Radiomics in Dentistry: A Paradigm Shift in Diagnostic Imaging and Treatment Planning in Oral Cancer

Abstract

Oral cancer is a substantial worldwide health burden. Improving results requires early diagnosis, precise staging, and well-thought-out treatment planning. Conventional imaging techniques and human radiologic interpretation, however, have limits in sensitivity, quantification, objectivity, and prognostic prediction. For dentistry and oral oncology, the developing area of radiomics—quantitative analysis of medical images using computational techniques and machine learning—offers a revolutionary paradigm. Radiomics can improve oral cancer detection, classification, prognosis, and individualized treatment planning by extracting high-dimensional, sub-visual information from imaging (CT, CBCT, MRI, optical coherence tomography). This review addresses the principles and workflow of radiomics, its current and potential uses in dentistry and oral cancer, highlights recent evidence (diagnostic and prognostic), identifies problems and limitations, and recommends options for future research and clinical translation.