ArticleJournal of personalized medicine2022
Preoperative Tumor Texture Analysis on MRI for High-Risk Disease Prediction in Endometrial Cancer: A Hypothesis-Generating Study.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 3 syntheses or guidelines pooled it, 16 citations in OpenAlex.
- Preoperative risk assessment of invasive endometrial cancer using MRI-based radiomics: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- 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
- Accuracy of radiomics in the diagnosis and preoperative high-risk assessment of endometrial cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Radiomics nomogram from multiparametric magnetic resonance imaging for preoperative prediction of substantial lymphovascular space invasion in endometrial cancer.Abdominal radiology (New York) · 2026Article
- Multi-modality MRI radiomics phenotypes in intermediate-high risk endometrial cancer: correlations with histopathology and prognosis.Japanese journal of radiology · 2025Article
- Multimodal MRI-based radiomics models for the preoperative prediction of lymphovascular space invasion of endometrial carcinoma.BMC medical imaging · 2024Article
- Diagnostic utility of apparent diffusion coefficient in preoperative assessment of endometrial cancer: are we ready for the 2023 FIGO staging?BMC medical imaging · 2024Article
- 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
- Combined deep-learning MRI-based radiomic models for preoperative risk classification of endometrial endometrioid adenocarcinoma.Frontiers in oncology · 2023Article
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Authors and funding
17 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop and validate magnetic resonance (MR) imaging-based radiomics models for high-risk endometrial cancer (EC) prediction preoperatively, to be able to estimate deep myometrial invasion (DMI) and lymphovascular space invasion (LVSI), and to discriminate between low-risk and other categories of risk as proposed by ESGO/ESTRO/ESP (European Society of Gynaecological Oncology-European Society for Radiotherapy & Oncology and European Society of Pathology) guidelines.
methodsThis retrospective study included 96 women with EC who underwent 1.5-T MR imaging before surgical staging between April 2009 and May 2019 in two referral centers divided into training (T = 73) and validation cohorts (V = 23). Radiomics features were extracted using the MODDICOM library with manual delineation of whole-tumor volume on MR images (axial T2-weighted). Diagnostic performances of radiomic models were evaluated by area under the receiver operating characteristic (ROC) curve in training (AUCT) and validation (AUCV) cohorts by using a subset of the most relevant texture features tested individually in univariate analysis using Wilcoxon-Mann-Whitney.
resultsA total of 228 radiomics features were extracted and ultimately limited to 38 for DMI, 29 for LVSI, and 15 for risk-classes prediction for logistic radiomic modeling. Whole-tumor radiomic models yielded an AUCT/AUCV of 0.85/0.68 in DMI estimation, 0.92/0.81 in LVSI prediction, and 0.84/0.76 for differentiating low-risk vs other risk classes (intermediate/high-intermediate/high).
conclusionMRI-based radiomics has great potential in developing advanced prognostication in EC.
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