SynthesisAbdominal radiology (New York)2025
Diagnostic performance of radiomics models for preoperative prediction of microsatellite instability status in endometrial cancer: a systematic review and meta-analysis.
Synthesis in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Prediction of cervical stromal invasion using ultrasound radiomics: from conventional ultrasound to intelligent diagnosis.Frontiers in oncology · 2026Article
- A radiomics model based on diffusion-weighted imaging developed using machine learning enables prediction of microsatellite instability in endometrial cancer.BMC medical imaging · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeMicrosatellite instability (MSI), caused by defects in mismatch repair (MMR) genes, serves as a critical molecular biomarker with therapeutic implications for endometrial cancer (EC). This study aims to assess the diagnostic performance of radiomics as a non-invasive approach for predicting MSI status in EC.
methodsA systematic search across PubMed, Scopus, Embase, Web of Science, Cochrane library and Clinical Trials was conducted. Quality assessment was performed using QUADAS-2 and METRICS. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were computed using a bivariate model. Separate meta-analyses for radiomics and combined models were conducted. Subgroup analysis and sensitivity analysis were conducted to find potential sources of heterogeneity. Likelihood ratio scattergram was used to evaluate the clinical applicability.
resultsA total of 9 studies (1650 patients) were included in the systematic review, with seven studies contributing to the meta-analysis of radiomics model and five for combined model. The pooled diagnostic performance of the radiomics model was as follows: sensitivity, 0.66; specificity, 0.89; PLR, 5.48; NLR, 0.43; DOR, 18.56; and AUC, 0.87. For combined model, the pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.58, 0.94, 7.37, 0.50, 16.43, and 0.85, respectively. Subgroup analysis of radiomics models revealed that studies employing non-linear classifiers achieved superior performance compared to those utilizing linear classifiers.
conclusionRadiomics showed promise as non-invasive tool for MSI prediction in EC, with potential clinical utility in guiding personalized treatments. However, further studies are required to validate these findings.
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Registered trials
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