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.