Machine Learning for Predicting Environmentally Induced Cancer Risk: A Review of Methods, Data, and Opportunities

Abstract

Environmentally induced cancers (those attributable to chemical, physical, and biological agents in air, water, soil, diet, and the occupational setting) constitute a substantial and, in principle, preventable fraction of the global cancer burden. The promise of machine learning (ML) is that it can integrate the high-dimensional, multi-modal data streams that characterize modern exposure science (remote-sensing and sensor-network measurements, exposure biomarkers, molecular and mutational signatures, and linked epidemiological cohorts) into individualized and community level risk predictions that support surveillance, screening triage, and targeted prevention. This review surveys the methods, data, and evaluation practices that define the field and argues that it is, at present, method-rich but deployment-poor. A large and growing body of work demonstrates strong discrimination on retrospective, internally validated datasets, yet comparatively little of it advances to the standards required for responsible clinical or public-health use: external and prospective validation, honest calibration, uncertainty quantification, transparent interpretability, and fairness auditing across the subpopulations most affected by environmental injustice. We organize the literature along two axes. The first is the data modality (environmental monitoring, exposure biomarkers such as 8-hydroxy-2'-deoxyguanosine (8-OHdG) and other DNA-damage endpoints, molecular and omics features including mutational signatures, and the epidemiological cohorts that link exposure to outcome) emphasizing the pervasive data bottleneck created by scarce, heterogeneous, and weakly harmonized labels. The second is the method family (regularized regression, tree ensembles and gradient boosting, deep learning, mechanistic and physics-guided models, and probabilistic and Bayesian approaches), each mapped to the regimes in which it is appropriate. We then examine deployment readiness through the lenses of validation practice (including data leakage and the failure of naïve cross-validation under spatial and temporal structure), calibration and uncertainty, dataset shift, interpretability, and equity. Synthesizing these threads, we propose a biomarker-anchored, mechanistically-informed research programme: harmonized datasets tied to measurable biological effect, models constrained by dose-response and toxicological knowledge, standardized validation reporting calibration and uncertainty by default, and deployment-aware design that treats the eventual decision context as a first-class constraint. We situate this programme within broader currents in AI-enabled and digital health and argue that it offers a credible path beyond the current state of the art.