ArticleBMC medical imaging2022
The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: a comparison with radiologists.
Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- Diagnostic accuracy of ovarian cancer using convolutional neural network: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Diagnosis Test Accuracy of Artificial Intelligence for Endometrial Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools.European radiology · 2025Pooled it
- An interpretable multimodal machine learning model integrating transvaginal ultrasound and clinical features for differentiating benign from malignant endometrial thickening in postmenopausal women.Frontiers in oncology · 2026Article
- MRI-derived deep-feature model for differentiating stage I endometrial carcinoma from atypical endometrial hyperplasia: a two-center retrospective study.Frontiers in oncology · 2026Article
- DCS-NET: a multi-task model for uterine ROI detection and automatic staging of early endometrial cancer in MRI.Scientific reports · 2025Article
- Artificial Intelligence in Obstetric and Gynecological MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Review
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- Review
- Estimation of TP53 mutations for endometrial cancer based on diffusion-weighted imaging deep learning and radiomics features.BMC cancer · 2025Article
- Preoperative prediction of aggressive endometrial cancer using multiparametric MRI-based deep transfer learning models.Frontiers in oncology · 2025Article
- Exploratory Analysis of Radiomics and Pathomics in Uterine Corpus Endometrial Carcinoma.Scientific reports · 2024Article
- Deep Learning for Grading Endometrial Cancer.The American journal of pathology · 2024Article
- Review
- Impact of artificial intelligence on the diagnosis, treatment and prognosis of endometrial cancer.Annals of medicine and surgery (2012) · 2024Review
- Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology.Journal of clinical medicine · 2024Review
- Big data and AI for gender equality in health: bias is a big challenge.Frontiers in big data · 2024Article
- A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles.PloS one · 2024Article
- A self-supervised classification model for endometrial diseases.Journal of cancer research and clinical oncology · 2023Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions.
methodsThis retrospective study included patients with endometrial cancer or non-cancerous lesions who underwent MRI between 2015 and 2020. In Experiment 1, single and combined image sets of several sequences from 204 patients with cancer and 184 patients with non-cancerous lesions were used to train CNNs. Subsequently, testing was performed using 97 images from 51 patients with cancer and 46 patients with non-cancerous lesions. The test image sets were independently interpreted by three blinded radiologists. Experiment 2 investigated whether the addition of different types of images for training using the single image sets improved the diagnostic performance of CNNs.
resultsThe AUC of the CNNs pertaining to the single and combined image sets were 0.88-0.95 and 0.87-0.93, respectively, indicating non-inferior diagnostic performance than the radiologists. The AUC of the CNNs trained with the addition of other types of single images to the single image sets was 0.88-0.95.
conclusionCNNs demonstrated high diagnostic performance for the diagnosis of endometrial cancer using MRI. Although there were no significant differences, adding other types of images improved the diagnostic performance for some single image sets.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.