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CC BY-NC-ND 4.0 · Endosc Int Open 2024; 12(06): E772-E780
DOI: 10.1055/a-2298-0147
Original article

Computed tomography-based prediction of pancreatitis following biliary metal stent placement with the convolutional neural network

Tsuyoshi Hamada 
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)
2   Department of Hepato-Biliary-Pancreatic Medicine, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
,
Koichiro Yasaka
3   Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)
,
Yousuke Nakai 
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)
4   Department of Endoscopy and Endoscopic Surgery, The University of Tokyo Hospital, Tokyo, Japan (Ringgold ID: RIN13143)
,
Rintaro Fukuda
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)
,
Ryunosuke Hakuta
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)
,
Kazunaga Ishigaki
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)
,
Sachiko Kanai
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)
,
Kensaku Noguchi
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)
,
Hiroki Oyama
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)
,
Tomotaka Saito
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)
,
Tatsuya Sato
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)
,
Tatsunori Suzuki
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)
,
Naminatsu Takahara
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)
,
Hiroyuki Isayama 
5   Department of Gastroenterology, Graduate School of Medicine, Juntendo University, Tokyo, Japan (Ringgold ID: RIN12847)
,
Osamu Abe
3   Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)
,
Mitsuhiro Fujishiro
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)
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Abstract

Background and study aims 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.

Patients and methods We included 70 patients who underwent endoscopic placement of a SEMS for nonresectable distal MBO. We constructed a convolutional neural network (CNN) model for pancreatitis prediction using a series of pre-procedure 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 with 0.72, 0.78, 0.77, and 0.96, respectively, without the probabilities.

Conclusions The CNN-based model may increase predictability for pancreatitis following endoscopic placement of a biliary SEMS. Our findings support the potential of deep learning technology to improve prognostic models in pancreatobiliary therapeutic endoscopy.

Keywords

Pancreatobiliary (ERCP/PTCD) - Strictures - ERC topics

Supplementary Material



Publication History

Received: 12 December 2023

Accepted after revision: 25 March 2024

Accepted Manuscript online:
02 April 2024

Article published online:
18 June 2024

© 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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