ArticleInternational journal of environmental research and public health2020
Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 82 citations in OpenAlex.
- Radiomics-Based Machine Learning for the Detection of Myometrial Invasion in Endometrial Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Development of an AI-based magnetic resonance imaging reading support program (AMP) for deep endometriosis diagnosis.Scientific reports · 2025Article
- DCS-NET: a multi-task model for uterine ROI detection and automatic staging of early endometrial cancer in MRI.Scientific reports · 2025Article
- Performance of CAD4TB artificial intelligence technology in TB screening programmes among the adult population in South Africa and Lesotho.Journal of clinical tuberculosis and other mycobacterial diseases · 2025Article
- A Radiomic-based model to predict the depth of myometrial invasion in endometrial cancer on ultrasound images.Scientific reports · 2025Article
- Deep learning-based automated detection of endometrioid endometrial carcinoma in histopathology.Frontiers in oncology · 2025Article
- Research progress of artificial intelligence in the early screening, diagnosis, precise treatment and prognosis prediction of three central gynecological malignancies.Frontiers in oncology · 2025Review
- Multiparametric MRI-based radiomics and deep learning for differentiating uterine serous carcinoma from endometrioid carcinoma: a multicenter retrospective study.Frontiers in oncology · 2025Article
- WOAENet: a whale optimization-guided ensemble deep learning with soft voting for uterine cancer diagnosis based on MRI images.Frontiers in artificial intelligence · 2025Article
- Preoperative prediction of aggressive endometrial cancer using multiparametric MRI-based deep transfer learning models.Frontiers in oncology · 2025Article
- Artificial intelligence-based magnetic resonance imaging for preoperative staging of patients with endometrial cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Article
- The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data.Zeitschrift fur medizinische Physik · 2024Review
- Achieving enhanced diagnostic precision in endometrial lesion analysis through a data enhancement framework.Frontiers in oncology · 2024Article
- Machine learning models using multiparametric MRI for preoperative risk stratification in endometrial cancer.American journal of cancer research · 2024Article
- A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles.PloS one · 2024Article
- Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows.Journal of clinical medicine · 2023Review
- A computer-aided determining method for the myometrial infiltration depth of early endometrial cancer on MRI images.Biomedical engineering online · 2023Article
- The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies-A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology.Cureus · 2023Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Myometrial invasion affects the prognosis of endometrial cancer. However, discrepancies exist between pre-operative magnetic resonance imaging staging and post-operative pathological staging. This study aims to validate the accuracy of artificial intelligence (AI) for detecting the depth of myometrial invasion using a deep learning technique on magnetic resonance images. We obtained 4896 contrast-enhanced T1-weighted images (T1w) and T2-weighted images (T2w) from 72 patients who were diagnosed with surgico-pathological stage I endometrial carcinoma. We used the images from 24 patients (33.3%) to train the AI. The images from the remaining 48 patients (66.7%) were used to evaluate the accuracy of the model. The AI then interpreted each of the cases and sorted them into stage IA or IB. Compared with the accuracy rate of radiologists' diagnoses (77.8%), the accuracy rate of AI interpretation in contrast-enhanced T1w was higher (79.2%), whereas that in T2w was lower (70.8%). The diagnostic accuracy was not significantly different between radiologists and AI for both T1w and T2w. However, AI was more likely to provide incorrect interpretations in patients with coexisting benign leiomyomas or polypoid tumors. Currently, the ability of this AI technology to make an accurate diagnosis has limitations. However, in hospitals with limited resources, AI may be able to assist in reading magnetic resonance images. We believe that AI has the potential to assist radiologists or serve as a reasonable alternative for pre-operative evaluation of the myometrial invasion depth of stage I endometrial cancers.
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