ArticleJournal of magnetic resonance imaging : JMRI2023
An Integrated Clinical-MR Radiomics Model to Estimate Survival Time in Patients With Endometrial Cancer.
Article in Journal of magnetic resonance imaging : JMRI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- MRI-Based Radiomics Model for Predicting Eligibility for Conservative Management of Endometrial Cancer.Cancer medicine · 2026Article
- Precision oncology in gynecologic cancers: molecular taxonomy, biomarker-guided therapeutics, and the challenge of therapeutic resistance.Frontiers in oncology · 2026Review
- Updated ESUR Guidelines for Endometrial Cancer: integrating MRI with the 2023 FIGO Staging Revolution.European radiology · 2026Review
- Functional, Structural, and AI-Based MRI Analysis: A Comprehensive Review of Recent Advances.Diagnostics (Basel, Switzerland) · 2025Review
- Predicting aggressive disease and poor outcome in endometrial cancer using preoperative [European journal of nuclear medicine and molecular imaging · 2025Article
- Application of artificial intelligence in assisting treatment of gynecologic tumors: a systematic review.Visual computing for industry, biomedicine, and art · 2025Review
- Machine learning-based radiomics model: prognostic prediction and mechanism exploration in patients with endometrial cancer.Biomarker research · 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
- Potential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancer.Scientific reports · 2025Article
- Intratumoral and peritumoral multiparametric MRI-based radiomics nomogram for preoperative risk stratification in patients with endometrial cancer.Frontiers in oncology · 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
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- Advances in Radiomics Research for Endometrial Cancer: A Comprehensive Review.Journal of Cancer · 2023Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundDetermination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning. PURPOSE: To identify clinical features and imaging signatures on T2-weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects. STUDY TYPE: Retrospective. POPULATION: Four hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years). FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T scanners with sagittal T2-weighted spin echo sequence. ASSESSMENT: Tumor regions were manually segmented on T2-weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets. STATISTICAL TESTS: A likelihood-ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model.
resultsThree radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respectively. DATA
conclusionThe proposed CPH model with radiomic signatures may serve as a tool to improve estimated survival time in women with endometrial cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.
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