| ISSN |
1948-5204 (online) |
| 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/ |
| Copyright |
© The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Highly accurate colorectal cancer prediction model based on Raman spectroscopy using patient serum
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Hiroaki Ito, Naoyuki Uragami, Tomokazu Miyazaki, William Yang, Kenji Issha, Kai Matsuo, Satoshi Kimura, Yuji Arai, Hiromasa Tokunaga, Saiko Okada, Machiko Kawamura, Noboru Yokoyama, Miki Kushima, Haruhiro Inoue, Takashi Fukagai and Yumi Kamijo |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Japanese Society for the Promotion of Science (JSPS), based on the JSPS KAKENHI Grants-in-Aid for Scientific Research (C) |
JP17K09022 |
|
| Corresponding Author |
Hiroaki Ito, MD, PhD, Associate Professor, Department of Surgery, Digestive Disease Center, Showa University Koto Toyosu Hospital, 5-1-38 Toyosu, Koto-ku, Tokyo 135-8577, Japan. h.ito@med.showa-u.ac.jp |
| Key Words |
Colorectal cancer; Raman spectroscopy; Machine learning; Blood; Serum; Diagnosis |
| Core Tip |
We developed a comprehensive, spontaneous, minimally invasive, label-free, blood-based colorectal cancer screening technique based on Raman spectroscopy. We used Raman spectra recorded using 184 serum samples obtained from patients undergoing colonoscopies. Use of the recorded Raman spectra as training data allowed the construction of a boosted tree colorectal cancer prediction model based on machine learning. The generalized R2 values for colorectal cancer was 0.9982. For machine learning using Raman spectral data, we are currently working on the construction of a more accurate colorectal cancer prediction model with a vast volume of additional data. |
| Publish Date |
2020-11-12 03:59 |
| Citation |
Ito H, Uragami N, Miyazaki T, Yang W, Issha K, Matsuo K, Kimura S, Arai Y, Tokunaga H, Okada S, Kawamura M, Yokoyama N, Kushima M, Inoue H, Fukagai T, Kamijo Y. Highly accurate colorectal cancer prediction model based on Raman spectroscopy using patient serum. World J Gastrointest Oncol 2020; 12(11): 1311-1324 |
| URL |
https://www.wjgnet.com/1948-5204/full/v12/i11/1311.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v12.i11.1311 |
