Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings. Issue 3 (13th February 2018)

• Record Type: Journal Article
• Authors: Sun, Yiqun; Hu, Panpan; Wang, Jiazhou; Shen, Lijun; Xia, Fan; Qing, Gan; Hu, Weigang; Zhang, Zhen; Xin, Chao; Peng, Weijun; Tong, Tong; Gu, Yajia
• Is Part Of: Journal of magnetic resonance imaging. Volume 48:Issue 3(2018)
• Page Start: 615
• Page End: 621
• Publication Date: 2018-02-13
• Subjects: rectal cancer -- magnetic resonance imaging -- radiomics -- feasibility; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.25969
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: Recent 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. Purpose: The 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 Type: Retrospective study. Population/Subjects: A cohort comprising 119 rectal cancer patients who underwent surgery between January 2015 and November 2016. Field Strength/Sequence: 3.0T, axial high‐resolution T2 ‐weighted turbo spin echo (TSE) sequence. Assessment: Patients 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 Tests: To 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. Results: The 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 Conclusion: The use of MRI‐derived radiomic features to identify the T stage is feasible in rectal cancer. Level of Evidence : 3 Technical Efficacy : Stage 2 J. Magn. Reson. Imaging 2018;48:615–621.