Authors
Suvarna Kotian, Priyanka -, Varsha R, Rajagopal Kadavigere, Saikiran Pendem, Kaushik Nayak
Published in
F1000Research. Volume 15. Pages 464. Epub Apr 02, 2026.
Abstract
Accurate characterization of adnexal masses is a key issue and a crucial step toward improving the outcome of managing a patient with a gynecologic oncology issue. Though ultrasound is a dominant tool for this process, it is subjected to operator variability and is less reliable from a diagnostic perspective. Advances in computed tomography-based radiomics and ML hold great promise as objective diagnostic solutions.
This systematic review was performed according to the guidelines suggested by PRISMA. The literature research using PubMed, Embase, Scopus, and Web of Science databases included studies that examined CT-based radiomics and ML model performances for classification of adnexal masses and reported diagnostic performance metrics, including AUC, sensitivity, and specificity. Quality assessment of included studies was performed using the QUADAS 2 tool.
Eleven studies were included in the review. The performance of CT-based ML models was found to be moderate to excellent, with an AUC ranging from 0.72 to 0.99. Hybrid radiomics-DL algorithms were found to have a higher performance compared to other algorithms. The studies were found to have low risk of bias.
CT-based radiomics and AI models also hold good prominence as adjunctive tools in differentiating between both benign and malignant adnexal masses and in predicting prognosis. PROSPERO registration: The study has been registered in PROSPERO under the registration number CRD420251266988, on 16 December 2025.
PMID:
42779737
Bibliographic data and abstract were imported from PubMed on 24 Sep 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 8
- Comments 0