Deep Learning for the Diagnosis of Esophageal Cancer in Endoscopic Images: A Systematic Review and Meta-Analysis

Md Mohaimenul Islam; Tahmina Nasrin Poly; Bruno Andreas Walther; Chih-Yang Yeh; Shabbir Seyed-Abdul; Yu-Chuan Jack Li; Ming-Chin Lin

doi:10.3390/cancers14235996

Deep Learning for the Diagnosis of Esophageal Cancer in Endoscopic Images: A Systematic Review and Meta-Analysis

Cancers (Basel). 2022 Dec 5;14(23):5996. doi: 10.3390/cancers14235996.

Authors

Md Mohaimenul Islam^{1

2

3}, Tahmina Nasrin Poly^{1

2

3}, Bruno Andreas Walther⁴, Chih-Yang Yeh¹, Shabbir Seyed-Abdul¹, Yu-Chuan Jack Li^{1

2

3

5

6}, Ming-Chin Lin^{1

7

8}

Affiliations

¹ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan.
² International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan.
³ Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan.
⁴ Deep Sea Ecology and Technology, Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Am Handelshafen 12, D-27570 Bremerhaven, Germany.
⁵ Department of Dermatology, Wan Fang Hospital, Taipei 116, Taiwan.
⁶ TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan.
⁷ Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan.
⁸ Taipei Neuroscience Institute, Taipei Medical University, Taipei 11031, Taiwan.

Abstract

Esophageal cancer, one of the most common cancers with a poor prognosis, is the sixth leading cause of cancer-related mortality worldwide. Early and accurate diagnosis of esophageal cancer, thus, plays a vital role in choosing the appropriate treatment plan for patients and increasing their survival rate. However, an accurate diagnosis of esophageal cancer requires substantial expertise and experience. Nowadays, the deep learning (DL) model for the diagnosis of esophageal cancer has shown promising performance. Therefore, we conducted an updated meta-analysis to determine the diagnostic accuracy of the DL model for the diagnosis of esophageal cancer. A search of PubMed, EMBASE, Scopus, and Web of Science, between 1 January 2012 and 1 August 2022, was conducted to identify potential studies evaluating the diagnostic performance of the DL model for esophageal cancer using endoscopic images. The study was performed in accordance with PRISMA guidelines. Two reviewers independently assessed potential studies for inclusion and extracted data from retrieved studies. Methodological quality was assessed by using the QUADAS-2 guidelines. The pooled accuracy, sensitivity, specificity, positive and negative predictive value, and the area under the receiver operating curve (AUROC) were calculated using a random effect model. A total of 28 potential studies involving a total of 703,006 images were included. The pooled accuracy, sensitivity, specificity, and positive and negative predictive value of DL for the diagnosis of esophageal cancer were 92.90%, 93.80%, 91.73%, 93.62%, and 91.97%, respectively. The pooled AUROC of DL for the diagnosis of esophageal cancer was 0.96. Furthermore, there was no publication bias among the studies. The findings of our study show that the DL model has great potential to accurately and quickly diagnose esophageal cancer. However, most studies developed their model using endoscopic data from the Asian population. Therefore, we recommend further validation through studies of other populations as well.

Keywords: artificial intelligence; automated diagnosis; convolutional neural network; esophageal cancer; gastrointestinal endoscopy.

Publication types

Review

Grants and funding

This research received no external funding.