Objective:To develop and validate a radiomics-based predictive risk score (RPRS) for preoperative prediction of lymph node (LN) metastasis in patients with resectable non-small cell lung cancer (NSCLC).Methods:We retrospectively analyzed 717 who underwent surgical resection for primary NSCLC with systematic mediastinal lymphadenectomy from October 2007 to July 2016.By using the method of radiomics analysis,591 computed tomography (CT)-based radiomics features were extracted,and the radiomics-based classifier was constructed.Then,using multivariable logistic regression analysis,a weighted score RPRS was derived to identify LN metastasis.Apparent prediction performance of RPRS was assessed with its calibration,discrimination,and clinical usefulness.Results:The radiomics-based classifier was constructed,which consisted of 13 selected radiomics features.Multivariate models demonstrated that radiomics-based classifier,age group,tumor diameter,tumor location,and CT-based LN status were independent predictors.When we assigned the corresponding score to each variable,parents with RPRSs of 0-3,4-5,6,7-8,and 9 had distinctly very low (0％-20％),low (21％-40％),intermediate (41％-60％),high (61％-80％),and very high (81％-100％) risks of LN involvement,respectively.The developed RPRS showed good discrimination and satisfactory calibration [C-index:0.785,95％ confidence interval (95％ CI):0.780-0.790].Additionally,RPRS outperformed the clinicopathologic-based characteristics model with net reclassification index (NRI) of0.711 (95％ CI:0.555-0.867).Conclusions:The novel clinical scoring system developed as RPRS can serve as an easy-to-use tool to facilitate the preoperatively individualized prediction of LN metastasis in patients with resectable NSCLC.This stratification of patients according to dheir LN status may provide a basis for individualized treatment.