ArticleBMC cancer2025
Preoperative clinical radiomics model based on deep learning in prognostic assessment of patients with gallbladder carcinoma.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Non-invasive predictive model for incidental gallbladder carcinoma based on multimodal features: Integrating clinical data, MRI radiomics, and deep transfer learning features.Translational oncology · 2026Article
- A novel machine learning-based model for precise prediction of postoperative recurrence in gallbladder cancer: development and multicenter validation across four centers.World journal of surgical oncology · 2026Article
- Effects of radiotherapy, chemotherapy, and chemoradiotherapy on survival outcomes in patients with gallbladder carcinoma: a real population-based study.Translational cancer research · 2025Article
- Predicting the prognosis of epithelial ovarian cancer patients based on deep learning models.Frontiers in oncology · 2025Article
- Radiomic nomograms in CT diagnosis of gall bladder carcinoma: a narrative review.Discover oncology · 2024Review
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Authors and funding
9 authors.
Funding
Abstract
objectiveWe aimed to develop a preoperative clinical radiomics survival prediction model based on the radiomics features via deep learning to provide a reference basis for preoperative assessment and treatment decisions for patients with gallbladder carcinoma (GBC).
methodsA total of 168 GBC patients who underwent preoperative upper abdominal enhanced CT from one high-volume medical center between January 2011 to December 2020 were retrospectively analyzed. The region of interest (ROI) was manually outlined by two physicians using 3D Slicer software to establish a nnU-Net model. The DeepSurv survival prediction model was developed by combining radiomics features and preoperative clinical variables.
resultsA total of 1502 radiomics features were extracted from the ROI results based on the nnU-Net model and manual segmentation, and 13 radiomics features were obtained through the 4-step dimensionality reduction methods, respectively. The C-index and AUC of 1-, 2-, and 3-year survival prediction for the nnU-Net based clinical radiomics DeepSurv model was higher than clinical and nnU-Net based radiomics DeepSurv models in the training and testing sets, and close to manual based clinical radiomics DeepSurv model. Delong-test was performed on the AUC of 1-, 2-, and 3-year survival prediction for the two preoperative clinical radiomics DeepSurv prediction models in the testing set, and the results showed that the two models had the same prediction efficiency (all P > 0.05).
conclusionsBy using the DeepSurv model via nnU-Net segmentation, postoperative survival outcomes for individual gallbladder carcinoma patients could be assessed and stratified, which can provide references for preoperative diagnosis and treatment decisions.
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