Pathologic Nodal Upstaging Predictive Models for CT-based Clinical Node Negative Non-Small Cell Lung Cancer

Abstract

AbstractBackgroundAccurate clinical nodal staging of non-small cell lung cancer (NSCLC) is essential for surgical management. Some clinical node negative cases diagnosed preoperatively by CT were later staged as pathological N1 (pN1) or pN2. Our study aimed to evaluate factors related to pathological nodal upstaging and develop statistical models for predicting upstaging.MethodsWe retrospectively reviewed 1,735 patients with clinical node negative NSCLC from 2011 to 2016 in the West China Lung Cancer database. Demographic and clinical data were analyzed via univariate and multivariate approaches. Predictive models were developed on a training set and validated with independent datasets.Results171 (9.9%) clinical node negative patients have pathologic nodal upstaging to pN1. 191(11.0%) patients were upstaged to p(N1+N2). 91(5.2%) patients have pSN2 pathologic nodal upstaging. Preoperative factors were used to establish 3 statistical models for predicting pathological nodal upstaging. The area under the receiver operator characteristic (AUC) were 0.815, 0.768, and 0.726, for pN1, p(N1+N2) and pSN2 respectively.ConclusionOur models may help evaluate the possibility of nodal upstaging for clinical node negative NSCLC and enable surgeons to form appropriate plans preoperatively. External validation in a prospective multi-site study is needed before adoption into clinical practice.