ArticleJournal of magnetic resonance imaging : JMRI2021
Whole-Volume Tumor MRI Radiomics for Prognostic Modeling in Endometrial Cancer.
Article in Journal of magnetic resonance imaging : JMRI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it, 82 citations in OpenAlex.
- Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis.La Radiologia medica · 2024Pooled it
- Toward reproducible metabolic tumor volume quantification in endometrial cancer: optimizing [¹⁸F]FDG PET/CT tumor segmentation methods.EJNMMI research · 2026Article
- Molecular and AI enabled prognostication in endometrial cancer a 2015 to 2024 bibliometric Atlas and critical review.Discover oncology · 2026Article
- A 3T Magnetic Resonance Fractional Order Calculus Model for the Diagnosis and Grading of Endometrial Cancer.Current medical imaging · 2026Article
- An integrated radiomics and deep learning model on multisequence MRI for preoperative prediction of lymphovascular space invasion in endometrial cancer.BMC medical imaging · 2025Article
- 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
- 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
- The qualitative and quantitative characteristics of serous endometrial carcinoma on MRI: applying a novel nomogram for predicting an aggressive histological type.Frontiers in oncology · 2025Article
- The Value of Texture Analysis of Multi-parameter MRI Images in Distinguishing Benign and Malignant Lesions of O-RADS MRI 4 Lesions.International journal of medical sciences · 2025Article
- Letter to the Editor: Nodal infiltration in endometrial cancer: a prediction model using best subset regression.European radiology · 2024Article
- Both intra- and peri-tumoral radiomics signatures can be used to predict lymphatic vascular space invasion and lymphatic metastasis positive status from endometrial cancer MR imaging.Abdominal radiology (New York) · 2024Article
- Endometrial cancer risk stratification using MRI radiomics: corroborating with choline metabolism.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- A prediction model based on deep learning and radiomics features of DWI for the assessment of microsatellite instability in endometrial cancer.Cancer medicine · 2024Article
- A comparison of 2D and 3D magnetic resonance imaging-based intratumoral and peritumoral radiomics models for the prognostic prediction of endometrial cancer: a pilot study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- MRI Radiomics Data Analysis for Differentiation between Malignant Mixed Müllerian Tumors and Endometrial Carcinoma.Cancers · 2024Article
- Impact of MRI radiomic feature normalization for prognostic modelling in uterine endometrial and cervical cancers.Scientific reports · 2024Article
- CT-based radiomics predicts CD38 expression and indirectly reflects clinical prognosis in epithelial ovarian cancer.Heliyon · 2024Article
- Multiparameter MRI-based radiomics analysis for preoperative prediction of type II endometrial cancer.Heliyon · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, while final tumor stage and grade are established by surgery and pathology. MRI-based radiomic tumor profiling may aid in preoperative risk-stratification and support clinical treatment decisions in EC. PURPOSE: To develop MRI-based whole-volume tumor radiomic signatures for prediction of aggressive EC disease. STUDY TYPE: Retrospective. POPULATION: A total of 138 women with histologically confirmed EC, divided into training (n FIELD STRENGTH/SEQUENCE: Axial oblique T ASSESSMENT: Primary tumors were manually segmented by two radiologists with 4 and 8 years' of experience. Radiomic tumor features were computed and used for prediction of surgicopathologically-verified deep (≥50%) myometrial invasion (DMI), lymph node metastases (LNM), advanced stage (FIGO III + IV), nonendometrioid (NE) histology, and high-grade endometrioid tumors (E3). Corresponding analyses were also conducted using radiomics extracted from the axial oblique image slice depicting the largest tumor area. STATISTICAL TESTS: Logistic least absolute shrinkage and selection operator (LASSO) was applied for radiomic modeling in the training cohort. The diagnostic performances of the radiomic signatures were evaluated by area under the receiver operating characteristic curve in the training (AUC
resultsThe whole-tumor radiomic signatures yielded AUC DATA
conclusionMRI-based whole-tumor radiomic signatures yield medium-to-high diagnostic performance for predicting aggressive EC disease. The signatures may aid in preoperative risk assessment and hence guide personalized treatment strategies in EC. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.