Radiomics-based predictive risk score: A scoring system for preoperatively predicting risk of lymph node metastasis in patients with resectable non-small cell lung cancer

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, patients 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) of 0.711 (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 their LN status may provide a basis for individualized treatment.