Tailored Treatments: Precision Medicine Approaches Utilizing Machine Learning in Cancer Patient Stratification

Abstract

Precision medicine has emerged as a promising approach to cancer treatment, aiming to tailor therapies based on individual patient characteristics and disease subtypes. However, effectively stratifying cancer patients for personalized treatments remains a significant challenge due to the complexity and heterogeneity of the disease. Machine learning (ML) techniques offer new opportunities to address this challenge by leveraging vast amounts of patient data to identify distinct cancer subtypes and predict optimal treatment strategies. This paper explores the role of ML in precision medicine approaches for cancer patient stratification, focusing on its potential to enhance treatment outcomes and minimize adverse effects. We review recent advancements in ML-based classification models for identifying cancer subtypes and predicting patient responses to various treatment modalities. Additionally, we discuss key considerations and challenges in implementing ML-driven precision medicine approaches, including data integration, model interpretability, and clinical validation. Furthermore, we highlight promising avenues for future research, such as multi-omics data integration and ensemble learning techniques, to improve the accuracy and generalizability of ML models in cancer patient stratification.