Advancing personalised care in ovarian cancer using CT and MRI radiomics

Radiomics, utilising quantitative feature extraction from CT and MR imaging, offers significant potential in advancing the diagnosis and management of ovarian cancer. Through the analysis of high-dimensional imaging data, radiomics models may capture subtle phenotypic variations in tumour heterogeneity, texture, and shape that extend beyond the capabilities of traditional imaging interpretation. CT-based radiomics excels in evaluating the prognostic significance of peritoneal disease dissemination and treatment response, while MRI-based models provide enhanced soft tissue characterisation, particularly in assessing tumour microstructure, vascularity, and cellularity. Studies demonstrate that these models can improve diagnostic accuracy, predict therapeutic outcomes and assist in risk stratification. However, standardisation of imaging acquisition protocols, feature extraction techniques and validation across diverse patient cohorts remains a challenge for the incorporation of radiomics into routine clinical practice. Evidence strongly supports the incorporation of radiomic features with molecular, genomic and clinical data in developing high-performance integrated radiomics models, which can facilitate precision oncology in ovarian cancer.