ArticleAbdominal radiology (New York)2024
Radiomics-based machine learning and deep learning to predict serosal involvement in gallbladder cancer.
Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 11 citations in OpenAlex.
- Deep learning-based segmentation of gallbladder cancer on abdominal computed tomography scans: a multicenter study.Abdominal radiology (New York) · 2025Article
- Update on the Role of Imaging in the Diagnosis, Staging, and Prognostication of Gallbladder Cancer.The Indian journal of radiology & imaging · 2025Review
- New trends in diagnosis and management of gallbladder carcinoma.World journal of gastrointestinal oncology · 2024Review
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveOur study aimed to determine whether radiomics models based on contrast-enhanced computed tomography (CECT) have considerable ability to predict serosal involvement in gallbladder cancer (GBC) patients. MATERIALS AND
methodsA total of 152 patients diagnosed with GBC were retrospectively enrolled and divided into the serosal involvement group and no serosal involvement group according to paraffin pathology results. The regions of interest (ROIs) in the lesion on all CT images were drawn by two radiologists using ITK-SNAP software (version 3.8.0). A total of 412 features were extracted from the CT images of each patient. The Mann‒Whitney U test was applied to identify features with significant differences between groups. Seven machine learning algorithms and a deep learning model based on fully connected neural networks (f-CNNs) were used for radiomics model construction. The prediction efficacy of the models was evaluated using receiver operating characteristic (ROC) curve analysis.
resultsThrough the Mann‒Whitney U test, 75 of the 412 features extracted from the CT images of patients were significantly different between groups (P < 0.05). Among all the algorithms, logistic regression achieved the highest performance with an area under the curve (AUC) of 0.944 (sensitivity 0.889, specificity 0.8); the f-CNN deep learning model had an AUC of 0.916, and the model showed high predictive power for serosal involvement, with a sensitivity of 0.733 and a specificity of 0.801.
conclusionRadiomics models based on features derived from CECT showed convincing performances in predicting serosal involvement in GBC.
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