BMC cancer · 2022
Evaluation of log odds of positive lymph nodes in predicting the survival of patients with non-small cell lung cancer treated with neoadjuvant therapy and surgery: a SEER cohort-based study.
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Wang Qing, Wang Suyu, Sun Zhiyong, Cao Min, Zhao Xiaojing
Аннотация
BACKGROUND: Log odds of positive lymph nodes (LODDS) is a novel lymph node (LN) descriptor that demonstrates promising prognostic value in many tumors. However, there is limited information regarding LODDS in patients with non-small cell lung cancer (NSCLC), especially those receiving neoadjuvant therapy followed by lung surgery.
METHODS: A total of 2059 patients with NSCLC who received neoadjuvant therapy and surgery were identified from the Surveillance, Epidemiology, and End Results (SEER) database. We used the X-tile software to calculate the LODDS cutoff value. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve analysis were performed to compare predictive values of the American Joint Committee on Cancer (AJCC) N staging descriptor and LODDS. Univariate and multivariate Cox regression and inverse probability of treatment weighting (IPTW) analyses were conducted to construct a model for predicting prognosis.
RESULTS: According to the survival analysis, LODDS had better differentiating ability than the N staging descriptor (log-rank test, P < 0.0001 vs. P = 0.031). The ROC curve demonstrated that the AUC of LODDS was significantly higher than that of the N staging descriptor in the 1-, 3-, and 5-year survival analyses (all P < 0.05). Univariate and multivariate Cox regression analyses showed that LODDS was an independent risk factor for patients with NSCLC receiving neoadjuvant therapy followed by surgery both before and after IPTW (all P < 0.001). A clinicopathological model with LODDS, age, sex, T stage, and radiotherapy could better predict prognosis.
CONCLUSIONS: Compared with the AJCC N staging descriptor, LODDS exhibited better predictive ability for patients with NSCLC receiving neoadjuvant therapy followed by surgery. A multivariate clinicopathological model with LODDS demonstrated a sound performance in predicting prognosis.
METHODS: A total of 2059 patients with NSCLC who received neoadjuvant therapy and surgery were identified from the Surveillance, Epidemiology, and End Results (SEER) database. We used the X-tile software to calculate the LODDS cutoff value. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve analysis were performed to compare predictive values of the American Joint Committee on Cancer (AJCC) N staging descriptor and LODDS. Univariate and multivariate Cox regression and inverse probability of treatment weighting (IPTW) analyses were conducted to construct a model for predicting prognosis.
RESULTS: According to the survival analysis, LODDS had better differentiating ability than the N staging descriptor (log-rank test, P < 0.0001 vs. P = 0.031). The ROC curve demonstrated that the AUC of LODDS was significantly higher than that of the N staging descriptor in the 1-, 3-, and 5-year survival analyses (all P < 0.05). Univariate and multivariate Cox regression analyses showed that LODDS was an independent risk factor for patients with NSCLC receiving neoadjuvant therapy followed by surgery both before and after IPTW (all P < 0.001). A clinicopathological model with LODDS, age, sex, T stage, and radiotherapy could better predict prognosis.
CONCLUSIONS: Compared with the AJCC N staging descriptor, LODDS exhibited better predictive ability for patients with NSCLC receiving neoadjuvant therapy followed by surgery. A multivariate clinicopathological model with LODDS demonstrated a sound performance in predicting prognosis.