Prognostic indicators and nomograms for postoperative survival among younger patients with non-small cell lung cancer

Introduction

Lung cancer continues to be the leading cause of cancer-related death worldwide (1). The National Cancer Center reported that about 828,100 new lung cancer cases and 657,000 new lung cancer deaths occurred in China in 2016 (2). Although the lifetime probability of being diagnosed with lung cancer is significantly higher for both male and females aged 50 years and older, 5–10% of lung cancer cases are diagnosed in younger patients (<50 years of age) (3).

Previous studies have primarily focused on comparisons between younger and older patients to investigate the survival outcome in younger adults with non-small cell lung cancer (NSCLC), with prognosis among younger patients differing from that of their older counterparts (4,5). However, the prognostic values of various clinical parameters for long-term postoperative survival in younger patients surgically treated for NSCLC have thus far not been clarified. Consequently, it is necessary to conduct comprehensive population-based research on the various prognostic indicators and develop nomograms to predict the long-term survival for younger patients with NSCLC treated with surgery.

In this study, we analyzed the surgical outcomes of younger patients with NSCLC who underwent surgery to investigate the prognostic values of various clinical factors associated with prolonged survival via data extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Moreover, we established a simple nomogram for these patients to accurately predict their survival prognosis, which can be facilitate clinicians to identify high-risk patients, guiding treatment strategies. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-348/rc).

Methods

Data source

We used 17 registries from the SEER cancer incidence database, spanning from 2000 to 2020, which was submitted to the SEER database in November 2022 and released in April 2023, to identify pathologically proven lung cancer cases aged 18 to 49 years. Cases of lung cancers with the site codes C34.0–C34.9 were extracted from the SEER database. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Patient population and data collection

Figure 1 presents the detailed inclusion and exclusion criteria. The stages of all the enrolled patients were reclassified according to the eighth edition of the American Joint Committee on Cancer Staging System.

Figure 1 Study flowchart of patient inclusion. SEER, Surveillance, Epidemiology, and End Results; TNM, tumor, node, and metastasis.

The collected data from the database included the following: sociodemographic details (age, sex, race, marital status, and median household income), tumor characteristics [primary site, laterality, histology, grade, size, and tumor, node, metastasis (TNM) stage], treatment specifics (surgical approaches and adjuvant therapy), and survival information (status, months, and cause of death).

Nomogram development and assessment

All included cases were randomly divided into training and validation cohorts at a ratio of 3:1. Following the identification of independent prognostic factors through multivariable logistic regression analysis, a comprehensive nomogram for survival outcome was developed, which could detail the predictive probabilities of overall survival (OS) and lung cancer-specific survival (LCSS) at 1-, 3-, and 5-year intervals for all patients. Subsequently, receiver operating characteristic (ROC) curves were constructed to assess the discriminatory performance of the nomogram model, with the area under the curve (AUC) serving as a metric for evaluation. Additionally, calibration curves were employed to gauge the accuracy and reliability of the nomograms.

Statistical analyses

Continuous and categorical data were summarized as the mean ± standard deviation (SD) and numbers with percentages, respectively. OS was defined as the time from the first diagnosis to death from any causes, and patients alive at the time of last record were defined as censored. LCSS was defined as the time from diagnosis date to death date caused by lung cancer, and patients were censored if died of other causes. OS and LCSS were estimated by Kaplan-Meier method, and log-rank tests were used for univariate analysis. Multivariate analyses were performed by Cox proportional hazard models to identify the prognostic parameters associated with survival. All statistical tests were two-sided, and a P value less than 0.05 was considered statistically significant. The statistical analyses were performed with R version 4.3.1 (The R Project for Statistical Computing, Vienna, Austria, https://www.r-project.org).

Results

Patient demographic and baseline characteristics

A total of 2,584 younger patients surgically treated for NSCLC who met the inclusion criteria were identified; their demographic and baseline characteristics are summarized in Table 1. All patients were randomly divided into a training cohort (n=1,938) and a validation cohort (n=646), with no statistically significant difference observed between the two cohorts.

Survival outcomes and predictors

The median follow-up period for all the enrolled patients was 54.0 months (range, 3.0–131.0 months). By the end of the follow-up period, there were 483 patients (18.7%) who were dead including 382 (14.8%) patients who died of lung cancer and 99 (3.9%) from other causes. The 5-year OS and LCSS rate for all the enrolled younger patients who were surgically treated were 84.3% and 87.0%, respectively, as shown in Figure 2A,2B). The 5-year OS and LCSS were 93.3% and 95.5% for stage I, 80.4% and 84.1% for stage II, 65.0% and 67.8% for stage III, and 46.3% and 50.0% for stage IV, respectively (Figure 3A,3B).

Figure 2 Kaplan-Meier estimates of (A) overall survival and (B) lung cancer-specific survival in younger patients.

Figure 3 Kaplan-Meier estimates of (A) overall survival and (B) lung cancer-specific survival in younger patients at each stage. TNM, tumor, node, and metastasis.

Results of the univariate and multivariate survival analyses for the cohort are presented in Table 2. The multivariate analysis indicated that age, gender, histology, T stage, TNM stage, postoperative radiation, and postoperative chemotherapy were independent prognosticators for the survival among younger patients who had undergone surgical treatment.

Nomogram evaluation

Independent prognostic factors identified by multivariate logistic analysis were subsequently used to develop nomograms for predicting OS and LCSS (Figure 4A,4B). As shown in Figure 4, histology was the most influential factor affecting long-term survival after surgery, followed by TNM stage, age, T stage, postoperative radiation, sex, and postoperative chemotherapy.

Figure 4 Nomograms for predicting (A) overall survival and (B) lung cancer-specific survival in younger patients treated surgically. TNM, tumor, node, and metastasis; OS, overall survival; LCSS, lung cancer-specific survival.

ROC analysis was performed in both the training cohort and validation cohort for validation of the nomogram performance. The AUC values for 1-, 3-, and 5-year OS were 0.829, 0.820, and 0.816 in the training cohort, and 0.849, 0.833, and 0.811, in the validation cohort, respectively (Figure 5A,5B). Calibration plots of the training and validation cohorts are shown in Figure 5C. The AUC values for 1-, 3-, and 5-year LCSS were 0.892, 0.852, and 0.845 in the training cohort, and 0.878, 0.861, and 0.848, in the validation cohort, respectively (Figure 5D,5E). Calibration plots are shown in Figure 5F.

Figure 5 ROC curves for the nomograms in predicting overall survival and lung cancer-specific survival in (A,D) the training cohort and (B,E) validation cohort. Calibration curves for the nomograms in predicting (C) overall survival and (F) lung cancer-specific survival. AUC, area under the curve; CI, confidence interval; OS, overall survival; LCSS, lung cancer-specific survival; ROC, receiver operating characteristic.

Discussion

In this study, we identified age, gender, histology, T stage, TNM stage, and postoperative therapies as significant prognostic indicators among younger NSCLC patients who had undergone surgical intervention. Furthermore, the nomograms developed in this study demonstrated capability to differentiate high-risk patients with a simple formula, which may offer insights into clinical decision-making for post-surgical treatment.

Previous studies have highlighted significant sex-associated variations in the survival outcomes of patients who have undergone surgical resection for NSCLC, with the female sex often being associated with a favorable prognosis, which aligns with our findings (6,7). We observed a female-to-male ratio of 1.69 for lung cancer in this study, suggesting a higher incidence rate of lung cancer among women, which diverges significantly from most previous studies (8,9). However, few studies have also highlighted this phenomenon (10,11). The higher susceptibility to lung cancer among young women might be related to germline gene variants, since most young women patients were never smoker (11).

One reason for the younger lung cancer patients with fair prognosis in this study is that about 1/3 of the included patients were with carcinoid tumors, which was relatively higher than the regular carcinoid tumors incidence rate among all lung cancer. This aligns with a previous study pointing out that patients aged 45 years and younger had a greater proportion of carcinoid tumors (26%) as compared to older patients (6%) (12). In our study, the atypical carcinoids constituted 12.6% of the total carcinoids. Atypical carcinoids exhibit more aggressive behavior with higher rates of recurrence and metastasis compared to typical carcinoids (13,14). The 5-year OS rates are 90–95% for patients who undergo surgical treatment with typical carcinoid tumor while with atypical carcinoid tumor, it is 86% (15-17). Yet, the prognosis of carcinoid patients is still much better than that of the lung cancer patients with other cell types.

Another reason for the younger lung cancer patients with fair prognosis in this study is that almost 2/3 of the included patients were in stage I. In a previous study, about 24.5% of all age range patients with stage I lung cancer developed recurrence or a second primary cancer after surgery (18), which is worse than the phenomenon in younger lung cancer patient cohort. The observation of this study is consistent with this, showing relative younger age, better survival. In another study, age is a protective factor in the prognose analysis; there are many reasons for this phenomenon since younger patients usually come with better health conditions and more possibility to undergo thoracic surgery (19). Fewer comorbidities of younger patients are also one of the reasons, since fewer comorbidities means longer overall survival.

Interestingly, LCSS in younger lung cancer patients was also lower than that of the relatively older lung cancer patients in this study. One unnoted reason is that younger lung cancer patients are usually with the likelihood of harboring targetable genomic alterations compared to older patients. In previous studies, the younger group had a significantly higher frequency of EGFR kinase mutations (33.5% vs. 24.6%) and ALK rearrangements (14.4% vs. 3.0%), along with a lower frequency of KRAS mutations (12.3% vs. 29.2%) (20,21). Another study reported that ROS1 rearrangements ratio peaked in patients under 30 years and gradually decreased, reaching a plateau at 50s (22). Another study reported that approximately 80% patients under 40 years had tumors harboring targetable driver alterations in EGFR, ALK, ROS1, RET, ERBB2, and MET, and those mutation were sensitive to targeted therapies with improved long-term survival (23). In this study, most patients did not undergo genetic testing and no such data could be used for survival analysis. Previous study also reported that the genomic landscape spectrum in surgically resected younger patients with early-stage NSCLC remains largely unknown due to insufficient tissue for testing (24).

Lung cancer screening for patients aged 50 years and younger, even at high-risk, is not recommended in the current guidelines (25). According to two of the largest randomized controlled trials conducted thus far—the National Lung Screening Trial (NLST) (26) in the United States and the Nederland-Leuvens Longkanker Screenings Onderzoek (NELSON) trial in Europe (27)—the efficacy of low-dose computer tomography screening in reducing lung cancer mortality has been sufficiently evaluated. Following the publication of the results from these two trials, national lung cancer screening guidelines for adults at high-risk for lung cancer have been developed across countries, with most guidelines recommending that screening should be started as early as 50 years of age (28). Younger patients with NSCLC constitute a relatively rare and distinct subgroup within lung cancer population, yet, the potential benefits of lung cancer screening in high-risk younger populations should be thoroughly considered.

In this study, we used tumor stage (T stage) and TNM stage as predictive factors in multivariate analysis, with lymph node stage (N stage) being excluded. There were several reasons that we used T stage and TNM stage together rather than T stage and N stage together in the multivariate analysis. Firstly, N stage was not well documented in the database with this subgroup patients, while TNM stage and T stage were well displayed instead. Secondly, the population that we enrolled in this study was surgically resected patients and we excluded the patients with preoperative neoadjuvant therapy, resulting in selection bias of the patients with clinical N0 stage. In this study, the patients with lymph node stage positive turned out to be of less than 10% among all the included patients. The N stage could be overwhelmed if that was being included in the multivariate analysis. Based on these reasons, we did not take the N stage into account for the multivariate analysis. We acknowledge that there is absolutely a multiple-collinearity between the T stage and TNM stage, however, it is hard for us to exclude the T stage in multivariate analysis because of its virtual importance in the early stage surgically resected lung cancer patients. Meanwhile, we cannot totally exclude the N stage influence in the multivariate analysis, therefore we included the TNM stage in the multivariate analysis. We believe that T stage and TNM stage together could be an option in the analysis of prognostic factors.

In the future studies, we may try to include more patients, in particular, from different databases with more completed clinical information. Incorporating patients from different databases means that we can perform crossed validation more effectively and describe results more precisely with the different demographic characteristics from different databases. With more completed clinical information, including information on comorbidity, gene expression results, etc., we could take into account of more variables for the multivariable analysis and may discover more independent factors which have never been reported in the prognosis studies of younger patient cohorts.

There are several limitations in this study. Firstly, we employed a retrospective approach based on population registry data, with unavoidable selection bias. Not all variables of interest associated with lung cancer or long-term survival, such as tobacco use, performance status, comorbidities, pulmonary function and histology subtype, are available in these databases. Secondly, we only included the younger patients who had undergone cancer-direct surgery as the first treatment approach, while excluding those who received neoadjuvant therapies. This exclusion might have underpowered the ability to comprehensively identify predictors associated with prognosis. Thirdly, given the rising prevalence of targeted therapy and immunotherapy for NSCLC, whether these advanced treatments are associated with long-term survival remains unclear and should be investigated in future studies.

Conclusions

Multivariate analysis identified age, gender, histology, T stage, TNM stage, and postoperative therapies as independent prognostic indicators for postoperative survival among younger patients with NSCLC. The nomograms developed in this study may offer insights into clinical decision-making for post-surgical treatment by identifying patients at high risk.

Acknowledgments

We would like to express our gratitude to the Surveillance, Epidemiology, and End Results program tumor registries for their invaluable contributions.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-348/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-348/prf

Funding: This study was funded by the Capital’s Funds for Health Improvement and Research (No. CFH, 2022-4-1064).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-348/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Gray)