Next Article in Journal
An Optimal Artificial Intelligence System for Real-Time Endoscopic Prediction of Invasion Depth in Early Gastric Cancer
Previous Article in Journal
Correlation of Transcriptomics and FDG-PET SUVmax Indicates Reciprocal Expression of Stemness-Related Transcription Factor and Neuropeptide Signaling Pathways in Glucose Metabolism of Ewing Sarcoma
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cancers
Volume 14
Issue 23
10.3390/cancers14235996
cancers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

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

by
Md. Mohaimenul Islam
1,2,3,
Tahmina Nasrin Poly
1,2,3,
Bruno Andreas Walther
4,
Chih-Yang Yeh
1,
Shabbir Seyed-Abdul
1,
Yu-Chuan (Jack) Li
1,2,3,5,6 and
Ming-Chin Lin
1,7,8,*
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
*
Author to whom correspondence should be addressed.
Cancers 2022, 14(23), 5996; https://doi.org/10.3390/cancers14235996
Submission received: 13 October 2022 / Revised: 17 November 2022 / Accepted: 2 December 2022 / Published: 5 December 2022
Browse Figures
Versions Notes

Abstract

:

Simple Summary

Esophageal cancer is the seventh leading cause of cancer-related mortality worldwide, with a 5-year survival rate of around 20%. Recently, deep learning (DL) models have shown great performance in image-based esophageal cancer diagnosis and prognosis prediction. In this study, a comprehensive literature search was conducted on studies published between 1 January 2012 and 1 August 2022 from the most popular databases, namely, PubMed, Embase, Scopus, and Web of Science. This study, thus, systematically summarizes the application of a DL model for esophageal cancer diagnosis and discusses the potential limitations and future directions of DL techniques in esophageal cancer therapy.

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.

1. Introduction

Rationale: Esophageal cancer is one of the most commonly diagnosed adenocarcinomas globally, with an estimated 0.5 million new cases annually [1]. The prognosis of esophageal cancer is poor and is the sixth leading cause of cancer-related mortality worldwide, with over 0.5 million deaths annually [2]. The 5-year overall survival rate of early EC is only 20% [3]; however, the survival rate depends on several factors, including the stages of esophageal cancer. The 5-year survival rate of localized (confined to the primary site) esophageal cancer is 46.4%, whereas the relative survival of distant esophageal cancer (spread to lymph nodes) is 5% [4]. The two primary histologic subtypes of esophageal cancer are esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), which contribute approximately 90 percent of total esophageal cancer [5]. Although the prevalence of ESCC is always high, recent years have witnessed an increasing trend of EAC in the United States of America (USA) and other Western countries [6,7]. Previous studies reported that patients with EAC have a better overall median survival than ESCC, particularly in early stage disease [8,9]. The risk factors of ESCC include smoking, alcohol, dietary, and male gender, whereas gastro-esophageal reflux disease (GERD) and obesity are two major risk factors for EAC [10,11]. Barrett’s esophagus (BE) is also an established premalignant lesion [12,13], which increases the risk of EAC up to 40-fold [14].
An early and accurate diagnosis of esophageal cancer is essential in determining the appropriate management of esophageal cancer patients and improving their overall survival rate [15]. Esophageal cancer is often detected in the advanced stage, which requires highly invasive treatments such as surgical resection and chemoradiotherapy [16,17]. The early detection of esophageal cancer through widely used screening programs has shown its effectiveness in reducing esophageal cancer-related mortality and improving the overall survival rate. The introduction of image-enhanced endoscopies, such as narrow-band imaging (NBI) and white-light imaging (WLI), has improved the early detection rate of esophageal cancer [18,19,20]. However, esophageal cancer detection is always challenging and depends on substantial expertise and experience [21]. Recently, DL models, especially the convolutional neural network (CNN) model, have performed remarkably well in various medical fields, including esophageal cancer diagnosis and prognosis [22].
Goal: Notwithstanding the growing interest in and opportunities for the application of the DL model for the diagnosis of esophageal cancer using endoscopic images, previous studies have not comprehensively reviewed the extant literature reporting on the application of the DL model to diagnose esophageal cancer. If the DL algorithm might be considered in the future for use in a real-world clinical setting, the performance of this algorithm should first undergo the same degree of assessment as current practices to evaluate their acceptability. Therefore, in the present study, we aimed to conduct a systematic review and meta-analysis from studies that applied the DL to determine the diagnostic performance for esophageal cancer.

2. Methods

2.1. Research Design

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) [23]. The study has not been registered.

2.2. Search Methods for Identification of Studies

Electronic Database Search

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 the diagnosis of esophageal cancer using endoscopic images, with the assistance of experts in systematic reviews and meta-analysis. We used appropriate MeSH (Medical Subject Headings) terms as given below: “Deep learning” OR “computer-aided system” OR “convolutional neural network/s” AND “esophageal cancer” OR “esophageal neoplasm” OR “esophageal adenocarcinoma” OR “Barrett’s esophagus”.

2.3. Inclusion and Exclusion Criteria

Studies were included if they satisfied all the following criteria: (a) studies evaluated the diagnostic test accuracy of DL for esophageal cancer using endoscopic images, (b) studies provided sensitivity, specificity, and accuracy, or studies provided adequate information to calculate these data, (c) a prospective or retrospective study design, (d) provided appropriate information regarding inclusion and exclusion criteria, and (e) studies were published in English. However, studies were excluded if any of the following criteria were met: (a) studies published in the form of review, letter, or case report, (b) studies used the same database (we only included recent studies), and (c) studies did not provide any information regarding the number of patients or images. Two authors (M.M.I and T.N.P.) independently assessed the eligibility criteria of all the retrieved studies.

2.4. Data Extraction

The same two authors read all the selected studies carefully and extracted the following information using a standardized form: (1) study characteristics (authors, country of origin, year of publication, total number of endoscopic images, and study design), (2) demographic characteristics (gender and number of patients), (3) model characteristics (algorithms, data partition, and model description), (4) results (sensitivity, specificity, area under receiver operating characteristic curve, positive predictive value, and negative predictive value).

2.5. Quality Assessment

The same two authors also evaluated the methodological quality of the selected studies using established questionnaires and criteria established in the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) [24]. This tool is widely accepted for assessing the risk of bias and the applicability of diagnostic studies. QUADAS-2 comprises four main domains: (a) patient selection, (b) index test, (c) reference standard, and (d) flow and timing.

2.6. Statistical Analysis

The diagnostic performance of DL for detecting esophageal cancer was the primary outcome of our meta-analysis. We calculated the pooled sensitivity and specificity with 95% confidence intervals (CIs) using the bivariate random effects model [25,26,27]. However, the random effects model by DerSimonian and Laird was used to calculate the independent proportions and their differences [28]. A summary receiver operating characteristic (SROC) curve with a 95% confidence region was plotted to visualize the study findings. We also determined the heterogeneity of the study‘s findings using the inconsistency index (I2) as follows: 0% to 25%, might not be low; 25% to 50%, considered as low; 50% to 75%, medium heterogeneity; and 75% to 100%, considerable heterogeneity [29,30]. We also calculated the positive likelihood ratio, negative likelihood ratio, and diagnostic odd ratio (Supplementary Materials S1) [21]. p values < 0.05 were considered statistically significant. R (R Core Team and the R Foundation for Statistical Computing, version: 4.2.1) and MedCalc (MedCalc Software Ltd, Ostend, Belgium) were used to perform all statistical analyses.

3. Results

3.1. Study Selection

A total of 2491 studies were retrieved during the initial search, after which 1375 duplicate studies were excluded. From 1116 nonduplicate studies, 1088 were excluded after reviewing the titles and abstracts. However, 39 studies underwent full-text evaluation. Afterward, eleven studies were excluded due to review, not using the DL model, and insufficient data. The remaining 28 studies were selected for inclusion in the meta-analysis [22,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57] (Supplementary Mayerials Figure S1).

3.2. Study Characteristics

All of the studies used in this systematic review and meta-analysis are presented in Table 1. The selected studies were published between 2019 [57] and 2022 [31] with a total of 703,006 endoscopic images, and each study followed the standard protocol to detect esophagus cancer. The included images were captured by white-light imaging (WLI), narrow-band image (NBI), or blue light image (BLI). Expert endoscopists checked the appropriateness of images and marked images as cancerous and normal. Most of the studies were retrospective study designs, except for three studies. However, all studies used the CNN model to train and validate their findings. Twenty-three studies were conducted on Asian populations, while only five studies were conducted on Western populations. Esophageal squamous cell cancer (ESCC) was the primary target for eighteen studies, while six studies reported Barret’s esophagus (BE) and four studies showed esophageal adenocarcinoma (EAC), including ESCC as the outcome. Nine studies developed a DL model using WLI, while six studies included NBI, and eleven studies utilized both WLI and NBI. Moreover, one study trained a DL model for the diagnosis of esophageal cancer using BLI, while another study trained their DLF model using volumetric laser endomicroscopy (VLE) [53] and endocytoscopic system image (ECS) [52].

3.3. Deep Learning Model for Esophageal Cancer Diagnosis

A total of 28 studies were included in our study. The pooled sensitivity for the diagnosis of esophageal cancer was 93.80% (95% CI: 93.64–93.96%; Figure 1). The pooled specificity for the diagnosis of esophageal cancer was 91.73% (95% CI: 91.52%–91.94%; Figure 2). There was significant heterogeneity among the studies (I2 = 86.6%, χ2 = 268.56, p < 0.001, and I2 = 97.6%, χ2 = 1176.58, p < 0.001).
However, the pooled positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio for the diagnosis of esophageal cancer were 6.41 (95% CI: 4.74–8.67), 0.09 (95% CI: 0.08–0.11), and 81.89 (95% CI: 61.90–108.34), respectively. There was significant heterogeneity among the studies (I2 = 99.0%, τ2 = 59.05, Cochrane-Q = 2865.73, p < 0.001; I2 = 91.4%, τ2 = 0.08, Cochrane-Q = 327.37, p < 0.001; I2= 94.7%, τ2 = 0.34, Cochrane-Q = 528.97, p < 0.001). The AUROC of the DL was 0.96 (95%CI: 0.94–0.98; Figure 3).

3.4. Subgroup Analysis

We also conducted subgroup analyses based on the region, study design, endoscopy types, histological types, and methodological quality (Table 2). Nine studies evaluated the diagnostic performance of the DL model for the diagnosis of esophageal cancer using WLI. The pooled sensitivity and specificity of the DL model for esophageal cancer were 92.60% (95% CI: 91.39%–93.69%) and 86.95% (95% CI: 85.58%–88.25%). Six studies used NBI to train and validate the DL model for esophageal cancer diagnosis. The pooled sensitivity and specificity of the DL model for esophageal cancer were 93.73% (93.56%–93.89%) and 92.66% (95% CI: 92.45%–92.86%).
While comparing the diagnostic performance between high- and low-quality studies, the diagnostic performance of the DL model for high-quality studies was higher than that for low-quality studies. The pooled accuracy, sensitivity, and specificity of high-quality studies were 92.96% (95% CI: 92.83%–93.09%), 93.85% (95% CI: 93.69%–94.01%), and 91.80% (95% CI: 91.59%–92.01%), respectively. The pooled accuracy, sensitivity, and specificity of low-quality studies were 89.16% (95% CI: 87.88%–90.35%), 90.40% (95% CI: 88.70%–91.93%), and 87.75% (95% CI: 85.75%–89.57%), respectively.

3.5. External Validation

Five studies externally validated their model and evaluated the performance for the diagnosis of esophageal cancer. At external validation, the pooled accuracy of the DL model was 90.31% (89.42%–91.14%), while the corresponding sensitivity, specificity, positive predictive value, and negative predictive value for the diagnosis of esophageal cancer were 95.26% (95% CI: 94.37%–96.04%), 84.02% (95% CI: 82.37%–85.58%), 88.33% (95% CI: 87.27%–89.32%), and 93.31% (95% CI: 92.15%–94.31%).

3.6. Performance Comparison between DL and Endoscopists

Ten studies also compared the diagnostic performance of the DL model with that of endoscopists. The performance measures are presented in Figure 4. The sensitivity of the DL model for the diagnosis of early esophageal cancer exceeded those of endoscopists (92.87% vs. 80.43%). A further analysis of specificity showed that the DL model had a similar performance when compared with that of the endoscopists (74.37% vs. 76.11%).

3.7. Publication Bias

Deeks’ funnel plot of the asymmetry test showed no evidence of publication bias (p = 0.15) (Figure 5).

4. Discussion

We conducted a meta-analysis to evaluate the performance of the DL model for the diagnosis of esophageal cancer using data from 28 studies. The overall pooled estimation showed that the DL performance for the diagnosis of esophageal cancer performed better in terms of sensitivity, specificity, and AUROC. To determine the generalizability of the DL model to external settings, the DL model also showed a better performance when used with different datasets. The findings of our study suggest that the accurate diagnosis performance of DL may help a physician to increase the early diagnosis of esophageal cancer and decrease mortality. Because the identification of early esophageal cancer has been a subject of concern and solely depends on expert endoscopists, the higher sensitivity and specificity of DL could correctly and accurately detect esophageal cancer in patients, provide supportive treatment, and improve patient outcomes.
Four previously published studies also assessed the impact of AI-assisted models for detecting esophageal cancer using endoscopic images [58,59,60,61]. Zhang et al. [58] included sixteen studies to provide scientific evidence for using AI-assisted models to detect esophageal neoplasm. The pooled sensitivity and specificity and AUROC of AI-assisted models for esophageal cancer detection were 0.94 (95% CI: 0.92–0.96), 0.85 (95% CI: 0.73–0.92), and 0.97 (95% CI: 0.95–0.98), respectively. They also reported that the performance of AI-based models was better than endoscopists in terms of the pooled sensitivity 0.94 [95% CI: 0.84–0.98] vs 0.82 [95% CI: 0.77–0.86]. Lui et al. [59] conducted a systematic review and meta-analysis to evaluate the diagnostic accuracy of AI models for gastric, esophageal neoplastic lesions, and Helicobacter pylori status. A total of 23 studies were included in that study; however, only 10 studies were used to evaluate early esophageal cancer detection. The pooled sensitivity, specificity, and AUROC on the detection of squamous esophagus neoplasm were 0.75 (95% CI: 0.48–0.92), 0.92 (95% CI: 0.66–0.99), and 0.88 (95% CI: 0.82–0.96), respectively. Bang et al. [60] included 21 studies in the systematic review. Among them, 19 studies were included in the meta-analysis to evaluate the diagnostic test accuracy of the deep learning or machine learning model of esophageal cancers. The pooled sensitivity, specificity, and AUROC of DL algorithms for the diagnosis of esophageal cancer were 0.94 (95% CI: 0.89–0.96), 0.88 (95% CI: 0.76–0.94), and 0.97 (95% CI: 0.95–0.99), respectively. Mohan et al. [61] performed a meta-analysis to examine the pooled performance rates for CNN-based AI in diagnosing gastrointestinal neoplasia from endoscopic images. Nineteen studies met all inclusion criteria for detecting gastrointestinal neoplasm; however, only five studies were used to evaluate the impact of CNN in diagnosing esophageal cancer. The pooled sensitivity, specificity, and accuracy of the CNN model for diagnosis of esophageal cancer were 0.87 (95% CI: 0.69–0.95), 0.87 (95% CI: 0.74–0.94), and 0.87 (95% CI: 0.76–0.93), respectively. This study included a higher number of studies to summarize the available evidence for the accuracy of the DL algorithm in diagnosing esophageal cancer. Moreover, this study showed not only sensitivity and specificity but accuracy and positive and negative predictive value, which are essential metrics for making clinical decisions. The findings of our study showed that the DL model could play a crucial role in diagnosing esophageal cancer in the near future when this algorithm might be employed in a busy daily clinical practice.
The detection rate of esophageal cancer is relatively poor (more than 40% percent of patients with esophageal cancer are detected at a late stage), and the 5-year survival rate is approximately 20% [62]; therefore, the early diagnosis of esophageal cancer is important for both clinicians and patients. The early diagnosis of esophageal cancer could assist clinicians in decision-making, improve patients’ management, and reduce healthcare costs. Endoscopy is one of the reliable methods for early esophageal cancer screening because of its high diagnostic performance [63]. Diagnostic accuracy depends on several factors such as the quality of images, instruments, and endoscopists. Traditional statistical models could hardly perform analyzing endoscopic images for the diagnosis of esophageal cancer; however, the DL model has been applied and has shown great performance in esophageal cancer diagnosis. To the best of our knowledge, this is the first comprehensive systematic review and meta-analysis of the diagnostic performance of the DL-based AI model for the diagnosis of esophageal cancer. The findings of this study demonstrate conclusively that the diagnostic performance of the DL model for the diagnosis of esophageal cancer using endoscopic images was clinically highly satisfactory in terms of sensitivity, specificity, and AUROC. For esophageal cancer diagnosis, DL achieved a pooled AUROC of 0.96 with a pooled sensitivity of 94% and a specificity of 92%. In terms of the generalizability of the DL model, the DL model also achieved good performance in the external validation.
The early detection of esophageal cancer offers a higher chance of survival [64]. Upper gastrointestinal endoscopy is still considered the standard method for the diagnosis of esophageal cancer [65,66]. Previous evidence shows that endoscopic techniques have made significant progress over the last decades [67,68,69]. WLI is widely used and the most basic endoscopy diagnostic modality to diagnose esophageal cancer [70]. However, the application of WLI for the diagnosis of esophageal cancer is limited because the DL model could not perform well. Therefore, the application of NBI and VLI has been increased for the early diagnosis of esophageal cancer. Consequently, researchers are now using both modalities together to develop DL models for identifying early esophageal cancer. The findings of our study show that the pooled sensitivity and specificity of the DL model, which used NBI images, had significantly better performance than those using WLI images.
There are several strengths and limitations associated with this meta-analysis. First, this is the first comprehensive study that summarized the performance of the DL model for the diagnosis of esophageal cancer using endoscopic images. Second, this study provided clinically important diagnosis metrics such as accuracy, positive predictive value, and negative predictive value, which may help a physician to make the appropriate decision for the patient with a high risk of esophageal cancer. Nevertheless, this study has some limitations that need to be addressed. First, most of the included studies were retrospective study designs. Although external validation and prospective evaluation showed great performance, more studies are needed that could evaluate the performance of the DL model using prospectively collected data or real-time evaluation. Second, two-thirds of the studies developed their model and tested the performance using the same continent’s population (namely, Asia); therefore, more data are warranted from other continents to validate the performance of the current model. Finally, heterogeneity among the studies was high, although it can be partially explained by regional effect, image quality, image modality, and histological types.

5. Conclusions

This is the first comprehensive systematic review and meta-analysis to show that the DL model was able to diagnose early esophageal cancer with high sensitivity and specificity. The performance of the DL model for esophageal cancer diagnosis was higher for NBI than for WLI. Most of the studies were from Asia and used a retrospective design; therefore, more prospective evaluations with various populations are warranted in the future.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers14235996/s1, Figure S1: PRISMA flowchart for identifying and selecting the study; Table S1: Evaluation metrics.

Author Contributions

Conceptualization, M.M.I. and T.N.P.; Methodology, M.M.I.; Software, M.M.I. and C.-Y.Y.; Validation, T.N.P., M.-C.L., S.S.-A.; Formal analysis, M.M.I.; Investigation, Y.-C.L.; Resources, M.M.I.; Data curation, M.M.I., M.-C.L.; Writing—original draft preparation, M.M.I., B.A.W.; Writing—review and editing, M.M.I. and T.N.P.; Visualization, M.M.I.; Supervision, M.-C.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Huang, J.; Koulaouzidis, A.; Marlicz, W.; Lok, V.; Chu, C.; Ngai, C.H.; Zhang, L.; Chen, P.; Wang, S.; Yuan, J. Global Burden, Risk Factors, and Trends of Esophageal Cancer: An Analysis of Cancer Registries from 48 Countries. Cancers 2021, 13, 141. [Google Scholar] [CrossRef] [PubMed]
  2. Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018, 68, 394–424. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Rock, C.L.; Thomson, C.; Gansler, T.; Gapstur, S.M.; McCullough, M.L.; Patel, A.V.; Andrews, K.S.; Bandera, E.V.; Spees, C.K.; Robien, K. American Cancer Society guideline for diet and physical activity for cancer prevention. CA Cancer J. Clin. 2020, 70, 245–271. [Google Scholar] [CrossRef] [PubMed]
  4. Guo, J.; Zhang, S.; Li, H.; Hassan, M.O.O.; Lu, T.; Zhao, J.; Zhang, L. Lung Metastases in Newly Diagnosed Esophageal Cancer: A Population-Based Study. Front. Oncol. 2021, 11, 119. [Google Scholar] [CrossRef] [PubMed]
  5. Uhlenhopp, D.J.; Then, E.O.; Sunkara, T.; Gaduputi, V. Epidemiology of esophageal cancer: Update in global trends, etiology and risk factors. Clin. J. Gastroenterol. 2020, 13, 1010–1021. [Google Scholar] [CrossRef] [PubMed]
  6. Lepage, C.; Rachet, B.; Jooste, V.; Faivre, J.; Coleman, M.P. Continuing rapid increase in esophageal adenocarcinoma in England and Wales. Off. J. Am. Coll. Gastroenterol. 2008, 103, 2694–2699. [Google Scholar] [CrossRef]
  7. Ilson, D.H.; van Hillegersberg, R. Management of patients with adenocarcinoma or squamous cancer of the esophagus. Gastroenterology 2018, 154, 437–451. [Google Scholar] [CrossRef]
  8. Hou, H.; Meng, Z.; Zhao, X.; Ding, G.; Sun, M.; Wang, W.; Wang, Y. Survival of esophageal cancer in China: A pooled analysis on hospital-based studies from 2000 to 2018. Front. Oncol. 2019, 9, 548. [Google Scholar] [CrossRef] [Green Version]
  9. Qin, J.; Zhu, H.; Guo, J.; Ni, C.; Wu, P.; Xu, H.; Mao, A.; Lu, J.; Su, C.; Teng, G. Factors associated with overall survival and relief of dysphagia in advanced esophageal cancer patients after 125I seed-loaded stent placement: A multicenter retrospective analysis. Dis. Esophagus 2019, 32, doz012. [Google Scholar] [CrossRef]
  10. Kountouras, J.; Doulberis, M.; Papaefthymiou, A.; Polyzos, S.A.; Vardaka, E.; Tzivras, D.; Dardiotis, E.; Deretzi, G.; Giartza-Taxidou, E.; Grigoriadis, S. A perspective on risk factors for esophageal adenocarcinoma: Emphasis on Helicobacter pylori infection. Ann. N. Y. Acad. Sci. 2019, 1452, 12–17. [Google Scholar] [CrossRef]
  11. Kambhampati, S.; Tieu, A.H.; Luber, B.; Wang, H.; Meltzer, S.J. Risk factors for progression of Barrett’s esophagus to High Grade Dysplasia and esophageal Adenocarcinoma. Sci. Rep. 2020, 10, 4899. [Google Scholar] [CrossRef] [Green Version]
  12. Abnet, C.C.; Arnold, M.; Wei, W.-Q. Epidemiology of esophageal squamous cell carcinoma. Gastroenterology 2018, 154, 360–373. [Google Scholar] [CrossRef]
  13. Coleman, H.G.; Xie, S.-H.; Lagergren, J. The epidemiology of esophageal adenocarcinoma. Gastroenterology 2018, 154, 390–405. [Google Scholar] [CrossRef]
  14. Pickens, A.; Orringer, M.B. Geographical distribution and racial disparity in esophageal cancer. Ann. Thorac. Surg. 2003, 76, S1367–S1369. [Google Scholar] [CrossRef]
  15. Arnal, M.J.D.; Arenas, Á.F.; Arbeloa, Á.L. Esophageal cancer: Risk factors, screening and endoscopic treatment in Western and Eastern countries. World J. Gastroenterol. 2015, 21, 7933. [Google Scholar] [CrossRef]
  16. Minashi, K.; Nihei, K.; Mizusawa, J.; Takizawa, K.; Yano, T.; Ezoe, Y.; Tsuchida, T.; Ono, H.; Iizuka, T.; Hanaoka, N. Efficacy of endoscopic resection and selective chemoradiotherapy for stage I esophageal squamous cell carcinoma. Gastroenterology 2019, 157, 382–390.e383. [Google Scholar] [CrossRef] [Green Version]
  17. Defize, I.; Van Der Horst, S.; Bülbul, M.; Mohammad, N.H.; Mook, S.; Meijer, G.; Brosens, L.; Ruurda, J.; van Hillegersberg, R. Salvage robot-assisted minimally invasive esophagectomy (RAMIE) for T4b esophageal cancer after definitive chemoradiotherapy. Ann. Surg. Oncol. 2021, 28, 2730–2738. [Google Scholar] [CrossRef]
  18. Ebi, M.; Shimura, T.; Yamada, T.; Mizushima, T.; Itoh, K.; Tsukamoto, H.; Tsuchida, K.; Hirata, Y.; Murakami, K.; Kanie, H. Multicenter, prospective trial of white-light imaging alone versus white-light imaging followed by magnifying endoscopy with narrow-band imaging for the real-time imaging and diagnosis of invasion depth in superficial esophageal squamous cell carcinoma. Gastrointest. Endosc. 2015, 81, 1355–1361.e2. [Google Scholar] [CrossRef]
  19. Muto, M.; Minashi, K.; Yano, T.; Saito, Y.; Oda, I.; Nonaka, S.; Omori, T.; Sugiura, H.; Goda, K.; Kaise, M. Early detection of superficial squamous cell carcinoma in the head and neck region and esophagus by narrow band imaging: A multicenter randomized controlled trial. J. Clin. Oncol. 2010, 28, 1566. [Google Scholar] [CrossRef] [Green Version]
  20. Chung, C.S.; Lo, W.C.; Lee, Y.C.; Wu, M.S.; Wang, H.P.; Liao, L.J. Image-enhanced endoscopy for detection of second primary neoplasm in patients with esophageal and head and neck cancer: A systematic review and meta-analysis. Head Neck 2016, 38, E2343–E2349. [Google Scholar] [CrossRef]
  21. Ishihara, R.; Takeuchi, Y.; Chatani, R.; Kidu, T.; Inoue, T.; Hanaoka, N.; Yamamoto, S.; Higashino, K.; Uedo, N.; Iishi, H. Prospective evaluation of narrow-band imaging endoscopy for screening of esophageal squamous mucosal high-grade neoplasia in experienced and less experienced endoscopists. Dis. Esophagus 2010, 23, 480–486. [Google Scholar] [CrossRef]
  22. Horie, Y.; Yoshio, T.; Aoyama, K.; Yoshimizu, S.; Horiuchi, Y.; Ishiyama, A.; Hirasawa, T.; Tsuchida, T.; Ozawa, T.; Ishihara, S. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest. Endosc. 2019, 89, 25–32. [Google Scholar] [CrossRef]
  23. McInnes, M.D.; Moher, D.; Thombs, B.D.; McGrath, T.A.; Bossuyt, P.M.; Clifford, T.; Cohen, J.F.; Deeks, J.J.; Gatsonis, C.; Hooft, L. Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: The PRISMA-DTA statement. JAMA 2018, 319, 388–396. [Google Scholar] [CrossRef]
  24. Whiting, P.F.; Rutjes, A.W.; Westwood, M.E.; Mallett, S.; Deeks, J.J.; Reitsma, J.B.; Leeflang, M.M.; Sterne, J.A.; Bossuyt, P.M. QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies. Ann. Intern. Med. 2011, 155, 529–536. [Google Scholar] [CrossRef]
  25. Suh, C.H.; Park, S.H. Successful publication of systematic review and meta-analysis of studies evaluating diagnostic test accuracy. Korean J. Radiol. 2016, 17, 5–6. [Google Scholar] [CrossRef] [Green Version]
  26. Kim, K.W.; Lee, J.; Choi, S.H.; Huh, J.; Park, S.H. Systematic review and meta-analysis of studies evaluating diagnostic test accuracy: A practical review for clinical researchers-part I. General guidance and tips. Korean J. Radiol. 2015, 16, 1175–1187. [Google Scholar] [CrossRef] [Green Version]
  27. Reitsma, J.B.; Glas, A.S.; Rutjes, A.W.; Scholten, R.J.; Bossuyt, P.M.; Zwinderman, A.H. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J. Clin. Epidemiol. 2005, 58, 982–990. [Google Scholar] [CrossRef]
  28. DerSimonian, R.; Laird, N. Meta-analysis in clinical trials. Control. Clin. Trials 1986, 7, 177–188. [Google Scholar] [CrossRef] [PubMed]
  29. Islam, M.M.; Yang, H.-C.; Poly, T.N.; Jian, W.-S.; Li, Y.-C.J. Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: A systematic review and meta-analysis. Comput. Methods Programs Biomed. 2020, 191, 105320. [Google Scholar] [CrossRef] [PubMed]
  30. Islam, M.; Poly, T.N.; Walther, B.A.; Yang, H.C.; Li, Y.-C.J. Artificial intelligence in ophthalmology: A meta-analysis of deep learning models for retinal vessels segmentation. J. Clin. Med. 2020, 9, 1018. [Google Scholar] [CrossRef] [PubMed]
  31. Gong, E.J.; Bang, C.S.; Jung, K.; Kim, S.J.; Kim, J.W.; Seo, S.I.; Lee, U.; Maeng, Y.B.; Lee, Y.J.; Lee, J.I. Deep-Learning for the Diagnosis of Esophageal Cancers and Precursor Lesions in Endoscopic Images: A Model Establishment and Nationwide Multicenter Performance Verification Study. J. Pers. Med. 2022, 12, 1052. [Google Scholar] [CrossRef] [PubMed]
  32. Liu, W.; Yuan, X.; Guo, L.; Pan, F.; Wu, C.; Sun, Z.; Tian, F.; Yuan, C.; Zhang, W.; Bai, S. Artificial Intelligence for Detecting and Delineating Margins of Early ESCC Under WLI Endoscopy. Clin. Transl. Gastroenterol. 2022, 13, e00433. [Google Scholar] [CrossRef] [PubMed]
  33. Yuan, X.L.; Guo, L.J.; Liu, W.; Zeng, X.H.; Mou, Y.; Bai, S.; Pan, Z.G.; Zhang, T.; Pu, W.F.; Wen, C. Artificial intelligence for detecting superficial esophageal squamous cell carcinoma under multiple endoscopic imaging modalities: A multicenter study. J. Gastroenterol. Hepatol. 2022, 37, 169–178. [Google Scholar] [CrossRef] [PubMed]
  34. Everson, M.A.; Garcia-Peraza-Herrera, L.; Wang, H.-P.; Lee, C.-T.; Chung, C.-S.; Hsieh, P.-H.; Chen, C.-C.; Tseng, C.-H.; Hsu, M.-H.; Vercauteren, T. A clinically interpretable convolutional neural network for the real-time prediction of early squamous cell cancer of the esophagus: Comparing diagnostic performance with a panel of expert European and Asian endoscopists. Gastrointest. Endosc. 2021, 94, 273–281. [Google Scholar] [CrossRef]
  35. Tang, D.; Wang, L.; Jiang, J.; Liu, Y.; Ni, M.; Fu, Y.; Guo, H.; Wang, Z.; An, F.; Zhang, K. A novel deep learning system for diagnosing early esophageal squamous cell carcinoma: A multicenter diagnostic study. Clin. Transl. Gastroenterol. 2021, 12, e00393. [Google Scholar] [CrossRef]
  36. Li, B.; Cai, S.-L.; Tan, W.-M.; Li, J.-C.; Yalikong, A.; Feng, X.-S.; Yu, H.-H.; Lu, P.-X.; Feng, Z.; Yao, L.-Q. Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging. World J. Gastroenterol. 2021, 27, 281. [Google Scholar] [CrossRef]
  37. Iwagami, H.; Ishihara, R.; Aoyama, K.; Fukuda, H.; Shimamoto, Y.; Kono, M.; Nakahira, H.; Matsuura, N.; Shichijo, S.; Kanesaka, T. Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma. J. Gastroenterol. Hepatol. 2021, 36, 131–136. [Google Scholar] [CrossRef]
  38. Waki, K.; Ishihara, R.; Kato, Y.; Shoji, A.; Inoue, T.; Matsueda, K.; Miyake, M.; Shimamoto, Y.; Fukuda, H.; Matsuura, N. Usefulness of an artificial intelligence system for the detection of esophageal squamous cell carcinoma evaluated with videos simulating overlooking situation. Dig. Endosc. 2021, 33, 1101–1109. [Google Scholar] [CrossRef]
  39. Wang, Y.-K.; Syu, H.-Y.; Chen, Y.-H.; Chung, C.-S.; Tseng, Y.S.; Ho, S.-Y.; Huang, C.-W.; Wu, I.-C.; Wang, H.-C. Endoscopic images by a single-shot multibox detector for the identification of early cancerous lesions in the esophagus: A pilot study. Cancers 2021, 13, 321. [Google Scholar] [CrossRef]
  40. Yang, X.X.; Li, Z.; Shao, X.J.; Ji, R.; Qu, J.Y.; Zheng, M.Q.; Sun, Y.N.; Zhou, R.C.; You, H.; Li, L.X. Real-time artificial intelligence for endoscopic diagnosis of early esophageal squamous cell cancer (with video). Dig. Endosc. 2021, 33, 1075–1084. [Google Scholar] [CrossRef]
  41. Shiroma, S.; Yoshio, T.; Kato, Y.; Horie, Y.; Namikawa, K.; Tokai, Y.; Yoshimizu, S.; Yoshizawa, N.; Horiuchi, Y.; Ishiyama, A. Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance. Sci. Rep. 2021, 11, 7759. [Google Scholar] [CrossRef] [PubMed]
  42. Ohmori, M.; Ishihara, R.; Aoyama, K.; Nakagawa, K.; Iwagami, H.; Matsuura, N.; Shichijo, S.; Yamamoto, K.; Nagaike, K.; Nakahara, M. Endoscopic detection and differentiation of esophageal lesions using a deep neural network. Gastrointest. Endosc. 2020, 91, 301–309.e1. [Google Scholar] [CrossRef] [PubMed]
  43. Guo, L.; Xiao, X.; Wu, C.; Zeng, X.; Zhang, Y.; Du, J.; Bai, S.; Xie, J.; Zhang, Z.; Li, Y. Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos). Gastrointest. Endosc. 2020, 91, 41–51. [Google Scholar] [CrossRef] [PubMed]
  44. Fukuda, H.; Ishihara, R.; Kato, Y.; Matsunaga, T.; Nishida, T.; Yamada, T.; Ogiyama, H.; Horie, M.; Kinoshita, K.; Tada, T. Comparison of performances of artificial intelligence versus expert endoscopists for real-time assisted diagnosis of esophageal squamous cell carcinoma (with video). Gastrointest. Endosc. 2020, 92, 848–855. [Google Scholar] [CrossRef]
  45. Tokai, Y.; Yoshio, T.; Aoyama, K.; Horie, Y.; Yoshimizu, S.; Horiuchi, Y.; Ishiyama, A.; Tsuchida, T.; Hirasawa, T.; Sakakibara, Y. Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma. Esophagus 2020, 17, 250–256. [Google Scholar] [CrossRef]
  46. Liu, G.; Hua, J.; Wu, Z.; Meng, T.; Sun, M.; Huang, P.; He, X.; Sun, W.; Li, X.; Chen, Y. Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network. Ann. Transl. Med. 2020, 8, 486. [Google Scholar] [CrossRef]
  47. Hashimoto, R.; Requa, J.; Dao, T.; Ninh, A.; Tran, E.; Mai, D.; Lugo, M.; Chehade, N.E.-H.; Chang, K.J.; Karnes, W.E. Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett’s esophagus (with video). Gastrointest. Endosc. 2020, 91, 1264–1271.e1. [Google Scholar] [CrossRef] [PubMed]
  48. De Groof, A.J.; Struyvenberg, M.R.; van der Putten, J.; van der Sommen, F.; Fockens, K.N.; Curvers, W.L.; Zinger, S.; Pouw, R.E.; Coron, E.; Baldaque-Silva, F. Deep-learning system detects neoplasia in patients with Barrett’s esophagus with higher accuracy than endoscopists in a multistep training and validation study with benchmarking. Gastroenterology 2020, 158, 915–929.e4. [Google Scholar] [CrossRef] [PubMed]
  49. De Groof, A.J.; Struyvenberg, M.R.; Fockens, K.N.; van der Putten, J.; van der Sommen, F.; Boers, T.G.; Zinger, S.; Bisschops, R.; Peter, H.; Pouw, R.E. Deep learning algorithm detection of Barrett’s neoplasia with high accuracy during live endoscopic procedures: A pilot study (with video). Gastrointest. Endosc. 2020, 91, 1242–1250. [Google Scholar] [CrossRef] [PubMed]
  50. Ebigbo, A.; Mendel, R.; Probst, A.; Manzeneder, J.; de Souza, L.A., Jr.; Papa, J.P.; Palm, C.; Messmann, H. Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma. Gut 2019, 68, 1143–1145. [Google Scholar] [CrossRef]
  51. García-Peraza-Herrera, L.C.; Everson, M.; Lovat, L.; Wang, H.-P.; Wang, W.L.; Haidry, R.; Stoyanov, D.; Ourselin, S.; Vercauteren, T. Intrapapillary capillary loop classification in magnification endoscopy: Open dataset and baseline methodology. Int. J. Comput. Assist. Radiol. Surg. 2020, 15, 651–659. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Kumagai, Y.; Takubo, K.; Kawada, K.; Aoyama, K.; Endo, Y.; Ozawa, T.; Hirasawa, T.; Yoshio, T.; Ishihara, S.; Fujishiro, M. Diagnosis using deep-learning artificial intelligence based on the endocytoscopic observation of the esophagus. Esophagus 2019, 16, 180–187. [Google Scholar] [CrossRef] [PubMed]
  53. Fonollà, R.; Scheeve, T.; Struyvenberg, M.R.; Curvers, W.L.; de Groof, A.J.; van der Sommen, F.; Schoon, E.J.; Bergman, J.J.; de With, P.H. Ensemble of deep convolutional neural networks for classification of early Barrett’s neoplasia using volumetric laser endomicroscopy. Appl. Sci. 2019, 9, 2183. [Google Scholar] [CrossRef] [Green Version]
  54. Cai, S.-L.; Li, B.; Tan, W.-M.; Niu, X.-J.; Yu, H.-H.; Yao, L.-Q.; Zhou, P.-H.; Yan, B.; Zhong, Y.-S. Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video). Gastrointest. Endosc. 2019, 90, 745–753.e2. [Google Scholar] [CrossRef]
  55. Ebigbo, A.; Mendel, R.; Probst, A.; Manzeneder, J.; Prinz, F.; de Souza, L.A., Jr.; Papa, J.; Palm, C.; Messmann, H. Real-time use of artificial intelligence in the evaluation of cancer in Barrett’s oesophagus. Gut 2020, 69, 615–616. [Google Scholar] [CrossRef] [Green Version]
  56. Zhao, Y.-Y.; Xue, D.-X.; Wang, Y.-L.; Zhang, R.; Sun, B.; Cai, Y.-P.; Feng, H.; Cai, Y.; Xu, J.-M. Computer-assisted diagnosis of early esophageal squamous cell carcinoma using narrow-band imaging magnifying endoscopy. Endoscopy 2019, 51, 333–341. [Google Scholar] [CrossRef]
  57. Everson, M.; Herrera, L.G.P.; Li, W.; Luengo, I.M.; Ahmad, O.; Banks, M.; Magee, C.; Alzoubaidi, D.; Hsu, H.; Graham, D. Artificial intelligence for the real-time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof-of-concept study. United Eur. Gastroenterol. J. 2019, 7, 297–306. [Google Scholar] [CrossRef] [Green Version]
  58. Zhang, S.M.; Wang, Y.J.; Zhang, S.T. Accuracy of artificial intelligence-assisted detection of esophageal cancer and neoplasms on endoscopic images: A systematic review and meta-analysis. J. Dig. Dis. 2021, 22, 318–328. [Google Scholar] [CrossRef]
  59. Lui, T.K.; Tsui, V.W.; Leung, W.K. Accuracy of artificial intelligence–assisted detection of upper GI lesions: A systematic review and meta-analysis. Gastrointest. Endosc. 2020, 92, 821–830.e9. [Google Scholar] [CrossRef]
  60. Bang, C.S.; Lee, J.J.; Baik, G.H. Computer-aided diagnosis of esophageal cancer and neoplasms in endoscopic images: A systematic review and meta-analysis of diagnostic test accuracy. Gastrointest. Endosc. 2021, 93, 1006–1015.e3. [Google Scholar] [CrossRef]
  61. Mohan, B.P.; Khan, S.R.; Kassab, L.L.; Ponnada, S.; Dulai, P.S.; Kochhar, G.S. Accuracy of convolutional neural network-based artificial intelligence in diagnosis of gastrointestinal lesions based on endoscopic images: A systematic review and meta-analysis. Endosc. Int. Open 2020, 8, E1584–E1594. [Google Scholar] [CrossRef] [PubMed]
  62. Jemal, A.; Ward, E.M.; Johnson, C.J.; Cronin, K.A.; Ma, J.; Ryerson, A.B.; Mariotto, A.; Lake, A.J.; Wilson, R.; Sherman, R.L. Annual report to the nation on the status of cancer, 1975–2014, featuring survival. J. Natl. Cancer Inst. 2017, 109, djx030. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Kaise, M. Advanced endoscopic imaging for early gastric cancer. Best Pract. Res. Clin. Gastroenterol. 2015, 29, 575–587. [Google Scholar] [CrossRef] [PubMed]
  64. Wang, G.-Q.; Jiao, G.-G.; Chang, F.-B.; Fang, W.-H.; Song, J.-X.; Lu, N.; Lin, D.-M.; Xie, Y.-Q.; Yang, L. Long-term results of operation for 420 patients with early squamous cell esophageal carcinoma discovered by screening. Ann. Thorac. Surg. 2004, 77, 1740–1744. [Google Scholar] [CrossRef]
  65. Abe, H.; Hatta, W.; Ogata, Y.; Koike, T.; Saito, M.; Jin, X.; Nakagawa, K.; Kanno, T.; Uno, K.; Asano, N. Prevention of delayed bleeding with vonoprazan in upper gastrointestinal endoscopic treatment. J. Gastroenterol. 2021, 56, 640–650. [Google Scholar] [CrossRef]
  66. Hatta, W.; Koike, T.; Abe, H.; Ogata, Y.; Saito, M.; Jin, X.; Kanno, T.; Uno, K.; Asano, N.; Imatani, A. Recent approach for preventing complications in upper gastrointestinal endoscopic submucosal dissection. DEN Open 2022, 2, e60. [Google Scholar] [CrossRef]
  67. Takayanagi, D.; Cho, H.; Machida, E.; Kawamura, A.; Takashima, A.; Wada, S.; Tsunoda, T.; Kohno, T.; Shiraishi, K. Update on Epidemiology, Diagnosis, and Biomarkers in Gastroenteropancreatic Neuroendocrine Neoplasms. Cancers 2022, 14, 1119. [Google Scholar] [CrossRef]
  68. Ay, B.; Turker, C.; Emre, E.; Ay, K.; Aydin, G. Automated classification of nasal polyps in endoscopy video-frames using handcrafted and CNN features. Comput. Biol. Med. 2022, 147, 105725. [Google Scholar] [CrossRef]
  69. Wolfson, P.; Ho, K.M.A.; Wilson, A.; McBain, H.; Hogan, A.; Lipman, G.; Dunn, J.; Haidry, R.; Novelli, M.; Olivo, A. Endoscopic eradication therapy for Barrett’s Esophagus related neoplasia. A final 10 year report from the United Kingdom National Halo Radiofrequency Ablation Registry. Gastrointest. Endosc. 2022, 96, 223–233. [Google Scholar] [CrossRef]
  70. Tsai, C.-L.; Mukundan, A.; Chung, C.-S.; Chen, Y.-H.; Wang, Y.-K.; Chen, T.-H.; Tseng, Y.-S.; Huang, C.-W.; Wu, I.-C.; Wang, H.-C. Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer. Cancers 2021, 13, 4593. [Google Scholar] [CrossRef]
Cancers 14 05996 g001 550
Figure 1. Pooled sensitivity of the DL model for the diagnosis of esophageal cancer.
Figure 1. Pooled sensitivity of the DL model for the diagnosis of esophageal cancer.
Cancers 14 05996 g001
Cancers 14 05996 g002 550
Figure 2. Pooled specificity of the DL model for the diagnosis of esophageal cancer.
Figure 2. Pooled specificity of the DL model for the diagnosis of esophageal cancer.
Cancers 14 05996 g002
Cancers 14 05996 g003 550
Figure 3. The summary of the area under the receiver operating curve for diagnosis of esophageal cancer.
Figure 3. The summary of the area under the receiver operating curve for diagnosis of esophageal cancer.
Cancers 14 05996 g003
Cancers 14 05996 g004 550
Figure 4. Diagnostic performance for esophageal cancer by the DL system vs. endoscopists.
Figure 4. Diagnostic performance for esophageal cancer by the DL system vs. endoscopists.
Cancers 14 05996 g004
Cancers 14 05996 g005 550
Figure 5. Deeks’ funnel plot of the performance of the DL model for the diagnosis of esophageal cancer.
Figure 5. Deeks’ funnel plot of the performance of the DL model for the diagnosis of esophageal cancer.
Cancers 14 05996 g005
Table
Table 1. The characteristics of the included studies.
Table 1. The characteristics of the included studies.
AuthorYearStudy TypeCountry/
Region		Modality Model Total ImagesTotal PatientNumber of EndoscopistsReal-TimeCompare with EndoscopistExternal ValidationVideo ValidationTarget Quality
Gong 2022REKoreaWLICNN5162NRNRNoNoYesNoECH
Liu 2022REChinaWLICNN13,083123914NoYesYesNoESCCH
Yuan 2022REChinaWLI, NBICNN53,933262111YesYesYesYesESCCH
Everson 2021RETaiwanNBICNN67,742NR3NoYesNoNoESCCH
Tang2021REChinaWLICNN4002107810YesYesYesYesESCCH
Li 2021REChinaWLI, NBICNN4735NR20NoYesNoNoESCCH
Iwagami 2021REJapanWLI, NBICNN2327915YesYesYesNoEACL
Waki 2021REJapanNBI/BLICNN17,336NR21YesYesNoYesESCCH
Wang 2021RETaiwanWLI, NBICNN935NRNRNoNoNoNoESCCL
Yang 2021REChinaWLI, NBICNN13,29761306YesYesYesYesESCCH
Shiroma 2021REJapanNBI, WLICNN8428NR18YesYesNoYesESCCH
Ohmori 2020REJapanNBI, WLI, BLICNN13510215NoYesNoNoESCCH
Guo 2020REChinaNBICNN6671NRNRYesNoYesYesESCCH
Fukuda 2020REJapanNBI, BLICNN238NR13YesYesYesYesESSCL
Tokai 2020REJapanNBI, WLICNN279NR13NoYesNoNoESCCH
Liu2020REChinaWLICNN127NRNRNoNoNoNoESCC/EACH
Hashimoto2020REJapanNBI, WLICNN45839NRYesNoNoNoBEH
de Groof 2020PRNetherlandsWLICNN14420NRNoNoNoNoBEH
de Groof 2020RENetherlandsWLICNN494,36415,28653NoYesYesNoBEL
Ebigbo 2020REEuropeWLICNN6214NRNoNoNoNoBEL
Herrera 2020REAsiaNBICNN67,742$114NRNRNRNoNoESCCH
Kumagai2019REJapanECSCNN152055NRNoNoNoNoESCCH
Fonolla 2019PREuropeVLICNN141NRNRNoNoNoNoBEL
Horie 2019REJapanNBI, WLICNN111897NRYesNoNoNoESCC/EACH
Cai 2019REChinaWLICNN1875216NoYesNoNoESCCH
Ebigbo 2019REGermanyNBI, WLICNN148NRNRNoNoNoNoBE/EACH
Ebigbo # 2019REGermanyWLICNN100NRNRNoNoNoNoBEH
Zhao 2019REChinaNBICNN1383NR9NoYesNoNoESCCH
Everson 2019RETaiwanNBICNN704617NRNoNoNoNoESCCL
Note: RE—retrospective; PR—prospective; NBI—narrow-band imaging; WLI—white-light imaging; ECS—endocytoscopic system image; BLI—blue-laser image; VLI/E—volumetric laser image/endomicroscopy; NR—not reported; EC—esophageal cancer; EAC—esophageal adenocarcinoma; ESCC—esophageal squamous cell cancer; BE—Barret’s esophagus; CNN—convolutional neural network; H—high; L—low; and #—same study.
Table
Table 2. Subgroup analyses for the diagnosis of esophageal cancer.
Table 2. Subgroup analyses for the diagnosis of esophageal cancer.
SubgroupStudies (n)Sensitivity (95%CI)Specificity (95%CI)Positive Predictive Value (95%CI)Negative Predictive Value (95%CI)Accuracy (95%CI)Disease Prevalence
All 2893.80 (93.64–93.96)91.73 (91.52–91.94)93.62 (93.47–93.77)91.97 (91.77–92.15)92.90 (92.77–93.03)56.38 (56.14–56.63)
Region
Asia2393.82 (93.66–93.98)91.75 (91.55–91.96)93.66 (93.51–93.80)91.97 (91.77–92.16)92.92 (92.80–93.05)56.48 (56.23–56.72)
West 588.20 (84.39–91.36)88.99 (86.03–91.51)84.18 (80.66–87.16)91.91 (89.51–93.79)88.68 (86.41–90.68)39.91 (36.68–43.21)
Study design
Retrospective2593.82 (93.66–93.98)91.75 (91.55–91.96)93.65 (93.50–93.80)91.97 (91.77–92.15)92.92 (92.79–93.05)56.46 (56.22–56.71)
Prospective385.78 (80.23–90.27)87.83 (84.10–90.95)79.19 (74.26–83.38)91.97 (89.08–94.14)87.11 (84.12–89.73)35.05 (31.17–39.08)
Endoscopy type
WLI992.60 (91.39–93.69)86.95 (85.58–88.25)85.42 (84.11–86.64)93.44 (92.44–94.32)89.51 (88.59–90.38)45.22 (43.78 46.67)
NBI593.73 (93.56–93.89)92.66 (92.45–92.86)94.39 (94.24–94.54)91.81 (91.61–92.01)93.27 (93.14–93.39)56.85 (56.60–57.11)
Mixed (WLI + NBI) 1195.70 (95.09–96.26)80.99 (79.72–82.21)86.15 (85.35–86.91)93.85 (93.03–94.58)89.12 (88.45–89.77)55.27 (54.21–56.32)
VLE/BLI/ECS388.24 (81.05–93.42)74.58 (67.50–80.81)70.00 (64.26–75.18)90.41 (85.12–93.95)80.07 (75.06–84.47)40.20 (34.57–46.03)
Histological type
ESCC1893.81 (93.65–93.97)91.79 (91.58–92.00)93.74 (93.59–93.89)91.88 (91.69–92.07)92.94 (92.81–93.06) 56.72 (56.47–56.96)
BE690.87 (88.05–93.22)91.41 (89.06–93.40)88.80 (86.12–91.02)93.04 (91.04–94.61)91.18 (89.43–92.72)42.85 (40.03–45.70)
EAC, including ESCC495.80 (90.47–98.62)52.38 (43.99–60.67)61.96 (57.79–65.96)93.90 (86.56–97.36)71.80 (65.99–77.13)44.74 (38.66–50.93)
Methodological quality
High2193.85 (93.69–94.01)91.80 (91.59–92.01)93.68 (93.53–93.8392.02 (91.82–92.21)92.96 (92.83–93.09)56.44 (56.19–56.69)
Low 790.40 (88.70–91.93)87.75 (85.75–89.57)89.27 (87.71–90.65)89.03 (87.30–90.55)89.16 (87.88–90.35)52.98 (51.0–54.94)
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Islam, M.M.; Poly, T.N.; Walther, B.A.; Yeh, C.-Y.; Seyed-Abdul, S.; Li, Y.-C.; Lin, M.-C. Deep Learning for the Diagnosis of Esophageal Cancer in Endoscopic Images: A Systematic Review and Meta-Analysis. Cancers 2022, 14, 5996. https://doi.org/10.3390/cancers14235996

AMA Style

Islam MM, Poly TN, Walther BA, Yeh C-Y, Seyed-Abdul S, Li Y-C, Lin M-C. Deep Learning for the Diagnosis of Esophageal Cancer in Endoscopic Images: A Systematic Review and Meta-Analysis. Cancers. 2022; 14(23):5996. https://doi.org/10.3390/cancers14235996

Chicago/Turabian Style

Islam, Md. Mohaimenul, Tahmina Nasrin Poly, Bruno Andreas Walther, Chih-Yang Yeh, Shabbir Seyed-Abdul, Yu-Chuan (Jack) Li, and Ming-Chin Lin. 2022. "Deep Learning for the Diagnosis of Esophageal Cancer in Endoscopic Images: A Systematic Review and Meta-Analysis" Cancers 14, no. 23: 5996. https://doi.org/10.3390/cancers14235996

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop