Radiomics-Based Machine Learning for the Detection of Myometrial Invasion in Endometrial Cancer: Systematic Review and Meta-Analysis
Abstract
Background: Preoperative endometrial cancer (EC) diagnosis often depends on radiologists' expertise, which introduces subjectivity. Recent studies have explored radiomics-based machine learning (ML) models for detecting myometrial invasion (MI), but a comprehensive evaluation of their diagnostic performance is lacking. Therefore, our study systematically assessed the diagnostic performance of radiomics-based ML approaches for identifying MI in EC, thereby providing evidence to guide the development or improvement of noninvasive diagnostic tools.
Objective: This study aims to systematically assess the diagnostic performance of radiomics-based ML approaches for identifying MI in EC and compare the diagnostic efficacy of conventional ML (CML) and deep learning (DL) models based on differences in data processing methods via subgroup analyses, thereby providing evidence to guide the development or improvement of noninvasive diagnostic tools.
Methods: PubMed, Cochrane Library, Embase, and Web of Science were searched through November 26, 2024, for studies evaluating radiomics-based ML for detecting MI in patients with EC. Study quality was appraised using the radiomics quality score. Pooled diagnostic metrics were estimated using a bivariate random-effects model. Subgroup analyses compared CML and DL models.
Results: We included 19 studies comprising 4373 patients with EC. Of these 19 studies, 18 (95%) used magnetic resonance imaging-based radiomics, and 1 (5%) used ultrasound…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health. Reported size: 4,373 participants or records.
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