Toward Scalable Electromyography in Oncology: A Narrative Review of Normalization Challenges and Machine Learning Innovations

Objectives: Electromyography (EMG) is increasingly applied in oncology to monitor neuromuscular impairment, treatment toxicities, and rehabilitation outcomes. However, reliance on maximal voluntary contraction (MVC) normalization limits scalability, as many patients cannot perform safe and reliable MVCs due to fatigue, pain, or treatment-induced impairments. This narrative review evaluates the feasibility and clinical utility of machine learning (ML)–predicted MVCs as an alternative normalization method in oncology care. Methods: Peer-reviewed articles published between 2015 and 2025 were retrieved from PubMed, IEEE Xplore, ScienceDirect, SpringerLink, and open-access repositories. Search terms included electromyography, oncology, maximum voluntary contraction, machine learning, sarcopenia, cachexia, and rehabilitation. Results: Thirty-eight studies were included. Findings highlight that traditional MVC-based normalization is frequently infeasible in cancer populations due to neuromuscular compromise, variability in body composition, and safety risks. ML approaches, leveraging demographic, anthropometric, and submaximal EMG data, show promise for estimating MVC indirectly. Predictive models such as artificial neural networks and ensemble learners demonstrate potential to improve accuracy, reduce patient burden, and enable broader EMG integration into rehabilitation and survivorship monitoring. Clinical applications include safer exercise prescription, individualized progress tracking, and remote continuous monitoring through wearable sensors. Conclusions: ML-predicted MVCs may overcome longstanding barriers to EMG standardization in oncology. By reducing dependence on direct maximal efforts, these approaches can improve functional assessment accuracy, optimize rehabilitation strategies, and enhance patient-centered care. Implications for Nursing Practice: Oncology nurses and rehabilitation specialists could incorporate ML-supported EMG assessments into clinical and home-based programs, supporting adaptive, real-time interventions that promote safety, engagement, and quality of life for individuals with cancer.