Retrospective Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jan 21, 2023; 29(3): 536-548
Published online Jan 21, 2023. doi: 10.3748/wjg.v29.i3.536
Magnetic resonance imaging-based deep learning model to predict multiple firings in double-stapled colorectal anastomosis
Zheng-Hao Cai, Qun Zhang, Zhan-Wei Fu, Abraham Fingerhut, Jing-Wen Tan, Lu Zang, Feng Dong, Shu-Chun Li, Shi-Lin Wang, Jun-Jun Ma
Zheng-Hao Cai, Zhan-Wei Fu, Abraham Fingerhut, Lu Zang, Feng Dong, Shu-Chun Li, Jun-Jun Ma, Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Zheng-Hao Cai, Shanghai Minimally Invasive Surgery Center, Shanghai 200025, China
Qun Zhang, Shi-Lin Wang, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 201100, China
Jing-Wen Tan, Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Author contributions: Cai ZH, Zhang Q, Fu ZW, Fingerhut A, Tan JW, Zang L, Dong F, Li SC, Wang SL, Ma JJ contributed to the study conception and design; Cai ZH, Zhang Q, Zang L, Dong F, Li SC, Tan JW, and Ma JJ contributed to material preparation; Cai ZH, Fu ZW and Tan JW contributed to data collection; Fu ZW, Fingerhut A, and Li SC contributed to data analysis; The first draft of the manuscript was written by Cai ZH, Zhang Q, Wang SL, and Fu Z; The final version and revisions of the manuscript were performed by Cai ZH, Zang L, Tan JW, Zhang Q, Li SC, Ma JJ, Fu ZW, and Fingerhut A; All authors read and approved the final manuscript and accept to be responsible for the contents.
Supported by Shanghai Jiaotong University, No. YG2019QNB24.
Institutional review board statement: This study was reviewed and approved by Ruijin Hospital Ethics Committee (Approval No. 2019-82).
Informed consent statement: Informed consent was waived by Ruijin Hospital Ethics committee due to the retrospective nature of the study. The analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author at marsnew1997@163.com. Consent was not obtained but the presented data are anonymized and risk of identification is low.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jun-Jun Ma, MD, Doctor, Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin No. 2 Rd, Shanghai 200025, China. marsnew1997@163.com
Received: October 9, 2022
Peer-review started: October 9, 2022
First decision: November 18, 2022
Revised: November 29, 2022
Accepted: January 3, 2023
Article in press: January 3, 2023
Published online: January 21, 2023
Processing time: 95 Days and 6.6 Hours
Abstract
BACKGROUND

Multiple linear stapler firings during double stapling technique (DST) after laparoscopic low anterior resection (LAR) are associated with an increased risk of anastomotic leakage (AL). However, it is difficult to predict preoperatively the need for multiple linear stapler cartridges during DST anastomosis.

AIM

To develop a deep learning model to predict multiple firings during DST anastomosis based on pelvic magnetic resonance imaging (MRI).

METHODS

We collected 9476 MR images from 328 mid-low rectal cancer patients undergoing LAR with DST anastomosis, which were randomly divided into a training set (n = 260) and testing set (n = 68). Binary logistic regression was adopted to create a clinical model using six factors. The sequence of fast spin-echo T2-weighted MRI of the entire pelvis was segmented and analyzed. Pure-image and clinical-image integrated deep learning models were constructed using the mask region-based convolutional neural network segmentation tool and three-dimensional convolutional networks. Sensitivity, specificity, accuracy, positive predictive value (PPV), and area under the receiver operating characteristic curve (AUC) was calculated for each model.

RESULTS

The prevalence of ≥ 3 linear stapler cartridges was 17.7% (58/328). The prevalence of AL was statistically significantly higher in patients with ≥ 3 cartridges compared to those with ≤ 2 cartridges (25.0% vs 11.8%, P = 0.018). Preoperative carcinoembryonic antigen level > 5 ng/mL (OR = 2.11, 95%CI 1.08-4.12, P = 0.028) and tumor size ≥ 5 cm (OR = 3.57, 95%CI 1.61-7.89, P = 0.002) were recognized as independent risk factors for use of ≥ 3 linear stapler cartridges. Diagnostic performance was better with the integrated model (accuracy = 94.1%, PPV = 87.5%, and AUC = 0.88) compared with the clinical model (accuracy = 86.7%, PPV = 38.9%, and AUC = 0.72) and the image model (accuracy = 91.2%, PPV = 83.3%, and AUC = 0.81).

CONCLUSION

MRI-based deep learning model can predict the use of ≥ 3 linear stapler cartridges during DST anastomosis in laparoscopic LAR surgery. This model might help determine the best anastomosis strategy by avoiding DST when there is a high probability of the need for ≥ 3 linear stapler cartridges.

Keywords: Deep learning; Image-reading artificial intelligence; Magnetic resonance imaging; Predictive model; Double stapling technique; Linear stapler; Rectal cancer; Laparoscopic surgery; Low anterior resection; Anastomotic leakage

Core Tip: Multiple linear stapler firings during double stapling technique (DST) anastomosis are associated with an increased risk of anastomotic leakage after laparoscopic low anterior resection. This retrospective study developed a deep learning model to predict the use of ≥ 3 linear stapler cartridges during DST anastomosis. With the help of the artificial intelligence to identify and extract information from pelvic magnetic resonance imaging, we developed a clinical-image integrated model with satisfactory accuracy. This model might help preoperatively to determine the anastomosis strategy for rectal cancer patients (suggesting not to perform DST when the risk for ≥ 3 firings is high).