ArticleBMC medical imaging2025
An integrated radiomics and deep learning model on multisequence MRI for preoperative prediction of lymphovascular space invasion in endometrial cancer.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and validate a multimodal model that integrates radiomics features (RFs) and deep learning features (DFs) derived from preoperative multisequence magnetic resonance imaging (MRI) for the prediction of lymphovascular space invasion (LVSI) in patients with endometrial cancer (EC).
methodsThis multicenter, retrospective study enrolled 892 patients with postoperative pathologically confirmed EC. Preoperative MRI comprised T2-weighted imaging, contrast-enhanced T1-weighted imaging, and apparent diffusion coefficient maps, were analyzed. Regions of interest (ROIs) were manually delineated for 2D and 3D analyses. RFs were extracted using PyRadiomics, and DFs were obtained using pretrained VGG 11, ResNet 101, and DenseNet 121 architectures. Five single-modality models (2D-RF, 3D-RF, VGG11-DF, ResNet101-DF, and DenseNet121-DF) were developed. In addition, the integration of RFs and DFs were explored to construct combined models. Models were trained in a training cohort (n = 378) and evaluated in both internal (n = 160) and external (n = 354) validation cohorts. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC).
resultsIn the training cohort, the 2D-RF and 3D-RF models showed comparable performance for LVSI prediction (AUC: 0.775 vs. 0.772, P = 0.89). Among the deep learning models, DenseNet121-DF achieved the highest AUC (0.757), which was significantly higher than ResNet-101-DF (AUC: 0.671; P = 0.01) and not statistically different from VGG11-DF (AUC: 0.720, P = 0.20). The optimal combined model, integrating features from 2D-RF and DenseNet121-DF, yielded the highest performance in the training cohort (AUC: 0.796). These findings were confirmed in both the internal and external validation cohorts.
conclusionsA multimodal MRI-based model integrating both RFs and DFs achieved superior performance for noninvasive prediction of LVSI in patients with EC. This approach holds potential to enhance preoperative risk stratification and guide personalized treatment planning.
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.