Objectives The study aim was to conduct a systematic review of the literature reporting the application of radiomics
to imaging techniques in patients with ovarian lesions.
Methods MEDLINE/PubMed, Web of Science, Scopus, EMBASE, Ovid and ClinicalTrials.gov were searched for relevant
articles. Using PRISMA criteria, data were extracted from short-listed studies. Validity and bias were assessed independently
by 2 researchers in consensus using the Quality in Prognosis Studies (QUIPS) tool. Radiomic Quality Score (RQS)
was utilised to assess radiomic methodology.
Results After duplicate removal, 63 articles were identified, of which 33 were eligible. Fifteen assessed lesion classifications,
10 treatment outcomes, 5 outcome predictions, 2 metastatic disease predictions and 1 classification/outcome
prediction. The sample size ranged from 28 to 501 patients. Twelve studies investigated CT, 11 MRI, 4 ultrasound
and 1 FDG PET-CT. Twenty-three studies (70%) incorporated 3D segmentation. Various modelling methods were
used, most commonly LASSO (least absolute shrinkage and selection operator) (10/33). Five studies (15%) compared
radiomic models to radiologist interpretation, all demonstrating superior performance. Only 6 studies (18%) included
external validation. Five studies (15%) had a low overall risk of bias, 9 (27%) moderate, and 19 (58%) high risk of bias.
The highest RQS achieved was 61.1%, and the lowest was − 16.7%.
Conclusion Radiomics has the potential as a clinical diagnostic tool in patients with ovarian masses and may allow
better lesion stratification, guiding more personalised patient care in the future. Standardisation of the feature
extraction methodology, larger and more diverse patient cohorts and real-world evaluation is required before clinical
translation.
Clinical relevance statement Radiomics shows promising results in improving lesion stratification, treatment
selection and outcome prediction. Modelling with larger cohorts and real-world evaluation is required before clinical
translation.
Key points
• Radiomics is emerging as a tool for enhancing clinical decisions in patients with ovarian masses.
• Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction.
• Modelling with larger cohorts and real-world evaluation is required before clinical translation
