Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings

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 ra...

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Bibliographic Details
Published in:Journal of magnetic resonance imaging 2018-09, Vol.48 (3), p.615-621
Main 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
Format: Article
Language:English
Subjects:
Cancer
Cluster analysis
Colorectal cancer
Confidence intervals
Diagnostic systems
feasibility
Feasibility studies
Feature extraction
Field strength
Magnetic resonance imaging
Mathematical models
Medical imaging
Patients
Population (statistical)
Population studies
Radiomics
rectal cancer
Rectum
Statistical analysis
Statistical tests
Surgery
Tumors
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Summary: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 (T2WI) 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 T2WI 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.
ISSN:1053-1807
1522-2586
DOI:10.1002/jmri.25969