Deep learning for hepatocellular carcinoma segmentation in MRI: A systematic review of models, performance, and challenges
Abstract
Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, necessitating accurate segmentation in magnetic resonance imaging (MRI) for diagnosis and treatment planning. Deep learning (DL) models, particularly convolutional neural networks, have shown promise in automating HCC segmentation, yet challenges like dataset limitations and MRI protocol variability persist. This systematic review evaluates DL models for HCC segmentation in MRI, focusing on model architectures, performance metrics, and implementation challenges.
Methods: Following preferred reporting items for systematic reviews and meta-analyses guidelines, we searched PubMed, Scopus, Web of Science, and Cochrane Library for peer-reviewed studies using DL for HCC segmentation in MRI. Inclusion criteria required quantitative metrics (e.g., dice similarity coefficient [DSC]) and human subjects. Two reviewers conducted screening, data extraction, and quality assessment using quality assessment of diagnostic accuracy studies-2. Narrative synthesis grouped studies by architecture and MRI sequence, analyzing performance and challenges.
Results: Of 2462 records, 13 studies met criteria, predominantly using U-Net-based models (e.g., nnU-Net, UNet++). DSCs ranged from 0.61 to 0.954, with transformers and hybrid models showing adaptability. Clinical applications included diagnosis, treatment planning, and risk assessment. Challenges included small datasets (e.g., 19-602 patients), lesion…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 602 participants or records.
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