Dynamic nomograms combining N classification with ratio-based nodal classifications to predict long-term survival for patients with lung adenocarcinoma after surgery: a SEER population-based study

A survival nomogram model for patients with resectable non-small cell lung cancer and lymph node metastasis (N1 or N2) based on the Surveillance, Epidemiology, and End Results Database and single-center data

Background

The ability to predict survival in patients with lymph node metastasis has long been elusive. After surgery, the basis for decision-making on the combination treatment of patients is not clear. The purpose of this study was thus to build a survival nomogram model to effectively predict the overall survival (OS) of patients with non-small cell lung cancer (NSCLC) and lymph node metastasis. The number of dissected lymph nodes (NDLN), number of positive lymph nodes (NPLN), lymph node ratio (LNR), and log odds of positive lymph nodes (LODDS) were included in this study to determine the risk factors in patients with advanced NSCLC.

Methods

The data of 5,132 patients with NSCLC and lymph node metastasis (N1 or N2) were extracted from the Surveillance, Epidemiology, and End Results (SEER) database according to inclusion and exclusion criteria and used as the training cohort. We enrolled 117 patients from the First Affiliated Hospital, Zhejiang University School of Medicine as the external validation cohort. Receiver operating characteristic (ROC) analyses were performed to determine the best cutoff values for predicting the prognosis of patients with NSCLC. Based on the risk factors affecting prognosis, a nomogram was constructed using univariate and multivariate Cox proportional hazard regression models. The discrimination ability of the nomogram was evaluated with the concordance index (C-index) and calibration curves. For the independent risk factors, survival curves were drawn using Kaplan-Meier analysis.

Results

ROC curve analysis showed that the optimal NPLN cut-off value was 4, LNR was 0.26, and LODDS was -0.25, respectively. However, LNR was nonsignificant in multivariate analysis, with a P value of 0.274. The novel survival nomogram model included seven independent risk factors, among which were NPLN, LODDS, and chemotherapy. Model 4, which included N stage, NPLN, and LODDS, had a higher likelihood ratio (LR) and C-index than did the other models. The C-index was 0.648 [95% confidence interval (CI): 0.636-0.659] in the training cohort and 0.807 (95% CI: 0.751-0.863) in the external validation cohort, showing good prognostic accuracy and discrimination ability. According to the median risk score, the patients in the training cohort and external validation cohort were divided into high-risk and low-risk groups, between which significant differences in OS were found. In the training cohort, age, sex, T stage, N stage, NPLN, LODDS, and chemotherapy were significantly associated with OS (P<0.001). In the external validation cohort, T stage, NPLN, LODDS, and chemotherapy were found to be correlated with OS.

Conclusions

The NPLN and LODDS nomogram is an accurate survival prediction tool for patients with N1 or N2 NSCLC. Patients with lymph node metastasis can benefit from chemotherapy, but no evidence shows that radiotherapy is necessary for patients with resectable NSCLC.

Development and validation of a nomogram for predicting overall survival of resected N2 non-small cell lung cancer patients undergoing neoadjuvant radiotherapy

INTRODUCTION

Currently, the prognosis of resected N2 non-small cell lung cancer patients undergoing neoadjuvant radiotherapy is poor. The goal of this research was to develop and validate a novel nomogram for exactly predicting the overall survival (OS) of resected N2 NSCLC patients undergoing neoadjuvant radiotherapy.

METHODS

The data applied in our research were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. We divided selected data into a training cohort and a validation cohort using R software, with a ratio of 7:3. Univariate Cox regression and multivariate Cox regression were utilized to select significant variables to build the nomogram. To validate our nomogram, calibration curves, receiver operating characteristic curves (ROC), decision curve analysis (DCA), and Kaplan-Meier survival curves were employed. The nomogram model was also compared with the tumor-node-metastasis (TNM) staging system by utilizing net reclassification index (NRI) and integrated discrimination improvement (IDI).

RESULTS

Eight variables-age, sex, operative type, LN removed number, chemotherapy, AJCC stage, M stage, histology-were statistically significant in the multivariate Cox regression analysis and were selected to develop our nomogram. Based on ROC curves, calibration curves, and DCA analysis, our novel nomogram demonstrated good predictive accuracy and clinical utility. Using Kaplan-Meier (KM) survival curves and log-rank tests, the risk stratification system was able to stratify patients based on their estimated mortality risk. The nomogram performed better than the TNM staging system based on the NRI and IDI indexes.

CONCLUSIONS

We developed and validated a nomogram to predict prognosis of resected N2 NSCLC patients undergoing neoadjuvant radiotherapy. Using this nomogram, clinicians may find this nomogram useful in predicting OS of targeted patients and making more appropriate treatment decisions.

Construction and validation of nomograms based on the log odds of positive lymph nodes to predict the prognosis of lung neuroendocrine tumors

Background

This research aimed to investigate the predictive performance of log odds of positive lymph nodes (LODDS) for the long-term prognosis of patients with node-positive lung neuroendocrine tumors (LNETs).

Methods

We collected 506 eligible patients with resected N1/N2 classification LNETs from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. The study cohort was split into derivation cohort (n=300) and external validation cohort (n=206) based on different geographic regions. Nomograms were constructed based on the derivation cohort and validated using the external validation cohort to predict the 1-, 3-, and 5-year cancer-specific survival (CSS) and overall survival (OS) of patients with LNETs. The accuracy and clinical practicability of nomograms were tested by Harrell’s concordance index (C-index), integrated discrimination improvement (IDI), net reclassification improvement (NRI), calibration plots, and decision curve analyses.

Results

The Cox proportional-hazards model showed the high LODDS group (-0.79≤LODDS) had significantly higher mortality compared to those in the low LODDS group (LODDS<-0.79) for both CSS and OS. In addition, age at diagnosis, sex, histotype, type of surgery, radiotherapy, and chemotherapy were also chosen as predictors in Cox regression analyses using stepwise Akaike information criterion method and included in the nomograms. The values of C-index, NRI, and IDI proved that the established nomograms were better than the conventional eighth edition of the TNM staging system. The calibration plots for predictions of the 1-, 3-, and 5-year CSS/OS were in excellent agreement. Decision curve analyses showed that the nomograms had value in terms of clinical application.

Conclusions

We created visualized nomograms for CSS and OS of LNET patients, facilitating clinicians to bring individually tailored risk assessment and therapy.

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

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09908-3.

Role of Pneumonectomy in T1–4N2M0 Non-Small Cell Lung Cancer: A Propensity Score Matching Analysis

Background

N2 stage disease constitutes approximately 20%–30% of all non-small cell lung cancer (NSCLC). Concurrently, surgery remains the first-choice treatment for patients with N2 NSCLC if feasible. However, the role of pneumonectomy in N2 NSCLC has rarely been investigated and remains controversial.

Methods

We enrolled 26,798 patients with T1–4N2M0 NSCLC (stage IIIA/IIIB) from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. We compared the overall survival (OS) and cancer-specific survival (CSS) between patients who received pneumonectomy and those who did not receive surgery. The Kaplan–Meier method, Cox regression analyses, and propensity score matching (PSM) were applied to demonstrate the effect of pneumonectomy.

Results

Patients receiving pneumonectomy had a significantly better OS and CSS than those without pneumonectomy both before [adjusted-HR (95% CI): 0.461 (0.425–0.501) for OS, 0.444 (0.406–0.485) for CSS] and after PSM [adjusted-HR (95% CI): 0.499 (0.445–0.560) for OS, 0.457 (0.405–0.517) for CSS] with all p-values <0.001. Subgroup analysis demonstrated concordant results stratified by demographic or clinicopathological variables. In sensitivity analysis, no significant difference was observed between patients receiving single pneumonectomy and chemoradiotherapy without surgery in OS and CSS both before [unadjusted-HR (95% CI): 1.016 (0.878–1.176) for OS, 0.934 (0.794–1.099) for CSS, p = 0.832] and after PSM [unadjusted-HR (95% CI): 0.988 (0.799–1.222) for OS, 0.938 (0.744–1.182) for CSS] with all p-values >0.4.

Conclusion

For patients with T1–4N2M0 NSCLC (stage IIIA/IIIB), pneumonectomy is an independent protective factor of OS and should be considered when applicable.