Deep Learning and Machine Learning Algorithms for Cervical Cancer Segmentation on Mri: A Systematic Review Publisher Pubmed

Summary: Can AI outline cervical tumors on MRI? A systematic review found promising automated segmentation, but calls for external validation before routine use. #MedicalImaging #CervicalCancer

Ahmadi S H ; Mohammadi S S ; Koozari A ; Elhaie M
Source: Journal of Medical Radiation Sciences Published:2026

Abstract

Introduction: Cervical cancer management relies heavily on magnetic resonance imaging (MRI) for staging and treatment planning; however, manual segmentation is time-consuming and prone to variability. This systematic review aimed to evaluate the performance and clinical potential of machine learning (ML) and deep learning (DL) algorithms for automated cervical cancer segmentation on MRI. Methods: A systematic review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (PROSPERO: CRD420251247441) was conducted by searching PubMed/MEDLINE, Embase, Scopus, Web of Science, IEEE Xplore, and Google Scholar until December 2025. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). MRI-based segmentation studies were primarily included, while computed tomography (CT)- and positron emission tomography (PET)-based multimodal studies were analysed as complementary evidence. Due to substantial heterogeneity, findings were synthesized narratively, and certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. Results: Thirty studies were included, mainly evaluating segmentation of tumours, high-risk clinical target volumes, and organs at risk. DL models, particularly U-Net variants, convolutional neural networks, and transformer-based architectures, demonstrated promising performance, with reported Dice similarity coefficients ranging from 0.60 to 0.93. However, performance varied according to target structure, tumour characteristics, and imaging protocols. Overall risk of bias showed “some concerns,” mainly due to patient selection, reference standards, and limited external validation. GRADE assessment indicated low certainty of evidence due to heterogeneity, methodological limitations, and imprecision. Conclusion: ML and DL algorithms show promising potential for automated cervical cancer segmentation on MRI, with potential benefits for efficiency and reproducibility. However, further multicenter studies with standardized protocols and external validation are required before routine clinical implementation. Trial Registration: PRISMA guidelines and was prospectively registered in PROSPERO: CRD420251247441. © 2026 The Author(s). Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology.