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automated cervical cancer segmentation on MRI using ML/DL

Source article: Deep Learning and Machine Learning Algorithms for Cervical Cancer Segmentation on MRI: A Systematic Review

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…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-1219· cite & verify
Deep Learning and Machine Learning Algorithms for Cervical Cancer Segmentation on MRI: A Systematic Review
Breast and Cervical Cancer Screening Patients by Springluver. CC BY-SA 3.0 · https://creativecommons.org/licenses/by-sa/3.0
Trace impact readingNegative state
P 73The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.

Both sides are scored from claims and sources, not community votes.

G 68The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.

In brief

This PRISMA-guided systematic review of 30 studies up to December 2025 evaluated machine learning and deep learning algorithms for automated cervical cancer segmentation on MRI, a task central to staging and treatment planning that is currently manual and variable.

While models showed Dice scores between 0.60 and 0.93 and potential efficiency gains, the review found some concerns for bias and low certainty of evidence, leaving uncertainty about generalizability across protocols and centers and the timeline for clinical adoption.

Main points

  1. Systematic review included 30 studies identified via PRISMA search of six databases until December 2025, registered as PROSPERO: CRD420251247441.
  2. Primary focus was MRI-based segmentation of tumours, high-risk clinical target volumes, and organs at risk, with CT and PET multimodal studies as complementary evidence.
  3. Risk of bias assessed with QUADAS-2 showed some concerns related to patient selection and reference standards.

The gain

Deep learning models, especially U-Net variants, achieved Dice scores up to 0.93 for automated segmentation of cervical tumors and related structures on MRI, suggesting improved efficiency and reproducibility over manual segmentation.

The problem

Current studies show some concerns for bias and low certainty of evidence due to heterogeneity, limited external validation, and variability by target structure and imaging protocol, requiring further multicenter standardized studies before clinical implementation.

The rundown

The review searched PubMed/MEDLINE, Embase, Scopus, Web of Science, IEEE Xplore, and Google Scholar until December 2025, with two reviewers screening and extracting data. Thirty studies were included, evaluating U-Net variants, convolutional neural networks, and transformer-based architectures.

Performance varied according to target structure, tumour characteristics, and imaging protocols, with Dice coefficients from 0.60 to 0.93. Authors concluded promising potential but called for multicenter studies with standardized protocols and external validation before routine use.

What this doesn’t fix

Evidence certainty is limited by heterogeneity across imaging protocols and targets, methodological limitations, and lack of external validation, preventing routine clinical use.

Sources

  1. Peer-reviewedJournal of Medical Radiation Sciences2026-09-27

The debate