• Yiqun Sun, Panpan Hu, Jiazhou Wang, Lijun Shen, Fan Xia, Gan Qing, Weigang Hu, Zhen Zhang, Chao Xin, Weijun Peng, Tong Tong, and Yajia Gu.
• Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
• J Magn Reson Imaging. 2018 Feb 13.

BackgroundRecent studies have shown that magnetic resonance (MR) radiomic analysis is feasible and has some value in identifying tumor characteristics, but there are few data regarding the role of MR-based radiomic features in rectal cancer.PurposeThe aim of this study was to determine whether radiomic features extracted from T2 -weighted imaging (T2 WI) can identify pathological features in rectal cancer.Study TypeRetrospective study.Population/SubjectsA cohort comprising 119 rectal cancer patients who underwent surgery between January 2015 and November 2016.Field Strength/Sequence3.0T, axial high-resolution T2 -weighted turbo spin echo (TSE) sequence.AssessmentPatients were classified according to pathological features such as T stage, N stage, perineural invasion, histological grade, lymph-vascular invasion, tumor deposits, and circumferential resection margin (CRM). The whole tumor volume (WTV) was distinguished, and segments were quantified on axial high-resolution T2 WI by a radiologist. A total of 256 radiomic features were extracted.Statistical TestsTo achieve reliable results, cluster analysis and least absolute shrinkage and selection operator (LASSO) were implemented. In the cluster analysis, the patients were divided into two groups, and chi-square tests were performed to investigate the relationship between the pathological features and the radiomic-based clusters. The area under the curve (AUC) was calculated to evaluate the predictability of the model in the LASSO analysis.ResultsThe cluster results revealed that patients could be stratified into two groups, and the chi-square test results indicated that the pT stage was correlated with the radiomic feature cluster results (P = 0.002). The prediction model AUC for the diagnostic T stage was 0.852 (95% confidence interval: 0.677-1; sensitivity: 79.0%, specificity: 82.0%).Data ConclusionThe use of MRI-derived radiomic features to identify the T stage is feasible in rectal cancer.Level Of Evidence3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.© 2018 International Society for Magnetic Resonance in Medicine.

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