Background and purpose: Accurate preoperative prediction of lymph node metastasis (LNM) is very important for the prognosis and recurrence of patients with colorectal cancer (CRC). The purpose of our study was to develop and validate a clinical-radiomics nomogram for preoperative prediction of LNM for patients with CRC. Methods: We enrolled 767 patients treated in Fudan University Shanghai Cancer Center (537 in the primary cohort and 230 in the validation cohort) with clinicopathologically confirmed CRC. We included nine significant clinical risk factors ［age

gender

preoperative carcinoembryonic antigen (CEA) level

preoperative carbohydrate antigen 19-9 (CA19-9) level

grade

histological type

tumor location

tumor size and M stage］ to build the clinical model. We used ANOVA

Relief and recursive feature elimination (RFE) for feature selection (including clinical risk factors

imaging features of primary lesions and peripheral lymph nodes)

established the classification models through logistic regression analysis and selected respective optimal models by one-standard-error rule. Then we combined the clinical risk factors

the primary lesion radiomics features and the peripheral lymph node radiomics features of the optimal models to establish combined prediction models. The performance of the model was assessed by area under curve (AUC) of the receiver operating characteristic (ROC). Finally

decision curve analysis (DCA) and nomogram were applied to assess the clinical usefulness. Results: The clinical-primary lesion radiomics-peripheral lymph node radiomics model with the highest AUC (0.743 0) was identified as the best model. This optimal clinical-radiomics model also showed good discrimination and calibration in both primary cohort and validation cohort. DCA demonstrated that the clinical-radiomics nomogram was useful for preoperative prediction in clinical practice. Conclusion: The present study proposed a clinical-radiomics nomogram created by the radiomics signature and clinical risk factors

which can be potentially applied in the individual preoperative prediction of LNM in patients with CRC.