Retrospective Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Sep 14, 2020; 26(34): 5156-5168
Published online Sep 14, 2020. doi: 10.3748/wjg.v26.i34.5156
Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis
Han Ma, Zhong-Xin Liu, Jing-Jing Zhang, Feng-Tian Wu, Cheng-Fu Xu, Zhe Shen, Chao-Hui Yu, You-Ming Li
Han Ma, Jing-Jing Zhang, Cheng-Fu Xu, Zhe Shen, You-Ming Li, Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang Province, China
Zhong-Xin Liu, College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
Feng-Tian Wu, State Key Laboratory for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang Province, China
Chao-Hui Yu, Department of Gastroenterology, Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang Province, China
Author contributions: Yu CH and Li YM conceived this project, and acted as guarantors; Ma H managed the data acquisition, data labeling, and contributed to the data analysis and manuscript drafting and editing, and experiments; Liu ZX developed the technology, implemented the deep-learning architectures, conducted the main experiments, and contributed to the data analysis and manuscript drafting and editing; Wu FT and Zhang JJ collected the data; Xu CF and Shen Z reviewed CT images and data labeling; Yu CH supervised the experiments, analyzed the results; all authors approved the final version of the manuscript.
Supported by the National Natural Science Foundation of China, No. 81900509; Fundamental Research Funds for the Central Universities, No. 2018XZZX002-10; and High-Level Talents Special Support Plan of Zhejiang Province (known as the Ten Thousand Talents Plan), No. ZJWR0108008.
Institutional review board statement: The study was reviewed and approved by the review board of Zhejiang University School of Medicine, Zhejiang Province, China.
Informed consent statement: Patients were not required to give written informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: We have no financial relationships to disclose.
Data sharing statement: 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: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Chao-Hui Yu, MD, PhD, Chief Doctor, Professor, Department of Gastroenterology, Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, No. 79 Qingchun Road, Hangzhou 310003, Zhejiang Province, China. zyyyych@zju.edu.cn
Received: April 20, 2020
Peer-review started: April 20, 2020
First decision: May 1, 2020
Revised: May 19, 2020
Accepted: August 26, 2020
Article in press: August 26, 2020
Published online: September 14, 2020
ARTICLE HIGHLIGHTS
Research background

Pancreatic cancer is a highly lethal malignancy with a very poor prognosis. With promising achievements in deep neural networks and increasing medical needs, computer-aided diagnosis systems have become a new research focus.

Research motivation

Efforts should be made to develop a deep-learning diagnosis system to distinguish pancreatic cancer from benign tissue due to the high morbidity of pancreatic cancer.

Research objectives

To identify pancreatic cancer in computed tomography (CT) images automatically by constructing a convolutional neural network (CNN) classifier.

Research methods

A CNN model was constructed using a dataset of 3494 CT images obtained from 222 patients with pathologically confirmed pancreatic cancer and 3751 CT images from 190 patients with normal pancreas from June 2017 to June 2018. We built three datasets from our images according to the image phases, evaluated our approach in terms of binary classification and ternary classification using 10-fold cross validation, and measured the effectiveness of the model with regard to the accuracy, sensitivity, and specificity.

Research results

In the binary classifiers, the performance of plain, arterial and venous phase showed no difference. Considering that plain phase had relatively same sensitivity, easier access, and lower radiation compared with arterial phase and venous phase, it is more sufficient for the binary classifier. Its accuracy on plain scans was 95.47%, sensitivity was 91.58%, and specificity was 98.27%. In the ternary classifier, the arterial phase had the highest sensitivity in detecting cancer in the head of the pancreas among three phases, but it achieved only moderate performances.

Research conclusions

In this study, we developed a deep learning-based, computer-aided pancreatic ductal adenocarcinoma classifier trained on medium-sized CT images. It was suitable for screening purposes in pancreatic cancer detection.

Research perspectives

Further improvement in the performance of models would be required before it could be integrated into a clinical strategy.