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DOI: 10.1055/a-2298-0147
Computed tomography-based prediction of pancreatitis following biliary metal stent placement with the convolutional neural network
Supported by: Takeda Science Foundation Not availableSupported by: Japan Society for the Promotion of Science JP19K08362,JP21K15368,JP22H02841
Supported by: Daiichi Sankyo Company Not available
Introduction: Pancreatitis is a potentially lethal adverse event of endoscopic transpapillary placement of a self-expandable metal stent (SEMS) for malignant biliary obstruction (MBO). Deep learning-based image recognition has not been investigated in predicting pancreatitis in this setting. Methods: We included 70 patients who received endoscopic placement of a SEMS for nonresectable distal MBO. We constructed a convolutional neural network (CNN) model for pancreatitis prediction using a series of preprocedural computed tomography images covering the whole pancreas (≥ 120,960 augmented images in total). We examined the additional effects of the CNN-based probabilities on the following machine learning models based on clinical parameters: logistic regression, support vector machine with a linear or RBF kernel, random forest classifier, and gradient boosting classifier. Model performance was assessed based on the area under the curve (AUC) in the receiver operating characteristic analysis, positive predictive value (PPV), accuracy, and specificity. Results: The CNN model was associated with moderate levels of performance metrics: AUC, 0.67; PPV, 0.45; accuracy, 0.66; and specificity, 0.63. When added to the machine learning models, the CNN-based probabilities increased the performance metrics. The logistic regression model with the CNN-based probabilities had an AUC of 0.74, PPV of 0.85, accuracy of 0.83, and specificity of 0.96, compared to 0.72, 0.78, 0.77, and 0.96, respectively, without the probabilities. Conclusions: The CNN-based model may increase the predictability for pancreatitis following endoscopic placement of a biliary SEMS. Our findings support the potential of deep learning technology in improving prognostic models in pancreatobiliary therapeutic endoscopy.
Publication History
Received: 12 December 2023
Accepted after revision: 25 March 2024
Accepted Manuscript online:
02 April 2024
© . The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
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