Comparison of 5-year survival rates and prognostic factors between adenoid cystic carcinoma and squamous cell carcinoma of the maxillary sinus: a SEER database analysis

Introduction

Malignant tumors of the paranasal sinuses (PNS) are exceedingly rare, accounting for less than 1% of all human cancers and fewer than 3% of head and neck malignancies (1). Among them, maxillary sinus carcinoma (MSC) represents the most common subtype, yet its overall incidence remains very low, with age-standardized rates typically below 1 per 100,000 population per year (2). Recent population-based studies have shown that sinonasal cancer incidence has remained relatively stable in Europe over the past decade, whereas Surveillance, Epidemiology, and End Results (SEER)-based analyses in the United States suggest a slight decline after 2012 (1). Despite advances in diagnosis and treatment, survival outcomes remain unsatisfactory, with 5-year survival rates generally around 40–50% depending on tumor site and histology (3). The European Society for Medical Oncology (ESMO) and European Reference Network on Rare Adult Solid Cancers (EURACAN) guidelines further emphasize the rarity and heterogeneity of sinonasal malignancies, highlighting the need for histology-specific evidence to optimize management (4).

Maxillary sinus squamous cell carcinoma (MSSCC) typically presents as an aggressive neoplasm, often diagnosed at advanced stages due to the silent expansion of tumors within the sinus cavity (5). Consequently, the prognosis of MSSCC has historically been poor, with published series reporting 5-year overall survival (OS) rates of only 30–50% (6), and outcomes remain unsatisfactory even with modern multimodal treatment (5). In contrast, maxillary sinus adenoid cystic carcinoma (MSACC) usually follows a more indolent yet relentless course. Patients with adenoid cystic carcinoma (ACC) may achieve relatively favorable short-term survival—a recent meta-analysis reported a 5-year OS of approximately 68% for sinonasal ACC—but the tumor is notorious for perineural invasion and a high propensity for distant metastasis, leading to late relapses (7). As a result, long-term outcomes for MSACC deteriorate significantly, with 10-year survival dropping to around 40%, and distant metastases eventually developing in a large proportion of patients (7,8). This striking dichotomy in clinical course underscores the importance of histology-specific prognostic understanding for maxillary sinus cancers (6). Nevertheless, there remains a paucity of research directly comparing outcomes between MSACC and MSSCC. Most existing studies either aggregate all maxillary sinus cancers or focus on a single histologic subtype, limiting the ability to discern prognostic nuances between ACC and SCC in this location (6,9). Moreover, survival statistics for rare histologies such as ACC remain scarce, and no dedicated predictive model has been established to estimate long-term survival based on tumor subtype.

The present study aims to address this gap by comparing the 5-year survival rates and prognostic factors of MSACC and MSSCC using a population-based cohort. We further evaluate OS and cancer-specific survival (CSS), identify independent prognostic factors, and construct a predictive model. By clarifying histology-specific survival patterns and risk factors, this work seeks to improve prognostic accuracy and guide personalized treatment strategies for MSC. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0355/rc).

Methods

Patient selection

Clinical data of 82 patients with MSACC and 391 patients with MSSCC diagnosed between 2004 and 2019 were extracted from the SEER database. The variables collected included age, sex, race, pathological grade, tumor-node-metastasis (TNM) stage, treatment information (surgery, radiotherapy, and chemotherapy), survival time (months), and survival status. Patients were eligible for inclusion if they had a primary site coded as C31.0 (maxillary sinus), histologically confirmed ACC (ICD-O-3 code 8200/3) or squamous cell carcinoma (ICD-O-3 codes 8070/3–8078/3), malignant behavior (ICD-O-3 code /3), and microscopic confirmation of diagnosis. Only cases diagnosed between 2004 and 2019 with complete follow-up and staging information were included, and patients were required to have either a single primary tumor or to be the first of multiple primaries. Patients were excluded if they were identified solely by death certificate or autopsy, if follow-up data or key information such as survival status or survival time were missing, or if the histology type was other than MSACC or MSSCC. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Statistical analysis

All eligible patients were randomly divided into a training set (n=331, 70%) and a validation set (n=142, 30%) using a 7:3 ratio. The training set was used to construct the prognostic model, while the validation set served for internal validation. Baseline characteristics were compared using the Chi-squared test, with P<0.05 considered statistically significant. OS and CSS were defined as the primary endpoints. Survival curves were estimated by the Kaplan-Meier method, and group differences were evaluated using the log-rank test. Prognostic factors were initially screened by univariate Cox proportional hazards regression, and significant variables were subsequently entered into multivariate Cox regression models to identify independent predictors. A nomogram was then developed based on the independent prognostic factors to predict 3- and 5-year OS and CSS. Model performance was assessed using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves at 3 and 5 years, and calibration plots comparing predicted versus observed outcomes. Decision curve analysis (DCA) was further applied to evaluate the clinical net benefit of the model. All analyses were performed in R software (version 4.4.2).

Results

Patient characteristics analysis

Baseline characteristics of the two sets are summarized in Table 1. No significant differences were observed between the training and validation sets in terms of tumor type, age, sex, race, pathological grading, T stage, M stage, surgery, radiotherapy, and chemotherapy (all P>0.05), except for N stage, which showed a higher proportion of positive cases in the training set (29.61% vs. 19.01%, P=0.01). These results indicate that the two cohorts were generally well balanced and suitable for subsequent model construction and validation.

Survival analysis

Survival outcomes are summarized in Table 2 and illustrated in Figure 1. Patients with MSACC exhibited markedly better survival compared with those with MSSCC. The 3- and 5-year median OS rates were 73.2% [interquartile range (IQR), 69.5–75.6%] and 58.5% (IQR, 54.9–62.2%) for MSACC, respectively, while the corresponding rates for MSSCC were 39.6% (IQR, 38.1–41.4%) and 32.7% (IQR, 31.2–34.3%). Similarly, the 3- and 5-year CSS rates were 78.0% (IQR, 75.6–81.7%) and 64.6% (IQR, 61.0–68.3%) for MSACC, compared with 51.2% (IQR, 49.6–52.9%) and 46.3% (IQR, 44.5–47.8%) for MSSCC. Kaplan-Meier analysis confirmed a significant survival advantage for MSACC patients compared with MSSCC patients (log-rank P<0.001).

Figure 1 Kaplan-Meier survival curves of patients with MSACC and MSSCC. (A) Overall survival; (B) cancer-specific survival. ACC, adenoid cystic carcinoma; CI, confidence interval; HR, hazard ratio; MSACC, maxillary sinus adenoid cystic carcinoma; MSSCC, maxillary sinus squamous cell carcinoma; NA, not available; SCC, squamous cell carcinoma.

Univariate and multivariable Cox regression analysis

For OS, univariate analysis showed that age ≥60 years, advanced T stage, N stage, M stage, surgery, and radiotherapy were significantly associated with prognosis. In the multivariable model, age ≥60 years, T3, T4a, and T4b, absence of surgery, and absence of radiotherapy remained independent predictors of worse OS.

For CSS, univariate analysis identified age, T stage, N stage, M stage, surgery, and radiotherapy as significant factors. Multivariable analysis confirmed that age ≥60 years, T4b stage, positive N stage, absence of surgery, and absence of radiotherapy were independent adverse prognostic factors.

Overall, age, tumor stage, N stage, surgery, and radiotherapy were the most important determinants of survival, while chemotherapy had no significant effect. These factors were incorporated into the prognostic model. The detailed results are summarized in Tables 3,4.

Model construction

All variables with P<0.05 in the univariate Cox regression analysis were incorporated into the prognostic model. A nomogram was then constructed to predict 3- and 5-year OS and CSS in patients with MSC. Each factor was assigned a weighted score according to its regression coefficient, and the total score corresponded to the predicted survival probability. The combined nomogram for OS and CSS is provided in Figure S1.

Model validation

The predictive performance and clinical utility of the constructed nomograms were comprehensively evaluated. For the OS-based model, ROC curves and time-dependent C-index analyses showed good discrimination, with AUCs of 0.719 and 0.761 in the training and validation sets, respectively, and C-index values of 0.69 and 0.71. Calibration plots indicated favorable agreement between predicted and observed survival probabilities, while DCA demonstrated a higher net clinical benefit across a wide range of threshold probabilities. Similar results were obtained for the CSS-based model, with stable discrimination, good calibration, and satisfactory clinical utility. Moreover, Schoenfeld residual analyses confirmed that the proportional hazards assumption was not violated, supporting the robustness of the Cox regression models (Figures 2-5).

Figure 2 Validation of the prognostic model for OS. (A) ROC curve for 3-year OS prediction in the training and validation sets. (B) ROC curve for 5-year OS prediction in the training and validation sets. (C) Time-dependent C-index curves for the training and validation sets. (D) Calibration plot for 3-year OS in the training set. (E) Calibration plot for 3-year OS in the validation set. (F) Calibration plot for 5-year OS in the training set. (G) Calibration plot for 5-year OS in the validation set. (H) DCA curve for 3-year OS in the training set. (I) DCA curve for 3-year OS in the validation set. (J) DCA curve for 5-year OS in the training set. (K) DCA curve for 5-year OS in the validation set. AUC, area under the curve; C-index, concordance index; CI, confidence interval; DCA, decision curve analysis; OS, overall survival; ROC, receiver operating characteristic.

Figure 3 Schoenfeld residual plots for OS. Schoenfeld residuals for each covariate included in the Cox proportional hazards model are displayed. The black points represent residuals, the red dashed line indicates the fitted smoothing curve, the blue dashed line represents the expected value under the proportional hazards assumption, and the shaded area indicates the 95% confidence interval of the fitted curve. No obvious violation of the proportional hazards assumption was observed. cM; cN; cT; OS, overall survival.

Figure 4 Validation of the prognostic model for CSS. (A) ROC curve for 3-year CSS prediction in the training and validation sets. (B) ROC curve for 5-year CSS prediction in the training and validation sets. (C) Time-dependent C-index curves for the training and validation sets. (D) Calibration plot for 3-year CSS in the training set. (E) Calibration plot for 3-year CSS in the validation set. (F) Calibration plot for 5-year CSS in the training set. (G) Calibration plot for 5-year CSS in the validation set. (H) DCA curve for 3-year CSS in the training set. (I) DCA curve for 3-year CSS in the validation set. (J) DCA curve for 5-year CSS in the training set. (K) DCA curve for 5-year CSS in the validation set. AUC, area under the curve; C-index, concordance index; CI, confidence interval; CSS, cancer-specific survival; DCA, decision curve analysis; ROC, receiver operating characteristic.

Figure 5 Schoenfeld residual plots for CSS. Schoenfeld residuals for each covariate included in the Cox proportional hazards model are displayed. The black points represent residuals, the red dashed line indicates the fitted smoothing curve, the blue dashed line represents the expected value under the proportional hazards assumption, and the shaded area indicates the 95% confidence interval of the fitted curve. No obvious violation of the proportional hazards assumption was observed. cM; cN; cT; CSS, cancer-specific survival.

Discussion

In this large SEER-based cohort study, we compared the survival outcomes of patients with MSACC and MSSCC and identified prognostic factors. Patients with MSACC had significantly higher 3- and 5-year OS and CSS compared with those with MSSCC, consistent with the distinct biological behavior of these histological subtypes. Importantly, multivariable Cox regression demonstrated that age ≥60 years, advanced T stage, and absence of surgery were independent adverse prognostic factors, while radiotherapy was associated with improved survival. These findings highlight that histology-specific differences strongly influence prognosis (3,5,7,10). However, MSACC and MSSCC should be regarded as biologically distinct diseases rather than interchangeable histologic variants. Therefore, the present comparison was intended to identify histology-specific prognostic patterns within the same anatomical site.

The observed survival advantage in MSACC over MSSCC aligns with prior reports showing that ACC tends to follow a more indolent course, despite frequent perineural invasion and late M stage metastasis (11,12). This biological distinction also has important clinical implications, because ACC is typically characterized by slow but relentless progression with a propensity for late distant relapse, whereas SCC more often follows a locally aggressive course (13,14). Published series consistently report 5-year OS rates of 60–70% for sinonasal ACC, compared to only 30–50% for sinonasal SCC (15,16). Our results are in line with these trends, as MSACC patients achieved 5-year OS of nearly 60%, whereas MSSCC patients demonstrated significantly poorer survival. Interestingly, our analysis also confirmed that even with advances in multimodal therapy, survival outcomes for MSSCC remain unsatisfactory, echoing prior European and North American studies (17). By contrast, the decline in long-term survival of MSACC patients underscores the need for vigilant follow-up, given the high risk of late relapses and M stage metastasis (18,19).

In addition to histology, several clinical variables were identified as key prognostic factors. Age ≥60 years was associated with significantly worse OS and CSS, consistent with prior SEER and institutional studies that highlight age as a robust predictor of poor outcomes in sinonasal malignancies (20). Advanced T stage (T3–T4b) was also independently associated with inferior prognosis, reflecting the challenges of achieving clear margins in locally advanced tumors occupying critical anatomical sites (21). Surgery emerged as the most important favorable prognostic factor, reaffirming that complete surgical resection remains the cornerstone of treatment whenever feasible (22). Moreover, radiotherapy significantly improved survival in both OS and CSS models, underscoring its value as an adjuvant modality, especially for unresectable or advanced stage cases (23). Chemotherapy, however, did not demonstrate a survival benefit in our analysis, echoing previous reports suggesting its limited role in MSC (1,24). Interestingly, although N stage was an independent predictor of CSS, it did not significantly affect OS in our multivariable analysis. This divergence may be explained by the influence of competing risks, particularly among older patients, where non-cancer-related mortality attenuates the prognostic effect of nodal metastasis on OS. Similar patterns have been documented in prior population-based analyses of head and neck malignancies, in which competing causes of death substantially reduced the apparent impact of nodal disease on OS (25,26). Moreover, when cause-specific survival is analyzed under competing-risk frameworks, nodal involvement consistently emerges as an independent adverse determinant. Hu et al. demonstrated that positive nodal status significantly increased the cumulative incidence of cancer-specific death in patients with MSC using a Fine-Gray competing-risk model (27). Likewise, Wang et al. found that higher lymph-node ratios provided stronger and more durable discrimination for CSS than for OS in hypopharyngeal SCC, underscoring that non-cancer mortality may obscure the true oncologic effect of nodal burden (28). Collectively, these findings support the interpretation that nodal metastasis primarily drives cancer-specific outcomes, while its prognostic effect on OS is diluted by age- and comorbidity-related competing events. Taken together, these results highlight the importance of integrating tumor biology, patient age, and treatment modality into personalized prognosis. Our predictive nomogram, constructed from these variables, may therefore provide a histology-aware reference for risk stratification and long-term survival estimation in MSC (2,17,27), although its interpretation should remain grounded in the distinct biology and treatment paradigms of ACC and SCC.

Limitations

First, the retrospective design and reliance on the SEER database introduce the possibility of selection bias and coding inaccuracies. Second, SEER lacks detailed information on several clinically important variables, including surgical margin status, perineural invasion, radiotherapy techniques and dose, systemic therapy details, and molecular biomarkers, which may affect outcomes and limit treatment-related interpretation (3,29). These missing variables may introduce residual confounding and should be considered when interpreting the observed associations between treatment modalities and survival. Third, the rarity of MSACC resulted in a limited sample size, which may reduce the statistical power of subgroup analyses. Fourth, ACC is characterized by a prolonged natural history with late recurrences and delayed distant metastasis; therefore, focusing on 5-year OS and CSS may not fully capture its long-term disease burden and may underestimate the true prognosis of MSACC. Finally, external validation of our predictive model in independent, non-SEER populations and prospective cohorts is warranted to confirm its generalizability (4,30). Although our nomogram integrates key demographic and treatment variables, it should be interpreted as an exploratory prognostic tool rather than a definitive clinical decision-support instrument.

Conclusions

In summary, patients with MSACC exhibited better survival outcomes than those with MSSCC in this SEER-based cohort. age, tumor stage, surgery, and radiotherapy were identified as key prognostic factors. However, these findings should be cautious given the retrospective design and the lack of external validation. The proposed nomogram may serve as a preliminary tool for prognostic evaluation and risk stratification in patients with MSC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0355/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0355/prf

Funding: The study was supported by the 2021 Qinghai Kunlun Elite High-end Innovation and Entrepreneurship Talent Program (2021-13), and the 2025 Kunlun Talents · High level Health Talents Project.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0355/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. All personal identifying information for patients was anonymized by Surveillance, Epidemiology, and End Results (SEER), so ethical approval and informed consent were waived.

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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