ArticleAbdominal radiology (New York)2025
Preoperative discrimination of absence or presence of myometrial invasion in endometrial cancer with an MRI-based multimodal deep learning radiomics model.
Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Review
- Prediction of cervical stromal invasion using ultrasound radiomics: from conventional ultrasound to intelligent diagnosis.Frontiers in oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
objectiveAccurate preoperative evaluation of myometrial invasion (MI) is essential for treatment decisions in endometrial cancer (EC). However, the diagnostic accuracy of commonly utilized magnetic resonance imaging (MRI) techniques for this assessment exhibits considerable variability. This study aims to enhance preoperative discrimination of absence or presence of MI by developing and validating a multimodal deep learning radiomics (MDLR) model based on MRI.
methodsDuring March 2010 and February 2023, 1139 EC patients (age 54.771 ± 8.465 years; range 24-89 years) from five independent centers were enrolled retrospectively. We utilized ResNet18 to extract multi-scale deep learning features from T2-weighted imaging followed by feature selection via Mann-Whitney U test. Subsequently, a Deep Learning Signature (DLS) was formulated using Integrated Sparse Bayesian Extreme Learning Machine. Furthermore, we developed Clinical Model (CM) based on clinical characteristics and MDLR model by integrating clinical characteristics with DLS. The area under the curve (AUC) was used for evaluating diagnostic performance of the models. Decision curve analysis (DCA) and integrated discrimination index (IDI) were used to assess the clinical benefit and compare the predictive performance of models.
resultsThe MDLR model comprised of age, histopathologic grade, subjective MR findings (TMD and Reading for MI status) and DLS demonstrated the best predictive performance. The AUC values for MDLR in training set, internal validation set, external validation set 1, and external validation set 2 were 0.899 (95% CI, 0.866-0.926), 0.874 (95% CI, 0.829-0.912), 0.862 (95% CI, 0.817-0.899) and 0.867 (95% CI, 0.806-0.914) respectively. The IDI and DCA showed higher diagnostic performance and clinical net benefits for the MDLR than for CM or DLS, which revealed MDLR may enhance decision-making support.
conclusionsThe MDLR which incorporated clinical characteristics and DLS could improve preoperative accuracy in discriminating absence or presence of MI. This improvement may facilitate individualized treatment decision-making for EC.
Indexed as
Identifiers
39747670What 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.