Comparative survival analysis of stage T1-T2N0M0 lung squamous cell carcinoma and adenocarcinoma using SEER data, and nomogram analysis for early-stage lung squamous cell carcinoma

Introduction

In 2020, global cancer statistics reported 2.2 million new lung cancer cases and 1.8 million deaths (1). The World Health Organization (WHO) classifies lung cancer into non-small cell lung cancer (NSCLC) and small cell lung cancer. Among NSCLC, adenocarcinoma (AC) and squamous cell carcinoma (SQCC) are predominant, accounting for 40–50% and 25–30%, respectively (2,3). Early-stage lung cancer is mainly treated with surgery, chemotherapy, and radiation, without multimodal therapy. The prognosis for early-stage AC vs. SQCC is debated. Some studies suggest that SQCC has a better prognosis (4,5). A previous study has reported no significant difference in survival between the two pathological types (6), and some studies show that AC has a better prognosis (7,8). Given the insufficient research on the differences in prognosis between the two, it is crucial to analyze clinical and pathological characteristics and prognostic factors. This study uses extensive clinical data for this analysis.

Current cancer treatment protocols and prognosis predictions primarily rely on the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system (9), which focuses on anatomical features, but does not take into account of other factors like age, sex, marital status, race, histology, or treatment modalities (10). Nomograms integrate these factors for a more accurate and intuitive prognosis prediction (10), but few are designed for early-stage lung cancer. To achieve personalized cancer prediction, predictive tools for early-stage lung cancer are essential.

Early screening is improving diagnosis and prognosis, highlighting the need to monitor high-risk groups for recurrence. This study used data from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute (NCI) to analyze T1-T2N0M0 early-stage lung SQCC and AC patients, compare survival differences under different treatments, and investigate prognostic factors. The goal was to develop a nomogram for early-stage SQCC to aid intervention strategies. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1602/rc).

Methods

Study population

The SEER database is a large-scale cancer registry in North America, covering approximately 48.0% of the U.S. population, with data on cancer survival and mortality (11). This study used SEER*Stat software version 8.4.2 (https://seer.cancer.gov/seerstat/) to download cases from the Incidence-SEER Research Plus data, 18 Registries, Nov 2020 Sub [2000–2018].

Inclusion criteria were: (I) single primary lung cancer diagnosed between 2004 and 2019; (II) clear follow-up times, survival status, and cause of death. Initially, 530,380 patients were screened. Exclusion criteria were: (I) lesions diameter >5 cm; (II) unknown tumor location, surgical status, grade, race, marital status, or laterality; (III) non-squamous or non-AC pathology per the International Classification of Diseases for Oncology-3rd Edition (ICD-O-3); (IV) lymph node or distant metastasis; (V) Tis (stage 0) disease. This resulted in 12,071 SQCC patients and 28,254 AC patients, totaling 40,325. The screening process is shown in Figure 1.

Figure 1 Data screening flow chart.

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). As the SEER database is public and does not contain sensitive personal information, informed consent and institutional review board approval were not required.

Demographic and clinical variables

The analysis included variables: demographic characteristics (gender, age, race, marital status), disease characteristics (pathological type, tumor laterality, location, grade, size), treatment information (surgery, chemotherapy, radiotherapy), survival time, and status. Patients had tumor diameters ≤5 cm and were classified as stage IA-IIA lung SQCC and AC per the 8th edition AJCC criteria for T1-T2N0M0. Events of interest were overall survival (OS) and lung cancer-specific survival (LCSS). OS is the time from lung cancer diagnosis to death from any cause or last follow-up. LCSS is the time from diagnosis to death specifically from lung cancer.

Statistical analysis

Clinical and pathological characteristics of early-stage lung AC and SQCC were compared using categorical variables represented by frequency and proportion, analyzed with the Chi-squared test. Survival analysis was conducted using Kaplan-Meier method to plot OS and LCSS curves, estimating median OS. Propensity score matching (PSM) at a 1:1 ratio eliminated differences in baseline characteristics, followed by log-rank testing to assess survival differences. Multivariable analysis on OS factors in early-stage lung SQCC used the Cox proportional hazards model to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Statistical significance was defined as P<0.05.

Nomograms predicting 1-, 3-, and 5-year survival rates for early-stage lung SQCC were constructed based on independent prognostic factors. Predictive ability and accuracy of nomograms were evaluated by discrimination (concordance index, C-index) and calibration (calibration curves) (12,13). Clinical utility was assessed using decision curve analysis (DCA) (14). A risk classification system was developed from the total scores to identify high and low prognostic groups. All analyses were conducted using R (version 4.3.2, https://www.rproject.org/).

Results

Clinical characteristics

This study included 12,071 patients with SQCC and 28,254 with AC. Chi-squared tests showed significant differences in demographics, disease features, and treatment modalities between the groups (Table 1). Patients under 60 were more prevalent in the AC group (19.9% vs. 10.5%, P<0.001), while those over 70 were more common in the SQCC group (57.6% vs. 46.3%, P<0.001). The AC group had 40.1% males and 59.9% females (P<0.001), compared to 56.4% males and 43.6% females in the SQCC group (P<0.001). Marital status showed both groups predominantly married (57.2% vs. 53.4%, P<0.001), with a higher proportion of widowed/divorced in the SQCC group (34.1% vs. 29.3%). Both cancers were more common on the right side (60.2% vs. 56.5%, P<0.001). About 60% of tumors were in the upper lobes for both types, followed by lower and middle lobes, with the main bronchus being least common. SQCC was more frequent in the main bronchus (1.2% vs. 0.1%, P<0.001). Histological grade showed AC cells better differentiated (26.9% vs. 3.5%, P<0.001). More SQCC patients received radiotherapy (19.3% vs. 8.8%, P<0.001) and fewer underwent surgery (77.3% vs. 89.5%, P<0.001).

Survival differences between lung AC and lung SQCC

Patients with stage T1-T2N0M0 NSCLC were categorized into four treatment groups: surgery-only (31,604 patients), radiotherapy-only (3,371 patients), chemotherapy-only (205 patients), and untreated (1,515 patients). Significant demographic differences were adjusted using PSM, considering age, race, gender, marital status, tumor laterality, tumor location, histological grade, and tumor size. Kaplan-Meier survival analysis was performed on the matched groups.

Comparison of survival prognosis in patients receiving only chemotherapy

The HR for LCSS in the chemotherapy-only group compared to the untreated group was 0.88 (95% CI: 0.670–1.114, P=0.28) (Figure 2A), and the HR for OS in the chemotherapy-only group was 0.82 (95% CI: 0.663–1.010, P=0.053) (Figure 2B), indicating no statistical difference.

Figure 2 Difference in survival between T1-T2N0M0 non-small cell lung cancer patients receiving chemotherapy and those not receiving chemotherapy after PSM. (A) LCSS. (B) OS. LCSS, lung cancer specific survival; OS, overall survival; PSM, propensity score matching.

Comparison of survival prognosis in patients receiving only radiotherapy

The HR for LCSS in the radiotherapy-only group compared to the untreated group was 0.38 (95% CI: 0.342–0.420, P<0.001) (Figure 3A), indicating that the risk of lung cancer-specific death in the radiotherapy-only group is 0.38 times that of the untreated group. The HR for OS in the radiotherapy-only group was 0.54 (95% CI: 0.500–0.590, P<0.001) (Figure 3B), showing that the risk of death in the radiotherapy-only group is 0.54 times that of the untreated group.

Figure 3 Difference in survival in patients receiving radiotherapy alone. (A,B) Difference in survival between T1-T2N0M0 non-small cell lung cancer patients receiving radiotherapy and those not receiving radiotherapy after PSM. (A) LCSS; (B) OS. (C,D) Difference in survival between patients with lung squamous cell carcinoma and adenocarcinoma after PSM. (C) LCSS; (D) OS. LCSS, lung cancer specific survival; OS, overall survival; PSM, propensity score matching.

When treated with radiotherapy alone, patients with SQCC had worse LCSS compared to those with AC (HR =1.20, 95% CI: 1.079–1.336, P<0.001) (Figure 3C). AC patients had better 3-year LCSS (62.7% vs. 55.9%) and better 5-year LCSS (47.8% vs. 43.6%). For OS, SQCC patients also had worse outcomes compared to AC patients (HR =1.32, 95% CI: 1.215–1.429, P<0.001) (Figure 3D). The median OS was 35 months (95% CI: 32–37) for AC patients and 27 months (95% CI: 25–28) for SQCC patients. AC patients had better 3-year OS (48.0% vs. 37.6%) and better 5-year OS (29.7% vs. 20.6%).

Comparison of survival prognosis in patients undergoing surgery alone

In the surgery-alone group, the HR for LCSS compared to the untreated group was 0.08 (95% CI: 0.069–0.088, P<0.001) (Figure 4A). For OS, the HR was 0.09 (95% CI: 0.085–0.104, P<0.001) (Figure 4B).

Figure 4 Difference in survival in patients receiving surgery. (A,B) Difference in survival between T1-T2N0M0 non-small cell lung cancer patients receiving surgery and those not receiving surgery after PSM. (A) LCSS; (B) OS. (C,D) Difference in survival between patients with lung squamous cell carcinoma and adenocarcinoma after PSM. (C) LCSS; (D) OS. LCSS, lung cancer specific survival; OS, overall survival; PSM, propensity score matching.

Under surgery alone, the HR for LCSS comparing SQCC to AC was 1.03 (95% CI: 0.965–1.092, P=0.41) (Figure 4C), indicating no significant difference. AC patients had better 3-year LCSS (83.1% vs. 81.7%) but slightly worse 5-year LCSS (73.9% vs. 74.4%). For OS, SQCC patients had worse outcomes (HR =1.25, 95% CI: 1.200–1.309, P<0.001) (Figure 4D). Median OS was 88 months (95% CI: 85–91) for AC and 72 months (95% CI: 69–74) for SQCC. AC patients had better 3-year OS (75.0% vs. 69.8%) and 5-year OS (61.3% vs. 55.9%).

After PSM, survival analysis showed that for T1-T2N0M0 stage SQCC and AC patients, both radiotherapy and surgery improved LCSS and OS. Chemotherapy alone showed no significant differences in LCSS and OS. Among surgery or radiotherapy-treated patients, those with SQCC had poorer OS compared to AC. For LCSS, SQCC patients receiving radiotherapy alone had worse outcomes than those with AC. However, among surgery-treated patients, there was no significant difference between the two groups.

Single and multifactorial analysis of early SQCC prognosis

The Cox proportional hazards model was used for single-factor analysis of all known variables, as detailed in Table 2. Factors that were statistically significant in this analysis were then included in a multifactor analysis. This revealed that age, gender, race, marital status, tumor location, tumor size, radiotherapy, and surgery are independent prognostic factors for OS in early-stage squamous cell lung cancer patients (Table 2). Age ≥60 (HR >1, P<0.001), male gender (HR =1.244, P<0.001), divorced or widowed marital status (HR =1.162, P<0.001), tumors located in the main bronchus (HR =1.28, P=0.01), and larger tumor size (HR >1, P<0.001) were identified as risk factors negatively impacting survival. Conversely, undergoing surgery (HR =0.294, P<0.001) or radiotherapy (HR =0.706, P<0.001) were found to be protective factors that improve survival. The P value for chemotherapy alone was 0.40, indicating no statistically significant difference (P>0.05).

Construction of nomogram and risk classification system

Using the independent factors identified, we developed a nomogram to predict 1-, 3-, and 5-year survival rates for early-stage squamous cell lung cancer patients. We enrolled 12,071 patients with tumors ≤5 cm in diameter, dividing them into a training set (n=8,449) and a validation set (n=3,622) at a 7:3 ratio, ensuring comparable baseline characteristics (Table 3). The nomogram, shown in Figure 5, was validated with the concordance index (C-index), calibration plots, and DCA. The C-index was 0.662 for the training set and 0.647 for the validation set, indicating good predictive accuracy. The calibration plots (Figure 6A-6F) demonstrated strong agreement between predicted and actual 1-, 3-, and 5-year OS probabilities. Additionally, the DCA plots (Figure 7A-7F) showed a significant net benefit across nearly all threshold probabilities for the 1-, 3-, and 5-year predictions.

Figure 5 Nomogram for predicting 1-, 3- and 5-year overall survival in patients with T1-T2N0M0 lung squamous cell carcinoma.

Figure 6 Calibration curves of nomograms for predicting 1-, 3- and 5-year OS in patients with lung squamous cell carcinoma: (A) Calibration curve for predicting 1-year OS in the training set. (B) Calibration curve for predicting 3-year OS in the training set. (C) Calibration curve for predicting 5-year OS in the training set. (D) Calibration curve for predicting 1-year OS in the validation set. (E) Calibration curve for predicting 3-year OS in the validation set. (F) Calibration curve for predicting 5-year OS in the validation set. OS, overall survival.

Figure 7 DCA curves of nomograms for predicting 1-, 3- and 5-year OS of patients with lung squamous cell carcinoma: (A) DCA curve of predicting 1-year OS in the training set; (B) DCA curve of predicting 3-year OS in the training set; (C) DCA curve of predicting 5-year OS in the training set; (D) DCA curve for predicting 1-year OS in the validation set; (E) DCA curve of predicting 3-year OS in the validation set; (F) DCA curve of predicting 5-year OS in the validation set. DCA, decision curve analysis; OS, overall survival.

The nomogram model allows for rapid prognosis prediction using clinical data (age, sex, race, marital status, tumor location, histologic grade, tumor size, surgery, and radiotherapy) for each patient. Finally, we established an OS risk classification system, stratifying patients into high and low-risk groups with cutoff values of 141.35 in the training set and 141.06 in the validation set. The low-risk group exhibited significant survival benefits (P<0.001, Figure 8A,8B).

Figure 8 Kaplan-Meier curves of OS in low-risk and high-risk patients in (A) training set and (B) validation set. OS, overall survival.

Discussion

Some studies suggest that treating lung AC and SQCC as separate clinical entities and adopting personalized management and treatment strategies are increasingly preferable in the future (15-17). Our study identified differences in clinical and pathological characteristics, as well as prognosis, between early-stage lung SQCC and AC patients. Irrespective of being treated with surgery or radiation therapy, patients with early-stage lung SQCC had a poorer prognosis. We analyzed factors affecting SQCC prognosis and created survival curves based on the data. The strength of our study lies in its large population data, sufficient follow-up duration, matched analysis of differences among early-stage lung cancer patients, and exploration of prognosis differences under a single treatment modality, adhering to a first principles approach. This comprehensive analysis is relatively rare. With the global increase in cancer incidence, early implementation and dissemination of diagnostic methods are crucial. Through this population-based research, we aim to enhance the understanding of the heterogeneity between early-stage lung SQCC and AC. Given the poorer prognosis of early-stage SQCC compared to AC, there should be increased post-diagnosis attention and consideration for personalized management strategies for these high-risk groups.

Clinical and pathological characteristics

Our study reveals that both lung AC and SQCC are more frequently found in the right lung, primarily affecting the upper and lower lobes. SQCC tends to occur slightly more in the main bronchus compared to AC. Consistent with previous reports, SQCC is more likely to occur in the central region, whereas AC is more common in the peripheral region (18,19). We also found that SQCC tends to have poorer differentiation (grades III–IV, 46.3% vs. 23.7%, P<0.001), occurs at an older age, and has a higher proportion of male patients. These findings align with earlier research (20,21).

Prognostic differences in patients with stage T1-T2N0M0 lung SQCC and AC

This study first adjusted for intergroup differences before analyzing the prognosis of stage T1-T2N0M0 SQCC and AC under various treatments. The findings suggest that surgery or radiation therapy can improve LCSS and OS. However, the impact of chemotherapy alone on LCSS and OS was not statistically significant. To our knowledge, this is one of the few large-scale comparative analyses of prognosis following different treatments for early-stage lung SQCC and AC, providing robust evidence supporting the poorer prognosis associated with SQCC histology.

For early-stage NSCLC, surgery is generally the preferred treatment (22,23), with 5-year OS rates ranging from 50% to 80% (2). Some postoperative studies suggest a better prognosis for SQCC. For example, Pfannschmidt et al. analyzed 2,083 postoperative lung cancer patients (stages I–IV) and found significantly better 5-year survival rates for SQCC compared to AC (P=0.008) (4). Similarly, Strand et al. included 3,211 postoperative lung cancer patients (stages I–IV) and reported through Cox regression analysis that AC had a worse prognosis than SQCC across all patients (5). Additionally, a multifactorial analysis of IA stage lung cancer patients post-surgery showed no significant correlation between histological type and OS (P=0.38) or disease-specific survival (P=0.39) (6). Conversely, other studies support a better prognosis for AC. For instance, a retrospective study by Izaki et al. (7) analyzed the prognosis of 628 patients with stage I–IIA lung cancer after surgery in Japan. The 5-year OS rate was 90% in patients with AC and 77% in patients with SQCC (P<0.01), confirming that patients with lung AC had better survival outcomes than those with lung SQCC. Maeda et al. reported statistically significant differences in OS between stage I–II lung AC and SQCC post-surgery, with 5-year survival rates of 79.7% and 63.8%, respectively (24). Similarly, JL Lopez Guerra et al. found a significantly increased risk of death in SQCC patients post-surgery in their study of N0-N1 NSCLC (8). Given the wide tumor staging inclusion and varied treatment approaches in the aforementioned studies, alongside clinical feature disparities, the present study specifically included only T1-T2N0M0 stage patients who underwent surgery for prognosis comparison. The conclusion drawn was that among early-stage lung cancer patients treated solely with surgery, there was no statistically significant difference in disease-specific survival, but OS favored lung AC over SQCC.

For patients with early-stage NSCLC who are not eligible for surgery or decline it, stereotactic body radiation therapy (SBRT) is often the preferred treatment option (2,22,25). SBRT has demonstrated over 90% local control at 3 years and favorable survival rates (26,27). Currently, radiation doses do not vary based on histological subtypes. Guidelines from the European Society for Radiotherapy and Oncology and the ESTRO Guidelines Committee [former Advisory Committee for Radiation Oncology Practice (ACROP)] recommend doses of 15 Gy ×3 fractions for peripheral lesions, and a maximum tolerated dose of 18 Gy ×3 fractions for patients without significant comorbidities and with a long life expectancy (28). In cervical cancer, SQCC regresses more rapidly, is more sensitive to radiation, and has a better prognosis compared to AC (29-32). Whether these differences exist in lung cancer remains debated. Some studies, like that by Mak et al. (33), which analyzed 75 peripheral lung cancer patients treated with SBRT, found no association between histology and local recurrence or distant metastasis. Another study involving 91 stage I NSCLC patients treated with SBRT suggested that while SQCC regressed faster than AC, especially at 2 and 4 months post-SBRT, there was no significant difference in 3-year and 4-year local control rates (34). Research on T1-T2N0M0 stage lung cancer patients treated with SBRT (35) indicated that SQCC patients appeared to derive more survival benefit compared to AC patients. However, other studies suggest that AC benefits more from radiation than SQCC. Woody et al. (3), studying lung cancer patients (T1-T3N0M0 AJCC 7th edition) treated with SBRT, reported that SQCC had twice the local failure rate compared to AC, with a 3-year cumulative local failure rate of approximately 19% for SQCC. Numerous studies (2,36-39) have confirmed that pathological type, tumor volume, and radiation dose influence local control outcomes.

Most previous prospective and retrospective studies did not use PSM and did not report the direct impact of SBRT on survival in different pathological subtypes. This study used PSM to explore differences in prognosis between SQCC and AC after exclusive radiotherapy, revealing poorer LCSS and OS outcomes for SQCC compared to AC. The reasons for these conclusions remain unclear, genetic diversity (40,41) and microenvironmental characteristics (42) may explain such differences. For instance, the three most common mutations (>50%) in AC are Kirsten rat sarcoma viral oncogene homologue (KRAS), epidermal growth factor receptor (EGFR), and anaplastic lymphoma kinase (ALK), whereas phosphatase and tensin homolog (PTEN), fibroblast growth factor receptor 1 (FGFR1), and phosphatidylinositol-4, 5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) are more prevalent in SQCC (43). Studies indicate that EGFR and ALK mutations tend to be sensitive to radiotherapy (44-46), while other listed mutations appear to confer radiotherapy resistance (47-50). Furthermore, programed-cell death ligand 1 (PD-L1) expression in SQCC appears higher than in AC, and high PD-L1 expression can inhibit T-cell activation, correlating with poorer prognosis (51). Additionally, studies demonstrate that thymidylate synthase (TS) promotes deoxyribonucleic acid (DNA) synthesis, enhancing cell proliferation. Lower TS levels in AC may reflect lower DNA repair capacity, potentially leading to a higher response to radiotherapy (52,53), and correlating with better clinical outcomes (54). Finally, high glucose metabolism reduces tumor antioxidant capacity, which is associated with radiotherapy resistance (55) Compared to AC, SQCC exhibits higher expression of hypoxia-inducible factor-1α (HIF-1α) (56), lower vascular density, restricted oxygen diffusion, and higher glycolysis rates at messenger ribonucleic acid(mRNA) and protein levels (42) all of which impact the tumor microenvironment, potentially contributing to lower local control rates in SQCC (57). These features collectively suggest enhanced radiotherapy resistance in SQCC.

This study represents the largest investigation to date on prognostic outcomes in early-stage lung cancer across different histological types after rigorous matching, focusing exclusively on radiotherapy. The observed differences in outcomes between these histological types suggest that a standardized radiotherapy protocol for early-stage lung cancer may be insufficient. Instead, optimizing treatment based on histological subtypes might be more beneficial. This finding has significant clinical implications. Currently, limited research suggests that tailoring treatments based on histology could enhance survival rates in NSCLC patients. For instance, radiotherapy resistance related to genes like KRAS and TP53 might be mitigated by increasing radiation doses (58). Higher doses could potentially reduce the outcome disparities between AC and SQCC. Hörner-Rieber et al. (36) reported that SQCC patients undergoing SBRT with a central planning-target volume (PTV) total dose ≥150 Gy (equivalent dose in 2 Gy fractions, EQD2) did not show significant differences in local control (P=0.36), and different pathologies did not significantly affect distant metastases (P=0.83). Liu et al. (2) found that, compared to AC, SQCC requires higher radiation doses for optimal local control. Similar research indicates that while SQCC of the lung tends to exhibit poorer local control with radiotherapy compared to AC, this can be overcome with higher biologically equivalent doses (BEDs) (3,59).

Additionally, studies on cervical cancer radiotherapy suggest that AC patients have poorer OS and disease-free survival (DFS) compared to SQCC patients (29-32). This contradicts the prognosis observed in early-stage lung cancer patients receiving radiotherapy. Therefore, another significant aspect of this study is that the histological subtypes of SQCC and AC may exhibit varying prognostic responses to radiotherapy depending on the primary tumor site.

Analysis of prognostic factors and nomograms for T1-T2N0M0 stage lung SQCC patients

This study conducted single-factor and multi-factor Cox regression analyses to investigate prognostic factors for T1-T2N0M0 stage lung SQCC patients. The results indicate that age, gender, race, marital status, tumor location, tumor size, radiotherapy, and surgery are independent factors influencing OS in early-stage lung SQCC patients. These findings are consistent with previous reports (13,60-65). By integrating various prognostic factors, nomograms can generate individual probabilities of clinical events to aid in clinical decision-making (10). Due to significant clinical and pathological differences between AC and SQCC, the current TNM staging system used for treatment and prediction appears broad. Therefore, we specifically categorized lung cancer patients and developed refined nomograms that incorporate multiple clinical variables to provide personalized predictions of 1-, 3-, and 5-year survival rates for each patient. Validation using C-index, calibration curves, and DCA demonstrated its predictive capability.

Limitations

There are several limitations in this study. Firstly, it is retrospective in nature, and inherent selection bias is unavoidable. Secondly, many variables are lacking in the SEER database, including smoking history, physical condition, comorbidities, tumor complications, details of chemotherapy regimens and cycles, radiation dosage/volume, surgical complications, genetic mutations, and targeted therapies, among others. This restricts further detailed exploration of early-stage lung cancer. Thirdly, although the nomograms developed in this study demonstrated good discrimination and calibration, validation was limited to internal data only, lacking external validation with independent datasets.

Conclusions

In summary, for stage T1-T2N0M0 NSCLC patients, surgery or radiation therapy can improve LCSS and OS. However, the impact of chemotherapy alone on LCSS and OS in early-stage lung cancer was not statistically significant. Furthermore, the prognosis of early-stage lung SQCC and AC is different under the same treatment mode. When only surgical treatment was performed, the difference in LCSS between the two cancers was not statistically significant. Lung SQCC had a worse LCSS than lung AC when treated with radiation alone. This finding suggests that radiation therapy needs to be optimized based on histological subtypes. Overall, early-stage lung SQCC has a worse OS compared to AC. Factors such as age, gender, race, marital status, tumor location, tumor size, receipt of radiotherapy, and receipt of surgery are independent prognostic factors for OS in T1-T2N0M0 SQCC patients. This study further developed nomograms to predict the 1-, 3-, and 5-year survival rates of lung SQCC patients. This model can assist clinicians in effectively assessing the disease and making treatment decisions.

In the era of personalized medicine, even when formulating treatment strategies for early-stage lung cancer patients, pathological differences should be considered separately for analysis, aiming to provide more accurate diagnosis and treatment in the future.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1602/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1602/prf

Funding: This work was supported by the First Batch of Key Disciplines on Public Health in Chongqing.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1602/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 (as revised in 2013).

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/.

References

1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. [Crossref] [PubMed]
2. Liu F, Farris MK, Ververs JD, et al. Histology-driven hypofractionated radiation therapy schemes for early-stage lung adenocarcinoma and squamous cell carcinoma. Radiother Oncol 2024;195:110257. [Crossref] [PubMed]
3. Woody NM, Stephans KL, Andrews M, et al. A Histologic Basis for the Efficacy of SBRT to the lung. J Thorac Oncol 2017;12:510-9. [Crossref] [PubMed]
4. Pfannschmidt J, Muley T, Bülzebruck H, et al. Prognostic assessment after surgical resection for non-small cell lung cancer: experiences in 2083 patients. Lung Cancer 2007;55:371-7. [Crossref] [PubMed]
5. Strand TE, Rostad H, Møller B, et al. Survival after resection for primary lung cancer: a population based study of 3211 resected patients. Thorax 2006;61:710-5. [Crossref] [PubMed]
6. Kawai H, Tada A, Kawahara M, et al. Smoking history before surgery and prognosis in patients with stage IA non-small-cell lung cancer--a multicenter study. Lung Cancer 2005;49:63-70. [Crossref] [PubMed]
7. Izaki Y, Mimae T, Kagimoto A, et al. Differences in postoperative prognosis between early-stage lung adenocarcinoma and squamous cell carcinoma. Jpn J Clin Oncol 2024;54:813-21. [Crossref] [PubMed]
8. Lopez Guerra JL, Gomez DR, Lin SH, et al. Risk factors for local and regional recurrence in patients with resected N0-N1 non-small-cell lung cancer, with implications for patient selection for adjuvant radiation therapy. Ann Oncol 2013;24:67-74. [Crossref] [PubMed]
9. Rami-Porta R, Nishimura KK, Giroux DJ, et al. The International Association for the Study of Lung Cancer Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groups in the Forthcoming (Ninth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol 2024;19:1007-27. [Crossref] [PubMed]
10. Piras A, Morelli I, Colciago RR, et al. The continuous improvement of digital assistance in the radiation oncologist's work: from web-based nomograms to the adoption of large-language models (LLMs). A systematic review by the young group of the Italian association of radiotherapy and clinical oncology (AIRO). Radiol Med 2024;129:1720-35. [Crossref] [PubMed]
11. Che WQ, Li YJ, Tsang CK, et al. How to use the Surveillance, Epidemiology, and End Results (SEER) data: research design and methodology. Mil Med Res 2023;10:50. [Crossref] [PubMed]
12. You H, Teng M, Gao CX, et al. Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study. Front Med (Lausanne) 2021;8:680679. [Crossref] [PubMed]
13. Chen S, Gao C, Du Q, et al. A prognostic model for elderly patients with squamous non-small cell lung cancer: a population-based study. J Transl Med 2020;18:436. [Crossref] [PubMed]
14. Zhao L, Leng Y, Hu Y, et al. Understanding decision curve analysis in clinical prediction model research. Postgrad Med J 2024;100:512-5. [Crossref] [PubMed]
15. Gálffy G, Morócz É, Korompay R, et al. Targeted therapeutic options in early and metastatic NSCLC-overview. Pathol Oncol Res 2024;30:1611715. [Crossref] [PubMed]
16. Wang M, Herbst RS, Boshoff C. Toward personalized treatment approaches for non-small-cell lung cancer. Nat Med 2021;27:1345-56. [Crossref] [PubMed]
17. Lau SCM, Pan Y, Velcheti V, et al. Squamous cell lung cancer: Current landscape and future therapeutic options. Cancer Cell 2022;40:1279-93. [Crossref] [PubMed]
18. Ciofiac CM, Mămuleanu M, Florescu LM, et al. CT Imaging Patterns in Major Histological Types of Lung Cancer. Life (Basel) 2024;14:462. [Crossref] [PubMed]
19. Wang BY, Huang JY, Chen HC, et al. The comparison between adenocarcinoma and squamous cell carcinoma in lung cancer patients. J Cancer Res Clin Oncol 2020;146:43-52. [Crossref] [PubMed]
20. Zhang Y, Liu H, Chang C, et al. Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics. PLoS One 2024;19:e0300170. [Crossref] [PubMed]
21. Oróstica KY, Saez-Hidalgo J, de Santiago PR, et al. Total mutational load and clinical features as predictors of the metastatic status in lung adenocarcinoma and squamous cell carcinoma patients. J Transl Med 2022;20:373. [Crossref] [PubMed]
22. Riely GJ, Wood DE, Ettinger DS, et al. Non-Small Cell Lung Cancer, Version 4.2024, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2024;22:249-74. [Crossref] [PubMed]
23. Tasoudis P, Loufopoulos G, Manaki V, et al. Long term outcomes after lobar versus sublobar resection for patients with Non-Small cell lung Cancer: Systematic review and individual patient data Meta-Analysis. Lung Cancer 2024;195:107929. [Crossref] [PubMed]
24. Maeda R, Yoshida J, Ishii G, et al. Prognostic impact of histology on early-stage non-small cell lung cancer. Chest 2011;140:135-45. [Crossref] [PubMed]
25. Hong H, He Y, Qu Y. Future Radiation Oncology: The Role of Personalization and Standardization in Stereotactic Body Radiotherapy. J Thorac Oncol 2024;19:e39-40. [Crossref] [PubMed]
26. Chang JY, Senan S, Paul MA, et al. Stereotactic ablative radiotherapy versus lobectomy for operable stage I non-small-cell lung cancer: a pooled analysis of two randomised trials. Lancet Oncol 2015;16:630-7. [Crossref] [PubMed]
27. Remon J, Soria JC, Peters S, et al. Early and locally advanced non-small-cell lung cancer: an update of the ESMO Clinical Practice Guidelines focusing on diagnosis, staging, systemic and local therapy. Ann Oncol 2021;32:1637-42. [Crossref] [PubMed]
28. de Jong EEC, Guckenberger M, Andratschke N, et al. Variation in current prescription practice of stereotactic body radiotherapy for peripherally located early stage non-small cell lung cancer: Recommendations for prescribing and recording according to the ACROP guideline and ICRU report 91. Radiother Oncol 2020;142:217-23. [Crossref] [PubMed]
29. Hu K, Wang W, Liu X, et al. Comparison of treatment outcomes between squamous cell carcinoma and adenocarcinoma of cervix after definitive radiotherapy or concurrent chemoradiotherapy. Radiat Oncol 2018;13:249. [Crossref] [PubMed]
30. Gadducci A, Guerrieri ME, Cosio S. Adenocarcinoma of the uterine cervix: Pathologic features, treatment options, clinical outcome and prognostic variables. Crit Rev Oncol Hematol 2019;135:103-14. [Crossref] [PubMed]
31. Liu P, Ji M, Kong Y, et al. Comparison of survival outcomes between squamous cell carcinoma and adenocarcinoma/adenosquamous carcinoma of the cervix after radical radiotherapy and chemotherapy. BMC Cancer 2022;22:326. [Crossref] [PubMed]
32. Ye Y, Zhang G, Li Z, et al. Initial treatment for FIGO 2018 stage IIIC cervical cancer based on histological type: A 14-year multicenter study. Cancer Med 2023;12:19617-32. [Crossref] [PubMed]
33. Mak RH, Hermann G, Lewis JH, et al. Outcomes by tumor histology and KRAS mutation status after lung stereotactic body radiation therapy for early-stage non-small-cell lung cancer. Clin Lung Cancer 2015;16:24-32. [Crossref] [PubMed]
34. Miyakawa A, Shibamoto Y, Kosaki K, et al. Early response and local control of stage I non-small-cell lung cancer after stereotactic radiotherapy: difference by histology. Cancer Sci 2013;104:130-4. [Crossref] [PubMed]
35. Koshy M, Malik R, Mahmood U, et al. Stereotactic body radiotherapy and treatment at a high volume facility is associated with improved survival in patients with inoperable stage I non-small cell lung cancer. Radiother Oncol 2015;114:148-54. [Crossref] [PubMed]
36. Hörner-Rieber J, Bernhardt D, Dern J, et al. Histology of non-small cell lung cancer predicts the response to stereotactic body radiotherapy. Radiother Oncol 2017;125:317-24. [Crossref] [PubMed]
37. Inagaki T, Doi H, Ishida N, et al. Escalated Maximum Dose in the Planning Target Volume Improves Local Control in Stereotactic Body Radiation Therapy for T1-2 Lung Cancer. Cancers (Basel) 2022;14:933. [Crossref] [PubMed]
38. Katz MS, Weissferdt A, Antonoff MB. Histologic Subtypes of Non-Small Cell Lung Cancer: Can We Further Personalize Radiation Therapy? Int J Radiat Oncol Biol Phys 2023;115:906-8. [Crossref] [PubMed]
39. Resova K, Knybel L, Parackova T, et al. Survival analysis after stereotactic ablative radiotherapy for early stage non-small cell lung cancer: a single-institution cohort study. Radiat Oncol 2024;19:50. [Crossref] [PubMed]
40. Kozono D, Hua X, Wu MC, et al. Lung-MAP Next-Generation Sequencing Analysis of Advanced Squamous Cell Lung Cancers (SWOG S1400). J Thorac Oncol 2024;19:1618-29. [Crossref] [PubMed]
41. Zhang Y, Fu F, Zhang Q, et al. Evolutionary proteogenomic landscape from pre-invasive to invasive lung adenocarcinoma. Cell Rep Med 2024;5:101358. [Crossref] [PubMed]
42. Schuurbiers OC, Meijer TW, Kaanders JH, et al. Glucose metabolism in NSCLC is histology-specific and diverges the prognostic potential of 18FDG-PET for adenocarcinoma and squamous cell carcinoma. J Thorac Oncol 2014;9:1485-93. [Crossref] [PubMed]
43. Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nature 2018;553:446-54. [Crossref] [PubMed]
44. Yagishita S, Horinouchi H, Katsui Taniyama T, et al. Epidermal growth factor receptor mutation is associated with longer local control after definitive chemoradiotherapy in patients with stage III nonsquamous non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 2015;91:140-8. [Crossref] [PubMed]
45. Kris MG, Johnson BE, Berry LD, et al. Using multiplexed assays of oncogenic drivers in lung cancers to select targeted drugs. JAMA 2014;311:1998-2006. [Crossref] [PubMed]
46. Hu C, Wu S, Deng R, et al. Radiotherapy with continued EGFR-TKIs for oligoprogressive disease in EGFR-mutated non-small cell lung cancer: A real-world study. Cancer Med 2023;12:266-73. [Crossref] [PubMed]
47. Sebastian NT, Webb A, Shilo K, et al. A PI3K gene expression signature predicts for recurrence in early-stage non-small cell lung cancer treated with stereotactic body radiation therapy. Cancer 2023;129:3971-7. [Crossref] [PubMed]
48. Jeong Y, Hoang NT, Lovejoy A, et al. Role of KEAP1/NRF2 and TP53 Mutations in Lung Squamous Cell Carcinoma Development and Radiation Resistance. Cancer Discov 2017;7:86-101. [Crossref] [PubMed]
49. Saleh MM, Scheffler M, Merkelbach-Bruse S, et al. Comprehensive Analysis of TP53 and KEAP1 Mutations and Their Impact on Survival in Localized- and Advanced-Stage NSCLC. J Thorac Oncol 2022;17:76-88. [Crossref] [PubMed]
50. Scalera S, Mazzotta M, Corleone G, et al. KEAP1 and TP53 Frame Genomic, Evolutionary, and Immunologic Subtypes of Lung Adenocarcinoma With Different Sensitivity to Immunotherapy. J Thorac Oncol 2021;16:2065-77. [Crossref] [PubMed]
51. Altorki NK, Bhinder B, Borczuk AC, et al. A signature of enhanced proliferation associated with response and survival to anti-PD-L1 therapy in early-stage non-small cell lung cancer. Cell Rep Med 2024;5:101438. [Crossref] [PubMed]
52. Sen A, Karati D. An insight into thymidylate synthase inhibitor as anticancer agents: an explicative review. Naunyn Schmiedebergs Arch Pharmacol 2024;397:5437-48. [Crossref] [PubMed]
53. D'Angelillo RM, Ramella S. Are We Ready for Histology-Driven Stereotactic Ablative Radiotherapy? J Thorac Oncol 2018;13:1441-2. [Crossref] [PubMed]
54. Nicolson MC, Fennell DA, Ferry D, et al. Thymidylate synthase expression and outcome of patients receiving pemetrexed for advanced nonsquamous non-small-cell lung cancer in a prospective blinded assessment phase II clinical trial. J Thorac Oncol 2013;8:930-9. [Crossref] [PubMed]
55. Zhang Y, Li X, Ren X, et al. Nanozymes as Glucose Scavengers and Oxygenerators for Enhancing Tumor Radiotherapy. ACS Appl Mater Interfaces 2024;16:61805-19. [Crossref] [PubMed]
56. Kokeza J, Strikic A, Ogorevc M, et al. The Effect of GLUT1 and HIF-1α Expressions on Glucose Uptake and Patient Survival in Non-Small-Cell Lung Carcinoma. Int J Mol Sci 2023;24:10575. [Crossref] [PubMed]
57. Klement RJ, Sweeney RA. Metabolic factors associated with the prognosis of oligometastatic patients treated with stereotactic body radiotherapy. Cancer Metastasis Rev 2023;42:927-40. [Crossref] [PubMed]
58. Gurtner K, Kryzmien Z, Koi L, et al. Radioresistance of KRAS/TP53-mutated lung cancer can be overcome by radiation dose escalation or EGFR tyrosine kinase inhibition in vivo. Int J Cancer 2020;147:472-7. [Crossref] [PubMed]
59. Verma V. Personalized Radiation Therapy-Spurning the "One Size Fits All" Approach. JAMA Oncol 2023;9:1534-5. [Crossref] [PubMed]
60. Wen H, Liang C. Effects of Lobectomy versus Sub-Lobar Resection on the Survival in Adults with Stage IA Left Upper Lobe Non-Small Cell Lung Cancer: A Retrospective Cohort Study Based on the Surveillance, Epidemiology, and End Results Database. Oncology 2024;102:525-32. [Crossref] [PubMed]
61. Qiu LH, Song JQ, Jiang F, et al. Marital status impacts survival of stage I non-small-cell lung cancer: a propensity-score matching analysis. Future Sci OA 2024;10:FSO926. [Crossref] [PubMed]
62. Huh Y, Sohn YJ, Kim HR, et al. Sex differences in prognosis factors in patients with lung cancer: A nationwide retrospective cohort study in Korea. PLoS One 2024;19:e0300389. [Crossref] [PubMed]
63. Dwyer LL, Vadagam P, Vanderpoel J, et al. Disparities in Lung Cancer: A Targeted Literature Review Examining Lung Cancer Screening, Diagnosis, Treatment, and Survival Outcomes in the United States. J Racial Ethn Health Disparities 2024;11:1489-500. [Crossref] [PubMed]
64. Sakurai H, Asamura H, Goya T, et al. Survival differences by gender for resected non-small cell lung cancer: a retrospective analysis of 12,509 cases in a Japanese Lung Cancer Registry study. J Thorac Oncol 2010;5:1594-601. [Crossref] [PubMed]
65. Moreira AL, Ocampo PSS, Xia Y, et al. A Grading System for Invasive Pulmonary Adenocarcinoma: A Proposal From the International Association for the Study of Lung Cancer Pathology Committee. J Thorac Oncol 2020;15:1599-610. [Crossref] [PubMed]