ArticleBMC medical imaging2025
Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Artificial Intelligence in Gynecologic Oncology: Current Applications, Clinical Challenges, and Future Perspectives.Cureus · 2026Review
- Development and clinical evaluation of an ultrasound-based predictive model for early diagnosis of endometrial cancer.Medicine · 2026Article
- HFSOF: A Hierarchical Feature Selection and Optimization Framework for Ultrasound-Based Diagnosis of Endometrial Lesions.Biomimetics (Basel, Switzerland) · 2026Article
- Prediction of cervical stromal invasion using ultrasound radiomics: from conventional ultrasound to intelligent diagnosis.Frontiers in oncology · 2026Article
- Exploring free radiomics software tools: a multiparametric evaluation for cancer classification.BMC medical imaging · 2025Article
- Improving the diagnosis of endometrial cancer in postmenopausal women in primary care settings using an artificial intelligence-based ultrasound detecting model.Frontiers in oncology · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundCurrent ultrasound-based screening for endometrial cancer (EC) primarily relies on endometrial thickness (ET) and morphological evaluation, which suffer from low specificity and high interobserver variability. This study aimed to develop and validate an artificial intelligence (AI)-driven diagnostic model to improve diagnostic accuracy and reduce variability.
methodsA total of 1,861 consecutive postmenopausal women were enrolled from two centers between April 2021 and April 2024. Super-resolution (SR) technique was applied to enhance image quality before feature extraction. Radiomics features were extracted using Pyradiomics, and deep learning features were derived from convolutional neural network (CNN). Three models were developed: (1) R model: radiomics-based machine learning (ML) algorithms; (2) CNN model: image-based CNN algorithms; (3) DLR model: a hybrid model combining radiomics and deep learning features with ML algorithms.
resultsUsing endometrium-level regions of interest (ROI), the DLR model achieved the best diagnostic performance, with an area under the receiver operating characteristic curve (AUROC) of 0.893 (95% CI: 0.847-0.932), sensitivity of 0.847 (95% CI: 0.692-0.944), and specificity of 0.810 (95% CI: 0.717-0.910) in the internal testing dataset. Consistent performance was observed in the external testing dataset (AUROC 0.871, sensitivity 0.792, specificity 0.829). The DLR model consistently outperformed both the R and CNN models. Moreover, endometrium-level ROIs yielded better results than uterine-corpus-level ROIs.
conclusionsThis study demonstrates the feasibility and clinical value of AI-enhanced ultrasound analysis for EC detection. By integrating radiomics and deep learning features with SR-based image preprocessing, our model improves diagnostic specificity, reduces false positives, and mitigates operator-dependent variability. This non-invasive approach offers a more accurate and reliable tool for EC screening in postmenopausal women. CLINICAL TRIAL NUMBER: Not applicable.
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