Machine Learning and Radiomics in Gliomas

Abstract

The integration of machine learning (ML) and radiomics is emerging as a pivotal advancement in glioma research, offering novel insights into the diagnosis, prognosis, and treatment of these complex tumors. Radiomics involves the extraction of a multitude of quantitative features from medical images. When these features are analyzed through ML algorithms, the precision of tumor characterization is enhanced beyond traditional methods.This chapter examines the application of both supervised and unsupervised ML techniques for interpreting radiomic data, highlighting their potential for accurately predicting tumor grade, identifying genetic mutations, estimating patient survival rates, and evaluating treatment responses. The ability of ML-based radiomic analysis to discern intricate patterns in tumor imaging, imperceptible to human observation, is particularly emphasized.Challenges in this field, including data diversity, overfitting risks, and the need for extensive, annotated datasets, are critically assessed. The necessity of integrating these advanced technologies into clinical practice through interdisciplinary collaboration is underscored as a crucial factor for their effective utilization.Overall, the synergy between ML and radiomics in glioma research represents a significant step toward personalized medicine, offering enhanced tools for patient-specific treatment strategies.

Keywords: Glioma; Machine learning; Radiomics; Texture analysis.