Predictive of a radiomic model based on enhanced CT portal vein images for predicting neoadjuvant chemotherapy in patients with locally advanced colorectal cancer
Xu Ruxin1, Cui Yanfen2, Yang Xiaotang2
1 Department of Medical Imaging, Shanxi Medical University, Taiyuan 030000, China; 2 Department of Radiology, Shanxi Province Tumor Hospital, Taiyuan 030000, China
Abstract：Objective This study aimed to investigate the potential value of a radiomic model based on computed tomography (CT) for predicting neoadjuvant chemotherapy (NAC) in patients with locally advanced colorectal cancer (LACRC). Methods The clinical and CT imaging data of 181 patients (96 males and 85 females, 23-85 years old) with colorectal adenocarcinoma who underwent preoperative NAC followed by surgery in Shanxi Province Tumor Hospital from January 2014 to September 2019 were retrospectively analyzed. Using a random method, 127 patients were classified into training cohort, and 54 patients were classified into validation cohort at a ratio of 7:3. These patients were divided into the good response cohort (0-1 grade, 81 patients) and non-good response cohort (2-3 grade, 100 patients) in accordance with the tumor regression grade (TRG) standard. All patients underwent enhanced CT examination before treatment. A total of 1037 imaging features were extracted from portal venous-phase CT images, and four steps, particularly the least absolute shrinkage and selection operator, were applied for feature extraction. Subsequently, the selected features were used to construct a radiomic model by using multivariate logistic regression. Then, clinicopathological independent risk factors were selected by using univariate and multivariate logistic regression and were used to construct a clinical model. Finally, the combined model integrating the radiomic signature and clinicopathological independent risk factors and the corresponding nomogram were constructed. Respectively, the predictive and calibration performances of the three models were evaluated by analyzing the receiver operating characteristic (ROC) curve and calibration curve analysis (CCA). Finally, decision curve analysis (DCA) was used to determine the clinical importance of the three models. Results No statistically significant differences were found in gender, clinical T stage, degree of N-stage pathological differentiation, and TRG were found between the training cohort and validation cohort (all P values>0.05). However, age showed statistically significant differences (Z=-3.47, P<0.05). In the training cohort, gender, clinical T stage, N stage, and degree of pathological differentiation between patients in the good response cohort (57 patients) and non-good response cohort (70 patients) were statistically significant (all P values<0.05). In the validation cohort, clinical T stage and N stage between patients in the good response cohort (24 patients) and non-good response cohort (30 patients) were statistically significant (all P values<0.05). Four key radiomic features derived from portal venous-phase CT images were selected for constructing the radiomic model. The clinical model included two independent risk factors, clinical T stage and pathological differentiation. The area under the ROC curve of the radiomic model, clinical model, and combined model was 0.822, 0.702, and 0.850 in the training cohort and 0.757, 0.706, and 0.824 in the validation cohort, respectively. The CCA showed that the radiomic model and the clinical model had good calibration. The DCA showed that the three predictive models had certain clinical importance, among which the combined model had the largest net profit. Conclusion The combined model integrating the radiomic signature based on contrast-enhanced CT and clinicopathological independent risk factors exhibit a potential value for predicting NAC outcomes in LACRC.
许汝鑫, 崔艳芬, 杨晓棠. 基于增强CT门静脉期影像组学模型对局部进展期结直肠癌新辅助化疗疗效的预测研究[J]. 中华解剖与临床杂志, 2022, 27(7): 449-457.
Xu Ruxin, Cui Yanfen, Yang Xiaotang. Predictive of a radiomic model based on enhanced CT portal vein images for predicting neoadjuvant chemotherapy in patients with locally advanced colorectal cancer. Chinese Journal of Anatomy and Clinics, 2022, 27(7): 449-457.
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2022, Vol. 27 
