ArticleCommunications biology2021
A radiogenomics application for prognostic profiling of endometrial cancer.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 3 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Diagnostic performance of radiomics models for preoperative prediction of microsatellite instability status in endometrial cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools.European radiology · 2025Pooled it
- What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies.International journal of molecular sciences · 2022Pooled it
- Toward reproducible metabolic tumor volume quantification in endometrial cancer: optimizing [¹⁸F]FDG PET/CT tumor segmentation methods.EJNMMI research · 2026Article
- Predicting aggressive disease and poor outcome in endometrial cancer using preoperative [European journal of nuclear medicine and molecular imaging · 2025Article
- Machine learning-based radiomics model: prognostic prediction and mechanism exploration in patients with endometrial cancer.Biomarker research · 2025Article
- Integrating Multi-Omics in Endometrial Cancer: From Molecular Insights to Clinical Applications.Cells · 2025Review
- An MRI-based radiomics nomogram to predict progression-free survival in patients with endometrial cancer.Future oncology (London, England) · 2025Article
- Development and validation of clinical-radiomics deep learning model based on MRI for endometrial cancer molecular subtypes classification.Insights into imaging · 2025Article
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- Artificial intelligence-based magnetic resonance imaging for preoperative staging of patients with endometrial cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Article
- Exploratory Analysis of Radiomics and Pathomics in Uterine Corpus Endometrial Carcinoma.Scientific reports · 2024Article
- Radiology and multi-scale data integration for precision oncology.NPJ precision oncology · 2024Review
- Impact of MRI radiomic feature normalization for prognostic modelling in uterine endometrial and cervical cancers.Scientific reports · 2024Article
- Radiomic profiles improve prognostication and reveal targets for therapy in cervical cancer.Scientific reports · 2024Article
- It is time to implement molecular classification in endometrial cancer.Archives of gynecology and obstetrics · 2024Review
- MRI radiomics captures early treatment response in patient-derived organoid endometrial cancer mouse models.Frontiers in oncology · 2024Article
- Multiparametric MRI radiomics for predicting disease-free survival and high-risk histopathological features for tumor recurrence in endometrial cancer.Frontiers in oncology · 2024Article
- Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows.Journal of clinical medicine · 2023Review
- Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey.European radiology · 2023Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers (n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors (n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm (n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts (n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC.
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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.