Retrospective Study
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
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
Abstract
BACKGROUND

Perineural invasion (PNI), as a key pathological feature of tumor spread, has emerged as an independent prognostic factor in patients with rectal cancer (RC). The preoperative stratification of RC patients according to PNI status is beneficial for individualized treatment and improved prognosis. However, the preoperative evaluation of PNI status is still challenging.

AIM

To establish a radiomics model for evaluating PNI status preoperatively in RC patients.

METHODS

This retrospective study 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) at a ratio of 8:2. A large number of intra- and peritumoral radiomics features were extracted from portal venous phase images of computed tomography (CT). After deleting redundant features, we tested different feature selection (n = 6) and machine-learning (n = 14) methods to form 84 classifiers. The best performing classifier was then selected to establish Rad-score. Finally, the clinicoradiological model (combined model) was developed by combining Rad-score with clinical factors. These models for predicting PNI were compared using receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC).

RESULTS

One hundred and forty-four of the 303 patients were eventually found to be PNI-positive. Clinical factors including CT-reported T stage (cT), N stage (cN), and carcinoembryonic antigen (CEA) level were independent risk factors for predicting PNI preoperatively. We established Rad-score by logistic regression analysis after selecting features with the L1-based method. The combined model was developed by combining Rad-score with cT, cN, and CEA. The combined model showed good performance to predict PNI status, with an AUC 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. For comparison of the models, the combined model achieved a higher AUC than the clinical model (cT + cN + CEA) achieved (P < 0.001 in the training cohort, and P = 0.045 in the validation cohort).

CONCLUSION

The combined model incorporating Rad-score and clinical factors can provide an individualized evaluation of PNI status and help clinicians guide individualized treatment of RC patients.

Keywords: Radiomics, Perineural invasion, Rectal cancer, Computed tomography, Preoperative prediction, Model building

Core Tip: In this study, a radiomics model integrating Rad-score and clinical factors was developed and validated for predicting perineural invasion status in patients with rectal cancer. Radiomics features were extracted from intra- and peritumoral regions. This radiomics model showed good performance and outperformed the clinical factors. Therefore, the combined model might assist in predicting perineural invasion status and improving prognosis of patients with rectal cancer.