PREDICTING REFRACTORY CANCER PAIN USING MACHINE LEARNING INTEGRATION OF CLINICAL, IMAGING, GENOMIC, AND OPIOID-RESPONSE PROFILES

Abstract

The management of refractory cancer pain is a significant clinical problem, and there is a substantial biological, psychological, genomic and treatment-response heterogeneity that is not always addressed by traditional cancer pain management strategies. This study aims to delve into the potential of machine learning in predicting refractory cancer pain using multimodal clinical, imaging, genomic, opioid-response, and longitudinal patient-reported outcome data. Machine learning can incorporate structured EHRs, radiomic markers, pharmacogenomic markers, and temporal pain trajectories to help identify patients at a high risk for opioid resistance, breakthrough pain, and poor analgesic response. The proposed framework will focus on three aspects: multimodal learning, federated data integration and explainable artificial intelligence, as well as prospective clinical validation, to enhance predictive capabilities without compromising patient privacy and clinical interpretability. The results indicate that multimodal machine learning techniques could be better than traditional single-source evaluation methods in providing more accurate risk stratification and tailoring pain-management approaches. For clinical translation to be successful, however, these matters must be addressed: external validation, reporting of clinical results, reduction of bias, integration of the workflow, and the clinician-centered design of the decision support. Overall, the present study confirms the potential for the evolution of precision pain medicine approaches that shift cancer pain care from a reactive approach of symptom management to a proactive, personalized, and data-informed approach.