ArticleWorld journal of clinical cases2024
Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer.
Article in World journal of clinical cases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Myokine-mediated mechanisms of immune checkpoint inhibitors-associated colorectal injury and repair in rectal cancer.Frontiers in immunology · 2026Review
- Preoperative risk stratification in endometrial carcinoma: a pragmatic, complementary nomogram using readily available clinical variables.Frontiers in oncology · 2026Article
- Prediction of cervical stromal invasion using ultrasound radiomics: from conventional ultrasound to intelligent diagnosis.Frontiers in oncology · 2026Article
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Authors and funding
5 authors.
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Abstract
backgroundPreoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC.
aimTo construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI.
methodsThe study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney
resultsHaving an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature.
conclusionThe MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.
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