ParetoRx: A Multiobjective Radiogenomic Framework Integrating Pareto Optimization and Q-Learning for Exploratory Compound Prioritization in Non-Small Cell Lung Cancer

Imaging-defined high-risk phenotypes may capture biologically aggressive disease states, but translating them into therapeutic hypotheses remains challenging. We developed ParetoRx, a radiogenomic imaging-informatics framework integrating computed tomography radiomics, pathway-level transcriptomic analysis, multiobjective Pareto optimization, and model-free Q-learning for non-small cell lung cancer (NSCLC). Pretreatment CT data from 402 patients were used for model development and internal validation, with 110 patients forming a held-out external cohort. The final radiomics model achieved an external C-index of 0.707 (95% CI 0.627-0.783); high-risk patients had worse overall survival (HR 3.461, 95% CI 1.674-7.153), with minimal change after adjustment for histology (HR 3.444, 95% CI 1.662-7.137). Gene set enrichment and single-sample pathway analyses identified exploratory associations involving E2F targets, MYC targets V1, inflammatory response, and hypoxia, although no pathway met FDR < 0.05. LINCS L1000 perturbational profiles were evaluated using transcriptomic reversal, pathway specificity, and a feasibility-related proxy, with 4188 compounds retained in the common complete-case analysis. Multiobjective Pareto optimization was combined with three-split consensus ranking and sequential policy evaluation. TWS-119 showed consistent prioritization across all three splits (ranks 1, 3, and 4) and was Pareto-optimal in each split. In simulation-based evaluation, model-free Q-learning achieved higher terminal-goal attainment than myopic and static policies across all three splits, with improvements of approximately 6.2-13.3 percentage points over the myopic comparator. These findings support ParetoRx as an exploratory framework for biologically informed compound prioritization and computational sequential decision-making, while independent drug response and clinical validation remain necessary.