictions via weighted-average ensemble [3]. Results: In 5-fold cross-validation, the multi-regional ensemble achieved AUC 0.719 and ACC 0.687, improving AUC by 0.048 over the best single-regional model and outperforming simple averaging (0.710), LogisticNet (0.664), and ElasticNet (0.661). Incorporating COPD-associated markers improved AUC to 0.724. Conclusion: We developed CT-LPI, predicting tissue-resident pollutant burden from routine CT using complementary multi-regional features. This framework non-invasively quantifies individual exposure to environmental carcinogens implicated in lung cancer initiation. By enabling scalable monitoring of pollution-driven biological alterations, CT-LPI may support environmental exposure assessment and personalized risk stratification. Next steps include external validation, application to screening datasets, and integration with other biomarkers. [1] Hill W, et al. Nature. 2023. [2] Pan et al., Nature Cancer, under review. [3] Shaheen A, et al. Front Neurosci. 2022.