An overview of data analytic-based strategies for assisting with post-surgical adjuvant treatment in melanoma

Summary: Abstract Body: Data-driven and deep-learning strategies can be critical in improving post-surgical adjuvant melanoma treatment by providing personalized recommendations to reduce recurrence. This paper explores multiple studies where data analytics was applied and suggests how machine learning (ML) can enhance outcomes. A study by Sha utilized ML models to predict recurrence risk based on patient demographics and tumor characteristics. This resulted in greater optimized selection of candidates for adjuvant immunotherapy1. A study by Liu combined genomics data with predictive algorithms to target adjuvant therapies for patients with specific genetic mutations, resulting in improved survival rates2. A study by Wang incorporated real-time data analytics using electronic health records to monitor treatment responses and adverse effects in melanoma patients receiving PD-1 inhibitors. This adaptive approach allowed rapid treatment adjustment, which reduced toxicity and maintained efficacy3. Liu's genomics-based predictive modeling proved most effective, as it achieved higher survival rates and fewer adverse effects. Integrating deep learning models could further improve the predictive capabilities. Recurrent neural networks (RNNs) can process sequential medical data. By banking on their ability to model time-dependent relationships, RNNs can enhance personalized treatment planning and improve predictive outcomes. RNNs with Gated Recurrent Units (GRUs) have outperformed traditional methods in predicting treatment outcomes4. RNN also has potential in epidemiological surveillance frameworks, which can further help in monitoring disease progression as well as tracking treatment effectiveness in poopulations5. Incorporating such advanced strategies can provide a more personalized approach to post-surgical adjuvant melanoma care6,7. Priyal Kancharla¹, Sahil Kapur², Kermanjot S. Sidhu³, Craig Burkhart⁴ 1. Georgia Institute of Technology, Atlanta, GA, United States. 2. The University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States. 3. Michigan State University College of Human Medicine, East Lansing, MI, United States. 4. The University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States. Bioinformatics, Computational Biology, and Imaging