Publication Name	World Journal of Gastroenterology
Manuscript ID	104280
Country	China

Received	2024-12-17 07:41
Peer-Review Started	2024-12-17 07:41
To Make the First Decision
Return for Revision	2025-02-18 02:32
Revised	2025-02-23 01:55
Second Decision	2025-03-24 02:37
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-03-24 06:54
Articles in Press	2025-03-24 06:54
Publication Fee Transferred	2025-02-24 09:34
Edit the Manuscript by Language Editor
Typeset the Manuscript	2025-04-01 01:25
Publish the Manuscript Online	2025-04-11 02:19

ISSN	1007-9327 (print) and 2219-2840 (online)
Open Access	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: http://creativecommons.org/Licenses/by-nc/4.0/
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Category	Gastroenterology & Hepatology
Manuscript Type	Retrospective Cohort Study
Article Title	Rapid pathologic grading-based diagnosis of esophageal squamous cell carcinoma via Raman spectroscopy and a deep learning algorithm
Manuscript Source	Unsolicited Manuscript
All Author List	Xin-Ying Yu, Jian Chen, Lian-Yu Li, Feng-En Chen and Qiang He
ORCID	Author(s) ORCID Number Qiang He http://orcid.org/0000-0001-5419-3360
Funding Agency and Grant Number	Funding Agency Grant Number Beijing Hospitals Authority Youth Programme QML20200505
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
Key Words	Raman spectroscopy; Esophageal neoplasia; Early diagnosis; Deep learning algorithm; Rapid pathologic grading
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.
Publish Date	2025-04-11 02:19
Citation	<p>Yu XY, Chen J, Li LY, Chen FE, He Q. Rapid pathologic grading-based diagnosis of esophageal squamous cell carcinoma via Raman spectroscopy and a deep learning algorithm. <i>World J Gastroenterol</i> 2025; 31(14): 104280</p>
URL	https://www.wjgnet.com/1007-9327/full/v31/i14/104280.htm
DOI	https://dx.doi.org/10.3748/wjg.v31.i14.104280

Full Article (PDF)	WJG-31-104280-with-cover.pdf
STROBE Statement	104280-STROBE-statement.pdf
Manuscript File	104280_Auto_Edited_075910.docx
Answering Reviewers	104280-answering-reviewers.pdf
Audio Core Tip	104280-audio.aiff
Biostatistics Review Certificate	104280-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	104280-conflict-of-interest-statement.pdf
Copyright License Agreement	104280-copyright-assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	104280-foundation-statement.pdf
Signed Informed Consent Form(s) or Document(s)	104280-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document	104280-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	104280-non-native-speakers.pdf
Peer-review Report	104280-peer-reviews.pdf
Scientific Misconduct Check	104280-scientific-misconduct-check.png
Scientific Editor Work List	104280-scientific-editor-work-list.pdf
CrossCheck Report	104280-crosscheck-report.pdf