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

Journal of Translational Medicine

Zongchao Liu¹  Yanxin Luo²  Juan Li³  Jiancong Hu³  Ping Lan³  Meijin Huang³  Zhuokai Zhuang³  Xiaochun Meng⁴  Fei Xiong⁴  Peiyi Xie⁴  Huichuan Yu⁵  Xiaolin Wang⁵  Yanhong Deng⁶ 

[1] Department of Biostatistics, Columbia University, 10032, New York, NY, USA;Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, 510655, Guangzhou, Guangdong, China;Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, 510655, Guangzhou, Guangdong, China;Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, 510655, Guangzhou, Guangdong, China;Department of Radiology, Sixth Affiliated Hospital of Sun Yat-sen University, 510655, Guangzhou, Guangdong, China;Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, 510655, Guangzhou, Guangdong, China;Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, 510655, Guangzhou, Guangdong, China;Department of Medical Oncology, Sixth Affiliated Hospital, Sun Yat-sen University, 510655, Guangzhou, Guangdong, China;

关键词: Radiomics;    Computed tomography;    Neoadjuvant treatment;    Rectal cancer;   

DOI  :  10.1186/s12967-021-02919-x

来源: Springer

【 摘 要 】

BackgroundWe 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.MethodsThis 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.ResultsWe 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.ConclusionsThe 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.

【 授权许可】

CC BY