RADIOMICS AND MACHINE LEARNING IN TUMOR CHARACTERIZATION AND TREATMENT PLANNING

Abstract

Recent advancements in radiomics and machine learning are revolutionizing cancer diagnostics, offering non-invasive and detailed insights into tumor characteristics that aid in treatment  planning. By transforming imaging data into quantitative features, radiomics enables predictive analyses when integrated with machine learning algorithms, allowing for highly personalized oncology approaches. This paper reviews the theoretical framework, methodologies, clinical applications, and challenges of radiomics in tumor characterization, alongside the utilization of machine learning in adaptive treatment planning. We highlight recent studies and clinical trials, illustrate case studies in different tumor types, and discuss the future direction of radiomics and machine learning in oncology. By addressing current limitations and exploring pathways for validation, this paper contributes to the ongoing conversation on precision medicine in oncology.