Radiomic signature based on MR T2-weighted images for predicting KRAS mutation statue in rectal cancer
Li Dandan1, Cui Yanfen2, Yang Xiaotang2
1Department of Medical Imaging, Shanxi Medical University, Taiyuan 030000, China; 2Department of Radiology, Shanxi Province Tumor Hospital, Taiyuan 030000, China
Abstract：Objective To investigate the potential value of a radiomic signature based on T2-weighted images(T2WI) for predicting KRAS mutation statue in rectal cancer.Methods The clinical and imaging data of 304 patients with pathologically confirmed rectal cancers in the Shanxi Province Tumor Hospital from April 2017 to April 2019 were retrospectively analyzed (175 males and 129 females; median age, 59.6 years). All patients underwent KRAS mutation tests and pelvic MRI examination before operation. These patients were randomly divided into the primary cohort (n=213) and the validation cohort (n=91). T2WI images of each patient were selected for manual segmentation and radiomic feature extraction. Subsequently, univariate statistical tests were applied for feature selection in the primary cohort. The logistic regression (LR), decision tree (DT), and support vector machine (SVM) algorithms were applied to build a radiomic signature for KRAS prediction in the primary cohort. Meanwhile, the predictive performance of the radiomic signature was evaluated by receiver operating characteristics curve (ROC) analysis and then validated in the validation cohort. Finally, decision curve analysis (DCA) was performed to determine the clinical usefulness of the three radiomic models.Results No significant differences were found in the distribution of baseline data between the primary and validation cohorts and in the clinicopathological characteristics between the mutated and wild-type groups (all P values>0.05). A total of 960 features were extracted from the T2WI image of each patient. Seven radiomic features were selected after feature selection, which was significantly associated with KRAS mutations (all P values<0.05). The area under the ROC curve (AUC) values of the LR, DT, and SVM algorithms were 0.677, 0.604, and 0.722 in the primary cohort, respectively, and 0.626, 0.600, and 0.682 in the validation cohort, respectively. Among the three models, the SVM algorithm showed the best performance for evaluating KRAS mutation, which was validated in the validation cohort with AUCs, sensitivity, specificity, and accuracy of 0.682, 0.713, 0.655, and 0.681, respectively. The DCA curve shows that the three radiomic models have certain clinical benefits, among which the SVM prediction model has the largest net profit.Conclusions The radiomic signature based on T2WI exhibits potential for predicting KRAS mutation status in rectal cancer.
李丹丹, 崔艳芬, 杨晓棠. 基于MR T2加权成像的影像组学标签预测直肠癌KRAS基因突变的价值[J]. 中华解剖与临床杂志, 2021, 26(1): 7-14.
Li Dandan, Cui Yanfen, Yang Xiaotang. Radiomic signature based on MR T2-weighted images for predicting KRAS mutation statue in rectal cancer. Chinese Journal of Anatomy and Clinics, 2021, 26(1): 7-14.
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