Innovations in Imaging and Endoscopic Diagnosis of Esophageal Cancer: From Early Detection to Clinical Decision Support
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: 30 August 2026 | Viewed by 182
Special Issue Editors
Interests: biomedical science and engineering; biomedical sensors and images; hyperspectral imaging; optoelectronic semiconductor materials and devices; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
2. Department of Computer Science and Engineering, Sanjivani University, Kopargaon, Maharashtra 423603, India
Interests: hyperspectral imaging; color engineering; optical inspection; aerodynamics; esophageal cancer; machine learning; artificial intelligence; mars colonization; lunar; skin cancer; cancer detection; cancer classification; multispectral imaging; cancer imaging
Interests: computer communications; artificial intelligence; distributed computing computer; security and reliability; information systems; computing in mathematics, natural sciences engineering, and medicine; programming languages; parallel computing; software engineering
Special Issue Information
Dear Colleagues,
Esophageal cancer remains one of the most aggressive gastrointestinal malignancies, characterized by elevated mortality rates, mostly due to late-stage diagnosis and the limited efficacy of conventional diagnostic methods. Recent advances in imaging technologies and endoscopic techniques have significantly enhanced the visualization of early neoplastic changes, enabling earlier detection, more accurate characterization, and better-informed clinical decision-making.
This Special Issue aims to highlight recent progress in imaging and endoscopic diagnostic techniques for esophageal cancer. Areas of interest include, but are not limited to, the following:
- Advanced endoscopic imaging modalities, such as narrow-band imaging, white light imaging, chromoendoscopy, confocal laser endomicroscopy, optical coherence tomography, hyperspectral and multispectral imaging, as well as novel image-enhanced endoscopy systems.
- Integration of artificial intelligence, machine learning, and computer-assisted diagnostics tools into endoscopic practice, particularly for lesion detection, classification, depth-of-invasion assessment, and risk stratification.
This Special Issue welcomes studies on multimodal imaging methods, radiomics, and data-driven clinical decision support systems that integrate diagnostic imaging with prognostic assessment and therapy planning. Original research articles, technical advances, systematic reviews, and meta-analyses are encouraged in both translational and clinical research, provided they demonstrate relevance and future potential.
Through contributions from clinicians, imaging scientists, and computational researchers, this Special Issue seeks to provide a comprehensive overview of current advances and emerging trends in imaging and diagnosis of esophageal cancer, ultimately supporting precision medicine and improving patient outcomes.
Prof. Dr. Hsiang-Chen Wang
Dr. Riya Karmakar
Dr. Mahendra Bhatu Gawali
Guest Editors
Manuscript Submission Information
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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-blind 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
- esophageal cancer
- endoscopic imaging
- early detection
- image-enhanced endoscopy
- optical imaging
- artificial intelligence
- computer-aided diagnosis
- multimodal imaging
- risk stratification
- clinical decision support systems
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