Artificial intelligence in esophageal cancer diagnosis and treatment: where are we now?—a narrative review
Review Article

Artificial intelligence in esophageal cancer diagnosis and treatment: where are we now?—a narrative review

Beatriz Merchán Gómez1, Lucía Milla Collado2, María Rodríguez2^

1Gastroenterology Department, Clínica Universidad de Navarra, Madrid, Spain; 2Thoracic Surgery Department, Clínica Universidad de Navarra, Madrid, Spain

Contributions: (I) Conception and design: All authors; (II) Administrative support: All authors; (III) Provision of study materials or patients: None; (IV) Collection and assembly of data: All authors; (V) Data analysis and interpretation: None; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

^ORCID: 0000-0002-3577-006X.

Correspondence to: Dr. María Rodríguez. Thoracic Surgery Department, Clínica Universidad de Navarra, C/Marquesado de Santa Marta 1, 28027 Madrid, Spain. Email: mery.rodriguez.perez@gmail.com.

Background and Objective: Artificial intelligence (AI) use is becoming increasingly prevalent directly or indirectly in daily clinical practice, including esophageal cancer (EC) diagnosis and treatment. Although the limits of its adoption and their clinical benefits are still unknown, any physician related to EC patients’ management should be aware of the status and future perspectives of AI use in their field. The purpose of this review is to summarize the existing literature regarding the role of AI in diagnosis and treatment of EC. We have focused on the aids AI entails in the management of this pathology and we have tried to offer an updated perspective to maximize current applications and to identify potential future uses of it.

Methods: Data concerning AI applied to EC diagnosis and treatment is not limited, including direct (those specifically related to them) and indirect (those referring to other specialties as radiology or pathology), applications. However, the clinical relevance of the discussed and presented models is still unknown. We performed a research in PubMed of English and Spanish written studies from January 1970 to June 2022.

Key Content and Findings: Information regarding the role of AI in EC diagnosis and treatment has increased exponentially in recent years. Several models, including different variables and features have been investigated and some of them internally and externally validated. However, the main challenge remains to apply and introduce all these data into clinical practice, and, as some of the discussed studies argue, if the models are able to enhance experienced endoscopists’ judgement. Although AI use is increasing steadily in different medical specialties, the truth is, most of the time, the gap between model development and clinical implementation is not closed. Learning to understand the routinely application of AI, as well as future improvements, would lead to a broadened adoption.

Conclusions: Physicians should be aware of the multiple current clinical uses of AI in EC diagnosis and treatment and should take part in their clinical application and future developments to enhance patient care.

Keywords: Artificial intelligence (AI); esophageal cancer; diagnosis; treatment

Submitted Aug 12, 2022. Accepted for publication Apr 21, 2023. Published online Jun 08, 2023.

doi: 10.21037/atm-22-3977

Introduction

Esophageal cancer (EC) remains a challenging disease regarding not only its diagnosis but also its management and clinical outcome. Different strategies based in artificial intelligence (AI), including endoscopic, radiologic, clinicopathologic and genetic variables have been developed to try to improve its poor prognosis.

As it happens with different tumors, early detection has proven to be the best weapon to improve treatment and prognosis of esophageal carcinoma. Thus, most of the efforts of AI in the EC field have been focused in detection of early stage squamous cell carcinoma and adenocarcinoma arising from Barrett’s esophagus (BE) (1).

Since its advantages in medical practice were perceived, AI set the frame for machine-learning (ML). ML is the field of AI where machines are trained by experts to recognize patterns based on different data and make predictions taking into account the inputs obtained from these data. Algorithms’ performance in ML improves as they are exposed to increased data. Furthermore, deep-learning (DL) is based on multilayered neural networks that learn from vast amounts of data and that improve over time based on their own accuracy and results.

Despite multiple models assessing their accuracy, sensitivity and specificity, large datasets, multicenter studies and internal and external validations, the results, when compared to those of experienced endoscopists, remain controversial (2).

Whether or not AI can help expert endoscopists to enhance their clinical decision-making appears still the main challenge to be answered.

Larger datasets, real time models, probably video-enhanced and randomized controlled trials are still needed to answer this important question, specifically taking into account EC patients’ particular needs.

The purpose of this review is to summarize the existing literature regarding the role of AI in diagnosis and treatment of EC. We have focused on the aids AI entails in the management of this pathology and we have tried to provide an updated perspective to maximize current applications and to identify potential future uses of it. We present this article in accordance with the Narrative Review reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-22-3977/rc).

Methods

The search strategy is described in Table 1. We searched PubMed for articles written from January 1970 to June 2022.

Table 1

Search strategy summary

Items Specification
Date of search 15th June–31st June 2022
Databases and other sources searched PubMed
Search terms used Search terms: ‘Artificial Intelligence’ and ‘Esophageal Cancer’ or ‘Barrett’s esophagus’ or ‘Esophageal Adenocarcinoma’ or ‘Esophageal Squamous carcinoma’
Timeframe From January 1970 to June 2022
Inclusion and exclusion criteria Inclusion criteria:
(I) English and Spanish language articles
(II) Article types:
   Original research including:
    Prospective cohorts
    Retrospective cohorts
   Reviews
   Editorials
Exclusion criteria:
(I) Articles without full text available
(II) Articles with incomplete or irrelevant data
Selection process There were two independent reviewers, MR and BMG, who reached a consensus about the manuscripts to be included in the review

Manuscripts in English and Spanish were taken into account. The search strategy included the words ‘Artificial Intelligence’ and ‘Esophageal Cancer’ ‘Barrett’s esophagus’ or ‘Esophageal Adenocarcinoma’ or ‘Esophageal Squamous carcinoma’.

With this criteria we found a total of 1,368 articles. Article types included original research, with both prospective and retrospective cohorts, previous reviews and editorials. Articles without full text available or incomplete or irrelevant data were excluded.

Furthermore, the risk of bias of the selected studies was assessed using standardized tools (3,4) by two investigators (Beatriz Merchán Gómez and María Rodríguez).

Current literature review and discussion

AI in EC diagnosis

EC includes mainly two types: esophageal adenocarcinoma and esophageal squamous cell carcinoma, which is the most frequent histological type, and it is more prevalent in Asian countries. In terms of incidence, EC remains the ninth most common cancer globally and it is the sixth cause of cancer mortality at the date (5). Despite several treatment advances (6), prognosis has not improved significantly, with an average 5-year overall survival of 20% (7). Furthermore, although the impact of early diagnosis is significant in survival, up to 6.5% of EC are missed during diagnostic gastroscopy (8,9). Endoscopic imaging diagnosis of EC and precursor lesions mainly depends on endoscopists’ experience, and in consequence, it has a high interobserver variability (9). To minimize this variability and to improve interobserver agreement, several techniques have been developed to improve diagnosis at earlier stages. For example, virtual chromoendoscopy and magnification, optical coherence tomography (OCT), high-resolution microendoscopy (HRM), confocal laser endomicroscopy (CLE) and volumetric laser endomicroscopy (VLE) (1,10-13). AI-based technologies, which are characterized by ML and DL algorithms, are the latest and subsequent technological step (14-16). Up to date, they have been tested in esophageal adenocarcinoma and esophageal squamous cell carcinoma precursors and have shown an improvement in the sensitivity and specificity of r EC diagnosis by refining the accuracy of endoscopic image diagnosis.

In the diagnosis of BE, the most important precursor of esophageal adenocarcinoma, AI may help in detecting and delineating suspicious lesions in endoscopic images. van der Sommen (2) tested an algorithm in 2013 that could identify changes in esophageal mucosas on high-definition endoscopy with an accuracy of 95.9% and an area under the curve (AUC) of 0.99. In the same lines, these authors employed a computer-aided diagnosis (CAD) system to automatically recognize a suspicious region in dysplastic BE (17) with high sensitivity and specificity, but with difficulties to locate early neoplastic lesions and to select the biopsy site. Horie et al. (18) created a deep convolutional network model to detect EC with light, virtual chromoendoscopy and near focus images with a 90% accuracy, with initial promising results for real time diagnosis. Furthermore, in order to improve the location of lesions, Ebigbo et al. (19) designed a CAD system based on DL using high definition white light images. Diagnostic accuracy was better when compared to general endoscopists’ diagnosis and the coincidence rate between the lesions identified by the system as well as the experts was up to 72%. This system was validated in two different datasets showing more than 90% of sensitivity. de Groof et al. (20) also trained a system using state-of-the-art ML techniques and validated their model in five sequential data files. They obtained a high accuracy in the validation process in two different external cohorts (89% and 88% respectively) (20), one of them including 53 endoscopists with a wide range of experiences. Continuing with the challenge to obtain an adequate diagnosis, Hong et al. (21) reported their experience with convolutional neural network as a classifier to distinguish neoplasia from BE employing endo microscopic images (21). The model’s accuracy was 80.77%. Swager et al. (22) also reported the use of a CAD system to identify neoplasia in ex-vivo VLE images (22). Later, van der Putten et al. (23) followed in his steps but this time using in-vivo images (23). The AUC, sensitivity and specificity were 0.95, 90%, and 93%, respectively.

In addition, as interpretation of endoscopic images alone was proven challenging, the role of pathologic morphology has also been investigated. Sabo et al. (24) developed and validated a computerized nuclear morphometry model to discriminate the degree of dysplasia in BE (24). This model could differentiate between BE without dysplasia and with low-grade dysplasia; and between BE with low-grade dysplasia and high-grade dysplasia, both with high accuracy (89% and 86% respectively).

Regarding esophageal squamous cell carcinoma, Liu et al. (25) developed an algorithm that detected 90.75% of EC with an AUC of 0.95 (25). Furthermore, Horie et al. (18), with their convolutional neural network model, reported a diagnostic accuracy of 99% for esophageal squamous cell carcinoma, 99% for superficial and 92% for advanced cancer, with high sensitivity and specificity. In 2019, Cai et al. (26) developed and validated a novel CAD system using a deep neural network to localize and identify early esophageal squamous cell carcinoma under conventional endoscopic white-light imaging. This model, trained with standard white-light images, could detect 91.4% of early esophageal squamous cell carcinoma (26). However, there weren’t s any significant differences between the CAD system and senior endoscopists’ diagnostic accuracy, questioning its utility. Similarly happened with the model of Ohmori et al. (27). They evaluated magnified and non-magnified images, including chromoendoscopy images using a convolutional neural network model. Although the accuracy was 77% or higher in all cases, with high sensitivity and moderate specificity, their results were similar to those achieved by experienced endoscopists. On the other hand, Zhao et al. obtained a higher sensitivity for type A microvascular intrapapillary capillary loop (IPCL) pattern than clinicians (71.5% vs. 28.2–64.9%), and the argued their model might avoid unnecessary radical treatment (28). Everson et al. validated another convolutional neural network model that could discriminate between types A and B with an accuracy of 93.3% (29). In the same research line, Nakagawa et al. aimed to predict invasion depths by developing a convolutional neural network system based on magnified and non-magnified images. The system’s accuracy to correctly describe the submucosal invasion and differentiate between submucosal microinvasive (SM1) cancers and submucosal deep invasive (SM2/SM3) was high (91% for the total, 92.9% for non-magnified images, and 89.7% for magnified images) (30). Luo et al. developed a multicenter study, to create a gastrointestinal AI diagnostic system (GRAIDS) based on the concept of DeepLab’s V3+®. For this purpose, they employed a total of 1,036,494 endoscopic images. The accuracy varied from 91.5% to 97.7% and it was validated in internal, external, and prospective validation datasets. This system obtained high sensitivities which were proximate to expert endoscopists’ and higher than junior endoscopists (31). In a systematic review and meta-analysis, Bang et al. determined the AUC, as well as the sensitivity, specificity, and diagnostic odds ratio of CAD algorithms in the diagnosis of esophageal cancer based on the analysis of endoscopic images (32). The study obtained an AUC of 0.97 [95% confidence interval (CI): 0.95–0.99], a sensitivity of 0.94 (95% CI: 0.89–0.96), a specificity of 0.88 (95% CI: 0.76–0.94), and a diagnostic odds ratio of 108 (95% CI: 43–273). The study also explored the depth of invasion for EC obtaining a pooled AUC of 0.96 (95% CI: 0.86–0.99), a sensitivity of 0.90 (95% CI: 0.88–0.92), a specificity of 0.88 (95% CI: 0.83–0.91), and a diagnostic odds ratio of 138 (95% CI: 12–1,569). No heterogeneity or publication bias were found.

Recently, Fang et al. (33) developed a model using semantic segmentation, a method of classification of pixels, so that various abnormal areas can be marked. In the method developed in this study, particularly the narrow band images, squamous cell carcinoma, and dysplasia were marked with high accuracy (84.72%). Tsai et al. (34) used hyperespectral imaging and a DL model using a single-shot multibox detector that classify the different stages of EC and identify its locations. The accuracy of this model using white-light images was 88% and 91% when narrow band images were used. An important remark is that the model’s accuracy using white light and narrow band images was increased by 5%, confirming that the hyperspectral imaging method significantly improves the diagnostic accuracy. Wang et al. (35) created a single-shot multibox detector using a convolutional neural network for diagnosing various histological grades of esophageal neoplasms. The diagnostic sensitivity, specificity, and accuracy were 96.2%, 70.4%, and 90.9%, respectively; and the accuracy o in differentiating the histological grade was 92%. AI systems with endo-cytoscopy (36) and high resolution microendoscopic images (37) have also been developed. The main characteristics of the previous studies are shown in Tables 2,3.

Table 2

Barrett’ esophagus/adenocarcinoma models

Author Year Number of images/
patients		 Type of image Model of AI Validation Accuracy (%) Sensitivity (%) Specificity (%)
van der Sommen et al. 2016 100 Endoscopy – Yes – 86 87
Horie et al. 2019 8,428 Endoscopy Convolutional neural network No 90 – –
Ebigbo et al. 2019 Two data sets of 148 and 139 Endoscopy, white light, NBI Convolutional neural network Yes – 92–97 80–100
de Groof et al. 2020 1,704 Endoscopy, high resolution Hybrid ResNet-UNet model CAD system Yes 88–89 90–93 83–88
Hong et al. 2017 262 Endomicroscopy Convolutional neural network No 80.77 – –
Swager et al. 2017 60 Endomicroscopy Convolutional neural network No – 90 93
van der Putten et al. 2020 23 Endomicroscopy Convolutional neural network Yes 82 – –
Sabo et al. 2006 97 Biopsies – No 86–89 – –

AI, artificial intelligence; NBI, narrow band images; CAD, computer-aided diagnosis.

Table 3

Squamous cell carcinoma models

Author Year Number of images/
patients		 Type of image Model of AI Validation Accuracy (%) Sensitivity (%) Specificity (%)
Liu et al. 2016 1,130 Endoscopy – No 90.75 – –
Horie et al. 2019 8,428 Endoscopy Convolutional neural network No 99 – –
Cai et al. 2019 2,428 Endoscopy, white light Deep neural network Yes 91.4 97.8 85.4
Ohmori et al. 2020 22,562 Endoscopy, white light, NBI, magnification Convolutional neural network Yes 75–77 90–100 56–76
Zhao et al. 2019 1,383 Endoscopy, white light, NBI, magnification Double-labeling fully convolutional network Yes 89.2–93 – –
Everson et al. 2019 7,046 Endoscopy, white light, NBI, magnification Convolutional neural network No 93.7 89.3 98
Nakagawa et al. 2019 14,338 Endoscopy, white light magnification Deep neural network Yes 91 90.1 95.8
Luo et al. 2019 1,036,496 Endoscopy Deep neural network Yes 91.5–97.7 – –
Fang et al. 2022 165 Endoscopy, white light, NBI Deep learning No 84.72 – –
Tsai et al. 2021 308 HSI, endoscopy, white light, NBI Convolutional neural network No 88–91 – –
Wang et al. 2021 936 Endoscopy, white light, NBI – No 90.9 96.2 70.4

AI, artificial intelligence; NBI, narrow band images; HSI, hyperspectral imaging.

AI in EC treatment and outcome prediction

AI has proven its utility not only in the diagnosis but also in the treatment and outcome prediction in EC patients. Opposing the traditional primary tumor, lymph nodes and distant metastasis (TNM) staging system, Sato et al. (38) trained an artificial neural network with 65 clinicopathologic, genetic and biologic variables for 1-year survival and 60 variables for 5-year survival. The AUC, sensitivity and specificity were 0.88, 78.1%, 84.7% for 1-year survival and 0.88, 80.7%, 86.5% for 5-year survival, respectively.

Another advantage of AI is the possibility of making real-time decisions. Although that is probably one of the theoretical-clinical practice more difficult to close gaps, an Iranian group trained a multilayer neural network with particle swarm optimization and error back propagation (EBP) algorithms, intending to clarify the most precise chemotherapy dose. The accuracy of their model was 77.3% (39). Not only to determine the best systemic treatment approach, but also to determine the best resection margin, Maktabi et al. (40) tested an hyperespectral image model that could detect cancerous tissue with 63% sensitivity and 69% specificity in one second. This system’s potential relies on the fact that it may assist surgeons in the process of identification of tumor margins intra-operatively and in real time for an adequate resection.

Another still unmet challenge in EC is predicting cancer response. To overcome it, several investigators have also designed different AI models. Warnecke-Eberz et al. (41) trained an artificial neural network system to predict histologic response in neoadjuvant chemoradiotherapy naïve patients. They analyzed 17 genes with the TaqMan® low-density arrays, obtaining 75.0% sensitivity, 81.0% specificity and 78.1% accuracy. Also, to predict response to neoadjuvant chemotherapy, Ypsilantis et al. (42) developed a three-slice convolutional neural network that was able to extract data from pre-treatment positron emission tomography-computed tomography (PET-CT) images to predict the response to neoadjuvant chemotherapy. The accuracy was 73.4%, with sensitivity of 80.7% and specificity of 81.6%. Finally, Li et al. (43) conducted a prospective, multicenter study to determine the therapeutic efficacy of chemoradiotherapy in patients with locally advanced thoracic esophageal squamous cell carcinoma. They developed a 3-dimensional DL radiomics model (3D-DLRM) based on pretreatment CT images to predict the response to treatment, with a positive predictive value (PPV) of 100% and AUC over 0.8 in radiation therapy plan, radiation field and prescription dose used.

Although with this narrative review we have tried to summarize the current evidence regarding AI in EC, our study could be affected by the main limitations of narrative reviews. Among them, is that narrative-review methodology does not follow systematic evidence based criteria. Furthermore, in this type of studies a higher selection bias (aligned with author’s expert opinion) have been observed. To mitigate this limitation two independent reviewers, from different departments have selected and reviewed each manuscript (44).

Furthermore, limitations of AI application in daily clinical practice, as patients data integration, clinical decision impact and influence in patients’ outcomes still need to be addressed in larger, multicentric studies.

Conclusions

In conclusion, as the reviewed manuscripts highlight, the role of AI in the care of EC patients has considerably increased in later years. The unmet challenges remain the validation and application of these algorithms into the daily clinical practice, the possibility to use them in real-time patient diagnosis and to test not only their efficacy, something that most of them have already proven, but also their real efficiency in different healthcare settings.

The future for AI in a complex pathology as EC seems bright, but we, as clinicians need to be aware of the most recent advances and help to introduce them in our daily practices.

Acknowledgments

Funding: None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://atm.amegroups.com/article/view/10.21037/atm-22-3977/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-3977/coif). MR serves as an unpaid editorial board member of Annals of Translational Medicine from April 2022 to March 2024. MR reports personal fees from Abex and AstraZeneca outside the submitted work. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

  1. Zhang YH, Guo LJ, Yuan XL, et al. Artificial intelligence-assisted esophageal cancer management: Now and future. World J Gastroenterol 2020;26:5256-71. [Crossref] [PubMed]
  2. van der Sommen F. The paradox of artificial intelligence diversification in endoscopy: creating blind spots by exposing them. Endoscopy 2022;54:778-9. [Crossref] [PubMed]
  3. Ma LL, Wang YY, Yang ZH, et al. Methodological quality (risk of bias) assessment tools for primary and secondary medical studies: what are they and which is better? Mil Med Res 2020;7:7. [Crossref] [PubMed]
  4. Moga C, Guo B, Schopflocher D, et al. Development of a quality appraisal tool for case series studies using a modified Delphi technique. Institute of Health Economics, 2012.
  5. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. [Crossref] [PubMed]
  6. Chau I, Doki Y, Ajani JA, et al. Nivolumab (NIVO) plus ipilimumab (IPI) or NIVO plus chemotherapy (chemo) versus chemo as first-line (1L) treatment for advanced esophageal squamous cell carcinoma (ESCC): First results of the CheckMate 648 study. J Clin Oncol 2021;39:LBA4001. [Crossref]
  7. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019;69:7-34. [Crossref] [PubMed]
  8. Naveed M, Kubiliun N. Endoscopic Treatment of Early-Stage Esophageal Cancer. Curr Oncol Rep 2018;20:71. [Crossref] [PubMed]
  9. Rodríguez de Santiago E, Hernanz N, Marcos-Prieto HM, et al. Rate of missed oesophageal cancer at routine endoscopy and survival outcomes: A multicentric cohort study. United European Gastroenterol J 2019;7:189-98. [Crossref] [PubMed]
  10. Inoue H, Kaga M, Ikeda H, et al. Magnification endoscopy in esophageal squamous cell carcinoma: a review of the intrapapillary capillary loop classification. Ann Gastroenterol 2015;28:41-8. [PubMed]
  11. Liu J, Li M, Li Z, et al. Learning curve and interobserver agreement of confocal laser endomicroscopy for detecting precancerous or early-stage esophageal squamous cancer. PLoS One 2014;9:e99089. [Crossref] [PubMed]
  12. Worrell SG, Boys JA, Chandrasoma P, et al. Inter-Observer Variability in the Interpretation of Endoscopic Mucosal Resection Specimens of Esophageal Adenocarcinoma: Interpretation of ER specimens. J Gastrointest Surg 2016;20:140-5. [Crossref] [PubMed]
  13. Gora MJ, Suter MJ, Tearney GJ, et al. Endoscopic optical coherence tomography: technologies and clinical applications Biomed Opt Express 2017;8:2405-44. [Invited]. [Crossref] [PubMed]
  14. Takeuchi M, Seto T, Hashimoto M, et al. Performance of a deep learning-based identification system for esophageal cancer from CT images. Esophagus 2021;18:612-20. [Crossref] [PubMed]
  15. Sui H, Ma R, Liu L, et al. Detection of Incidental Esophageal Cancers on Chest CT by Deep Learning. Front Oncol 2021;11:700210. [Crossref] [PubMed]
  16. Gehrung M, Crispin-Ortuzar M, Berman AG, et al. Triage-driven diagnosis of Barrett's esophagus for early detection of esophageal adenocarcinoma using deep learning. Nat Med 2021;27:833-41. [Crossref] [PubMed]
  17. van der Sommen F, Zinger S, Curvers WL, et al. Computer-aided detection of early neoplastic lesions in Barrett's esophagus. Endoscopy 2016;48:617-24. [Crossref] [PubMed]
  18. Horie Y, Yoshio T, Aoyama K, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc 2019;89:25-32. [Crossref] [PubMed]
  19. Ebigbo A, Mendel R, Probst A, et al. Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma. Gut 2019;68:1143-5. [Crossref] [PubMed]
  20. de Groof AJ, Struyvenberg MR, van der Putten J, et al. 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;158:915-29.e4. [Crossref] [PubMed]
  21. Hong J, Park BY, Park H. Convolutional neural network classifier for distinguishing Barrett's esophagus and neoplasia endomicroscopy images. Annu Int Conf IEEE Eng Med Biol Soc 2017;2017:2892-5. [Crossref] [PubMed]
  22. Swager AF, van der Sommen F, Klomp SR, et al. Computer-aided detection of early Barrett's neoplasia using volumetric laser endomicroscopy. Gastrointest Endosc 2017;86:839-46. [Crossref] [PubMed]
  23. van der Putten J, Struyvenberg M, de Groof J, et al. Deep principal dimension encoding for the classification of early neoplasia in Barrett's Esophagus with volumetric laser endomicroscopy. Comput Med Imaging Graph 2020;80:101701. [Crossref] [PubMed]
  24. Sabo E, Beck AH, Montgomery EA, et al. Computerized morphometry as an aid in determining the grade of dysplasia and progression to adenocarcinoma in Barrett's esophagus. Lab Invest 2006;86:1261-71. [Crossref] [PubMed]
  25. Liu DY, Gan T, Rao NN, et al. Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process. Med Image Anal 2016;32:281-94. [Crossref] [PubMed]
  26. Cai SL, Li B, Tan WM, et al. Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video). Gastrointest Endosc 2019;90:745-53.e2. [Crossref] [PubMed]
  27. Ohmori M, Ishihara R, Aoyama K, et al. Endoscopic detection and differentiation of esophageal lesions using a deep neural network. Gastrointest Endosc 2020;91:301-9.e1. [Crossref] [PubMed]
  28. Zhao YY, Xue DX, Wang YL, et al. Computer-assisted diagnosis of early esophageal squamous cell carcinoma using narrow-band imaging magnifying endoscopy. Endoscopy 2019;51:333-41. [Crossref] [PubMed]
  29. Everson M, Herrera L, Li W, et al. 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 European Gastroenterol J 2019;7:297-306. [Crossref] [PubMed]
  30. Nakagawa K, Ishihara R, Aoyama K, et al. Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists. Gastrointest Endosc 2019;90:407-14. [Crossref] [PubMed]
  31. Luo H, Xu G, Li C, et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Lancet Oncol 2019;20:1645-54. [Crossref] [PubMed]
  32. Bang CS, Lee JJ, Baik GH. Computer-aided diagnosis of esophageal cancer and neoplasms in endoscopic images: a systematic review and meta-analysis of diagnostic test accuracy. Gastrointest Endosc 2021;93:1006-15.e13. [Crossref] [PubMed]
  33. Fang YJ, Mukundan A, Tsao YM, et al. Identification of Early Esophageal Cancer by Semantic Segmentation. J Pers Med 2022;12:1204. [Crossref] [PubMed]
  34. Tsai CL, Mukundan A, Chung CS, et al. Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer. Cancers (Basel) 2021;13:4593. [Crossref] [PubMed]
  35. Wang YK, Syu HY, Chen YH, et al. Endoscopic Images by a Single-Shot Multibox Detector for the Identification of Early Cancerous Lesions in the Esophagus: A Pilot Study. Cancers (Basel) 2021;13:321. [Crossref] [PubMed]
  36. Kodashima S, Fujishiro M, Takubo K, et al. Ex vivo pilot study using computed analysis of endo-cytoscopic images to differentiate normal and malignant squamous cell epithelia in the oesophagus. Dig Liver Dis 2007;39:762-6. [Crossref] [PubMed]
  37. Shin D, Protano MA, Polydorides AD, et al. Quantitative analysis of high-resolution microendoscopic images for diagnosis of esophageal squamous cell carcinoma. Clin Gastroenterol Hepatol 2015;13:272-9.e2. [Crossref] [PubMed]
  38. Sato F, Shimada Y, Selaru FM, et al. Prediction of survival in patients with esophageal carcinoma using artificial neural networks. Cancer 2005;103:1596-605. [Crossref] [PubMed]
  39. Zahedi H, Mehrshad N, Anvari K. Intelligent modelling of oesophageal cancer treatment and its use to determine the dose of chemotherapy drug. J Med Eng Technol 2012;36:261-6. [Crossref] [PubMed]
  40. Maktabi M, Köhler H, Ivanova M, et al. Tissue classification of oncologic esophageal resectates based on hyperspectral data. Int J Comput Assist Radiol Surg 2019;14:1651-61. [Crossref] [PubMed]
  41. Warnecke-Eberz U, Metzger R, Bollschweiler E, et al. TaqMan low-density arrays and analysis by artificial neuronal networks predict response to neoadjuvant chemoradiation in esophageal cancer. Pharmacogenomics 2010;11:55-64. [Crossref] [PubMed]
  42. Ypsilantis PP, Siddique M, Sohn HM, et al. Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks. PLoS One 2015;10:e0137036. [Crossref] [PubMed]
  43. Li X, Gao H, Zhu J, et al. 3D Deep Learning Model for the Pretreatment Evaluation of Treatment Response in Esophageal Carcinoma: A Prospective Study (ChiCTR2000039279). Int J Radiat Oncol Biol Phys 2021;111:926-35. [Crossref] [PubMed]
  44. Schmidt LM, Gotzsche PC. Of mites and men: reference bias in narrative review articles: a systematic review. J Fam Pract 2005;54:334-8. [PubMed]
Cite this article as: Merchán Gómez B, Milla Collado L, Rodríguez M. Artificial intelligence in esophageal cancer diagnosis and treatment: where are we now?—a narrative review. Ann Transl Med 2023;11(10):353. doi: 10.21037/atm-22-3977

Article Options

Download Citation

Share