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Journal of Translational Medicine
Published in: Journal of Translational Medicine 1/2021

Open Access 01-12-2021 | Magnetic Resonance Imaging | Research

Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer

Authors: Zhuokai Zhuang, Zongchao Liu, Juan Li, Xiaolin Wang, Peiyi Xie, Fei Xiong, Jiancong Hu, Xiaochun Meng, Meijin Huang, Yanhong Deng, Ping Lan, Huichuan Yu, Yanxin Luo

Published in: Journal of Translational Medicine | Issue 1/2021

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Abstract

Background

We aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature.

Methods

This was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score.

Results

We developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990–1.000] and 0.822 [95% CI 0.649–0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did.

Conclusions

The CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making.
Appendix
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Metadata
Title
Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer
Authors
Zhuokai Zhuang
Zongchao Liu
Juan Li
Xiaolin Wang
Peiyi Xie
Fei Xiong
Jiancong Hu
Xiaochun Meng
Meijin Huang
Yanhong Deng
Ping Lan
Huichuan Yu
Yanxin Luo
Publication date
01-12-2021
Publisher
BioMed Central
Published in
Journal of Translational Medicine / Issue 1/2021
Electronic ISSN: 1479-5876
DOI
https://doi.org/10.1186/s12967-021-02919-x

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