Prognosis of surgery and nomogram for patients with IIIA lung squamous cell carcinoma: a propensity score matched SEER database analysis

Introduction

Lung cancer remains the most prevalent cancer worldwide, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all cases. Among the subtypes of NSCLC, lung squamous cell carcinoma (LSCC) represents about 25% of these cases (1-3). In the early stages of LSCC, clinical symptoms are often subtle and non-specific, typically manifesting as cough, sputum production, chest tightness, and shortness of breath. Due to these vague symptoms, many patients are not diagnosed until the disease has reached an advanced stage. Consequently, the survival rate for LSCC remains low (4,5).

Currently, the American Joint Committee on Cancer (AJCC) Tumor, Node, Metastasis (TNM) staging system is the most widely used tool in clinical practice for assessing the prognosis of lung cancer. The TNM system primarily evaluates prognosis based on tumor size, lymph node involvement, and distant metastasis. However, it does not consider other factors that may influence patient outcomes (6-8). Recent studies have increasingly demonstrated that, beyond clinical indicators like age, factors such as marital status, insurance coverage, race, and ethnicity significantly influence patient prognosis. Consequently, nomogram prediction models have seen growing use in cancer prognosis in recent years. Compared to the traditional TNM staging system, nomograms often offer more accurate predictions by integrating a wider range of variables (9,10).

In this study, we developed a prognostic nomogram to predict overall survival (OS) in patients with stage IIIA LSCC using data from the Surveillance, Epidemiology, and End Results (SEER) database. Additionally, we evaluated its reliability and feasibility through validation with an independent internal cohort. We present this article in accordance with the TRIPOD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1320/rc).

Methods

Data source

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This research utilizes data from the SEER database (https://seer.cancer.gov/data/), which includes information from 18 population-based cancer registries. The SEER database is one of the largest cancer databases in the United States, covering approximately 28% of the U.S. population. For this study, LSCC patients from 2010 to 2015 were selected using SEER*Stat (version 8.4.1) with the following criteria: (I) the primary site was the lung and bronchus; (II) the diagnosis period was between 2010 and 2015; and (III) histological codes included “8070/3, 8071/3, 8072/3, 8073/3, 8074/3, 8075/3, 8076/3, 8078/3, 8083/3, 8084/3.” After filtering the preliminary data and excluding cases with incomplete information, a total of 4,268 patients were included in the analysis.

Data collection

The detailed patient selection process is shown in Figure 1, which includes a total of 4,268 stage IIIA LSCC cases. The study variables encompass sex, age, race, T stage, N stage, grade, marital status, radiotherapy, chemotherapy, and others. The primary endpoint of the study was OS, defined as the time from diagnosis to death. Patients who were alive at the time of the last follow-up were censored at that date.

Figure 1 Flowchart illustrating the inclusion and exclusion of patients. CDS, cancer-directed surgery; LSCC, lung squamous cell carcinoma; PSM, propensity score matching.

Statistical analysis

Given that the distribution of patients in the SEER database was non-random and potential selection bias in baseline characteristics could influence the final outcomes, we assessed differences in these baseline characteristics using the Chi-squared test. To address data bias and minimize the impact of confounding variables, propensity score matching (PSM) was employed to adjust for potential baseline confounders. Specifically, the R package “MatchIt” was utilized to perform 1:1 PSM between the cancer-directed surgery (CDS) and no-CDS groups. Key baseline covariates included in the analysis were sex, age, race, T stage, N stage, tumor grade, marital status, and treatment modalities such as radiotherapy and chemotherapy, among others.

For the comparison of the surgery and no-surgery groups, survival curves were generated using the Kaplan-Meier method, and the log-rank test was applied to assess differences between these curves. Univariate and multivariate Cox regression models were utilized to evaluate the prognostic significance of various variables, with hazard ratios (HR) and 95% confidence intervals (CI) calculated to quantify the strength of associations. Statistical significance was defined as P<0.05. Based on the identified prognostic factors, a nomogram was developed to predict 1-, 3-, and 5-year OS rates for stage IIIA LSCC patients. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%) for model development and verification. The predictive accuracy and reliability of the model were evaluated for both the training and validation sets using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. DCA was applied to evaluate the clinical net benefit of the nomogram across a range of threshold probabilities. Calibration curves were used to assess the agreement between predicted and observed outcomes of the nomogram. All statistical analyses were performed using R software (http://www.R-project.org) and relevant packages, including “Table1”, “rms”, “ggDCA”, “survivalROC”, and “survival” (11-13).

Results

Baseline characteristics

This study included patients diagnosed with LSCC from 2010 to 2015, of whom 1,157 underwent surgical treatment. Tables 1,2 present the demographic and clinicopathological characteristics of the CDS and non-CDS groups, respectively. Before PSM, there were statistically significant differences between the two groups of patients in terms of sex, age, T-stage, N-stage, marital status, primary site, laterality, race, radiotherapy, and chemotherapy. After balancing the baseline characteristics of the two groups, each group included 1,095 patients. Most demographic and clinical characteristics were well balanced between the two groups after PSM. However, differences in N stage and radiotherapy remained statistically significant.

Cox regression analysis of OS

In the univariate Cox regression analysis of the training set, variables such as sex, age, marital status, primary site, laterality, radiotherapy, surgery, and chemotherapy showed statistical significance (Table 3). Based on the univariate analysis, a multivariate Cox regression analysis was conducted to further evaluate the impact of these factors on OS. The multivariate Cox regression results indicated that sex, age, marital status, surgery, and chemotherapy were independent prognostic factors affecting patients with stage IIIA LSCC (Table 3, Figure 2). After reviewing the baseline characteristics, Kaplan-Meier analysis and the log-rank test were used to examine the effect of surgery on prognosis. Notably, patients who underwent surgery had significantly longer OS compared to those who did not, regardless of whether PSM was applied (Figure 3).

Figure 2 The forest plot of HRs comparing patients with IIIA LSCC between the CDS group and no-CDS group according to different variables. **, P<0.01; ***, P<0.001. AIC, Akaike information criterion; CDS, cancer-directed surgery; HRs, hazard ratios; LSCC, lung squamous cell carcinoma.

Figure 3 Comparison of overall survival OS between patients in the CDS group and no-CDS group before PSM (A) and after PSM (B). CDS, cancer-directed surgery; OS, overall survival; PSM, propensity score matching.

Establishment of nomogram

Using the independent prognostic factors identified through multivariate Cox regression analysis—namely sex, age, marital status, surgery, and chemotherapy—a nomogram was constructed to predict OS at 1-, 3-, and 5-year for LSCC patients (Figure 4). In the nomogram, each variable is assigned a score on the “point axis” based on its contribution to survival. To estimate a patient’s survival probability, the scores of each variable are summed to calculate the total score. Then, a perpendicular line is drawn from the “total score axis” to the corresponding point on the “survival axis”, which provides the estimated probability of survival for 1-, 3-, or 5-year.

Figure 4 Nomograms for predicting 1-, 3- and 5-year OS in patients with IIIA LSCC (training cohort). ***, P<0.001. LSCC, lung squamous cell carcinoma; OS, overall survival.

Verification of nomogram

We validated the accuracy of the nomogram using several methods. First, ROC curve analysis demonstrated that the area AUC values for 1-, 3-, and 5-year OS in the training set were 0.727, 0.726, and 0.730, respectively, indicating that the nomogram had good discriminative ability (Figure 5). The results of the calibration curves further indicated an ideal agreement between the predicted and actual survival probabilities (Figure 6). Additionally, we assessed the clinical validity of the nomogram using DCA and found that the prognostic models provided favorable positive net benefits across a range of threshold probabilities at different time points (Figure 7).

Figure 5 ROC curves for 1-, 3- and 5-year OS in the training cohort (A) and validation cohort (B). AUC, area under the curve; OS, overall survival; ROC, receiver operating characteristic.

Figure 6 The calibration curves to predict 1-, 3- and 5-year OS in the training cohort (A-C) and validation cohort (D-F). OS, overall survival.

Figure 7 DCA curves of the nomogram for predicting 1-, 3- and 5-year OS in the training cohort (A-C) and validation cohort (D-F). DCA, decision curve analysis; OS, overall survival.

Discussion

Currently, LSCC remains the malignant tumor with the highest morbidity and mortality worldwide. This is particularly true when the disease progresses to advanced stages, as the prognosis is often poor in such cases (14,15). Therefore, evaluating the prognosis of LSCC patients remains a challenging task for physicians. Nomograms can quantify the effects of individual risk factors on outcomes, allowing for a visual representation of each patient’s prognosis. Currently, nomograms have emerged as essential decision-making tools for predicting disease risk and long-term survival outcomes (9,16,17). This study aims to identify prognostic factors for patients with stage IIIA LSCC by analyzing a range of clinicopathological characteristics. Through the development of a nomogram-based prognostic model, we aim to improve the identification of high-risk patients and optimize survival outcomes in this population.

In this study, we analyzed the OS of patients diagnosed with stage IIIA LSCC between 2010 and 2015 using data extracted from the SEER database. Univariate and multivariate Cox regression analyses revealed that sex, age, marital status, surgery, and chemotherapy were independent prognostic factors for OS in this patient population. Additionally, a previous study identified radiotherapy as another independent prognostic factor for OS in patients with stage IIIA LSCC (18). However, it is important to note that in this study, radiotherapy was not significantly associated with OS in patients with stage IIIA LSCC. This finding may be explained by the severe bone marrow suppression induced by high-dose and prolonged radiotherapy regimens, which could negatively impact prognosis, particularly in patients with advanced-stage tumors (19,20). Age has been consistently identified as an independent prognostic factor for survival in patients with lung cancer. With advancing age, the risk of mortality tends to increase, potentially due to diminished physiological reserves, reduced tolerance to cancer therapies, and a higher likelihood of treatment-related adverse effects in elderly patients. Additionally, older patients tend to have reduced OS, partly due to the natural decline in life expectancy with age. Similar results were observed in our study, where the prognosis for patients of advanced age was found to be worse. Age emerged as an independent prognostic factor for OS in our analysis.

Previous studies have indicated that the mortality rate for male patients diagnosed with lung cancer is often higher than that for females. This disparity may be primarily attributed to the higher prevalence of smoking among males compared to females (21,22). In this study, we found that the male-to-female ratio for stage IIIA LSCC was approximately 1.8:1. The multivariate Cox regression analysis indicated that sex is an independent prognostic factor for patients, with male patients exhibiting a worse prognosis compared to female patients.

In this study, we also discovered that marital status is related to patient prognosis, which is quite intriguing. Previous research has shown that marital status is significantly correlated with the survival of cancer patients. The survival advantages associated with marriage can typically be attributed to increased social support, improved mental health, and practical assistance, including better navigation of the medical system (23,24).

The tumor treatment plan should be personalized according to the patient’s clinical manifestations and preferences. To date, lobectomy remains the most important surgical approach in the treatment of lung cancer (25,26). Previous studies have shown that surgical resection of the primary lesion can improve the prognosis for patients with stage IV NSCLC (27). This study further confirmed that surgical resection can improve the survival rate of patients with stage IIIA LSCC, identifying it as an independent prognostic factor.

The role of chemotherapy in cancer treatment has always been a subject of controversy, as it may serve as an alternative option for advanced cancer patients who cannot undergo surgical resection. Most patients receiving chemotherapy have advanced tumors, and their conditions are often severe and progress rapidly, resulting in a poor prognosis. However, a study found that chemotherapy is an independent prognostic risk factor for lung adenocarcinoma (LUAD) with bone metastasis, suggesting that systemic chemotherapy can improve patient outcomes (28). In this study, we also found that chemotherapy is an independent prognostic factor for OS in patients.

By utilizing the extensive population data from the SEER database to examine pathological characteristics such as age, sex, race, marital status, primary site, radiotherapy, chemotherapy, and surgery, we can better evaluate the prognosis risk for patients with stage IIIA LSCC. In this study, the area AUC for the training and validation sets at 1, 3, and 5 years was 0.727, 0.726, and 0.730, and 0.699, 0.717, and 0.721, respectively, indicating good discriminative ability. The results from the calibration curves demonstrate that the predicted values closely align with the actual outcomes. Additionally, the DCA indicates that the model provides good clinical net benefits.

Certainly, there are some limitations in this study. First, while the SEER database includes many prognostic factors such as age, grade, and metastasis status, it does not encompass other important prognostic factors, including symptoms, chemotherapy dosage, smoking history, family history of cancer, and occupational exposure. Second, Because the SEER database lacks information on immunotherapy, the nomogram developed in this study could not incorporate this key treatment factor. Therefore, the model is primarily applicable to patients treated before the widespread use of immunotherapy or to those who are not candidates for immunotherapy. Future studies should incorporate immunotherapy data to further enhance the clinical applicability of the model. Third, the data in the SEER database are primarily derived from the United States, with a predominantly white and Black population. This may lead to inaccuracies when using this nomogram to predict the prognosis of stage IIIA LSCC patients in China, where the population is primarily of Asian descent. Lastly, as a retrospective study, our model still requires validation through prospective clinical trials to confirm its effectiveness.

Conclusions

In summary, our study identified risk factors for stage IIIA LSCC through both univariate and multivariate analyses, and we constructed a nomogram to predict OS at 1, 3, and 5 years. We also performed ROC analysis and established calibration and DCA curves to verify the accuracy of the nomogram. This model may offer valuable insights for the clinical prognosis of patients with stage IIIA LSCC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1320/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1320/prf

Funding: The study was supported by Zhejiang Provincial Medical and Health Technology Plan (Grant No. 2021PY029).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1320/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. This 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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