Oncology & Cancer Case Reports
Open Access
ISSN: 2471-8556
ISSN: 2471-8556
Ruochuan Zang
Peking Union Medical College, China
Posters & Accepted Abstracts: Oncol Cancer Case Rep
Objective: To evaluate the relationship between the clinical variables and lymph node disease, and develop the predictive model for lymph node involvement. Methods: We reviewed the clinical information of 474 patients with clinical stage T1aN0-2M0 NSCLC. Logistic regression analysis of the clinical characteristics was used to estimate the independent predictors of lymph node metastasis. A prediction model was developed and validated. Results: 82 patients were diagnosed with positive lymph nodes (17.3%) and 4 independent predictors of lymph node disease were identified: central tumor location, abnormal status of tumor-marker, consolidation size, and clinical N1-2 stage(P<0.05). The model showed good calibration (Hosmer-Lemeshow goodness fit, P=0.766) with an area under the receiver operating characteristics curve (AUC) of 0.842 (95% confidence interval 0.797-0.886). For the validation group, the AUC was 0.810 (95% confidence interval 0.731-0.889). Conclusions: The predictive model can assess the probability of lymph node involvement for patients with clinical stage T1aN0-2M0 NSCLC, enable clinicians perform an individualized prediction preoperatively and assist the clinical decisionmaking procedure.