A Model Predicting Lymph Node Status for Patients with Clinical Stage T1aN0-2M0 Nonsmall Cell Lung Cancer

Abstract

Background: Lymph node status of patients with early-stage nonsmall cell lung cancer has an influence on the choice of surgery. To assess the lymph node status more correspondingly and accurately, we evaluated the relationship between the preoperative clinical variables and lymph node status and developed one model for predicting lymph node involvement. Methods: We collected clinical and dissected lymph node information of 474 patients with clinical stage T1aN0-2M0 nonsmall cell lung cancer (NSCLC). Logistic regression analysis of clinical characteristics was used to estimate independent predictors of lymph node metastasis. The prediction model was validated by another group. Results: Eighty-two patients were diagnosed with positive lymph nodes (17.3%), and four independent predictors of lymph node disease were identified: larger consolidation size (odds ratio [OR] = 2.356, 95% confidence interval [CI]: 1.517–3.658, P < 0.001,), central tumor location (OR = 2.810, 95% CI: 1.545–5.109, P = 0.001), abnormal status of tumor marker (OR = 3.190, 95% CI: 1.797–5.661, P < 0.001), and clinical N1–N2 stage (OR = 6.518, 95% CI: 3.242–11.697, P < 0.001). The model showed good calibration (Hosmer–Lemeshow goodness-of-fit, P < 0.766) with an area under the receiver operating characteristics curve (AUC) of 0.842 (95% [CI]: 0.797–0.886). For the validation group, the AUC was 0.810 (95% CI: 0.731–0.889). Conclusions: The model can assess the lymph node status of patients with clinical stage T1aN0-2M0 NSCLC, enable surgeons perform an individualized prediction preoperatively, and assist the clinical decision-making procedure.

Keywords

• Carcinoma; Diagnosis; Lymph Nodes; Nonsmall Cell Lung Cancer; Predictive Models