MRI-Based Radiomics Predicts Tumor Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer - Yi, Xiaoping; Pei, Qian; Zhang, Youming; Zhu, Hong; Wang, Zhongjie; Chen, Chen; Li, Qingling; Long, Xueying; Tan, Fengbo; Zhou, Zhongyi; Liu, Wenxue; Li, Chenglong; et al. (Frontiers in Oncology, 2019)
Background: Conventional methods for predicting treatment response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) are limited. Methods: This study retrospectively recruited 134 LARC patients who underwent standard nCRT followed by total mesorectal excision surgery in our institution. Based on preoperative axial T2-weighted images, machine learning radiomics was performed. A receiver operating characteristic (ROC) curve was performed to test the efficiencies of the predictive model. Results: Among the 134 patients, 32 (23.9%) achieved pathological complete response (pCR), 69 (51.5%) achieved a good response, and 91 (67.9%) achieved down-staging. For prediction of pCR, good-response, and down-staging, the predictive model demonstrated high classification efficiencies, with an AUC value of 0.91(95% CI: 0.83 to 0.98), 0.90 (95% CI: 0.83 to 0.97) and 0.93(95% CI: 0.87 to 0.98), respectively. Conclusion: Our machine learning radiomics model showed promise for predicting response to nCRT in patients with LARC. Our predictive model based on the commonly used T2-weighted images on pelvic MRI scans has the potential to be adapted in clinical practice.