ArticleCancers2023
A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study.
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.
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
15 citing papers in PubMed.
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- Multiparametric MRI-based radiomics machine learning nomogram for predicting aggressive histology in endometrial cancer.Abdominal radiology (New York) · 2026Article
- Article
- Explainable machine learning for preoperative relapse prediction in molecularly stratified endometrial cancer: A single-center finnish cohort study.Computational and structural biotechnology journal · 2026Article
- Evaluate lymphovascular space invasion in endometrial cancer using diffusion-weighted imaging-based habitat imaging.Scientific reports · 2025Article
- CT radiomics-based explainable machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two-center study.Biomedical engineering online · 2025Article
- Machine learning-based radiomics model: prognostic prediction and mechanism exploration in patients with endometrial cancer.Biomarker research · 2025Article
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- Machine learning model reveals the risk, prognosis, and drug response of histamine-related signatures in pancreatic cancer.Discover oncology · 2025Article
- 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
- Intratumoral and peritumoral multiparametric MRI-based radiomics nomogram for preoperative risk stratification in patients with endometrial cancer.Frontiers in oncology · 2025Article
- The value of multi-sequence magnetic resonance imaging and whole-tumor apparent diffusion coefficient histogram analysis in differentiating p53 abnormal from non-p53 abnormal endometrial carcinoma.Frontiers in oncology · 2025Article
- Predicting recurrence risk in endometrial cancer: a multisequence MRI intratumoral and peritumoral radiomics nomogram approach.Frontiers in oncology · 2025Article
- Hybrid ResNet-ViT Framework for Endometrial Lesion Analysis: A Comparative Study of MRI and CT in Endometrial Cancer Classification.International journal of women's health · 2025Article
- Explainable machine learning predicts survival of retroperitoneal liposarcoma: A study based on the SEER database and external validation in China.Cancer medicine · 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
17 authors.
Funding
Abstract
backgroundCurrent prognostic models lack the use of pre-operative CT images to predict recurrence in endometrial cancer (EC) patients. Our study aimed to investigate the potential of radiomic features extracted from pre-surgical CT scans to accurately predict disease-free survival (DFS) among EC patients.
methodsContrast-Enhanced CT (CE-CT) scans from 81 EC cases were used to extract the radiomic features from semi-automatically contoured volumes of interest. We employed a 10-fold cross-validation approach with a 6:4 training to test set and utilized data augmentation and balancing techniques. Univariate analysis was applied for feature reduction leading to the development of three distinct machine learning (ML) models for the prediction of DFS: LASSO-Cox, CoxBoost and Random Forest (RFsrc).
resultsIn the training set, the ML models demonstrated AUCs ranging from 0.92 to 0.93, sensitivities from 0.96 to 1.00 and specificities from 0.77 to 0.89. In the test set, AUCs ranged from 0.86 to 0.90, sensitivities from 0.89 to 1.00 and specificities from 0.73 to 0.90. Patients classified as having a high recurrence risk prediction by ML models exhibited significantly worse DSF (
conclusionsOur findings demonstrate the potential of radiomics in predicting EC recurrence. While further validation studies are needed, our results underscore the promising role of radiomics in forecasting EC outcomes.
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.