GenomicRL: A DRL framework for cancer treatment recommendation using genomic and metastatic markers
Date
2025
Authors
Beg, Heebatullah
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Abstract
Even with significant advances in cancer treatment, Gastroesophageal Junction (GEJ) cancers continue to present therapeutic challenges with a 5-year survival rate of just 21%. This work develops GenomicRL, a deep reinforcement learning (DRL) framework that integrates genomic, metastatic, and clinical markers to optimize treatment recommendations. Initially, a supervised learning (SL) baseline using ElasticNet achieves 64% exact match ratio (EMR) with clinician decisions. Augmenting training with synthetic data improves EMR to 70%, demonstrating generated data’s utility in mitigating limited real-world samples. However, SL’s reliance on historical decisions neglects post-treatment outcomes. To address this, a novel outcome-driven DRL agent is trained. Although the approach, incorporating survival, metastasis, and genomic stability into its reward function, reduces EMR from 99% (for clinician-mimicking reward function) to 73%, it achieves a higher average reward. Incorporating post-treatment signals, however, leads the agent to deviate from historical choices in ways that improve long-term outcome metrics—trading some immediate agreement for better anticipated patient benefit. This shift from pure imitation to outcome-oriented optimization highlights the promise of data-driven recommendation strategies that leverage diverse clinical and molecular information. Importantly, the proposed framework is not designed to supplant medical professionals but to assist them in refining treatment planning through personalized insights that account for individual patient variability.