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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Apr 14, 2025; 31(14): 104280
Published online Apr 14, 2025. doi: 10.3748/wjg.v31.i14.104280
Published online Apr 14, 2025. doi: 10.3748/wjg.v31.i14.104280
Rapid pathologic grading-based diagnosis of esophageal squamous cell carcinoma via Raman spectroscopy and a deep learning algorithm
Xin-Ying Yu, Qiang He, Department of Gastroenterology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China
Jian Chen, Department of Cancer Prevention Center, Feicheng People’s Hospital, Feicheng 271000, Shandong Province, China
Lian-Yu Li, Department of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan 430000, Hubei Province, China
Feng-En Chen, Department of Chemistry, Tsinghua University, Beijing 100080, China
Author contributions: Yu XY and He Q designed the study and acquired funding; Yu XY, He Q and Li LY were responsible for developing the methodology; Yu XY, Chen J and Chen FE participated in the formal analysis and investigation; Yu XY and Li LY wrote the original draft; Yu XY, Chen J, Chen FE, Li LY and He Q participated in the review and editing.
Supported by Beijing Hospitals Authority Youth Programme, No. QML20200505.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Board of Beijing Tiantan Hospital, Capital Medical University (Approval No. KY2022-042-02).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: No additional data are available.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Qiang He, MD, Department of Gastroenterology, Beijing Tiantan Hospital, Capital Medical University, No. 119 South Fourth Ring West Road, Fengtai District, Beijing 100071, China. 229476289@qq.com
Received: December 17, 2024
Revised: February 23, 2025
Accepted: March 24, 2025
Published online: April 14, 2025
Processing time: 115 Days and 22 Hours
Revised: February 23, 2025
Accepted: March 24, 2025
Published online: April 14, 2025
Processing time: 115 Days and 22 Hours
Core Tip
Core Tip: Raman spectroscopy has become a new method for the early diagnosis of tumors. This study employed Raman spectroscopy to detect alterations in Raman spectral information across different stages of esophageal neoplasia, and a deep learning algorithm was designed to classify spectral data. In conclusion, Raman spectroscopy revealed significantly different waveforms for the different stages of esophageal neoplasia. The combination of Raman spectroscopy and deep learning methods could significantly improve the accuracy of classification and yielded high accuracy and specificity levels for the rapid pathologic grading-based diagnosis.