A Novel Nomogram for Identifying Candidates for Adjuvant Chemotherapy in Patients With Stage IB Non-small Cell Lung Cancer.

Abstract

This study aimed to develop a novel predictive nomogram to identify specific stage IB non-small cell lung cancer (NSCLC) populations who could benefit from adjuvant chemotherapy (ACT). Stage IB NSCLC patients were included in the Surveillance, Epidemiology, and End Results (SEER) database and divided into the ACT and non-ACT groups. Then the methods of Kaplan-Meier analysis, propensity score matching (PSM), Least absolute shrink and selection operator (LASSO) regression, and multivariate logistic regression analyses were implemented. Finally, the predictive nomogram was constructed and validated. 9055 stage IB NSCLC patients were enrolled from the SEER database while 47 patients from Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University were identified as the external validation cohort. Of these patients, 1334 cases underwent ACT while the other 7721 patients didn't receive ACT. After PSM, the patients in the ACT group presented longer median overall survival (100 vs 82months, P < .001). Among the ACT group, 482 (49.6%) patients achieving more prolonged overall survival than 82months were regarded as the beneficiary population. Then the LASSO regression and multivariate logistic regression analyses were implemented. Finally, 8 predictors were selected for model construction, including age, gender, marital status, laterality, pathology, tumor size, regional nodes examined, and tumor size. The predictive nomogram demonstrated good discrimination in the training cohort (AUC = .781), internal validation cohort (AUC = .772), and external validation cohort (AUC = .851). And calibration curves indicated ideal consistency between the predicted and observed probabilities. Decision curve analysis presented a clinically useful model. The practical nomogram could guide treatment decision-making and select optimal ACT candidates among stage IB NSCLC patients.