ArticleCancers2023
Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 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
- MRI-Based Radiomics Model for Predicting Eligibility for Conservative Management of Endometrial Cancer.Cancer medicine · 2026Article
- Review
- A combined model based on clinical, radiomics, and deep transfer learning features for differentiating endometrial hyperplasia with polyps from endometrial cancer.Frontiers in oncology · 2026Article
- Functional, Structural, and AI-Based MRI Analysis: A Comprehensive Review of Recent Advances.Diagnostics (Basel, Switzerland) · 2025Review
- Additional Risk Factors Lead to a Measurable Inflammatory Response in Stage I Endometrial Cancer-A Prospective Multicentric Observational Study.Indian journal of surgical oncology · 2025Article
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- FedCMC: a federated learning model with contribution fairness based on multi-center core data extraction for assessing the myometrial invasion status of endometrial cancer.Frontiers in oncology · 2025Article
- Preoperative prediction of aggressive endometrial cancer using multiparametric MRI-based deep transfer learning models.Frontiers in oncology · 2025Article
- Multi-modality MRI radiomics phenotypes in intermediate-high risk endometrial cancer: correlations with histopathology and prognosis.Japanese journal of radiology · 2025Article
- Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features.BMC medical research methodology · 2024Article
- Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology.Journal of clinical medicine · 2024Review
- Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows.Journal of clinical medicine · 2023Review
- Article
- Multi-parametric MRI-based radiomics for preoperative prediction of multiple biological characteristics in endometrial cancer.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo predict deep myometrial infiltration (DMI), clinical risk category, histological type, and lymphovascular space invasion (LVSI) in women with endometrial cancer using machine learning classification methods based on clinical and image signatures from T2-weighted MR images.
methodsA training dataset containing 413 patients and an independent testing dataset consisting of 82 cases were employed in this retrospective study. Manual segmentation of the whole tumor volume on sagittal T2-weighted MRI was performed. Clinical and radiomic features were extracted to predict: (i) DMI of endometrial cancer patients, (ii) endometrial cancer clinical high-risk level, (iii) histological subtype of tumor, and (iv) presence of LVSI. A classification model with different automatically selected hyperparameter values was created. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, F1 score, average recall, and average precision were calculated to evaluate different models.
resultsBased on the independent external testing dataset, the AUCs for DMI, high-risk endometrial cancer, endometrial histological type, and LVSI classification were 0.79, 0.82, 0.91, and 0.85, respectively. The corresponding 95% confidence intervals (CI) of the AUCs were [0.69, 0.89], [0.75, 0.91], [0.83, 0.97], and [0.77, 0.93], respectively.
conclusionIt is possible to classify endometrial cancer DMI, risk, histology type, and LVSI using different machine learning methods.
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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.