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

Tsuyoshi Hamada; Koichiro Yasaka; Yousuke Nakai; Rintaro Fukuda; Ryunosuke Hakuta; Kazunaga Ishigaki; Sachiko Kanai; Kensaku Noguchi; Hiroki Oyama; Tomotaka Saito; Tatsuya Sato; Tatsunori Suzuki; Naminatsu Takahara; Hiroyuki Isayama; Osamu Abe; Mitsuhiro Fujishiro

doi:10.1055/a-2298-0147

Endoscopy International Open, Table of Contents

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

¹Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)

²Department of Hepato-Biliary-Pancreatic Medicine, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan

,

Koichiro Yasaka

³Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)

,

Yousuke Nakai

¹Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)

⁴Department of Endoscopy and Endoscopic Surgery, The University of Tokyo Hospital, Tokyo, Japan (Ringgold ID: RIN13143)

,

Rintaro Fukuda

¹Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)

,

Ryunosuke Hakuta

¹Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)

,

Kazunaga Ishigaki

¹Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)

,

Sachiko Kanai

¹Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)

,

Kensaku Noguchi

¹Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)

,

Hiroki Oyama

¹Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)

,

Tomotaka Saito

¹Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)

,

Tatsuya Sato

¹Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)

,

Tatsunori Suzuki

¹Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)

,

Naminatsu Takahara

¹Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)

,

Hiroyuki Isayama

⁵Department of Gastroenterology, Graduate School of Medicine, Juntendo University, Tokyo, Japan (Ringgold ID: RIN12847)

,

Osamu Abe

³Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)

,

Mitsuhiro Fujishiro

¹Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (Ringgold ID: RIN13143)

› Author Affiliations

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

Full Text

References

References
1 Pan Y, He L, Chen W. et al. The current state of artificial intelligence in endoscopic diagnosis of early esophageal squamous cell carcinoma. Front Oncol 2023; 13: 1198941
2 Jahagirdar V, Bapaye J, Chandan S. et al. Diagnostic accuracy of convolutional neural network-based machine learning algorithms in endoscopic severity prediction of ulcerative colitis: a systematic review and meta-analysis. Gastrointest Endosc 2023; 98: 145-154.e148
3 Sharma P, Hassan C. Artificial intelligence and deep learning for upper gastrointestinal neoplasia. Gastroenterology 2022; 162: 1056-1066
4 Xie X, Xiao YF, Zhao XY. et al. Development and validation of an artificial intelligence model for small bowel capsule endoscopy video review. JAMA Netw Open 2022; 5: e2221992
5 Tonozuka R, Mukai S, Itoi T. The role of artificial intelligence in endoscopic ultrasound for pancreatic disorders. Diagnostics (Basel, Switzerland) 2020; 11: 18
6 Tonozuka R, Itoi T, Nagata N. et al. Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study. J Hepatobiliary Pancreat 2021; 28: 95-104
7 Kuwahara T, Hara K, Mizuno N. et al. Usefulness of deep learning analysis for the diagnosis of malignancy in intraductal papillary mucinous neoplasms of the pancreas. Clin Transl Gastroenterol 2019; 10: 1-8
8 Eisenberg I, Gaidhane M, Kahaleh M. et al. Drainage approach for malignant biliary obstruction: a changing paradigm. J Clin Gastroenterol 2023; 57: 546-552
9 Nakai Y, Isayama H, Wang HP. et al. International consensus statements for endoscopic management of distal biliary stricture. J Gastroenterol Hepatol 2020; 35: 967-979
10 Bill JG, Mullady DK. Stenting for benign and malignant biliary strictures. Gastrointest Endosc Clin N Am 2019; 29: 215-235
11 Almadi MA, Barkun A, Martel M. Plastic vs. self-expandable metal stents for palliation in malignant biliary obstruction: a series of meta-analyses. Am J Gastroenterol 2017; 112: 260-273
12 Buxbaum JL, Freeman M, Amateau SK. et al. American Society for Gastrointestinal Endoscopy guideline on post-ERCP pancreatitis prevention strategies: summary and recommendations. Gastrointest Endosc 2023; 97: 153-162
13 Dumonceau JM, Kapral C, Aabakken L. et al. ERCP-related adverse events: European Society of Gastrointestinal Endoscopy (ESGE) Guideline. Endoscopy 2020; 52: 127-149
14 Tamura T, Yamai T, Uza N. et al. Adverse events of self-expandable metal stent placement for malignant distal biliary obstruction: a large multicenter study. Gastrointest Endosc 2024; 99: 61-72.e68
15 Kim GH, Ryoo SK, Park JK. et al. Risk factors for pancreatitis and cholecystitis after endoscopic biliary stenting in patients with malignant extrahepatic bile duct obstruction. Clin Endosc 2019; 52: 598-605
16 Shimizu S, Naitoh I, Nakazawa T. et al. Predictive factors for pancreatitis and cholecystitis in endoscopic covered metal stenting for distal malignant biliary obstruction. J Gastroenterol Hepatol 2013; 28: 68-72
17 Kawakubo K, Isayama H, Nakai Y. et al. Risk factors for pancreatitis following transpapillary self-expandable metal stent placement. Surg Endosc 2012; 26: 771-776
18 Takeda T, Sasaki T, Mie T. et al. Novel risk factors for recurrent biliary obstruction and pancreatitis after metallic stent placement in pancreatic cancer. Endosc Int Open 2020; 8: E1603-E1610
19 Isayama H, Nakai Y, Toyokawa Y. et al. Measurement of radial and axial forces of biliary self-expandable metallic stents. Gastrointest Endosc 2009; 70: 37-44
20 Kogure H, Ryozawa S, Maetani I. et al. A prospective multicenter study of a fully covered metal stent in patients with distal malignant biliary obstruction: WATCH-2 Study. Digest Dis Sci 2018; 63: 2466-2473
21 Yokota Y, Fukasawa M, Takano S. et al. Partially covered metal stents have longer patency than uncovered and fully covered metal stents in the management of distal malignant biliary obstruction: a retrospective study. BMC Gastroenterol 2017; 17: 105
22 Isayama H, Mukai T, Itoi T. et al. Comparison of partially covered nitinol stents with partially covered stainless stents as a historical control in a multicenter study of distal malignant biliary obstruction: the WATCH study. Gastrointest Endosc 2012; 76: 84-92
23 Cotton PB, Lehman G, Vennes J. et al. Endoscopic sphincterotomy complications and their management: an attempt at consensus. Gastrointest Endosc 1991; 37: 383-393
24 Isayama H, Hamada T, Yasuda I. et al. TOKYO criteria 2014 for transpapillary biliary stenting. Dig Endosc 2015; 27: 259-264
25 Cotton PB, Eisen GM, Aabakken L. et al. A lexicon for endoscopic adverse events: report of an ASGE workshop. Gastrointest Endosc 2010; 71: 446-454
26 Hamada T, Isayama H, Nakai Y. et al. Tips and troubleshooting for transpapillary metal stenting for distal malignant biliary obstruction. J Hepatobiliary Pancreat 2014; 21: E12-18
27 Kato S, Kuwatani M, Onodera M. et al. Risk of pancreatitis following biliary stenting with/without endoscopic sphincterotomy: a randomized controlled trial. Clin Gastroenterol Hepatol 2022; 20: 1394-1403.e1391
28 Silva GFS, Fagundes TP, Teixeira BC. et al. Machine learning for hypertension prediction: a systematic review. Curr Hypertension Rep 2022; 24: 523-533
29 Spann A, Yasodhara A, Kang J. et al. Applying machine learning in liver disease and transplantation: a comprehensive review. Hepatology 2020; 71: 1093-1105
30 Wu CCH, Lim SJM, Khor CJL. Endoscopic retrograde cholangiopancreatography-related complications: risk stratification, prevention, and management. Clin Endosc 2023; 56: 433-445
31 Akshintala VS, Sperna Weiland CJ, Bhullar FA. et al. Non-steroidal anti-inflammatory drugs, intravenous fluids, pancreatic stents, or their combinations for the prevention of post-endoscopic retrograde cholangiopancreatography pancreatitis: a systematic review and network meta-analysis. Lancet Gastroenterol Hepatol 2021; 6: 733-742
32 Chen YI, Sahai A, Donatelli G. et al. Endoscopic ultrasound-guided biliary drainage of first intent with a lumen-apposing metal stent vs endoscopic retrograde cholangiopancreatography in malignant distal biliary obstruction: a multicenter randomized controlled study (ELEMENT trial). Gastroenterology 2023; 165: 1249-1261.e1245
33 Teoh AYB, Napoleon B, Kunda R. et al. EUS-guided choledocho-duodenostomy using lumen apposing stent versus ERCP with covered metallic stents in patients with unresectable malignant distal biliary obstruction: a multicenter randomized controlled trial (DRA-MBO Trial). Gastroenterology 2023; 165: 473-482.e472
34 Fukuda R, Hakuta R, Nakai Y. et al. Development and external validation of a nomogram for prediction of post-endoscopic retrograde cholangiopancreatography pancreatitis. Pancreatology 2023; 23: 789-796
35 Fujita K, Yazumi S, Uza N. et al. New practical scoring system to predict post-endoscopic retrograde cholangiopancreatography pancreatitis: Development and validation. JGH Open 2021; 5: 1078-1084

Supplementary Material

Supplementary Material