MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer

// Yankai Meng 1 , Chongda Zhang 1 , Shuangmei Zou 2 , Xinming Zhao 1 , Kai Xu 3 , Hongmei Zhang 1 and Chunwu Zhou 1 1 Department of Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China 2 Department of Pathology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China 3 Department of Radiology, The Affliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China Correspondence to: Chunwu Zhou, email: cjr.zhouchunwu@vip.163.com Hongmei Zhang, email: zhanghongmei1973@163.com Keywords: rectal cancer; texture analysis; magnetic resonance imaging; response assessment; neoadjuvant chemoradiotherapy Received: July 26, 2017      Accepted: October 28, 2017      Published: December 22, 2017 ABSTRACT To evaluate the importance of MRI texture analysis in prediction and early assessment of treatment response before and early neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). This retrospective study comprised of 59 patients. The tumoral texture parameters were compared between pre- and early nCRT. Area Under receiver operating characteristic (ROC) Curves [AUCs] were used to compare the diagnostic performance of statistically significant difference parameters and logistic regression analysis predicted probabilities for discriminating responders and nonresponders. The Standard Deviation (SD), kurtosis and uniformity were statistically significantly difference between pre- and early nCRT ( p = 0.0012, 0.0001, and < 0.0001, respectively). In pathological complete response (pCR) group, pre-uniformity and pre-Energy were significantly higher than that of nonresponders ( p = 0.03 and p < 0.01, respectively), while the pre-entropy in nonresponder was reverse ( p = 0.01). The diagnostic performance of pre-kurtosis and pre-Energy were higher in tumor regression grade (TRG) and pCR group (AUC = 0.67, 0.73, respectively). Logistic regression analysis showed that diagnostic performance for prediction responder and nonresponder did not significantly improve compared with to pre-uniformity, energy and entropy in pCR group (AUC = 0.76, p = 0.2794, 0.4222 and 0.3512, respectively). Texture parameters as imaging biomarkers have the potential to prediction and early assessment of tumoral treatment response to neoadjuvant chemoradiotherapy in patients with LARC.