Letter to the Editor: Balancing efficacy and toxicity: The critical role of predictive models in colorectal cancer chemotherapy

Abstract

While the pioneering study by Liu et al published in World Journal of Gastroenterology masterfully addresses the critical challenge of predicting chemotherapy-induced myelosuppression, it also illuminates the necessary path for future research: The development of integrated models that concurrently predict a spectrum of common toxicities, such as gastrointestinal distress, neurotoxicity, and hand-foot syndrome, and crucially, link these predictions to efficacy outcomes. Personalized oncology aims to identify the regimen offering the maximal therapeutic benefit for each patient. A decision tool that flags both severe gut toxicity risk and a high probability of profound pathological response would provide clinicians with an invaluable, holistic framework. By expanding the present robust methodology into an integrated platform predicting multi-organ adverse events and tumor response, we can advance beyond isolated risk assessment and truly optimize the balance between treatment safety and anti-tumor activity, ensuring that therapeutic choices are as comprehensive and patient-centric as possible.

Keywords: Clinic-machine learning nomogram; Efficacy; Toxicity; Colorectal cancer; Chemotherapy

Core Tip: We propose expanding Liu et al’s interpretable machine learning-based nomogram beyond myelosuppression to an integrated efficacy-toxicity platform that simultaneously forecasts tumor response and common multi-organ adverse events. By embedding these dual predictions into a real-time, utility-based clinical decision support system, designed for practical clinical use, clinicians can instantly identify the regimen offering each patient the maximal therapeutic index, thereby optimizing personalized treatment decisions.