Prognostic factors and survival outcomes of multimodal treatment in sinonasal neuroendocrine tumors in a population based SEER analysis.

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Francesca Carosi, Laura Fabbri, Giuseppe Lamberti, Elisa Andrini, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9193689/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Purpose Sinonasal neuroendocrine carcinomas (SNECs) are rare and aggressive malignancies with heterogeneous histopathology and limited evidence guiding optimal treatment. This study aims to characterize clinicopathological features, treatment patterns, and prognostic factors using a large population-based database. Methods We queried the SEER database (2000–2022) for histologically confirmed SNECs. Kaplan-Meier analysis was used to estimate overall survival (OS) and disease-specific survival (DSS). Univariate and multivariate Cox models were employed to identify independent prognostic factors. Results A total of 389 cases were identified, mostly male (58.4%) with a median age of 60 years and nasal cavity as primary site (48.1%). Neuroendocrine carcinoma not otherwise specified (50.1%) was the most frequent, followed by small cell (35.0%) and large cell carcinoma (10.5%). Nearly 38% of patients presented with metastatic disease. Surgery, radiotherapy, and chemotherapy were administered in 54.1%, 66.9%, and 69.9% of cases, respectively. Kaplan-Meier analysis showed improved OS and DSS in patients with localized/locally advanced stage, lower tumor grade, nasal cavity primary site, and those treated with surgery, radiotherapy (particularly adjuvant), or chemotherapy (all p < 0.05). In multivariate models, primary sites outside the nasal cavity were independently associated with worse OS and DSS, while surgery and radiotherapy remained the only treatment modality significantly associated with improved outcomes. Conclusion In this large population-based study, SNECs were characterized by advanced-stage presentation and use of multimodal therapy. Radiotherapy was independently associated with improved OS/DSS in multivariable models, supporting aggressive local treatment in SNECs and underscoring the need for therapeutic strategies tailored to disease stage and tumor location. Sinonasal malignancies neuroendocrine carcinoma multimodal treatment rare tumors head and neck cancer Figures Figure 1 Figure 2 1. Introduction Neuroendocrine neoplasms (NENs) of the head and neck are extremely rare, accounting for less than 1% of malignancies in this region, with an estimated incidence of 0.1–0.15 per million in Europe [ 1 , 2 ]. Among these, sinonasal NENs comprise a heterogeneous group of tumors arising in the paranasal sinuses, such as olfactory neuroblastomas and neuroendocrine carcinomas (NECs), and represent approximately 5% of malignancies at these sites [ 3 ]. The histopathological classification of sinonasal NENs is challenging due to their rarity and histologic heterogeneity. The 5th edition of the Head and Neck World Health Organization (WHO) classification defines well-differentiated epithelial NENs as neuroendocrine tumors (NETs), previously referred to as carcinoid tumors, which are further subclassified into typical and atypical according to differentiation grade, and poorly differentiated forms as NECs, which are further subtyped into small-cell (SCNECs) and large-cell neuroendocrine carcinomas (LCNECs) [ 4 ]. Accurate histopathological diagnosis is difficult, as NECs must be distinguished from esthesioneuroblastoma, sinonasal undifferentiated carcinoma (SNUC), melanoma, and other small round blue cell tumors. Primary carcinoid tumors of the sinonasal tract are particularly rare, comprising less than 0.5% of sinonasal tumors, most frequently occurring in males aged 40–60 years. [ 5 , 6 ] SCNECs typically present at locally advanced stages but show a relatively low rate of distant metastases, with nasal cavity and septum and ethmoid sinus as the most commonly involved sites. [ 7 – 9 ] In contrast, LCNECs tend to affect older patients, are often associated with p53 overexpression and have worse prognosis. [ 10 ] Due to nonspecific symptoms caused by the anatomical characteristics of the sinonasal region, these tumors are often diagnosed late, when rapid tumor growth leads to mass effect-related complaints. Unlike other head and neck cancers, traditional staging appears to have limited prognostic value; instead, histopathological grade has emerged as a more reliable predictor of outcome [ 9 , 11 ]. Irrespective of histological subtype, surgery remains a mainstay of treatment. However, the proximity of tumors to critical structures, such as the orbit and anterior skull base, further complicates management and limits surgical options. Retrospective studies suggest that well-differentiated tumors may be managed by surgery alone, whereas moderately or poorly differentiated tumors often require multimodal approaches incorporating postoperative RT. Notably, chemotherapy as unimodal treatment has demonstrated poor outcomes, with no long-term survivors reported [ 9 ]. A major challenge lies in the complexity of the histopathological classification and the risk of misdiagnosis of these neoplasms. Additionally, although several population-based analyses have examined sinonasal malignancies using large registries such as SEER or NCDB, most studies have focused on heterogeneous histologic groups or broader sinonasal cancer populations. Data specifically addressing SNECs remain limited and are often derived from smaller cohorts [ 12 – 17 ], shorter timeframes, or analyses not incorporating disease-specific survival. The present study aims to address this gap by providing a contemporary population-based analysis of SNECs over a long study period, with dedicated evaluation of overall and disease-specific survival and real-world treatment patterns. In the present study, we analyzed patients with SNECs from the Surveillance, Epidemiology, and End Results (SEER) database to assess demographic characteristics, prognostic factors, treatment patterns, overall survival (OS), and disease-specific survival (DSS) of these extremely rare tumors. 2. Materials and methods 2.1. Data extraction We queried the Incidence-SEER research data (17 Registries, November 2024 submission) for years 2000 to 2022 for sinonasal tract malignancies with confirmed histological diagnosis using International Statistical Classification of Diseases for Oncology, 3rd edition (ICD-O-3) topography codes. The codes included were those for structures comprising the sinonasal tract, namely the nasal cavity (C30.0) and paranasal sinuses (C31.0, C31.1, C31.2, C31.3, C31.8, and C31.9). Subsequently, we selected cases with ICD-O-3 histology and behavior codes corresponding to typical carcinoid (8240/3), atypical carcinoid (8249/3), small cell neuroendocrine carcinoma (8041/3), large cell neuroendocrine carcinoma (8013/3), and neuroendocrine carcinoma, not otherwise specified (NOS) (8246/3). SEER data were extracted using SEER*Stat 8.4.5 (National Cancer Institute, Bethesda, MD) software. The study was conducted in accordance with the SEER data-use agreement. The data were extracted and compiled into an Excel database prior to statistical analysis. Data collected from the SEER database included demographic, clinicopathological, and treatment-related variables, specifically: age, sex, ethnicity, marital status, year of diagnosis, histology, primary tumor site, grade, stage, type of surgical treatment, radiotherapy (RT) and its sequence with surgery, chemotherapy and its sequence, metastatic sites, and cause of death. For multivariate analysis, categorical variables were dichotomized based on clinically and prognostically relevant characteristics to enhance model interpretability. Primary tumor site was grouped as nasal cavity (NC) versus non-nasal cavity (non-NC, all other sites), given the generally more favorable outcomes associated with tumors arising in the NC. Because AJCC T and M categories were missing or not recorded for a substantial proportion of cases across the study period, these variables were not used as primary stratifiers in the main analyses; instead, disease extent was assessed using SEER Summary Stage, which provides more complete and consistent coverage in population-based datasets spanning multiple decades. The SEER summary stage was collapsed into L + R (localized and regional) versus D (distant) to reflect the well-established prognostic distinction between confined and disseminated disease. Tumor grade was divided into low grade (G1-G2) and high grade (G3-G4), consistent with the poorer prognosis typically associated with high-grade tumors. However, it is important to note that the SEER grading system does not accurately capture the biological heterogeneity of neuroendocrine carcinomas, which cannot be fully resolved by a simple low- versus high-grade dichotomy. This categorization was therefore adopted for analytic purposes, despite these intrinsic limitations. Histological subtype (hist) was grouped as SC (small cell), LC (large cell), NEC NOS, carcinoid, atypical carcinoid. For multivariable analyses, histology was modeled using a dichotomous classification (small cell vs non–small cell neuroendocrine carcinoma) to ensure model stability and adequate statistical power, given the limited sample size of individual histologic subgroups and the high proportion of neuroendocrine carcinoma NOS. Surgical treatment was analyzed as Yes versus No. For survival analyses, the original SEER variable “surgery type” was reclassified into five clinically meaningful categories as follows: No surgery, Surgery not otherwise specified (SurNOS), Therapeutic procedures (including radical surgery, total surgical removal, and simple or partial surgery), Local/diagnostic procedures (including polipectomy, excisional biopsy, and local excision), and Debulking surgery. This categorization allowed differentiation between the absence of surgery, diagnostic or limited surgical approaches, definitive therapeutic resections, and non-curative debulking interventions. RT was treated as Yes versus No. The RT sequence was dichotomized as RT ADJ (adjuvant RT) versus No, excluding neoadjuvant and other sequences. Chemotherapy sequence (cht_sur) was categorized as CHT ADJ (adjuvant chemotherapy) versus No, excluding perioperative or neoadjuvant regimens. Additionally, a binary variable for chemotherapy administration (CHT) was included (Yes vs No). Age was analyzed as a continuous variable in all models. Multimodal treatment was operationalized as predefined combinations of surgery, radiotherapy, and chemotherapy. Hazard Ratio (HR) with 95% confidence intervals (CI) and p-values were reported. A p-value < 0.05 was considered statistically significant. The primary endpoint was to identify clinicopathological and treatment-related prognostic factors for OS and DSS. 2.2. Statistical analysis All statistical analyses were performed using R software (version 4.4.3), with packages including survival, survminer, and forestmodel. OS and DSS were estimated using Kaplan-Meier survival analysis. OS was defined as the time from diagnosis to death from any cause or last follow-up, while DSS was defined as the time from diagnosis to death specifically attributed to the tumor. For DSS, the event was defined based on the SEER cause-specific death classification: deaths coded as “T” (tumor-related) were considered events, whereas deaths from other causes (“Other”) or patients alive at the last follow-up were censored. Analyses were conducted on a complete-case basis. Observations with missing values for any of the variables included in the multivariate model were excluded. Survival curves were compared using the log-rank test. Survival probabilities at 12 months were extracted from Kaplan–Meier estimates to calculate OS and DSS rates at one year. Cox proportional hazards regression was used to identify prognostic factors. Univariate Cox models were first applied to all available covariates. Variables with p < 0.05 in univariate analysis were then considered for multivariate analysis. Multivariate Cox models for OS and DSS were constructed using a complete-case approach. The proportional hazards assumption was verified using Schoenfeld residuals. 3. Results 3.1. Patient characteristics A total of 389 cases of sinonasal neuroendocrine tumors were reported in the SEER database between 2000 and 2022 (Table 1 ). The median age at diagnosis was 60 years (range, 17–89 years), with a slight male predominance (58.4%). Most patients were Caucasian (83.0%) and married (60.8%) at the time of diagnosis. The majority of cases (mean primaries per patient: 1.34) represented single primary tumors. The nasal cavity was the most common primary site (48.1%), followed by the ethmoid sinus (17.7%), and maxillary sinus (16.2%). Regarding histological classification, over half of tumors were neuroendocrine carcinoma NOS (50.1%), while SCNEC accounted for 35.0% and LCNEC for 10.5%. Carcinoid and atypical carcinoid types were rare (3.1% and 1.3%, respectively), and were excluded from later survival analysis due to small numbers. Tumor differentiation was frequently high: 22.4% were poorly differentiated (grade 3) and 26.7% undifferentiated/anaplastic (grade 4), while nearly half (45.8%) had unspecified histologic grade. In terms of disease extent at presentation according to SEER summary stage, 37.9% had distant metastases (D), 48.4% regional disease (R), and only 13.7% localized (L). Treatment patterns mirrored the severe presentation: just over half (54.1%) underwent surgery, mostly less-extensive procedures such as local excision or partial surgery. Lymph node surgery was rare. RT was used in 66.9%, predominantly in the adjuvant setting (37.6%), and chemotherapy was employed in nearly 70% of cases, with adjuvant chemotherapy (37.4%) being the most common sequence. The mean delay from diagnosis to any treatment was 26.5 days (SD, 39.0), and 20.9 days (SD, 23.0) to surgery. Table 1 Demographic and clinicopathological characteristics. Variable Category N (%) Age at diagnosis Median (range) 60 years (17–89) Sex Female 162 (41.6) Male 227 (58.4) Race White 323 (83.0) Black 28 (7.2) Other 37 (9.5) Marital status Married 227 (60.8) Not married 146 (39.2) NA 16 Histology Neuroendocrine carcinoma NOS 195 (50.1) Small cell carcinoma 136 (35.0) Large cell carcinoma 41 (10.5) Carcinoid 12 (3.1) Atypical carcinoid 5 (1.3) Primary tumor site Nasal cavity 187 (48.1) Ethmoid sinus 69 (17.7) Maxillary sinus 63 (16.2) Sphenoid sinus 34 (8.7) Accessory sinus 18 (4.6) Other sinus 18 (4.6) SEER stage Localized 51 (13.7) Regional 180 (48.4) Distant 141 (37.9) NA 17 AJCC T category T1-T2 52 (25.1) T3-T4 155 (74.9) Tx/Unknown 175 AJCC N category N0 150 (60.7) N+ 59 (39.3) NA 180 AJCC M category M0 182 (87.0) M1 35 (13.0) NA 172 Surgery Yes 210 (54.1) No 178 (45.9) NA 1 Radiotherapy Yes 253 (66.9) No 125 (33.1) NA 11 Type of radiotherapy treatment Adjuvant radiotherapy 142 (37.6) None/Unknown 235 (62.4) Chemotherapy Yes 272 (69.9) No 117 (30.1) Type of chemotherapy Adjuvant chemotherapy 106 (37.4) None/Unknown 177 (62.6) NA: Not available. AJCC: American Joint Committee on Cancer. 3.2. Survival analysis Kaplan-Meier analysis identified several variables significantly associated with OS. Patients with regional or localized SEER stage had significantly better OS compared to those with distant stage (p < 0.0001) (Fig. 1 ), similarly to AJCC stage (p = 0.038). In particular, median OS varied as follows: 15 months (95% CI, 12–22) for distant (D), 45 months (95% CI, 27–104) for regional (R), and 112 months (95% CI, 62–not reached) for localized disease (L). Surgical treatment was significantly associated with improved OS (p < 0.0001). Surgical treatment type also had a prognostic impact (p < 0.0001), with therapeutic and local/diagnostic procedures (polipectomy, local excision, or excisional biopsy) associated with improved OS compared with therapeutic or debulking surgery, while all surgical approaches conferred a survival advantage over no surgery. Adjuvant RT was associated with a significant survival benefit compared with no RT or neoadjuvant RT (p = 0.001). Patients receiving any RT demonstrated superior OS compared with those who did not (p = 0.002). Similarly, the use of adjuvant chemotherapy was significantly correlated with improved OS (p = 0.032). Tumor grade showed a prognostic effect, with low grade tumors associated with longer OS than high-grade tumors (p = 0.0045). Finally, marital status (married vs. not married, p = 0.035) and primary site (nasal vs. non-nasal cavity, p = 0.026) were also significantly associated with OS. In the Kaplan-Meier analysis, no significant association with OS was observed for sex, ethnicity, histologic subtype (small cell vs others), AJCC T and N category, as the log-rank test did not yield statistically significant differences between groups (p > 0.05). Kaplan-Meier curves for all significantly associated with OS are provided in the Supplementary Materials . OS: overall survival. D: distant disease. L: localized disease. R: regional disease. Kaplan-Meier analysis revealed that DSS was significantly improved in patients who underwent surgery (p < 0.0001) (with the highest survival observed in patients undergoing therapeutic procedures), received RT (p < 0.0001), particularly in the adjuvant setting (p < 0.0001), and those treated with chemotherapy (p = 0.0019). In contrast, adjuvant chemotherapy versus no chemotherapy did not reach statistical significance (p = 0.091). Patients with localized or regional disease had significantly better DSS compared to those with distant stage (p = 0.002) (Fig. 2 ). High-grade tumors (p = 0.011) and histological subtype (LC, NOSNE, SC; p = 0.021) was also characterized by worse DSS. All survival curves are provided in the Supplementary Materials . DSS: disease-specific survival. D: distant disease. L: localized disease. R: regional disease. 3.3. Multimodal treatment strategies Among surgically treated patients, RT was the only treatment modality associated with improved OS. Both surgery plus RT and surgery plus adjuvant RT showed significantly better OS compared with surgery alone (p = 0.0047 and p = 0.039, respectively), with concordant reductions in all-cause mortality in Cox analyses. Chemotherapy, either overall or adjuvant, was not associated with OS benefit. In the four-group multimodal analysis, OS differed significantly across strategies (p = 0.00062), with surgery plus RT showing the most favorable outcomes, while surgery plus chemotherapy alone was associated with significantly worse OS. No significant DSS benefit was observed with the addition of RT or chemotherapy among surgically treated patients. In the four-group multimodal model, DSS differed significantly across treatment strategies (p = 0.031), driven by worse DSS in patients treated with chemoradiotherapy without surgery. Kaplan–Meier curves and univariate analyses are reported in the Supplementary Figures and Tables . 3.4. Prognostic factors for OS In univariate Cox regression analysis, several variables were significantly associated with OS (Table 2 ). Among demographic factors, marital status was the only significant prognostic factor, with non-married patients experiencing worse outcomes (HR 1.33, p = 0.036). Regarding tumor characteristics, non-nasal cavity (non-NC) primary site (HR 1.35, p = 0.025), high-grade histology (HR 2.57, p = 0.006), and distant stage at diagnosis (HR 3.51, p < 0.001) were associated with worse OS, whereas patients with localized or regional disease had a more favorable prognosis (HR 0.56, p < 0.001). All treatment modalities showed protective effects, including surgery (HR 0.54, p < 0.001), RT (HR 0.43, p < 0.001), chemotherapy (HR 0.66, p = 0.002), adjuvant RT (HR 0.54, p < 0.001), and adjuvant chemotherapy (HR 0.70, p = 0.032). Histology, as well as T and N categories, did not show significant associations with survival. In the multivariate analysis including clinically relevant covariates, primary tumor site, disease stage, and local treatments remained independent prognostic factors. Non-NC tumors (HR 2.00, p < 0.001) and disseminated disease (HR 0.48, p < 0.001) were confirmed as adverse features, while surgery (HR 0.40, p < 0.001) and RT (HR 0.36, p < 0.001) independently improved OS. Conversely, adjuvant RT sequencing conferred no additional benefit beyond RT alone, and systemic therapy did not retain statistical significance in the multivariate model. Table 2 Univariate and multivariate Cox regression analysis for Overall Survival. Variable Univariate Analysis Multivariate Analysis HR (Univ.) 95% CI (Univ.) p-value (Univ.) HR (Multiv.) 95% CI (Multiv.) p-value (Multiv.) Primary site (NC vs non-NC) 1.35 1.04–1.74 0.025 2.0 1.4–2.86 < 0.001 Stage (L/R vs D) 0.56 0.43–0.73 < 0.001 0.48 0.33–0.7 < 0.001 Surgery (Yes vs No) 0.54 0.41–0.69 < 0.001 0.4 0.25–0.66 < 0.001 RT (Yes vs No) 0.43 0.33–0.56 < 0.001 0.36 0.2–0.64 < 0.001 RT adjuvant (Yes vs No) 0.54 0.41–0.72 < 0.001 1.11 0.52–2.38 0.784 CHT (Yes vs No) 0.66 0.50–0.86 0.002 0.69 0.38–1.23 0.206 CHT ADJ (Yes vs No) 0.7 0.51–0.97 0.032 1.87 0.87–4.01 0.110 OS: overall survival. HR: hazard ratio. CI: confidence interval. NC: nasal cavity. L: localized disease. R: regional disease. D: distant disease. RT: radiotherapy. CHT: chemotherapy. 3.5. Prognostic factors for DSS Univariate analysis identified several significant prognostic factors for DSS (Table 3 ). Primary tumor site and grade were strongly prognostic, with non-NC tumors (HR 2.33, p < 0.001) and high-grade tumors (HR 8.54, p = 0.034) associated with markedly worse DSS. Patients with localized/regional disease had improved outcomes (HR 0.53, p = 0.004), whereas distant metastases conferred poor prognosis (HR 2.60, p = 0.013). Histology showed a borderline adverse effect for small cell tumors (HR 1.54, p = 0.048). Surgical resection (HR 0.38, p < 0.001) and RT (HR 0.54, p = 0.006), particularly in the adjuvant setting, significantly reduced disease-specific mortality, while chemotherapy showed no benefit, with only a non-significant trend toward improved DSS for adjuvant CHT (HR 0.60, p = 0.066). In the multivariate analysis, non-NC primary site (HR 3.21, p < 0.001), surgery (HR 0.25, p = 0.002), and RT (HR 0.24, p < 0.001) remained independent prognostic factors. Systemic therapy, including adjuvant chemotherapy, was not independently associated with DSS, and disease stage showed only a non-significant trend. Overall, primary site, surgery, and radiotherapy emerged as the most robust independent determinants of DSS. Table 3 Univariate and multivariate Cox regression analysis for Disease-specific survival. Variable Univariate Analysis Multivariate Analysis HR (Univ.) 95% CI (Univ.) p-value (Univ.) HR (Multiv.) 95% CI (Multiv.) p-value (Multiv.) Primary site (NC vs non-NC) 2.33 1.47–3.69 < 0.001 3.21 1.78–5.77 < 0.001 Stage (L + R vs D) 0.53 0.34–0.81 0.004 0.59 0.33–1.07 0.083 Surgery (Yes vs No) 0.38 0.24–0.58 < 0.001 0.25 0.11–0.59 0.002 RT (Yes vs No) 0.54 0.34–0.84 0.006 0.24 0.11–0.52 < 0.001 RT adjuvant (Yes vs No) 0.53 0.33–0.83 0.006 1.33 0.4–4.41 0.639 CHT (Yes vs No) 0.655 0.501–0.856 0.002 1.58 0.67–3.75 0.296 CHT ADJ (Yes vs No) 0.6 0.35–1.03 0.066 1.32 0.39–4.5 0.653 DSS: disease-specific survival. HR: hazard ratio. CI: confidence interval. NC: nasal cavity. L: localized disease. R: regional disease. D: distant disease. RT: radiotherapy. CHT: chemotherapy. 4. Discussion This population-based analysis of 389 patients with sinonasal neuroendocrine carcinoma (SNEC) from the SEER database (2000–2022) represents one of the largest cohorts to date and provides valuable insights into prognostic determinants and therapeutic outcomes for these rare malignancies. Compared with previously published SEER- and NCDB-based series on sinonasal malignancies, the present analysis specifically focuses on neuroendocrine carcinoma histologies and spans a contemporary timeframe from 2000 to 2022. In addition to OS, we report DSS using SEER cause-specific death classification, thereby providing complementary prognostic information beyond all-cause mortality. Furthermore, we describe real-world treatment patterns, including RT and chemotherapy sequencing variables available in SEER, allowing a population-level assessment of multimodal management in this rare disease. Consistent with prior institutional and pooled series, our data confirm that primary tumor site, stage at presentation, and treatment modality remain the strongest predictors of both OS and DSS. These findings resonate with earlier institutional reports in high-grade sinonasal malignancies. For example, Kuo et al. [ 18 ] documented poor outcomes in sinonasal undifferentiated carcinoma (SNUC), with 5-year OS below 30% and highlighted the potential prognostic role of surgery and combined-modality therapy. Although SNUC represents a distinct pathological entity, these observations provide historical context for the importance of aggressive locoregional management in high-grade sinonasal tumors. Similarly, Gamez et al. [ 19 ] demonstrated improved outcomes with trimodality approaches, particularly in patients undergoing surgical resection. In a recent single-institution series of 19 high-grade head and neck NECs [ 14 ], median OS was 27 months, with markedly superior survival in radically treated patients (51 months) compared to those with metastatic disease (3 months), underscoring the critical role of multimodality therapy in localized cases. Our SEER data align with these observations, confirming the dismal prognosis of metastatic disease and the benefit of surgery as a cornerstone of therapy in non-metastatic patients. In our analysis, a strong association between RT and improved OS and DSS also emerged. Stefanovic et al. [ 20 ] demonstrated that RT was associated with reduced disease-specific mortality across sinonasal cancers, particularly when combined with surgery. Likewise, a large pooled meta-analysis of 701 cases [ 9 ] showed a survival advantage for surgery combined with postoperative RT, especially in poorly differentiated histologies, while chemotherapy as monotherapy was ineffective. The role of systemic therapy in SNEC remains controversial. In our cohort, chemotherapy showed a modest association with improved OS in univariate analysis but failed to retain significance in multivariate models, likely reflecting patient selection and the absence of treatment details in registry data. Although induction chemotherapy has been extensively investigated in SNUC and other high-grade sinonasal malignancies [ 16 , 21 – 23 ], extrapolation of these findings to SNEC remains limited, given the biological and pathological differences between entities. Accordingly, the value of chemotherapy in SNEC is likely context-dependent and should be individualized rather than routinely applied. Population-based data provide additional confirmation. The NCDB analysis by Issa et al. [ 24 ] on 415 patients with SNEC/SNUC confirmed the adverse prognostic impact of advanced T and N categories and demonstrated the benefit of multimodality therapy. However, given the mixed histologic composition of these cohorts, such findings should be interpreted as supportive rather than definitive for SNEC-specific management. Likewise, Patel et al. [ 25 ] reported a median DSS of 36 months for SNEC, with 5-year DSS of 70.2%, superior to SNUC and sinonasal small cell carcinoma, underscoring the distinct and somewhat more favorable biology of SNEC. As in Patel’s study, we found that tumors arising outside the nasal cavity carried a significantly worse prognosis, reflecting both biological aggressiveness and anatomic challenges to achieving radical local therapy. Guideline-based perspectives further contextualize our findings. The recent ESMO Clinical Practice Guidelines for head and neck neuroendocrine neoplasms [ 26 ] recommend multimodality management, with induction platinum-etoposide followed by RT or surgery in selected patients, reflecting the chemosensitive yet aggressive biology of these tumors. Similarly, the Italian consensus panel on recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) [ 27 ] emphasized multidisciplinary decision-making and integration of systemic therapies in selected scenarios, though evidence for chemotherapy efficacy in SNEC remains inconsistent. Our data, showing limited impact of chemotherapy on survival, align with the caution expressed in both consensus and guideline documents regarding the routinary use of systemic therapy outside of multimodal settings. Finally, our data corroborate a recent pooled analysis [ 20 ], which confirmed the prognostic value of stage, site, and treatment modality across sinonasal cancers, and add granularity by focusing specifically on SNEC. With regard for multimodal treatment associations, RT emerged as the only treatment component consistently associated with improved OS among surgically treated patients, whereas no clear survival benefit was observed with the addition of chemotherapy. Notably, the observed advantage of RT was confined to OS and did not translate into a significant improvement in DSS, suggesting that its apparent effect may be influenced, at least in part, by non–cancer-related mortality or by differences in patient selection. Indeed, patients receiving RT may represent a subgroup with better baseline performance status, improved access to care, or more favorable disease characteristics not fully captured within the SEER database. The violation of the proportional hazards assumption in some OS models further supports the possibility of a time-dependent or indirect effect of RT rather than a sustained disease-specific benefit. The lack of association between chemotherapy and survival outcomes, both in isolation and in combination with surgery and RT, should be interpreted with caution. SEER does not provide information on chemotherapy regimens, timing, dose intensity, or treatment intent, limiting the ability to distinguish potentially effective systemic approaches from less intensive or palliative strategies. Similarly, the heterogeneity observed in the four-group multimodal analysis likely reflects substantial clinical diversity, particularly among non-surgically treated patients, in whom worse DSS may be driven by advanced stage, comorbidity burden, or contraindications to surgery rather than by treatment strategy alone. In addition, the relatively small number of events in certain treatment subgroups reduces statistical power and limits the precision of hazard ratio estimates. While our findings are generally consistent with prior population-based analyses of high-grade sinonasal malignancies, the present study adds granularity by restricting the cohort to neuroendocrine carcinoma histologies and by contextualizing outcomes within contemporary treatment patterns. Nevertheless, as with other registry-based studies, the absence of central pathology review and detailed treatment parameters limits direct comparisons with institutional series and should be considered when interpreting the incremental contribution of our results. However, collectively, the evidence supports a treatment paradigm anchored in surgery and RT, while chemotherapy should be individualized, potentially serving as an induction or radiosensitizing strategy rather than a standalone modality and guided by treatment response or molecular predictors. This study has several limitations inherent to the SEER database. It should be emphasized that the SEER database lacks central pathology review, raising the possibility of histologic misclassification, particularly for neuroendocrine carcinoma not otherwise specified (NOS), which represent the vast majority, and for cases diagnosed in earlier years, when immunohistochemical and molecular characterization was less standardized. Therefore, our findings should be interpreted as population-level associations within sinonasal poorly differentiated neuroendocrine carcinomas as coded in SEER, rather than as evidence of uniform biology or behavior across distinct histopathological subtypes. Although descriptive analyses suggested histology-specific differences in DSS, histology was retained as a dichotomous variable (SCNEC vs other histology) in the multivariable models. This decision reflects a pragmatic balance between biological granularity and methodological robustness, given the limited size of individual histologic subgroups and the predominance of neuroendocrine carcinoma NOS. Accordingly, this approach was intended to capture major histologic distinctions while minimizing model instability, and the resulting associations should be interpreted in this context. In addition, SEER does not capture immunohistochemical or molecular data, limiting validation of diagnostic criteria and assessment of biologic heterogeneity across neuroendocrine carcinoma subtypes. Moreover, the high rate of missing AJCC T and M information represents an inherent limitation of SEER-based analyses and precludes detailed TNM-driven prognostic stratification; consequently, our findings should be interpreted in the context of broader disease extent as captured by SEER Summary Stage rather than formal AJCC staging. As for treatment modality, given the non-randomized nature of registry data, the observed associations between RT and survival outcomes may be influenced by confounding by indication and treatment selection bias, as patients selected for RT are likely to differ systematically from those who did not receive it in terms of disease extent, performance status, and other unmeasured clinical factors. To address the heterogeneity of surgical procedures recorded in SEER, we conducted additional survival analyses stratified by type of surgical treatment. Despite this refinement, the lack of information on surgical intent and margin status precludes definitive interpretation, and residual misclassification cannot be excluded. In summary, our large SEER-based study highlights the prognostic relevance of disease extent and treatment patterns in patients with SNECs, according to prior institutional, meta-analytic, and guideline-driven evidence. The observed associations between RT, surgery, and improved survival underscore the potential importance of effective locoregional management within a multimodal treatment approach. However, given the inherent limitations of registry data, including the lack of detailed information on RT dose and technique, surgical margins, and treatment intent, these findings should be interpreted as hypothesis-generating and not as prescriptive evidence for specific therapeutic strategies. These results reinforce the need to manage SNECs in high-expertise centers where multidisciplinary tumor boards, dedicated and skilled pathologists, and access to radiation oncology and medical oncology expertise can be leveraged. It also highlights multimodal management, with surgery and RT as a cornerstone of SNECs treatment, and suggests that chemotherapy, while beneficial in many cases, must be contextualized within disease stage and tumor biology. Declarations No funding was received for conducting this study. The authors have no relevant financial or non-financial interests to disclose. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author Contribution F.C., L.F., and D.C. conceptualized the study. D.C. developed the methodology. F.C. and L.F. performed the formal analysis and investigation. F.C. wrote the original draft of the manuscript. F.C., L.F., D.C., D.M.F., E.N., E.A., and G.L. contributed to manuscript review and editing. D.C., D.M.F., E.N., E.A., and G.L. supervised the study. All authors reviewed and approved the final manuscript Data Availability The data that support the findings of this study were obtained from the Surveillance, Epidemiology, and End Results Program (SEER) database. We used the Incidence SEER Research Data (17 Registries, November 2024 submission) for the years 2000–2022.Data were accessed using SEER*Stat software (version 8.4.5; National Cancer Institute, Bethesda, MD, USA). The SEER database is publicly available upon registration and agreement with the SEER Data-Use Policy at https://seer.cancer.gov/data/The specific SEER*Stat case listing session, cohort selection criteria, and variable definitions used in this study are available from the corresponding author upon reasonable request. References van der Zwan JM, Mallone S, van Dijk B, Bielska-Lasota M, Otter R, Foschi R, Baudin E, Links TP, RARECARE WG. Carcinoma of endocrine organs: results of the RARECARE project. Eur J Cancer. 2012;48(13):1923–31. 10.1016/j.ejca.2012.01.029 . Epub 2012 Feb 21. PMID: 22361014. Filippini DM, Abeshi A, Tober N, Marchese PV, Andrini E, Lamberti G, Agosti R, Molinari G, Fermi M, Presutti L. 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Oral Oncol. 2023;144:106486. 10.1016/j.oraloncology.2023.106486 . Epub 2023 Jul 4. PMID: 37413771. Resteghini C, Castelnuovo P, Nicolai P, Orlandi E, Bossi P, Vischioni B, Schreiber A, Gambazza S, Iacovelli NA, Battaglia P, Guzzo M, Turri-Zanoni M, Mattavelli D, Facchinetti N, Calareso G, Ravanelli M, Facco C, Tartaro T, Licitra L. The SINTART 1 study. A phase II non-randomised controlled trial of induction chemotherapy, surgery, photon-, proton- and carbon ion-based radiotherapy integration in patients with locally advanced resectable sinonasal tumours. Eur J Cancer. 2023;187:185–94. 10.1016/j.ejca.2023.03.033 . Epub 2023 Apr 5. PMID: 37164774. Keilin CA, VanKoevering KK, McHugh JB, McKean EL. Sinonasal Neuroendocrine Carcinoma: 15 Years of Experience at a Single Institution. J Neurol Surg B Skull Base. 2022;84(1):51–9. 10.1055/s-0041-1740968 . PMID: 36743710; PMCID: PMC9897894. Kuo P, Manes RP, Schwam ZG, Judson BL. Survival Outcomes for Combined Modality Therapy for Sinonasal Undifferentiated Carcinoma. Otolaryngol Head Neck Surg. 2017;156(1):132–6. 10.1177/0194599816670146 . Epub 2016 Oct 5. PMID: 27703092. Gamez ME, Lal D, Halyard MY, Wong WW, Vargas C, Ma D, Ko SJ, Foote RL, Patel SH. Outcomes and patterns of failure for sinonasal undifferentiated carcinoma (SNUC): The Mayo Clinic Experience. Head Neck. 2017;39(9):1819–24. 10.1002/hed.24834 . Epub 2017 May 31. PMID: 28561906. Stefanovic M, Hernando-Calvo A, Castro JB, Huang SH, Su J, O'Sullivan B, Ringash J, Galiana IL, Borbalas AL, Cirauqui BC, Hahn E, Teruel I, de Almeida JR, Querol JM, Witterick IJ, Rubió-Casadevall J, Goldstein DP, Siu LL, Waldron J, Oliva M, Spreafico A. Prognostic Factors in Sinonasal Cancers: A Multicenter Pooled Analysis. Laryngoscope. 2025 Aug;26. 10.1002/lary.70044 . Epub ahead of print. PMID: 40856008. Moran, Amit, et al. Induction Chemotherapy Response as a Guide for Treatment Optimization in Sinonasal Undifferentiated Carcinoma. J Clin Oncol. 2019;37:504–12. 10.1200/JCO.18.00353 . Bossi P, Orlandi E, Resteghini C, Vischioni B, Nicolai P, Castelnuovo P, Gambazza S, Locati LD, Turri-Zanoni M, Ferrari M, Facchinetti N, Iacovelli NA, Calareso G, Quattrone P, Cavallo A, Tuzi A, Licitra L. The SINTART 2 Study. A phase II non-randomised controlled trial of induction chemotherapy, photon-, proton- and carbon-ion-based radiotherapy integration in patients with locally advanced unresectable sinonasal tumours. Eur J Cancer. 2023;187:134–43. 10.1016/j.ejca.2023.03.034 . Epub 2023 Apr 7. PMID: 37163806. Rondi P, Mattavelli D, Rampinelli V, Schreiber A, Orlandi E, Vischioni B, Iacovelli NA, Calareso G, Bergamini C, Alfieri S, Castelnuovo P, Battaglia P, Turri-Zanoni M, Nicolai P, Ferrari M, Licitra L, Bossi P, Farina D, Ravanelli M, Resteghini C. The role of volumetry in sinonasal cancer response assessment to induction chemotherapy, results from two phase II non-randomized controlled prospective multicentric trials: SINTART-1 and SINTART-2. Oral Oncol. 2025;166:107388. 10.1016/j.oraloncology.2025.107388 . Epub 2025 May 25. PMID: 40418879. Issa K, Ackall F, Jung SH, Li J, Jang DW, Rangarajan SV, Abi Hachem R. Survival outcomes in sinonasal carcinoma with neuroendocrine differentiation: A NCDB analysis. Am J Otolaryngol. 2021 Mar-Apr;42(2):102851. 10.1016/j.amjoto.2020.102851 . Epub 2020 Dec 17. PMID: 33385873. Patel TD, Vazquez A, Dubal PM, Baredes S, Liu JK, Eloy JA. Sinonasal neuroendocrine carcinoma: a population-based analysis of incidence and survival. Int Forum Allergy Rhinol. 2015;5(5):448–53. 10.1002/alr.21497 . Epub 2015 Feb 27. PMID: 25727332. Hadoux J, Lamarca A, Grande E, Deandreis D, Kaltsas G, Janson ET, Tombal B, Pavel M, Thariat J, van Velthuysen MF, Herman P, Dromain C, Baudin E, Berruti A, ESMO Guidelines Committee. Electronic address: [email protected] . Neuroendocrine neoplasms of head and neck, genitourinary and gynaecological systems, unknown primaries, parathyroid carcinomas and intrathyroid thymic neoplasms: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. ESMO Open. 2024;9(10):103664. 10.1016/j.esmoop.2024.103664 . Epub 2024 Oct 1. PMID: 39461777; PMCID: PMC11549527. Bossi P, Alfieri S, Bonomo P, Botticelli A, De Felice F, Ghi MG, Ghiani M, Molteni G, Morbini P, Perri F, Rampinelli V, Ravanelli M, Valentini V, Vecchio S, Locati LD. Erratum to Improving the multidisciplinary therapeutic management of head and neck squamous cell carcinoma: Consensus statements from an Italian expert panel [Crit. Rev. Oncol. / Hematol. 210 (2025) 104709]. Crit Rev Oncol Hematol. 2025;211:104753. 10.1016/j.critrevonc.2025.104753 . Epub 2025 May 10. Erratum for: Crit Rev Oncol Hematol. 2025;210:104709. doi: 10.1016/j.critrevonc.2025.104709. PMID: 40348642. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialsSNECSEER30.10.251.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 May, 2026 Reviews received at journal 04 May, 2026 Reviews received at journal 03 May, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers invited by journal 17 Apr, 2026 Editor invited by journal 14 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 02 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9193689","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624953199,"identity":"f996b67d-7ab4-4d14-80ba-ff491a1c29c6","order_by":0,"name":"Francesca Carosi","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"","lastName":"Carosi","suffix":""},{"id":624953200,"identity":"f39f1561-372d-4919-87d7-edcdd5286549","order_by":1,"name":"Laura Fabbri","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Fabbri","suffix":""},{"id":624953201,"identity":"7db3ddc8-fd19-4aa9-877c-18a10376143c","order_by":2,"name":"Giuseppe Lamberti","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Giuseppe","middleName":"","lastName":"Lamberti","suffix":""},{"id":624953203,"identity":"d71df6b5-63ed-4db8-a392-f33355d15890","order_by":3,"name":"Elisa Andrini","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Elisa","middleName":"","lastName":"Andrini","suffix":""},{"id":624953206,"identity":"824c4228-8d00-4127-a272-0c3cdcb0c5d9","order_by":4,"name":"Elisabetta Nobili","email":"","orcid":"","institution":"IRCCS Azienda Ospedaliero-Universitaria di Bologna","correspondingAuthor":false,"prefix":"","firstName":"Elisabetta","middleName":"","lastName":"Nobili","suffix":""},{"id":624953207,"identity":"02338910-368b-4aa7-92d8-913078858136","order_by":5,"name":"Daria Maria Filippini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYDCCA0BcYWDBw8DA2MCQUAHkMTM3ENZyxkACquUMSAsjMVoYJCAcxjYwiV8L3wHuBIYDBRIy5tLNbQ8ezquN5m8HavlRsQ2nFskDvBsYDgAdZjnnYLtB4rbjuTMOMzYw9py5jVOLAVAL8wegFoMbiW0SiduO5TYAtTAztuHXArYFomXOsdz5JGppqMndQEiL5GHeDQegfmmTSDh2IHcjUMtBfH7hO9678cGBPzb25tLtzyR/1NTlzjt/+OCDHxW4tTAwQ6KGwQASNYfB5AHc6pE9BdFSR5TiUTAKRsEoGFkAAA0dXk5FxScmAAAAAElFTkSuQmCC","orcid":"","institution":"IRCCS Azienda Ospedaliero-Universitaria di Bologna","correspondingAuthor":true,"prefix":"","firstName":"Daria","middleName":"Maria","lastName":"Filippini","suffix":""},{"id":624953208,"identity":"1de220dc-98c5-4f88-83f2-b11dc74d1e23","order_by":6,"name":"Davide Campana","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Davide","middleName":"","lastName":"Campana","suffix":""}],"badges":[],"createdAt":"2026-03-22 21:09:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9193689/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9193689/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107835009,"identity":"768c981c-d2a1-46a2-a506-66a494a40743","added_by":"auto","created_at":"2026-04-26 15:51:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curve for OS based on SEER stage.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOS: overall survival. D: distant disease. L: localized disease. R: regional disease.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9193689/v1/deab1b72585d94e5e9cfad49.jpg"},{"id":107870398,"identity":"e3c735f8-6393-4e2c-bbb2-a961bac742b3","added_by":"auto","created_at":"2026-04-27 07:39:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30479,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curve for DSS based on the SEER stage.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDSS: disease-specific survival. D: distant disease. L: localized disease. R: regional disease.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9193689/v1/f41c073b3ac917f45a83641e.jpg"},{"id":108490971,"identity":"47d43cf5-1af9-4c18-a232-433984887c41","added_by":"auto","created_at":"2026-05-05 09:50:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":444368,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9193689/v1/179d2fbc-f32b-42bd-9d6b-de33e033e6d0.pdf"},{"id":107835008,"identity":"1a75f7d5-91f9-4caf-8c20-44cfd04c3a63","added_by":"auto","created_at":"2026-04-26 15:51:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3640470,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsSNECSEER30.10.251.docx","url":"https://assets-eu.researchsquare.com/files/rs-9193689/v1/8172f96ee020f4042bc19ddc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic factors and survival outcomes of multimodal treatment in sinonasal neuroendocrine tumors in a population based SEER analysis.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNeuroendocrine neoplasms (NENs) of the head and neck are extremely rare, accounting for less than 1% of malignancies in this region, with an estimated incidence of 0.1\u0026ndash;0.15 per million in Europe [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among these, sinonasal NENs comprise a heterogeneous group of tumors arising in the paranasal sinuses, such as olfactory neuroblastomas and neuroendocrine carcinomas (NECs), and represent approximately 5% of malignancies at these sites [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe histopathological classification of sinonasal NENs is challenging due to their rarity and histologic heterogeneity. The 5th edition of the Head and Neck World Health Organization (WHO) classification defines well-differentiated epithelial NENs as neuroendocrine tumors (NETs), previously referred to as carcinoid tumors, which are further subclassified into typical and atypical according to differentiation grade, and poorly differentiated forms as NECs, which are further subtyped into small-cell (SCNECs) and large-cell neuroendocrine carcinomas (LCNECs) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Accurate histopathological diagnosis is difficult, as NECs must be distinguished from esthesioneuroblastoma, sinonasal undifferentiated carcinoma (SNUC), melanoma, and other small round blue cell tumors.\u003c/p\u003e \u003cp\u003ePrimary carcinoid tumors of the sinonasal tract are particularly rare, comprising less than 0.5% of sinonasal tumors, most frequently occurring in males aged 40\u0026ndash;60 years. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eSCNECs typically present at locally advanced stages but show a relatively low rate of distant metastases, with nasal cavity and septum and ethmoid sinus as the most commonly involved sites. [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] In contrast, LCNECs tend to affect older patients, are often associated with p53 overexpression and have worse prognosis. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eDue to nonspecific symptoms caused by the anatomical characteristics of the sinonasal region, these tumors are often diagnosed late, when rapid tumor growth leads to mass effect-related complaints.\u003c/p\u003e \u003cp\u003eUnlike other head and neck cancers, traditional staging appears to have limited prognostic value; instead, histopathological grade has emerged as a more reliable predictor of outcome [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIrrespective of histological subtype, surgery remains a mainstay of treatment. However, the proximity of tumors to critical structures, such as the orbit and anterior skull base, further complicates management and limits surgical options. Retrospective studies suggest that well-differentiated tumors may be managed by surgery alone, whereas moderately or poorly differentiated tumors often require multimodal approaches incorporating postoperative RT. Notably, chemotherapy as unimodal treatment has demonstrated poor outcomes, with no long-term survivors reported [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA major challenge lies in the complexity of the histopathological classification and the risk of misdiagnosis of these neoplasms. Additionally, although several population-based analyses have examined sinonasal malignancies using large registries such as SEER or NCDB, most studies have focused on heterogeneous histologic groups or broader sinonasal cancer populations. Data specifically addressing SNECs remain limited and are often derived from smaller cohorts [\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], shorter timeframes, or analyses not incorporating disease-specific survival. The present study aims to address this gap by providing a contemporary population-based analysis of SNECs over a long study period, with dedicated evaluation of overall and disease-specific survival and real-world treatment patterns.\u003c/p\u003e \u003cp\u003eIn the present study, we analyzed patients with SNECs from the Surveillance, Epidemiology, and\u003c/p\u003e \u003cp\u003eEnd Results (SEER) database to assess demographic characteristics, prognostic factors, treatment patterns, overall survival (OS), and disease-specific survival (DSS) of these extremely rare tumors.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data extraction\u003c/h2\u003e \u003cp\u003eWe queried the Incidence-SEER research data (17 Registries, November 2024 submission) for years 2000 to 2022 for sinonasal tract malignancies with confirmed histological diagnosis using International Statistical Classification of Diseases for Oncology, 3rd edition (ICD-O-3) topography codes. The codes included were those for structures comprising the sinonasal tract, namely the nasal cavity (C30.0) and paranasal sinuses (C31.0, C31.1, C31.2, C31.3, C31.8, and C31.9). Subsequently, we selected cases with ICD-O-3 histology and behavior codes corresponding to typical carcinoid (8240/3), atypical carcinoid (8249/3), small cell neuroendocrine carcinoma (8041/3), large cell neuroendocrine carcinoma (8013/3), and neuroendocrine carcinoma, not otherwise specified (NOS) (8246/3).\u003c/p\u003e \u003cp\u003eSEER data were extracted using SEER*Stat 8.4.5 (National Cancer Institute, Bethesda, MD) software. The study was conducted in accordance with the SEER data-use agreement. The data were extracted and compiled into an Excel database prior to statistical analysis.\u003c/p\u003e \u003cp\u003eData collected from the SEER database included demographic, clinicopathological, and treatment-related variables, specifically: age, sex, ethnicity, marital status, year of diagnosis, histology, primary tumor site, grade, stage, type of surgical treatment, radiotherapy (RT) and its sequence with surgery, chemotherapy and its sequence, metastatic sites, and cause of death.\u003c/p\u003e \u003cp\u003eFor multivariate analysis, categorical variables were dichotomized based on clinically and prognostically relevant characteristics to enhance model interpretability. Primary tumor site was grouped as nasal cavity (NC) versus non-nasal cavity (non-NC, all other sites), given the generally more favorable outcomes associated with tumors arising in the NC. Because AJCC T and M categories were missing or not recorded for a substantial proportion of cases across the study period, these variables were not used as primary stratifiers in the main analyses; instead, disease extent was assessed using SEER Summary Stage, which provides more complete and consistent coverage in population-based datasets spanning multiple decades. The SEER summary stage was collapsed into L\u0026thinsp;+\u0026thinsp;R (localized and regional) versus D (distant) to reflect the well-established prognostic distinction between confined and disseminated disease. Tumor grade was divided into low grade (G1-G2) and high grade (G3-G4), consistent with the poorer prognosis typically associated with high-grade tumors. However, it is important to note that the SEER grading system does not accurately capture the biological heterogeneity of neuroendocrine carcinomas, which cannot be fully resolved by a simple low- versus high-grade dichotomy. This categorization was therefore adopted for analytic purposes, despite these intrinsic limitations. Histological subtype (hist) was grouped as SC (small cell), LC (large cell), NEC NOS, carcinoid, atypical carcinoid. For multivariable analyses, histology was modeled using a dichotomous classification (small cell vs non\u0026ndash;small cell neuroendocrine carcinoma) to ensure model stability and adequate statistical power, given the limited sample size of individual histologic subgroups and the high proportion of neuroendocrine carcinoma NOS.\u003c/p\u003e \u003cp\u003eSurgical treatment was analyzed as Yes versus No. For survival analyses, the original SEER variable \u0026ldquo;surgery type\u0026rdquo; was reclassified into five clinically meaningful categories as follows: No surgery, Surgery not otherwise specified (SurNOS), Therapeutic procedures (including radical surgery, total surgical removal, and simple or partial surgery), Local/diagnostic procedures (including polipectomy, excisional biopsy, and local excision), and Debulking surgery. This categorization allowed differentiation between the absence of surgery, diagnostic or limited surgical approaches, definitive therapeutic resections, and non-curative debulking interventions. RT was treated as Yes versus No. The RT sequence was dichotomized as RT ADJ (adjuvant RT) versus No, excluding neoadjuvant and other sequences. Chemotherapy sequence (cht_sur) was categorized as CHT ADJ (adjuvant chemotherapy) versus No, excluding perioperative or neoadjuvant regimens. Additionally, a binary variable for chemotherapy administration (CHT) was included (Yes vs No). Age was analyzed as a continuous variable in all models. Multimodal treatment was operationalized as predefined combinations of surgery, radiotherapy, and chemotherapy.\u003c/p\u003e \u003cp\u003eHazard Ratio (HR) with 95% confidence intervals (CI) and p-values were reported. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eThe primary endpoint was to identify clinicopathological and treatment-related prognostic factors for OS and DSS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.4.3), with packages including survival, survminer, and forestmodel. OS and DSS were estimated using Kaplan-Meier survival analysis. OS was defined as the time from diagnosis to death from any cause or last follow-up, while DSS was defined as the time from diagnosis to death specifically attributed to the tumor. For DSS, the event was defined based on the SEER cause-specific death classification: deaths coded as \u0026ldquo;T\u0026rdquo; (tumor-related) were considered events, whereas deaths from other causes (\u0026ldquo;Other\u0026rdquo;) or patients alive at the last follow-up were censored.\u003c/p\u003e \u003cp\u003eAnalyses were conducted on a complete-case basis. Observations with missing values for any of the variables included in the multivariate model were excluded.\u003c/p\u003e \u003cp\u003eSurvival curves were compared using the log-rank test. Survival probabilities at 12 months were extracted from Kaplan\u0026ndash;Meier estimates to calculate OS and DSS rates at one year.\u003c/p\u003e \u003cp\u003eCox proportional hazards regression was used to identify prognostic factors. Univariate Cox models were first applied to all available covariates. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis were then considered for multivariate analysis. Multivariate Cox models for OS and DSS were constructed using a complete-case approach. The proportional hazards assumption was verified using Schoenfeld residuals.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Patient characteristics\u003c/h2\u003e \u003cp\u003eA total of 389 cases of sinonasal neuroendocrine tumors were reported in the SEER database between 2000 and 2022 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The median age at diagnosis was 60 years (range, 17\u0026ndash;89 years), with a slight male predominance (58.4%). Most patients were Caucasian (83.0%) and married (60.8%) at the time of diagnosis. The majority of cases (mean primaries per patient: 1.34) represented single primary tumors.\u003c/p\u003e \u003cp\u003eThe nasal cavity was the most common primary site (48.1%), followed by the ethmoid sinus (17.7%), and maxillary sinus (16.2%). Regarding histological classification, over half of tumors were neuroendocrine carcinoma NOS (50.1%), while SCNEC accounted for 35.0% and LCNEC for 10.5%. Carcinoid and atypical carcinoid types were rare (3.1% and 1.3%, respectively), and were excluded from later survival analysis due to small numbers.\u003c/p\u003e \u003cp\u003eTumor differentiation was frequently high: 22.4% were poorly differentiated (grade 3) and 26.7% undifferentiated/anaplastic (grade 4), while nearly half (45.8%) had unspecified histologic grade. In terms of disease extent at presentation according to SEER summary stage, 37.9% had distant metastases (D), 48.4% regional disease (R), and only 13.7% localized (L).\u003c/p\u003e \u003cp\u003eTreatment patterns mirrored the severe presentation: just over half (54.1%) underwent surgery, mostly less-extensive procedures such as local excision or partial surgery. Lymph node surgery was rare. RT was used in 66.9%, predominantly in the adjuvant setting (37.6%), and chemotherapy was employed in nearly 70% of cases, with adjuvant chemotherapy (37.4%) being the most common sequence. The mean delay from diagnosis to any treatment was 26.5 days (SD, 39.0), and 20.9 days (SD, 23.0) to surgery.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinicopathological characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 years (17\u0026ndash;89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162 (41.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e227 (58.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e323 (83.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (7.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (9.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e227 (60.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (39.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeuroendocrine carcinoma NOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195 (50.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmall cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (35.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (10.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarcinoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtypical carcinoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary tumor site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNasal cavity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187 (48.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthmoid sinus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (17.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaxillary sinus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (16.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSphenoid sinus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (8.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccessory sinus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther sinus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEER stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocalized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (13.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180 (48.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141 (37.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJCC T category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1-T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (25.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3-T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (74.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTx/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJCC N category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (60.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (39.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJCC M category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (87.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210 (54.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178 (45.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e253 (66.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125 (33.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of radiotherapy treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjuvant radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142 (37.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235 (62.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272 (69.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117 (30.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjuvant chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (37.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177 (62.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNA: Not available.\u003c/p\u003e \u003cp\u003eAJCC: American Joint Committee on Cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Survival analysis\u003c/h2\u003e \u003cp\u003eKaplan-Meier analysis identified several variables significantly associated with OS.\u003c/p\u003e \u003cp\u003ePatients with regional or localized SEER stage had significantly better OS compared to those with distant stage (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), similarly to AJCC stage (p\u0026thinsp;=\u0026thinsp;0.038). In particular, median OS varied as follows: 15 months (95% CI, 12\u0026ndash;22) for distant (D), 45 months (95% CI, 27\u0026ndash;104) for regional (R), and 112 months (95% CI, 62\u0026ndash;not reached) for localized disease (L).\u003c/p\u003e \u003cp\u003eSurgical treatment was significantly associated with improved OS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Surgical treatment type also had a prognostic impact (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with therapeutic and local/diagnostic procedures (polipectomy, local excision, or excisional biopsy) associated with improved OS compared with therapeutic or debulking surgery, while all surgical approaches conferred a survival advantage over no surgery. Adjuvant RT was associated with a significant survival benefit compared with no RT or neoadjuvant RT (p\u0026thinsp;=\u0026thinsp;0.001). Patients receiving any RT demonstrated superior OS compared with those who did not (p\u0026thinsp;=\u0026thinsp;0.002). Similarly, the use of adjuvant chemotherapy was significantly correlated with improved OS (p\u0026thinsp;=\u0026thinsp;0.032). Tumor grade showed a prognostic effect, with low grade tumors associated with longer OS than high-grade tumors (p\u0026thinsp;=\u0026thinsp;0.0045).\u003c/p\u003e \u003cp\u003eFinally, marital status (married vs. not married, p\u0026thinsp;=\u0026thinsp;0.035) and primary site (nasal vs. non-nasal cavity, p\u0026thinsp;=\u0026thinsp;0.026) were also significantly associated with OS.\u003c/p\u003e \u003cp\u003eIn the Kaplan-Meier analysis, no significant association with OS was observed for sex, ethnicity, histologic subtype (small cell vs others), AJCC T and N category, as the log-rank test did not yield statistically significant differences between groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eKaplan-Meier curves for all significantly associated with OS are provided in the \u003cb\u003eSupplementary Materials\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOS: overall survival. D: distant disease. L: localized disease. R: regional disease.\u003c/p\u003e \u003cp\u003eKaplan-Meier analysis revealed that DSS was significantly improved in patients who underwent surgery (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (with the highest survival observed in patients undergoing therapeutic procedures), received RT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), particularly in the adjuvant setting (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and those treated with chemotherapy (p\u0026thinsp;=\u0026thinsp;0.0019). In contrast, adjuvant chemotherapy versus no chemotherapy did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.091). Patients with localized or regional disease had significantly better DSS compared to those with distant stage (p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). High-grade tumors (p\u0026thinsp;=\u0026thinsp;0.011) and histological subtype (LC, NOSNE, SC; p\u0026thinsp;=\u0026thinsp;0.021) was also characterized by worse DSS.\u003c/p\u003e \u003cp\u003eAll survival curves are provided in the \u003cb\u003eSupplementary Materials\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDSS: disease-specific survival. D: distant disease. L: localized disease. R: regional disease.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Multimodal treatment strategies\u003c/h2\u003e \u003cp\u003eAmong surgically treated patients, RT was the only treatment modality associated with improved OS. Both surgery plus RT and surgery plus adjuvant RT showed significantly better OS compared with surgery alone (p\u0026thinsp;=\u0026thinsp;0.0047 and p\u0026thinsp;=\u0026thinsp;0.039, respectively), with concordant reductions in all-cause mortality in Cox analyses. Chemotherapy, either overall or adjuvant, was not associated with OS benefit. In the four-group multimodal analysis, OS differed significantly across strategies (p\u0026thinsp;=\u0026thinsp;0.00062), with surgery plus RT showing the most favorable outcomes, while surgery plus chemotherapy alone was associated with significantly worse OS.\u003c/p\u003e \u003cp\u003eNo significant DSS benefit was observed with the addition of RT or chemotherapy among surgically treated patients. In the four-group multimodal model, DSS differed significantly across treatment strategies (p\u0026thinsp;=\u0026thinsp;0.031), driven by worse DSS in patients treated with chemoradiotherapy without surgery.\u003c/p\u003e \u003cp\u003eKaplan\u0026ndash;Meier curves and univariate analyses are reported in the \u003cb\u003eSupplementary Figures\u003c/b\u003e and \u003cb\u003eTables\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Prognostic factors for OS\u003c/h2\u003e \u003cp\u003eIn univariate Cox regression analysis, several variables were significantly associated with OS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong demographic factors, marital status was the only significant prognostic factor, with non-married patients experiencing worse outcomes (HR 1.33, p\u0026thinsp;=\u0026thinsp;0.036).\u003c/p\u003e \u003cp\u003eRegarding tumor characteristics, non-nasal cavity (non-NC) primary site (HR 1.35, p\u0026thinsp;=\u0026thinsp;0.025), high-grade histology (HR 2.57, p\u0026thinsp;=\u0026thinsp;0.006), and distant stage at diagnosis (HR 3.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with worse OS, whereas patients with localized or regional disease had a more favorable prognosis (HR 0.56, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). All treatment modalities showed protective effects, including surgery (HR 0.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RT (HR 0.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), chemotherapy (HR 0.66, p\u0026thinsp;=\u0026thinsp;0.002), adjuvant RT (HR 0.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and adjuvant chemotherapy (HR 0.70, p\u0026thinsp;=\u0026thinsp;0.032).\u003c/p\u003e \u003cp\u003eHistology, as well as T and N categories, did not show significant associations with survival.\u003c/p\u003e \u003cp\u003eIn the multivariate analysis including clinically relevant covariates, primary tumor site, disease stage, and local treatments remained independent prognostic factors. Non-NC tumors (HR 2.00, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and disseminated disease (HR 0.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were confirmed as adverse features, while surgery (HR 0.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and RT (HR 0.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) independently improved OS. Conversely, adjuvant RT sequencing conferred no additional benefit beyond RT alone, and systemic therapy did not retain statistical significance in the multivariate model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate Cox regression analysis for Overall Survival.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (Univ.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI (Univ.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value (Univ.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (Multiv.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI (Multiv.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value (Multiv.)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary site (NC vs non-NC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026ndash;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.4\u0026ndash;2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage (L/R vs D)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u0026ndash;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33\u0026ndash;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery (Yes vs No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u0026ndash;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u0026ndash;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRT (Yes vs No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u0026ndash;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2\u0026ndash;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRT adjuvant (Yes vs No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u0026ndash;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u0026ndash;2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCHT (Yes vs No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u0026ndash;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.38\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCHT ADJ (Yes vs No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u0026ndash;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.87\u0026ndash;4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOS: overall survival. HR: hazard ratio. CI: confidence interval. NC: nasal cavity. L: localized disease. R: regional disease. D: distant disease. RT: radiotherapy. CHT: chemotherapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Prognostic factors for DSS\u003c/h2\u003e \u003cp\u003eUnivariate analysis identified several significant prognostic factors for DSS (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrimary tumor site and grade were strongly prognostic, with non-NC tumors (HR 2.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and high-grade tumors (HR 8.54, p\u0026thinsp;=\u0026thinsp;0.034) associated with markedly worse DSS. Patients with localized/regional disease had improved outcomes (HR 0.53, p\u0026thinsp;=\u0026thinsp;0.004), whereas distant metastases conferred poor prognosis (HR 2.60, p\u0026thinsp;=\u0026thinsp;0.013). Histology showed a borderline adverse effect for small cell tumors (HR 1.54, p\u0026thinsp;=\u0026thinsp;0.048). Surgical resection (HR 0.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and RT (HR 0.54, p\u0026thinsp;=\u0026thinsp;0.006), particularly in the adjuvant setting, significantly reduced disease-specific mortality, while chemotherapy showed no benefit, with only a non-significant trend toward improved DSS for adjuvant CHT (HR 0.60, p\u0026thinsp;=\u0026thinsp;0.066).\u003c/p\u003e \u003cp\u003eIn the multivariate analysis, non-NC primary site (HR 3.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), surgery (HR 0.25, p\u0026thinsp;=\u0026thinsp;0.002), and RT (HR 0.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) remained independent prognostic factors. Systemic therapy, including adjuvant chemotherapy, was not independently associated with DSS, and disease stage showed only a non-significant trend. Overall, primary site, surgery, and radiotherapy emerged as the most robust independent determinants of DSS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate Cox regression analysis for Disease-specific survival.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (Univ.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI (Univ.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value (Univ.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (Multiv.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI (Multiv.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value (Multiv.)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary site (NC vs non-NC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.47\u0026ndash;3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.78\u0026ndash;5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage (L\u0026thinsp;+\u0026thinsp;R vs D)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u0026ndash;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery (Yes vs No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24\u0026ndash;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u0026ndash;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRT (Yes vs No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u0026ndash;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u0026ndash;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRT adjuvant (Yes vs No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u0026ndash;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4\u0026ndash;4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCHT (Yes vs No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.501\u0026ndash;0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.67\u0026ndash;3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCHT ADJ (Yes vs No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39\u0026ndash;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDSS: disease-specific survival. HR: hazard ratio. CI: confidence interval. NC: nasal cavity. L: localized disease. R: regional disease. D: distant disease. RT: radiotherapy. CHT: chemotherapy.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis population-based analysis of 389 patients with sinonasal neuroendocrine carcinoma (SNEC) from the SEER database (2000\u0026ndash;2022) represents one of the largest cohorts to date and provides valuable insights into prognostic determinants and therapeutic outcomes for these rare malignancies.\u003c/p\u003e \u003cp\u003eCompared with previously published SEER- and NCDB-based series on sinonasal malignancies, the present analysis specifically focuses on neuroendocrine carcinoma histologies and spans a contemporary timeframe from 2000 to 2022. In addition to OS, we report DSS using SEER cause-specific death classification, thereby providing complementary prognostic information beyond all-cause mortality. Furthermore, we describe real-world treatment patterns, including RT and chemotherapy sequencing variables available in SEER, allowing a population-level assessment of multimodal management in this rare disease.\u003c/p\u003e \u003cp\u003eConsistent with prior institutional and pooled series, our data confirm that primary tumor site, stage at presentation, and treatment modality remain the strongest predictors of both OS and DSS.\u003c/p\u003e \u003cp\u003eThese findings resonate with earlier institutional reports in high-grade sinonasal malignancies. For example, Kuo et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] documented poor outcomes in sinonasal undifferentiated carcinoma (SNUC), with 5-year OS below 30% and highlighted the potential prognostic role of surgery and combined-modality therapy. Although SNUC represents a distinct pathological entity, these observations provide historical context for the importance of aggressive locoregional management in high-grade sinonasal tumors.\u003c/p\u003e \u003cp\u003eSimilarly, Gamez et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] demonstrated improved outcomes with trimodality approaches, particularly in patients undergoing surgical resection. In a recent single-institution series of 19 high-grade head and neck NECs [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], median OS was 27 months, with markedly superior survival in radically treated patients (51 months) compared to those with metastatic disease (3 months), underscoring the critical role of multimodality therapy in localized cases.\u003c/p\u003e \u003cp\u003eOur SEER data align with these observations, confirming the dismal prognosis of metastatic disease and the benefit of surgery as a cornerstone of therapy in non-metastatic patients.\u003c/p\u003e \u003cp\u003eIn our analysis, a strong association between RT and improved OS and DSS also emerged. Stefanovic et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] demonstrated that RT was associated with reduced disease-specific mortality across sinonasal cancers, particularly when combined with surgery. Likewise, a large pooled meta-analysis of 701 cases [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] showed a survival advantage for surgery combined with postoperative RT, especially in poorly differentiated histologies, while chemotherapy as monotherapy was ineffective.\u003c/p\u003e \u003cp\u003eThe role of systemic therapy in SNEC remains controversial. In our cohort, chemotherapy showed a modest association with improved OS in univariate analysis but failed to retain significance in multivariate models, likely reflecting patient selection and the absence of treatment details in registry data.\u003c/p\u003e \u003cp\u003eAlthough induction chemotherapy has been extensively investigated in SNUC and other high-grade sinonasal malignancies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], extrapolation of these findings to SNEC remains limited, given the biological and pathological differences between entities. Accordingly, the value of chemotherapy in SNEC is likely context-dependent and should be individualized rather than routinely applied.\u003c/p\u003e \u003cp\u003ePopulation-based data provide additional confirmation. The NCDB analysis by Issa et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] on 415 patients with SNEC/SNUC confirmed the adverse prognostic impact of advanced T and N categories and demonstrated the benefit of multimodality therapy. However, given the mixed histologic composition of these cohorts, such findings should be interpreted as supportive rather than definitive for SNEC-specific management.\u003c/p\u003e \u003cp\u003eLikewise, Patel et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] reported a median DSS of 36 months for SNEC, with 5-year DSS of 70.2%, superior to SNUC and sinonasal small cell carcinoma, underscoring the distinct and somewhat more favorable biology of SNEC. As in Patel\u0026rsquo;s study, we found that tumors arising outside the nasal cavity carried a significantly worse prognosis, reflecting both biological aggressiveness and anatomic challenges to achieving radical local therapy.\u003c/p\u003e \u003cp\u003eGuideline-based perspectives further contextualize our findings. The recent ESMO Clinical Practice Guidelines for head and neck neuroendocrine neoplasms [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] recommend multimodality management, with induction platinum-etoposide followed by RT or surgery in selected patients, reflecting the chemosensitive yet aggressive biology of these tumors. Similarly, the Italian consensus panel on recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] emphasized multidisciplinary decision-making and integration of systemic therapies in selected scenarios, though evidence for chemotherapy efficacy in SNEC remains inconsistent. Our data, showing limited impact of chemotherapy on survival, align with the caution expressed in both consensus and guideline documents regarding the routinary use of systemic therapy outside of multimodal settings.\u003c/p\u003e \u003cp\u003eFinally, our data corroborate a recent pooled analysis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which confirmed the prognostic value of stage, site, and treatment modality across sinonasal cancers, and add granularity by focusing specifically on SNEC.\u003c/p\u003e \u003cp\u003eWith regard for multimodal treatment associations, RT emerged as the only treatment component consistently associated with improved OS among surgically treated patients, whereas no clear survival benefit was observed with the addition of chemotherapy. Notably, the observed advantage of RT was confined to OS and did not translate into a significant improvement in DSS, suggesting that its apparent effect may be influenced, at least in part, by non\u0026ndash;cancer-related mortality or by differences in patient selection. Indeed, patients receiving RT may represent a subgroup with better baseline performance status, improved access to care, or more favorable disease characteristics not fully captured within the SEER database. The violation of the proportional hazards assumption in some OS models further supports the possibility of a time-dependent or indirect effect of RT rather than a sustained disease-specific benefit. The lack of association between chemotherapy and survival outcomes, both in isolation and in combination with surgery and RT, should be interpreted with caution. SEER does not provide information on chemotherapy regimens, timing, dose intensity, or treatment intent, limiting the ability to distinguish potentially effective systemic approaches from less intensive or palliative strategies. Similarly, the heterogeneity observed in the four-group multimodal analysis likely reflects substantial clinical diversity, particularly among non-surgically treated patients, in whom worse DSS may be driven by advanced stage, comorbidity burden, or contraindications to surgery rather than by treatment strategy alone. In addition, the relatively small number of events in certain treatment subgroups reduces statistical power and limits the precision of hazard ratio estimates.\u003c/p\u003e \u003cp\u003eWhile our findings are generally consistent with prior population-based analyses of high-grade sinonasal malignancies, the present study adds granularity by restricting the cohort to neuroendocrine carcinoma histologies and by contextualizing outcomes within contemporary treatment patterns. Nevertheless, as with other registry-based studies, the absence of central pathology review and detailed treatment parameters limits direct comparisons with institutional series and should be considered when interpreting the incremental contribution of our results.\u003c/p\u003e \u003cp\u003eHowever, collectively, the evidence supports a treatment paradigm anchored in surgery and RT, while chemotherapy should be individualized, potentially serving as an induction or radiosensitizing strategy rather than a standalone modality and guided by treatment response or molecular predictors.\u003c/p\u003e \u003cp\u003eThis study has several limitations inherent to the SEER database. It should be emphasized that the SEER database lacks central pathology review, raising the possibility of histologic misclassification, particularly for neuroendocrine carcinoma not otherwise specified (NOS), which represent the vast majority, and for cases diagnosed in earlier years, when immunohistochemical and molecular characterization was less standardized. Therefore, our findings should be interpreted as population-level associations within sinonasal poorly differentiated neuroendocrine carcinomas as coded in SEER, rather than as evidence of uniform biology or behavior across distinct histopathological subtypes.\u003c/p\u003e \u003cp\u003eAlthough descriptive analyses suggested histology-specific differences in DSS, histology was retained as a dichotomous variable (SCNEC vs other histology) in the multivariable models. This decision reflects a pragmatic balance between biological granularity and methodological robustness, given the limited size of individual histologic subgroups and the predominance of neuroendocrine carcinoma NOS. Accordingly, this approach was intended to capture major histologic distinctions while minimizing model instability, and the resulting associations should be interpreted in this context.\u003c/p\u003e \u003cp\u003eIn addition, SEER does not capture immunohistochemical or molecular data, limiting validation of diagnostic criteria and assessment of biologic heterogeneity across neuroendocrine carcinoma subtypes.\u003c/p\u003e \u003cp\u003eMoreover, the high rate of missing AJCC T and M information represents an inherent limitation of SEER-based analyses and precludes detailed TNM-driven prognostic stratification; consequently, our findings should be interpreted in the context of broader disease extent as captured by SEER Summary Stage rather than formal AJCC staging. As for treatment modality, given the non-randomized nature of registry data, the observed associations between RT and survival outcomes may be influenced by confounding by indication and treatment selection bias, as patients selected for RT are likely to differ systematically from those who did not receive it in terms of disease extent, performance status, and other unmeasured clinical factors. To address the heterogeneity of surgical procedures recorded in SEER, we conducted additional survival analyses stratified by type of surgical treatment. Despite this refinement, the lack of information on surgical intent and margin status precludes definitive interpretation, and residual misclassification cannot be excluded.\u003c/p\u003e \u003cp\u003eIn summary, our large SEER-based study highlights the prognostic relevance of disease extent and treatment patterns in patients with SNECs, according to prior institutional, meta-analytic, and guideline-driven evidence. The observed associations between RT, surgery, and improved survival underscore the potential importance of effective locoregional management within a multimodal treatment approach. However, given the inherent limitations of registry data, including the lack of detailed information on RT dose and technique, surgical margins, and treatment intent, these findings should be interpreted as hypothesis-generating and not as prescriptive evidence for specific therapeutic strategies. These results reinforce the need to manage SNECs in high-expertise centers where multidisciplinary tumor boards, dedicated and skilled pathologists, and access to radiation oncology and medical oncology expertise can be leveraged. It also highlights multimodal management, with surgery and RT as a cornerstone of SNECs treatment, and suggests that chemotherapy, while beneficial in many cases, must be contextualized within disease stage and tumor biology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eF.C., L.F., and D.C. conceptualized the study. D.C. developed the methodology. F.C. and L.F. performed the formal analysis and investigation. F.C. wrote the original draft of the manuscript. F.C., L.F., D.C., D.M.F., E.N., E.A., and G.L. contributed to manuscript review and editing. D.C., D.M.F., E.N., E.A., and G.L. supervised the study. All authors reviewed and approved the final manuscript\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study were obtained from the Surveillance, Epidemiology, and End Results Program (SEER) database. We used the Incidence SEER Research Data (17 Registries, November 2024 submission) for the years 2000\u0026ndash;2022.Data were accessed using SEER*Stat software (version 8.4.5; National Cancer Institute, Bethesda, MD, USA). 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PMID: 40348642.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sinonasal malignancies, neuroendocrine carcinoma, multimodal treatment, rare tumors, head and neck cancer","lastPublishedDoi":"10.21203/rs.3.rs-9193689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9193689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eSinonasal neuroendocrine carcinomas (SNECs) are rare and aggressive malignancies with heterogeneous histopathology and limited evidence guiding optimal treatment. This study aims to characterize clinicopathological features, treatment patterns, and prognostic factors using a large population-based database.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe queried the SEER database (2000\u0026ndash;2022) for histologically confirmed SNECs. Kaplan-Meier analysis was used to estimate overall survival (OS) and disease-specific survival (DSS). Univariate and multivariate Cox models were employed to identify independent prognostic factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 389 cases were identified, mostly male (58.4%) with a median age of 60 years and nasal cavity as primary site (48.1%). Neuroendocrine carcinoma not otherwise specified (50.1%) was the most frequent, followed by small cell (35.0%) and large cell carcinoma (10.5%). Nearly 38% of patients presented with metastatic disease. Surgery, radiotherapy, and chemotherapy were administered in 54.1%, 66.9%, and 69.9% of cases, respectively. Kaplan-Meier analysis showed improved OS and DSS in patients with localized/locally advanced stage, lower tumor grade, nasal cavity primary site, and those treated with surgery, radiotherapy (particularly adjuvant), or chemotherapy (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In multivariate models, primary sites outside the nasal cavity were independently associated with worse OS and DSS, while surgery and radiotherapy remained the only treatment modality significantly associated with improved outcomes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn this large population-based study, SNECs were characterized by advanced-stage presentation and use of multimodal therapy. Radiotherapy was independently associated with improved OS/DSS in multivariable models, supporting aggressive local treatment in SNECs and underscoring the need for therapeutic strategies tailored to disease stage and tumor location.\u003c/p\u003e","manuscriptTitle":"Prognostic factors and survival outcomes of multimodal treatment in sinonasal neuroendocrine tumors in a population based SEER analysis.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-26 15:50:58","doi":"10.21203/rs.3.rs-9193689/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-05T14:22:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T17:21:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T14:51:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226789213683613905898958798969574997408","date":"2026-04-17T08:00:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217953337171486428645877231686402616297","date":"2026-04-17T05:16:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-17T05:07:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-14T19:17:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T09:20:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T18:34:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2026-04-02T18:30:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2bf56855-322b-408a-ae07-e4d324e6e4de","owner":[],"postedDate":"April 26th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-05T14:22:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T17:21:59+00:00","index":37,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T14:51:45+00:00","index":36,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T05:08:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-26 15:50:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9193689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9193689","identity":"rs-9193689","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}