Retrospective Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Sep 7, 2020; 26(33): 5008-5021
Published online Sep 7, 2020. doi: 10.3748/wjg.v26.i33.5008
Radiomics of rectal cancer for predicting distant metastasis and overall survival
Mou Li, Yu-Zhou Zhu, Yong-Chang Zhang, Yu-Feng Yue, Hao-Peng Yu, Bin Song
Mou Li, Yu-Feng Yue, Hao-Peng Yu, Bin Song, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
Yu-Zhou Zhu, Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Yong-Chang Zhang, Department of Radiology, Chengdu Seventh People’s Hospital, Chengdu 610213, Sichuan Province, China
Author contributions: All authors helped to perform the research; Song B and Zhu YZ designed the research; Li M and Zhang YC performed the research; Li M, Yue YF, Song B, and Yu HP analyzed the data; Li M and Song B wrote the paper.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of West China Hospital of Sichuan University.
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: All 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, 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
Received: May 27, 2020
Peer-review started: May 27, 2020
First decision: June 4, 2020
Revised: June 16, 2020
Accepted: August 13, 2020
Article in press: August 13, 2020
Published online: September 7, 2020
Processing time: 99 Days and 15.5 Hours
Abstract
BACKGROUND

Rectal cancer (RC) patient stratification by different factors may yield variable results. Therefore, more efficient prognostic biomarkers are needed for improved risk stratification, personalized treatment, and prognostication of RC patients.

AIM

To build a novel model for predicting the presence of distant metastases and 3-year overall survival (OS) in RC patients.

METHODS

This was a retrospective analysis of 148 patients (76 males and 72 females) with RC treated with curative resection, without neoadjuvant or postoperative chemoradiotherapy, between October 2012 and December 2015. These patients were allocated to a training or validation set, with a ratio of 7:3. Radiomic features were extracted from portal venous phase computed tomography (CT) images of RC. The least absolute shrinkage and selection operator regression analysis was used for feature selection. Multivariate logistic regression analysis was used to develop the radiomics signature (Rad-score) and the clinicoradiologic risk model (the combined model). Receiver operating characteristic curves were constructed to evaluate the diagnostic performance of the models for predicting distant metastasis of RC. The association of the combined model with 3-year OS was investigated by Kaplan-Meier survival analysis.

RESULTS

A total of 51 (34.5%) patients had distant metastases, while 26 (17.6%) patients died, and 122 (82.4%) patients lived at least 3 years post-surgery. The values of both the Rad-score (consisted of three selected features) and the combined model were significantly different between the distant metastasis group and the non-metastasis group (0.46 ± 0.21 vs 0.32 ± 0.24 for the Rad-score, and 0.60 ± 0.23 vs 0.28 ± 0.26 for the combined model; P < 0.001 for both models). Predictors contained in the combined model included the Rad-score, pathological N-stage, and T-stage. The addition of histologic grade to the model failed to show incremental prognostic value. The combined model showed good discrimination, with areas under the curve of 0.842 and 0.802 for the training set and validation set, respectively. For the survival analysis, the combined model was associated with an improved OS in the whole cohort and the respective subgroups.

CONCLUSION

This study presents a clinicoradiologic risk model, visualized in a nomogram, that can be used to facilitate individualized prediction of distant metastasis and 3-year OS in patients with RC.

Keywords: Radiomics; Rectal cancer; Overall survival; Distant metastasis; Computed tomography

Core tip: We developed and validated a combined model that incorporated radiomic features and clinical factors. This model showed excellent potential for predicting distant metastasis of rectal cancer (RC) within 3 years after surgery. We used this model to stratify the patients with RC into low-risk and high-risk groups for the survival analysis. Overall survival rates between the low-risk and high-risk groups were significantly different. This model may aid in individualized prediction of distant metastasis and 3-year overall survival in patients with RC.