ISSN |
1007-9327 (print) and 2219-2840 (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 |
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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 |
Radiology, Nuclear Medicine & Medical Imaging |
Manuscript Type |
Retrospective Study |
Article Title |
Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer
|
Manuscript Source |
Invited Manuscript |
All Author List |
Zheng-Yan Li, Xiao-Dong Wang, Mou Li, Xi-Jiao Liu, Zheng Ye, Bin Song, Fang Yuan, Yuan Yuan, Chun-Chao Xia, Xin Zhang and Qian Li |
ORCID |
|
Funding Agency and Grant Number |
Funding Agency |
Grant Number |
Research Grant of National Nature Science Foundation of China |
81971571 |
Multimodal MR Imaging and Radiomics of Rectal Cancer, Science and Technology Department of Sichuan Province |
2019YFS0431 |
Sichuan University Training Program of Innovation and Entrepreneurship for Undergraduates |
C2019104739 |
|
Corresponding Author |
Bin Song, MD, PhD, Chief Doctor, Professor, Department of Radiology, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu 610041, Sichuan Province, China. songlab_radiology@163.com |
Key Words |
Radiomics; Rectal cancer; Neoadjuvant chemotherapy; Magnetic resonance imaging; Computed tomography; |
Core Tip |
Our study developed and validated a radiomics model that incorporated computed tomography and magnetic resonance imaging radiomics features for noninvasive and individualized prediction of clinical response to neoadjuvant chemotherapy in patients with locally advanced rectal cancer. The combination of computed tomography and magnetic resonance imaging radiomics features was associated with better performance than any individual sequence. In contrast, the clinical model based on extramural venous invasion achieved relatively low diagnostic performance. Multi-modal nomogram facilitated easy and noninvasive estimation of clinical response to neoadjuvant chemotherapy. The proposed radiomics model performs well and thereby guiding clinical decision-making and preoperative assessment of neoadjuvant chemotherapy for locally advanced rectal cancer. |
Publish Date |
2020-05-21 12:02 |
Citation |
Li ZY, Wang XD, Li M, Liu XJ, Ye Z, Song B, Yuan F, Yuan Y, Xia CC, Zhang X, Li Q. Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer. World J Gastroenterol 2020; 26(19): 2388-2402 |
URL |
https://www.wjgnet.com/1007-9327/full/v26/i19/2388.htm |
DOI |
https://dx.doi.org/ 10.3748/wjg.v26.i19.2388 |