Development of a Nomogram Combining Clinical Risk Factors and Dual-Energy Spectral CT Parameters for the Preoperative Prediction of Lymph Node Metastasis in Patients With Colorectal Cancer - Cao, Yuntai; Zhang, Jing; Bao, Haihua; Zhang, Guojin; Yan, Xiaohong; Wang, Zhan; Ren, Jialiang; Chai, Yanjun; Zhao, Zhiyong; Zhou, Junlin (Frontiers in Oncology, 2021)
Objective: To develop a dual-energy spectral CT (DESCT) nomogram that incorporated both clinical factors and DESCT parameters for the individual preoperative prediction of lymph node metastasis (LNM) in patients with colorectal cancer (CRC). Material and Methods: We retrospectively reviewed 167 pathologically confirmed CRC patients who underwent enhanced DESCT pre-operatively, and this patients were categorised into training (n = 117) and validation cohorts (n = 50). The monochromatic CT value, iodine concentration value (IC), and effective atomic number (Eff-Z) of the primary tumors were measured independently by two radiologists in the arterial phase (AP) and venous phase (VP). DESCT parameters together with clinical factors were entered into the prediction model for predicting LNM in CRC patients. Logistic regression analyses were used to screen for significant predictors of LNM and these predictors were presented as an easy-to-use nomogram. The receiver operating characteristic curve and decision curve analysis (DCA) was used to evaluate the clinical usefulness of the nomogram. Results: The logistic regression analysis showed that carcinoembryonic antigen, carbohydrate antigen 199, pericolorectal fat invasion, ICAP, ICVP and Eff-ZVP were independent predictors in the predictive model. Based on these predictors, a quantitative nomogram was developed to predict individual LNM probability. The AUC values of the nomogram were 0.876 in the training cohort and 0.852 in the validation cohort. DCA showed that our nomogram has outstanding clinical utility. Conclusions: This study presents a clinical nomogram that incorporates clinical factors and DESCT parameters, which can potentially be used as a clinical tool for individual preoperative prediction of LNM in CRC patients.