Retrospective Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. May 21, 2020; 26(19): 2388-2402
Published online May 21, 2020. doi: 10.3748/wjg.v26.i19.2388
Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer
Zheng-Yan Li, Xiao-Dong Wang, Mou Li, Xi-Jiao Liu, Zheng Ye, Bin Song, Fang Yuan, Yuan Yuan, Chun-Chao Xia, Xin Zhang, Qian Li
Zheng-Yan Li, Mou Li, Xi-Jiao Liu, Zheng Ye, Bin Song, Fang Yuan, Yuan Yuan, Chun-Chao Xia, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
Xiao-Dong Wang, Qian Li, Department of Gastrointestinal Surgery, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
Xin Zhang, Life Science, PDx, IPM team, GE Healthcare, Shanghai 210000, China
Author contributions: All authors helped to perform the research; Li ZY wrote the manuscript and performed the procedures and data analysis; Li ZY and Wang XD conceived of and designed the study, and performed the experiments and data analysis; Song B contributed to writing of the manuscript and to conception and design of the study; Li M, Ye Z, Yuan F and Liu XJ contributed to writing of the manuscript; Yuan Y, Xia CC, Li Q and Zhang X performed the data collection and data analysis.
Supported by Research Grant of National Nature Science Foundation of China, No. 81971571; Multimodal MR Imaging and Radiomics of Rectal Cancer, Science and Technology Department of Sichuan Province, No. 2019YFS0431; and Sichuan University Training Program of Innovation and Entrepreneurship for Undergraduates, No. C2019104739.
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:
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.
Received: December 30, 2019
Peer-review started: December 30, 2019
First decision: February 16, 2020
Revised: March 27, 2020
Accepted: April 21, 2020
Article in press: April 21, 2020
Published online: May 21, 2020
Research background

Neoadjuvant chemotherapy is currently recommended as preoperative treatment for locally advanced rectal cancer (LARC); however, evaluation of treatment response to neoadjuvant chemotherapy is still challenging.

Research motivation

Several studies have reported that there were still 7-37% of LARC patients who do not respond to neoadjuvant CRT, which may not only increase CRT-related side effects and economic burden, but also delay surgery time. Therefore, it is necessary to identify which patients can benefit from neoadjuvant CRT treatment.

Research objectives

To create a multi-modal radiomics model to assess therapeutic response after neoadjuvant chemotherapy for LARC.

Research methods

This retrospective study consecutively included 118 patients with LARC who underwent both computed tomography (CT) and magnetic resonance imaging (MRI) before neoadjuvant chemotherapy between October 2016 and June 2019. Histopathological findings were used as the reference standard for pathological response. Patients were randomly divided into a training set (n = 70) and a validation set (n = 48). The performance of different models based on CT and MRI, including apparent diffusion coefficient (ADC), dynamic contrast enhanced T1 images (DCE-T1), high resolution T2-weighted imaging (HR-T2WI), and imaging features, was assessed by using the receiver operating characteristic curve (ROC) analysis and was demonstrated as area under the curve (AUC) and accuracy (ACC). Calibration plots with Hosmer-Lemeshow tests were used to investigate the agreement and performance characteristics of the nomogram.

Research results

Eighty of 118 patients (68%) achieved a pathological response. For an individual radiomics model, HR-T2WI performed better (AUC 0.859, ACC 0.896) than CT (AUC = 0.766, ACC = 0.792), DCE-T1 (AUC = 0.812, ACC = 0.854), and ADC (AUC = 0.828, ACC = 0.833) in the validation set. The imaging performance for extramural venous invasion (EMVI) detection was relatively low in both the training (AUC = 0.73, ACC = 0.714) and validation (AUC = 0.578, ACC = 0.583) sets. The multi-modal radiomics model reached an AUC of 0.925 and ACC of 0.886 in the training set, and an AUC of 0.93 and ACC of 0.875 in the validation set. For the clinical radiomics nomogram, good agreement was found between the nomogram prediction and actual observation.

Research conclusions

A multi-modal nomogram using traditional imaging features and radiomics of preoperative CT and MRI adds accuracy to the prediction of treatment outcome, and thus contributes to the personalized selection of neoadjuvant chemotherapy for LARC.

Research perspectives

Some biological characteristics such as overexpression of human epidermal growth factor receptor 2 and Ki-67 were reported to have good prediction of response to neoadjuvant chemotherapy; however, in our study, the above biological markers were not available in all included patients. Thus, the multi-modal nomogram combined with biological characteristics is desirable in the future.