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Advances in Endoscopic Management of Esophageal Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: 1 March 2027 | Viewed by 696

Editors


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Guest Editor
1. Endoscopy Unit, Gastroenterology Department, Hospital Universitari Germans Trias i Pujol, Badalona, Barcelona, Spain
2. Endoscopy Unit, Teknon Medical Center, Barcelona, Spain
Interests: early gastrointestinal cancer; endoscopic submucosal dissection (ESD); third space endoscopy; esophageal squamous cell carcinoma; Barrett’s esophagus; Barrett’s eradication

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Guest Editor
Department of Medical Oncology, ICO-Badalona (Catalan Institute of Oncology-Badalona), Barcelona, Spain
Interests: gastrointestinal tumors; medical oncology; esophageal cancer

Special Issue Information

Dear Colleagues,

Endoscopic management has substantially reshaped the therapeutic landscape of esophageal cancer, particularly for early-stage disease. Over the past two decades, the development and refinement of advanced endoscopic resection techniques—most notably endoscopic submucosal dissection (ESD)—have enabled organ-preserving treatment strategies with favorable oncologic outcomes in carefully selected patients without evidence of extraluminal disease. These approaches have markedly reduced the need for surgical esophagectomy in early neoplasia while achieving high rates of en bloc and R0 resection. In selected scenarios, local endoscopic resection followed by structured surveillance has emerged as a potential management strategy when the estimated risk of lymph node metastasis is low or when oncologic outcomes appear comparable to surgical resection. In this context, accumulating evidence suggests that surveillance strategies after complete endoscopic resection may represent a reasonable alternative to immediate surgery in carefully selected cases, including some patients with superficial submucosal invasion (pT1b), provided that risk stratification and follow-up protocols are rigorously applied. Ongoing research continues to refine the criteria that may identify patients who could safely benefit from organ-preserving approaches. Beyond therapeutic resection, the field continues to evolve rapidly through improvements in diagnostic and staging technologies. Advances in high-definition endoscopy, virtual chromoendoscopy, and artificial intelligence-assisted detection and characterization systems are improving lesion recognition and risk stratification. In parallel, endoscopic ablative therapies are expanding the therapeutic armamentarium for Barrett’s esophagus-associated neoplasia. This Special Issue aims to provide an updated overview of recent advances in the endoscopic diagnosis, staging, and treatment of esophageal cancer. We welcome original research articles and reviews addressing innovations in endoscopic techniques, clinical outcomes, training and standardization, and emerging technologies such as robotics and artificial intelligence. By bringing together contributions from leading experts, this Special Issue seeks to highlight current progress and explore future directions in minimally invasive, endoscopy-based management strategies for esophageal neoplasia.

Dr. Hugo Uchima
Dr. Cristina Bugés Sánchez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • endoscopic submucosal dissection (ESD)
  • esophageal squamous cell carcinoma
  • Barrett’s esophagus

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Published Papers (1 paper)

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Research

17 pages, 2811 KB  
Article
Efficacy of Spectral-Aided Visual Enhancer in Classification of Esophageal Cancer
by Kok-Yean Koh, Arvind Mukundan, Riya Karmakar, Chaudhary Tirth Atulbhai, Tsung-Hsien Chen, Wei-Chun Weng and Hsiang-Chen Wang
Cancers 2026, 18(10), 1609; https://doi.org/10.3390/cancers18101609 - 15 May 2026
Viewed by 499
Abstract
Background/Objectives: Esophageal cancer is one of the major global causes of cancer mortality, and the 5-year survival rate remains below 20% because many cases are detected late. In this study, a Spectral-Aided Vision Enhancer (SAVE) algorithm was utilized to convert conventional white-light endoscopic [...] Read more.
Background/Objectives: Esophageal cancer is one of the major global causes of cancer mortality, and the 5-year survival rate remains below 20% because many cases are detected late. In this study, a Spectral-Aided Vision Enhancer (SAVE) algorithm was utilized to convert conventional white-light endoscopic images (WLI) into hyperspectral-like narrow-band imaging (NBI) images for machine-learning classification of Dysplasia, Normal, and Squamous Cell Carcinoma (SCC). Methods: A total of 762 WLI images obtained from Kaohsiung Medical University were augmented to 1074 using the Al bumentations library, employing vertical flipping, horizontal flipping, and rotations. The SAVE conversion pipeline employs a 24-patch Macbeth color checker for calibration, γ-correction, CIE XYZ transformation, and multivariate regression to interpolate spectral bands, yielding an average color difference of 2.79 (CIEDE2000) from true NBI. The training outcomes and performance metrics illustrate the versatility of the machine learning/deep learning models—Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)—which were trained and evaluated on both the original WLI and SAVE datasets. Performance metrics were analyzed based on precision, recall, accuracy, and F1-score. Results: The CNN sample achieved an accuracy of 100 percent on SAVE data, compared to 93 percent for WLI. The accuracy of RF improved, with WLI at 91% and SAVE at 96%, while SVM increased from 79% to 84%. These improvements indicate the diagnostically valuable spectral variations that can be amplified with SAVE, resulting in significant enhancements in pre-cancer/SCC sensitivity. Conclusions: The proposed SAVE method demonstrates significant potential for enhancing endoscopic imaging and advancing computer-aided diagnosis in esophageal cancer screening, with applicability in other gastrointestinal imaging scenarios as well. Full article
(This article belongs to the Special Issue Advances in Endoscopic Management of Esophageal Cancer)
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