Li M, Jin YM, Zhang YC, Zhao YL, Huang CC, Liu SM, Song B. Radiomics for predicting perineural invasion status in rectal cancer. World J Gastroenterol 2021; 27(33): 5610-5621 [PMID: 34588755 DOI: 10.3748/wjg.v27.i33.5610]
Corresponding Author of This Article
Bin Song, MD, 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
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
World J Gastroenterol. Sep 7, 2021; 27(33): 5610-5621 Published online Sep 7, 2021. doi: 10.3748/wjg.v27.i33.5610
Radiomics for predicting perineural invasion status in rectal cancer
Mou Li, Yu-Mei Jin, Yong-Chang Zhang, Ya-Li Zhao, Chen-Cui Huang, Sheng-Mei Liu, Bin Song
Mou Li, Yu-Mei Jin, Sheng-Mei Liu, Bin Song, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
Yong-Chang Zhang, Department of Radiology, Chengdu Seventh People’s Hospital, Chengdu 610213, Sichuan Province, China
Ya-Li Zhao, Chen-Cui Huang, Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing 100080, China
Author contributions: Song B and Li M designed the research; Jin YM and Liu SM collected the data; Li M, Zhang YC, Zhao YL, and Huang CC analyzed the data; Li M and Song B wrote the paper; all authors have read and approved the final manuscript.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of West China Hospital of Sichuan University (Approved No.1159).
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: Zhao YL and Huang CC are employed by the company Beijing Deepwise & League of PHD Technology Co., Ltd. The remaining authors declare no conflicts-of-interest related to this article.
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: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bin Song, MD, 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
Received: June 2, 2021 Peer-review started: June 2, 2021 First decision: June 25, 2021 Revised: July 3, 2021 Accepted: August 11, 2021 Article in press: August 11, 2021 Published online: September 7, 2021
ARTICLE HIGHLIGHTS
Research background
Perineural invasion (PNI) has emerged as an independent prognostic factor in patients with rectal cancer (RC). The preoperative prediction of PNI status is beneficial for individualized treatment and improved prognosis.
Research motivation
Nowadays, preoperative assessment of PNI status is still challenging.
Research objectives
To build a radiomics prediction model for evaluating PNI status preoperatively in RC patients.
Research methods
We enrolled 303 RC patients in a single institution from March 2018 to October 2019. These patients were classified as the training cohort (n = 242) and validation cohort (n = 61). A large number of intra- and peritumoral radiomics features were extracted to build the Rad-score and combined model.
Research results
Our study enrolled more patients (144 PNI+ and 159 PNI-) than previous studies[6,17,18]. Rad-score was built by logistic regression analysis. The combined model was developed by combining Rad-score with computed tomography (CT)-reported T stage and N stage, and carcinoembryonic antigen. The combined model showed good performance to predict PNI status, with an area under the receiver operating characteristic curve of 0.828 [95% confidence interval (CI): 0.774-0.873] in the training cohort and 0.801 (95%CI: 0.679-0.892) in the validation cohort.
Research conclusions
The combined model incorporating Rad-score and clinical factors helps to provide an individualized PNI status evaluation.
Research perspectives
Other biological characteristics besides PNI are also related to the prognosis of RC patients; for instance, intramural lymphovascular invasion (LVI). Intramural LVI cannot be determined by magnetic resonance imaging and CT. Therefore, using radiomics or deep learning to predict intramural LVI of RC is valuable in the future.
