Age Influence on the Prognosis and Management of Non-Functional Pancreatic Neuroendocrine Tumors in the Elderly

Age Influence on the Prognosis and Management of Non-Functional Pancreatic Neuroendocrine Tumors in the Elderly | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Age Influence on the Prognosis and Management of Non-Functional Pancreatic Neuroendocrine Tumors in the Elderly Zhengqiang Wang, Chaoqun Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4729493/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction Pancreatic neuroendocrine tumors (PanNETs) account for about 7% of pancreatic tumors and are increasing in incidence. Non-functional PanNETs in the elderly often present asymptomatically, leading to more conservative treatment due to age bias. The impact of age on prognosis and management needs further clarification. Methods Clinical data for patients over 65 with non-functional PanNETs were collected from the SEER database. Kaplan-Meier curves analyzed overall survival between younger elderly (age 65–75 years) and older elderly (age ≥ 75 years) groups. Competing risk models assessed cancer-specific mortality, and Cox regression models identified independent survival risk factors. A prognostic model was constructed and evaluated for performance. Results Among 2,157 patients, older elderly patients (n = 695) had larger tumors, poorer differentiation, worse M stage, and lower surgery rates compared to younger elderly patients (n = 1,462). Younger elderly had better overall survival (p < 0.0001) and lower cancer-specific mortality. Surgery improved survival in younger elderly (p 75, male, higher grades, advanced TNM stage, N1 stage, and lack of surgery as significant risk factors. A predictive model with a C-index of 0.77 was developed. Conclusion Age is an independent prognostic factor for elderly patients with non-functional PanNETs. Younger elderly patients benefit more from surgical treatment. Biological sciences/Cancer Health sciences/Endocrinology Health sciences/Medical research Health sciences/Risk factors Pancreatic Neuroendocrine Tumors elderly patients Surgery Age Figures Figure 1 Figure 2 Figure 3 Introduction Neuroendocrine tumors (NETs) are a heterogeneous group of neoplasms originating from neuroendocrine cells and occurring throughout the body. The incidence of NETs has increased significantly, rising from 1.09 per 100,000 in 1973 to 6.98 per 100,000 in 2012 in the United States. Pancreatic neuroendocrine tumors (PanNETs) comprise approximately 7% of all pancreatic tumors, with an annual incidence of about 1 per 100,000 population [ 1 ] . PanNETs are classified based on hormone secretion into functional and non-functional types, with the latter being more common and often diagnosed at a later stage due to the absence of symptoms. According to the World Health Organization (WHO), PanNETs are classified into well-differentiated NETs and poorly differentiated neuroendocrine carcinomas (NECs), with well-differentiated NETs further categorized into grades G1, G2, and G3 based on mitotic count and Ki-67 index [ 2 ] . The treatment of diseases in elderly patients is an increasingly important topic due to the global aging population. Ageism, which involves discrimination based on age, can influence medical decision-making and often results in undertreatment of older patients [ 3 ] . Studies indicate that elderly patients may receive less aggressive treatment options compared to younger counterparts, potentially impacting outcomes and overall survival [ 4 ] . For pancreatic neuroendocrine tumors (PanNETs), surgical resection is challenging and traumatic, particularly for elderly patients, who may therefore opt for more conservative management, especially as non-functional tumors often remain asymptomatic and are managed through surveillance alone. Regarding the significance of age in non-functional pancreatic neuroendocrine tumors, some studies suggest that younger patients tend to have better tumor differentiation and earlier staging, which are favorable for surgical intervention and prognosis [ 5 ] . Other research indicates that early diagnosis of PanNETs in younger patients correlates with longer overall survival, even when the tumors are larger and have worse staging, highlighting better outcomes following curative surgical resection [ 6 ] . The relevance of age-related factors in non-functional pancreatic neuroendocrine tumors among elderly patients requires further empirical support. This research focuses on elucidating the influence of age on the prognosis and management of non-functional PanNETs in elderly patients. By leveraging data from the Surveillance, Epidemiology, and End Results (SEER) database, we aim to delineate age-related disparities in tumor characteristics, treatment patterns, and survival outcomes. Understanding these nuances is crucial for optimizing therapeutic strategies and enhancing outcomes in elderly patients with PanNETs. Methods Data source and patients This study employed data extracted from the Surveillance, Epidemiology, and End Results (SEER) cancer registry database, adhering to the SEER data use agreement ( www.seer.cancer.gov ). Data extraction was facilitated using the SEER*Stat software program (version 8.4.3). Pathological diagnoses were established based on the primary site, following the International Classification of Diseases for Oncology, third edition (ICD-O-3) criteria. Specifically, our study encompassed cases with Site recode ICD-O-3/WHO 2008 designation for the pancreas, including histology codes 8013/3 (large cell neuroendocrine carcinoma), 8150/3 (Pancreatic endocrine tumor, malignant), 8240/3 (neuroendocrine neoplasms, not otherwise specified), 8246/3 (neuroendocrine carcinoma, not otherwise specified), and 8249/3 (atypical carcinoid tumor). Exclusion criteria included cases documented solely as "Autopsy only" or "Death certificate only." Furthermore, the study cohort was confined to individuals aged 65 years and older. Samples with missing crucial variables were excluded from the analysis. Clinical variables For analytical purposes, age groups were delineated into two categories: 65–75 years and ≥ 75 years. Patient race was classified as white, black, and other, based on the SEER coding scheme. The primary site of tumors was subdivided into the head, body, tail of the pancreas, and other locations. Tumor size was stratified into three groups: ≤2cm, >2cm and ≤ 4cm, and > 4cm. The number of lymph nodes removed during surgery was categorized into three groups: no lymph nodes removed, ≤ 3 lymph nodes removed, and ≥ 4 lymph nodes removed. Tumor grade was categorized as well-differentiated (Grade 1), moderately differentiated (Grade 2), poorly differentiated (Grade 3), and undifferentiated (Grade 4). Therapy received was classified utilizing SEER site-specific therapy of primary site codes and segregated into two main groups: surgical resection and no surgery. The 7th edition of the American Joint Committee on Cancer (AJCC) staging system was utilized for staging purposes within this investigation. Univariate and multivariate COX regression analyses To identify variables influencing overall survival, we performed univariate Cox regression analysis on a set of potential prognostic factors. Variables with a p-value of less than 0.05 in the univariate analysis were subsequently included in the multivariate Cox regression model. This approach allowed us to control for confounding factors and assess the independent effect of each variable on overall survival. The hazard ratios (HRs) and 95% confidence intervals (CIs) for each variable were calculated. The significance of each variable was assessed, with a focus on those maintaining statistical significance in the multivariate model. Construction of prognostic nomogram To construct a prognostic nomogram for predicting overall survival in elderly patients with pancreatic neuroendocrine tumors, we utilized variables identified as significant in the multivariate Cox regression analysis. These included age, tumor grade, N stage, M stage, and surgical treatment. Variable Selection: Based on the multivariate analysis, variables with a significant impact on overall survival (p < 0.05) were chosen for inclusion in the nomogram. Model Development: We used these selected variables to develop a prognostic model. The nomogram was constructed using the 'rms' package in R, which allows for the visualization of the Cox regression model as a user-friendly tool for clinicians. Nomogram Construction: The nomogram graphically represents the survival probability at specific time points by assigning a score to each level of the prognostic variables. The total score, obtained by summing the individual scores, corresponds to the estimated survival probabilities. Model Validation: To evaluate the performance of the nomogram, we calculated the concordance index (C-index). Statistical analysis Descriptive statistics for categorical variables were computed in terms of proportions. The Chi-square test of independence was employed for assessing associations between categorical variables. Survival analysis was conducted utilizing non-parametric Kaplan-Meier estimates and Cox proportional hazard models. All statistical analyses were conducted using R version 4.3.3. A significance level of p < 0.05 was adopted for all analyses to determine statistical significance. Results Age-Related Differences in Non-Functional Pancreatic Neuroendocrine Tumors Based on the inclusion and exclusion criteria, a total of 2,157 patients were included in this study. Patients were divided into two age groups: the younger elderly group (65–75 years) and the older elderly group (≥ 75 years). No significant differences were observed between the two groups in terms of gender, race, primary tumor site, TNM stage, T stage, N stage, pulmonary metastasis, chemotherapy, and radiotherapy (p > 0.05). However, significant differences were found between the groups in terms of tumor size, grade, M stage, liver metastasis, surgical treatment, and lymph node dissection (p < 0.05). The older elderly group had a higher proportion of tumors larger than 2 cm (61.3% vs. 55.2%), a lower proportion of well-differentiated tumors (68.8% vs. 73.6%), a worse M stage (18.7% vs. 14.4%), and a higher incidence of liver metastasis (16.1% vs. 12.5%). Additionally, the older elderly group had a lower rate of surgical treatment (63% vs. 82%) and fewer patients undergoing lymph node dissection (50.2% vs. 67.1%) (see Table 1 ). These findings suggest that older elderly patients are likely to have tumors detected at a more advanced stage and receive relatively conservative treatment. Table 1 The clinical characteristics of patients included in the study Characteristic 65–75 years ≥ 75 years P test n 1462 695 Sex = Male (%) 877 (60.0) 419 (60.3) 0.931 Race (%) 0.339 Black 121 (8.3) 45 (6.5) White 1215 (83.1) 588 (84.6) Other 126 (8.6) 62 (8.9) Primary site (%) 0.267 Head 403 (27.6) 200 (28.8) Body 236 (16.1) 128 (18.4) Tail 605 (41.4) 281 (40.4) Others 218 (14.9) 86 (12.4) Tumor size (%) 0.018 ≤ 2 cm 655 (44.8) 269 (38.7) 2–4 cm 460 (31.5) 231 (33.2) > 4 cm 347 (23.7) 195 (28.1) Differentiation grade 0.041 I 1076 (73.6) 478 (68.8) II 267 (18.3) 136 (19.6) III 88 (6.0) 61 (8.8) IV 31 (2.1) 20 (2.9) TNM (%) 0.052 I 800 (54.7) 371 (53.4) II 389 (26.6) 162 (23.3) III 62 (4.2) 32 (4.6) IV 211 (14.4) 130 (18.7) T stage (%) 0.147 T1 597 (40.8) 253 (36.4) T2 485 (33.2) 244 (35.1) T3 325 (22.2) 162 (23.3) T4 55 (3.8) 36 (5.2) N stage = Yes (%) 337 (23.1) 167 (24.0) 0.655 M stage = Yes (%) 211 (14.4) 130 (18.7) 0.013 Liver metastasis 0.027 Yes (%) 183 (12.5) 112 (16.1) Lung metastasis 0.174 Yes (%) 17 (1.2) 14 (2.0) Surgery = Yes (%) 1200 (82.1) 438 (63.0) < 0.001 LN removed (%) < 0.001 0 481 (32.9) 346 (49.8) 1–3 172 (11.8) 50 (7.2) ≥ 4 Chemotherapy Yes (%) Radiation Yes (%) 809 (55.3) 148 (10.1) 40 (2.7) 299 (43.0) 76 (10.9) 13 (1.9) 0.615 0.287 Survival Analysis in Younger and Older Elderly Groups with Non-Functional Pancreatic Neuroendocrine Tumors K-M analysis revealed that the younger elderly group (65–75 years) had significantly better overall survival compared to the older elderly group (≥ 75 years) (p < 0.0001) (Fig. 1 A). Considering the competing risks of death from other comorbidities, a competing risks model analysis showed that the cumulative incidence of cancer-specific mortality was lower in the younger elderly group (Fig. 2 ). Stratified analysis by surgical treatment showed no significant difference in overall survival between the two age groups among patients who did not undergo surgery (p = 0.44). In contrast, among those who underwent surgery, the younger elderly group had significantly better overall survival (p < 0.0001) (Fig. 1 B). Further stratification by both surgery and chemotherapy revealed no significant difference in overall survival between the age groups for patients who received only chemotherapy (p = 0.32). However, significant differences were observed in overall survival for patients who had surgery without chemotherapy (p < 0.0001) and for those who received both surgery and chemotherapy (p = 0.022) (Fig. 1 C). These results indicate that age may be an independent risk factor for prognosis in elderly patients with neuroendocrine tumors, with surgery serving as a beneficial factor for prognosis, while chemotherapy alone may not improve survival. Multivariate Analysis of Prognostic Factors in Elderly Patients with Pancreatic Neuroendocrine Tumors Further analysis using a univariate Cox regression model identified variables influencing overall survival with p < 0.05, which were then included in a multivariate Cox regression model. Statistically significant variables identified were as follows: Age 65–75 years (HR = 0.61, p = 0), Sex Male (HR = 1.26, p = 0.0085), Grade III (HR = 3.86, p = 0), Grade IV (HR = 4.14, p = 0), TNM stage IV (HR = 1.85, p = 0.0082), N1 stage (HR = 1.28, p = 0.0248), and undergoing surgery (HR = 0.39, p = 0). Variables such as tumor size, T stage, primary site, liver metastasis, lung metastasis, M stage, chemotherapy, radiation, and lymph node removal were not statistically significant (Table 2 ). Table 2 Univariate and multivariate analyses of factors linked to OS prognosis Characteristic Univariate analysis Multivariate analysis Hazard rate(95%CI) P value Hazard rate(95%CI) P value Age ≥ 75 years 1 1 65–75 years 0.49(0.42–0.58) 0 0.61(0.51–0.72) 0 Sex Female 1 1 Male 1.2(1.01–1.42) 0.033 1.26(1.06–1.49) 0.0085 Race Black 1 White 0.7(0.46–1.06) 0.088 Others 0.89(0.66–1.2) 0.45 Primary site Body 1 1 Head 1.73(1.34–2.22) 0 1.16(0.90–1.51) 0.2539 Tail 1.32(0.98–1.77) 0.069 1.02(0.78–1.32) 0.8994 Others 0.96(0.75–1.24) 0.776 1.02(0.76–1.39) 0.8821 Tumor size ≤ 2 cm 1 1 2–4 cm 1.72(1.41–2.11) 0 1.02(0.61–1.7) 0.9303 > 4 cm 2.4(1.97–2.93) 0 0.95(0.57–1.57) 0.8368 Differentiation grade I 1 1 II 1.46(1.18–1.81) 0.001 1.17(0.94–1.46) 0.1629 III 7.18(5.8–8.9) 0 3.86(2.96–5.04) 0 IV 8.43(6.1-11.65) 0 4.14(2.89–5.94) 0 TNM I 1 1 II 1.71(1.38–2.11) 0 1.38(1-1.91) 0.0502 III IV T stage T1 T2 T3 T4 N stage No Yes M stage No Yes Liver metastasis No Yes Lung metastasis No Yes Surgery No Yes LN removed 0 1–3 ≥ 4 Chemotherapy No Yes Radiation No Yes 2.02(1.25–3.28) 5.19(4.3–6.27) 1 1.62(1.32–1.99) 2.36(1.91–2.92) 4.19(3.03–5.79) 1 2.05(1.74–2.43) 1 4.24(3.59–5.01) 1 3.89(3.27–4.62) 1 6.44(4.4–9.42) 1 0.24(0.2–0.28) 1 0.27(0.18–0.39) 0.42(0.35–0.5) 1 4.8(4.01–5.75) 1 2.37(1.63–3.44) 0.004 0 0 0 0 0 0 0 0 0 0 0 0 0 0.79(0.43–1.46) 1.85(1.17–2.91) 1 0.97(0.56–1.68) 1.06(0.61–1.84) 1.08(0.57–2.05) 1 1.28(1.03–1.59) 1 NA 1 1.07(0.72–1.58) 1 1.02(0.66–1.56) 1 0.39(0.28–0.53) 1 0.83(0.53–1.3) 0.95(0.7–1.3) 1 1.22(0.95–1.55) 1 0.98(0.66–1.45) 0.453 0.0082 0.9203 0.8297 0.8227 0.0248 NA 0.751 0.938 0 0.4219 0.7503 0.113 0.9131 These results indicate that Sex, Age, Grade stage, N stage, TNM stage, and surgery are independent prognostic factors. Although lymph node metastasis was identified as a prognostic risk factor, intraoperative lymph node dissection did not show a benefit. Tumor size and T stage were significant in univariate analysis but not in multivariate analysis, suggesting that tumor size may not be a primary consideration in treatment decisions for pancreatic neuroendocrine tumors. Similarly, M stage, liver metastasis, and lung metastasis were significant in univariate analysis but not in multivariate analysis, which contrasts with the finding that TNM stage IV is an independent prognostic factor. Development and Evaluation of a Prognostic Model for Overall Survival in Elderly Patients with Pancreatic Neuroendocrine Tumors To address collinearity issues and better understand prognostic factors, we included M stage in the prognostic prediction model and excluded the TNM stage variable. We developed an overall survival prediction model incorporating sex, age, grade stage, N stage, M stage, and surgery, and created a nomogram to visualize this model (Fig. 3 ). The model's performance was evaluated using the C-index, resulting in a C-index of 0.77. These metrics suggest that the model was successfully constructed, though its predictive performance could be further improved. Discussion This study utilizes the SEER database to investigate the impact of age on overall survival and disease management in elderly patients with non-functional pancreatic neuroendocrine tumors (PanNETs), a topic of significant concern for both doctors and patients. To ensure the reliability of the results, samples with missing variables were excluded, and a sufficiently large sample size was included. The study found that age is an independent risk factor for overall survival, with younger elderly patients benefiting more from surgical treatment. Additionally, tumor differentiation and N stage were identified as independent risk factors for overall survival, while tumor location, size, and T stage were not associated with overall survival. Regarding the relationship between age and PanNETs, studies have shown that younger patients (< 50 years) with PanNETs tend to have fewer comorbidities, higher tumor staging, and larger tumors, but they have better overall survival after radical surgical resection [ 6 ] . Our study indicates that younger elderly patients benefit more from surgery, consistent with previous research. For younger elderly patients, more aggressive surgical treatment may be considered. A meta-analysis showed that compared to non-surgical treatments, surgical resection provides better overall survival, even for small non-functional neuroendocrine tumors. Studies have also shown that enucleation has shorter operation time and less intraoperative bleeding compared to standard surgery, but a higher incidence of postoperative pancreatic fistula [ 7 ] . Another study comparing endoscopic ultrasound-guided radiofrequency ablation (EUS-RFA) with surgical resection for insulinomas found that EUS-RFA is safer and more effective, potentially becoming a first-line therapy [ 8 ] . For elderly patients with locally growing neuroendocrine tumors, EUS-RFA may replace surgery and see broader clinical application. Our research indicates that gender is an independent prognostic factor. A retrospective study from Italy revealed that compared to male patients, female patients have a higher incidence and earlier onset of disease, and when combined with type 2 diabetes, they present with more advanced tumor staging [ 9 ] . Another study confirmed that female patients have a longer overall survival, suggesting that prognosis and treatment should consider gender factors [ 10 ] . Regarding underlying mechanisms, a study investigated the correlation between female estrogen exposure and clinical characteristics of pancreatic neuroendocrine tumors, finding that estrogen exposure correlates with tumor size and may inhibit tumor growth [ 11 ] . Patients with better prognosis showed higher expression of ERβ in tumor tissues [ 12 ] . These studies collectively support the influence of gender on the prognosis of pancreatic neuroendocrine tumors. This study shows that N stage is an independent risk factor for prognosis. A meta-analysis indicated that lymph node metastasis is common even in small, well-differentiated neuroendocrine tumors and is associated with poor prognosis, suggesting that the wait-and-see strategy for small tumors should be reconsidered, and lymph node dissection should be performed during surgery [ 13 ] . Another retrospective study found that tumors larger than 1.5 cm, primary tumors in the pancreatic head, Ki-67 index greater than 20%, and lymphovascular invasion are associated with a higher likelihood of lymph node metastasis, and patients with lymph node metastasis have a shorter median disease-free survival. The study recommends lymph node dissection during surgery even if preoperative variables do not reliably predict a low probability of lymph node involvement [ 14 ] . Studies have shown that the size of non-functional PanNETs is closely related to malignant phenotype, and incidentally discovered tumors smaller than 2 cm are recommended for non-surgical management [ 15 ] . Our study found that in univariate Cox analysis, tumor size and T stage were related to prognosis, but in multivariate Cox analysis, tumor size was not associated with prognosis, suggesting an indirect or spurious relationship rather than a true independent risk factor. As one study showed, tumor size is positively correlated with the Ki-67 index in well-differentiated neuroendocrine tumors [ 16 ] , but not necessarily in poorly differentiated or undifferentiated tumors. Therefore, making treatment decisions based solely on tumor size may delay appropriate treatment. This study also shows that tumor differentiation is an independent risk factor for prognosis. Studies have indicated that patients with G3 neuroendocrine tumors have significantly better overall survival if the tumors are well-differentiated compared to poorly differentiated ones [ 17 ] . In clinical practice, there can be discrepancies between mitotic count and Ki-67 index-based grading in PanNETs, with clinical outcomes being worse when the mitotic count is G2 and the Ki-67 index is G3. More importantly, G3 well-differentiated PanNETs are significantly less aggressive than true poorly differentiated neuroendocrine carcinomas (NECs) [ 18 ] . Tumor differentiation plays a crucial role in prognosis. A limitation of our study is the lack of Ki-67 index grading data. The Ki-67 index grading in well-differentiated neuroendocrine tumors is important for predicting prognosis, and our prognostic model needs further improvement. Conclusion Age is an independent prognostic factor for elderly patients with non-functional PanNETs. Younger elderly patients may benefit more from surgical treatment. Declarations Conflicts of interest The authors declare that there is no conflict of interest regarding the publication of this paper Funding This work received support from the following funds: 1 Natural Science Foundation of Hubei Provincial，China（2023AFB347）. 2 Major Project of Wuhan Municipal Health Commission，China（WZ21A04）. 3 Knowledge Innovation Program of Wuhan-Shuguang Project of Wuhan Science and Technology Bureau，China（2022020801020584）. Author Contribution Zhengqiang Wang and Chaoqun Huang conceived and designed the research. Zhengqiang Wang collected and analyzed the data, interpreted the results, and wrote the manuscript. Chaoqun Huang reviewed and edited the manuscript and secured funding. All authors have read and approved the final manuscript. Data Availability The data supporting the findings of this study are freely available from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute at https://seer.cancer.gov/. 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Treatment Response and Outcomes of Grade 3 Pancreatic Neuroendocrine Neoplasms Based on Morphology: Well Differentiated Versus Poorly Differentiated. Pancreas, 46 (3), 296–301. https://doi.org/10.1097/MPA.0000000000000735 Basturk, O., Yang, Z., Tang, L. H., Hruban, R. H., Adsay, V., McCall, C. M., Krasinskas, A. M., Jang, K.-T., Frankel, W. L., Balci, S., Sigel, C., & Klimstra, D. S. (2015). The high-grade (WHO G3) pancreatic neuroendocrine tumor category is morphologically and biologically heterogenous and includes both well differentiated and poorly differentiated neoplasms. The American Journal of Surgical Pathology, 39 (5), 683–690. https://doi.org/10.1097/PAS.0000000000000408 Additional Declarations No competing interests reported. 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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-4729493","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":335184918,"identity":"f2abcbea-753c-49e6-88b7-5fd4dd56a112","order_by":0,"name":"Zhengqiang Wang","email":"","orcid":"","institution":"Department of Gastroenterology, Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Zhengqiang","middleName":"","lastName":"Wang","suffix":""},{"id":335184919,"identity":"e590371c-a688-4615-a160-88a5cd3b98b8","order_by":1,"name":"Chaoqun Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACefkHiQ8+VNjIsbE3EKnFsCHhseGMM2nGfDwHiLXmQOIzYd62w4nzJBKI1MHYcDiNmbctLbFN8vHGGww1NtEEtbAztqU9nHPOxrhNOq3YguFYWm4DQVuaedIN3pSlybZJ55hJAO0krIXhGP83CR62w4xtkmeI1XKGIU2Sp+2wYpsED5FaDGcwJIMDmY0H6JcEYvwiL8EAiUr59sMbb3yosSHCYUjAgOioQdJCqo5RMApGwSgYGQAAxHtA/PqvaZIAAAAASUVORK5CYII=","orcid":"","institution":"Hubei Provincial Hospital of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Chaoqun","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-07-12 10:07:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4729493/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4729493/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62223161,"identity":"ee2aa73c-d54f-41bb-b6a6-540575a5461b","added_by":"auto","created_at":"2024-08-11 12:42:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2686660,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier Overall Survival Analysis in Elderly Patients with Non-Functional Neuroendocrine Tumors.\u003c/p\u003e\n\u003cp\u003eA. Differences in Overall Survival by Age Group.\u003c/p\u003e\n\u003cp\u003eB. Differences in Overall Survival Stratified by Surgery Status According to Age Group.\u003c/p\u003e\n\u003cp\u003eC. Differences in Overall Survival Stratified by Age Group, Surgery Status, and Chemotherapy Status.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4729493/v1/eeadea4cad78601ccb94dd1c.jpg"},{"id":62221883,"identity":"fbe56417-d9f4-4bdb-9caa-0ebfc8b20e70","added_by":"auto","created_at":"2024-08-11 12:34:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":782158,"visible":true,"origin":"","legend":"\u003cp\u003eCompeting Risk Analysis of Cancer-Specific Mortality in Elderly Patients with Non-Functional Neuroendocrine Tumors\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4729493/v1/714fb0a0faf6b89dd93b9ba4.jpg"},{"id":62221885,"identity":"d3a38c63-b145-43a8-aa03-3059560702f4","added_by":"auto","created_at":"2024-08-11 12:34:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1358884,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for Predicting 1-, 3-, and 5-Year Overall Survival in Elderly Patients with Non-Functional Neuroendocrine Tumors.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4729493/v1/efd234e4580fcbee9b66a747.jpg"},{"id":62742843,"identity":"03da42a0-e58d-4955-837b-29ebc7a17575","added_by":"auto","created_at":"2024-08-19 03:19:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5527957,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4729493/v1/567e24f2-3296-4246-8998-7e5a25649243.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Age Influence on the Prognosis and Management of Non-Functional Pancreatic Neuroendocrine Tumors in the Elderly","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeuroendocrine tumors (NETs) are a heterogeneous group of neoplasms originating from neuroendocrine cells and occurring throughout the body. The incidence of NETs has increased significantly, rising from 1.09 per 100,000 in 1973 to 6.98 per 100,000 in 2012 in the United States. Pancreatic neuroendocrine tumors (PanNETs) comprise approximately 7% of all pancreatic tumors, with an annual incidence of about 1 per 100,000 population\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. PanNETs are classified based on hormone secretion into functional and non-functional types, with the latter being more common and often diagnosed at a later stage due to the absence of symptoms. According to the World Health Organization (WHO), PanNETs are classified into well-differentiated NETs and poorly differentiated neuroendocrine carcinomas (NECs), with well-differentiated NETs further categorized into grades G1, G2, and G3 based on mitotic count and Ki-67 index\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe treatment of diseases in elderly patients is an increasingly important topic due to the global aging population. Ageism, which involves discrimination based on age, can influence medical decision-making and often results in undertreatment of older patients\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Studies indicate that elderly patients may receive less aggressive treatment options compared to younger counterparts, potentially impacting outcomes and overall survival\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. For pancreatic neuroendocrine tumors (PanNETs), surgical resection is challenging and traumatic, particularly for elderly patients, who may therefore opt for more conservative management, especially as non-functional tumors often remain asymptomatic and are managed through surveillance alone.\u003c/p\u003e \u003cp\u003eRegarding the significance of age in non-functional pancreatic neuroendocrine tumors, some studies suggest that younger patients tend to have better tumor differentiation and earlier staging, which are favorable for surgical intervention and prognosis\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Other research indicates that early diagnosis of PanNETs in younger patients correlates with longer overall survival, even when the tumors are larger and have worse staging, highlighting better outcomes following curative surgical resection\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The relevance of age-related factors in non-functional pancreatic neuroendocrine tumors among elderly patients requires further empirical support.\u003c/p\u003e \u003cp\u003eThis research focuses on elucidating the influence of age on the prognosis and management of non-functional PanNETs in elderly patients. By leveraging data from the Surveillance, Epidemiology, and End Results (SEER) database, we aim to delineate age-related disparities in tumor characteristics, treatment patterns, and survival outcomes. Understanding these nuances is crucial for optimizing therapeutic strategies and enhancing outcomes in elderly patients with PanNETs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and patients\u003c/h2\u003e \u003cp\u003eThis study employed data extracted from the Surveillance, Epidemiology, and End Results (SEER) cancer registry database, adhering to the SEER data use agreement (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.seer.cancer.gov\" target=\"_blank\"\u003ewww.seer.cancer.gov\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.seer.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Data extraction was facilitated using the SEER*Stat software program (version 8.4.3). Pathological diagnoses were established based on the primary site, following the International Classification of Diseases for Oncology, third edition (ICD-O-3) criteria. Specifically, our study encompassed cases with Site recode ICD-O-3/WHO 2008 designation for the pancreas, including histology codes 8013/3 (large cell neuroendocrine carcinoma), 8150/3 (Pancreatic endocrine tumor, malignant), 8240/3 (neuroendocrine neoplasms, not otherwise specified), 8246/3 (neuroendocrine carcinoma, not otherwise specified), and 8249/3 (atypical carcinoid tumor).\u003c/p\u003e \u003cp\u003eExclusion criteria included cases documented solely as \"Autopsy only\" or \"Death certificate only.\" Furthermore, the study cohort was confined to individuals aged 65 years and older. Samples with missing crucial variables were excluded from the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinical variables\u003c/h2\u003e \u003cp\u003eFor analytical purposes, age groups were delineated into two categories: 65\u0026ndash;75 years and \u0026ge;\u0026thinsp;75 years. Patient race was classified as white, black, and other, based on the SEER coding scheme. The primary site of tumors was subdivided into the head, body, tail of the pancreas, and other locations. Tumor size was stratified into three groups: \u0026le;2cm, \u0026gt;2cm and \u0026le;\u0026thinsp;4cm, and \u0026gt;\u0026thinsp;4cm. The number of lymph nodes removed during surgery was categorized into three groups: no lymph nodes removed, \u0026le;\u0026thinsp;3 lymph nodes removed, and \u0026ge;\u0026thinsp;4 lymph nodes removed. Tumor grade was categorized as well-differentiated (Grade 1), moderately differentiated (Grade 2), poorly differentiated (Grade 3), and undifferentiated (Grade 4). Therapy received was classified utilizing SEER site-specific therapy of primary site codes and segregated into two main groups: surgical resection and no surgery. The 7th edition of the American Joint Committee on Cancer (AJCC) staging system was utilized for staging purposes within this investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate and multivariate COX regression analyses\u003c/h2\u003e \u003cp\u003eTo identify variables influencing overall survival, we performed univariate Cox regression analysis on a set of potential prognostic factors. Variables with a p-value of less than 0.05 in the univariate analysis were subsequently included in the multivariate Cox regression model. This approach allowed us to control for confounding factors and assess the independent effect of each variable on overall survival.\u003c/p\u003e \u003cp\u003eThe hazard ratios (HRs) and 95% confidence intervals (CIs) for each variable were calculated. The significance of each variable was assessed, with a focus on those maintaining statistical significance in the multivariate model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of prognostic nomogram\u003c/h2\u003e \u003cp\u003eTo construct a prognostic nomogram for predicting overall survival in elderly patients with pancreatic neuroendocrine tumors, we utilized variables identified as significant in the multivariate Cox regression analysis. These included age, tumor grade, N stage, M stage, and surgical treatment.\u003c/p\u003e \u003cp\u003eVariable Selection: Based on the multivariate analysis, variables with a significant impact on overall survival (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were chosen for inclusion in the nomogram.\u003c/p\u003e \u003cp\u003eModel Development: We used these selected variables to develop a prognostic model. The nomogram was constructed using the 'rms' package in R, which allows for the visualization of the Cox regression model as a user-friendly tool for clinicians.\u003c/p\u003e \u003cp\u003eNomogram Construction: The nomogram graphically represents the survival probability at specific time points by assigning a score to each level of the prognostic variables. The total score, obtained by summing the individual scores, corresponds to the estimated survival probabilities.\u003c/p\u003e \u003cp\u003eModel Validation: To evaluate the performance of the nomogram, we calculated the concordance index (C-index).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics for categorical variables were computed in terms of proportions. The Chi-square test of independence was employed for assessing associations between categorical variables. Survival analysis was conducted utilizing non-parametric Kaplan-Meier estimates and Cox proportional hazard models. All statistical analyses were conducted using R version 4.3.3. A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was adopted for all analyses to determine statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAge-Related Differences in Non-Functional Pancreatic Neuroendocrine Tumors\u003c/h2\u003e \u003cp\u003eBased on the inclusion and exclusion criteria, a total of 2,157 patients were included in this study. Patients were divided into two age groups: the younger elderly group (65\u0026ndash;75 years) and the older elderly group (\u0026ge;\u0026thinsp;75 years). No significant differences were observed between the two groups in terms of gender, race, primary tumor site, TNM stage, T stage, N stage, pulmonary metastasis, chemotherapy, and radiotherapy (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, significant differences were found between the groups in terms of tumor size, grade, M stage, liver metastasis, surgical treatment, and lymph node dissection (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The older elderly group had a higher proportion of tumors larger than 2 cm (61.3% vs. 55.2%), a lower proportion of well-differentiated tumors (68.8% vs. 73.6%), a worse M stage (18.7% vs. 14.4%), and a higher incidence of liver metastasis (16.1% vs. 12.5%). Additionally, the older elderly group had a lower rate of surgical treatment (63% vs. 82%) and fewer patients undergoing lymph node dissection (50.2% vs. 67.1%) (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings suggest that older elderly patients are likely to have tumors detected at a more advanced stage and receive relatively conservative treatment.\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\u003eThe clinical characteristics of patients included in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u0026ndash;75 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;75 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u0026thinsp;=\u0026thinsp;Male (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e877 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e419 (60.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.931\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1215 (83.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e588 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary site (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e403 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e605 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281 (40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e655 (44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269 (38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;4 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e460 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e231 (33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e347 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195 (28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1076 (73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e478 (68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e267 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e800 (54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e371 (53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e389 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e597 (40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e253 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e485 (33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e244 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e325 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e337 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM stage\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1200 (82.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e438 (63.0)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN removed (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e481 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e346 (49.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003cp\u003eYes (%)\u003c/p\u003e \u003cp\u003eRadiation\u003c/p\u003e \u003cp\u003eYes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e809 (55.3)\u003c/p\u003e \u003cp\u003e148 (10.1)\u003c/p\u003e \u003cp\u003e40 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e299 (43.0)\u003c/p\u003e \u003cp\u003e76 (10.9)\u003c/p\u003e \u003cp\u003e13 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis in Younger and Older Elderly Groups with Non-Functional Pancreatic Neuroendocrine Tumors\u003c/h2\u003e \u003cp\u003eK-M analysis revealed that the younger elderly group (65\u0026ndash;75 years) had significantly better overall survival compared to the older elderly group (\u0026ge;\u0026thinsp;75 years) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Considering the competing risks of death from other comorbidities, a competing risks model analysis showed that the cumulative incidence of cancer-specific mortality was lower in the younger elderly group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStratified analysis by surgical treatment showed no significant difference in overall survival between the two age groups among patients who did not undergo surgery (p\u0026thinsp;=\u0026thinsp;0.44). In contrast, among those who underwent surgery, the younger elderly group had significantly better overall survival (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eFurther stratification by both surgery and chemotherapy revealed no significant difference in overall survival between the age groups for patients who received only chemotherapy (p\u0026thinsp;=\u0026thinsp;0.32). However, significant differences were observed in overall survival for patients who had surgery without chemotherapy (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and for those who received both surgery and chemotherapy (p\u0026thinsp;=\u0026thinsp;0.022) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eThese results indicate that age may be an independent risk factor for prognosis in elderly patients with neuroendocrine tumors, with surgery serving as a beneficial factor for prognosis, while chemotherapy alone may not improve survival.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate Analysis of Prognostic Factors in Elderly Patients with Pancreatic Neuroendocrine Tumors\u003c/h2\u003e \u003cp\u003eFurther analysis using a univariate Cox regression model identified variables influencing overall survival with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, which were then included in a multivariate Cox regression model. Statistically significant variables identified were as follows: Age 65\u0026ndash;75 years (HR\u0026thinsp;=\u0026thinsp;0.61, p\u0026thinsp;=\u0026thinsp;0), Sex Male (HR\u0026thinsp;=\u0026thinsp;1.26, p\u0026thinsp;=\u0026thinsp;0.0085), Grade III (HR\u0026thinsp;=\u0026thinsp;3.86, p\u0026thinsp;=\u0026thinsp;0), Grade IV (HR\u0026thinsp;=\u0026thinsp;4.14, p\u0026thinsp;=\u0026thinsp;0), TNM stage IV (HR\u0026thinsp;=\u0026thinsp;1.85, p\u0026thinsp;=\u0026thinsp;0.0082), N1 stage (HR\u0026thinsp;=\u0026thinsp;1.28, p\u0026thinsp;=\u0026thinsp;0.0248), and undergoing surgery (HR\u0026thinsp;=\u0026thinsp;0.39, p\u0026thinsp;=\u0026thinsp;0). Variables such as tumor size, T stage, primary site, liver metastasis, lung metastasis, M stage, chemotherapy, radiation, and lymph node removal were not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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 analyses of factors linked to OS prognosis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard rate(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHazard rate(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;75 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;75 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49(0.42\u0026ndash;0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61(0.51\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2(1.01\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26(1.06\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0085\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7(0.46\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89(0.66\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.73(1.34\u0026ndash;2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16(0.90\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32(0.98\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02(0.78\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96(0.75\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02(0.76\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;4 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.72(1.41\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02(0.61\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4(1.97\u0026ndash;2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95(0.57\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46(1.18\u0026ndash;1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17(0.94\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.18(5.8\u0026ndash;8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.86(2.96\u0026ndash;5.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.43(6.1-11.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.14(2.89\u0026ndash;5.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.71(1.38\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38(1-1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003cp\u003eIV\u003c/p\u003e \u003cp\u003eT stage\u003c/p\u003e \u003cp\u003eT1\u003c/p\u003e \u003cp\u003eT2\u003c/p\u003e \u003cp\u003eT3\u003c/p\u003e \u003cp\u003eT4\u003c/p\u003e \u003cp\u003eN stage\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eM stage\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eLiver metastasis\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eLung metastasis\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eLN removed\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eRadiation\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.02(1.25\u0026ndash;3.28)\u003c/p\u003e \u003cp\u003e5.19(4.3\u0026ndash;6.27)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.62(1.32\u0026ndash;1.99)\u003c/p\u003e \u003cp\u003e2.36(1.91\u0026ndash;2.92)\u003c/p\u003e \u003cp\u003e4.19(3.03\u0026ndash;5.79)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2.05(1.74\u0026ndash;2.43)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e4.24(3.59\u0026ndash;5.01)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e3.89(3.27\u0026ndash;4.62)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e6.44(4.4\u0026ndash;9.42)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.24(0.2\u0026ndash;0.28)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.27(0.18\u0026ndash;0.39)\u003c/p\u003e \u003cp\u003e0.42(0.35\u0026ndash;0.5)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e4.8(4.01\u0026ndash;5.75)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2.37(1.63\u0026ndash;3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79(0.43\u0026ndash;1.46)\u003c/p\u003e \u003cp\u003e1.85(1.17\u0026ndash;2.91)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.97(0.56\u0026ndash;1.68)\u003c/p\u003e \u003cp\u003e1.06(0.61\u0026ndash;1.84)\u003c/p\u003e \u003cp\u003e1.08(0.57\u0026ndash;2.05)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.28(1.03\u0026ndash;1.59)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003eNA\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.07(0.72\u0026ndash;1.58)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.02(0.66\u0026ndash;1.56)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.39(0.28\u0026ndash;0.53)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.83(0.53\u0026ndash;1.3)\u003c/p\u003e \u003cp\u003e0.95(0.7\u0026ndash;1.3)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.22(0.95\u0026ndash;1.55)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.98(0.66\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003cp\u003e0.0082\u003c/p\u003e \u003cp\u003e0.9203\u003c/p\u003e \u003cp\u003e0.8297\u003c/p\u003e \u003cp\u003e0.8227\u003c/p\u003e \u003cp\u003e0.0248\u003c/p\u003e \u003cp\u003eNA\u003c/p\u003e \u003cp\u003e0.751\u003c/p\u003e \u003cp\u003e0.938\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0.4219\u003c/p\u003e \u003cp\u003e0.7503\u003c/p\u003e \u003cp\u003e0.113\u003c/p\u003e \u003cp\u003e0.9131\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\u003eThese results indicate that Sex, Age, Grade stage, N stage, TNM stage, and surgery are independent prognostic factors. Although lymph node metastasis was identified as a prognostic risk factor, intraoperative lymph node dissection did not show a benefit. Tumor size and T stage were significant in univariate analysis but not in multivariate analysis, suggesting that tumor size may not be a primary consideration in treatment decisions for pancreatic neuroendocrine tumors. Similarly, M stage, liver metastasis, and lung metastasis were significant in univariate analysis but not in multivariate analysis, which contrasts with the finding that TNM stage IV is an independent prognostic factor.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDevelopment and Evaluation of a Prognostic Model for Overall Survival in Elderly Patients with Pancreatic Neuroendocrine Tumors\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo address collinearity issues and better understand prognostic factors, we included M stage in the prognostic prediction model and excluded the TNM stage variable. We developed an overall survival prediction model incorporating sex, age, grade stage, N stage, M stage, and surgery, and created a nomogram to visualize this model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The model's performance was evaluated using the C-index, resulting in a C-index of 0.77. These metrics suggest that the model was successfully constructed, though its predictive performance could be further improved.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study utilizes the SEER database to investigate the impact of age on overall survival and disease management in elderly patients with non-functional pancreatic neuroendocrine tumors (PanNETs), a topic of significant concern for both doctors and patients. To ensure the reliability of the results, samples with missing variables were excluded, and a sufficiently large sample size was included. The study found that age is an independent risk factor for overall survival, with younger elderly patients benefiting more from surgical treatment. Additionally, tumor differentiation and N stage were identified as independent risk factors for overall survival, while tumor location, size, and T stage were not associated with overall survival.\u003c/p\u003e \u003cp\u003eRegarding the relationship between age and PanNETs, studies have shown that younger patients (\u0026lt;\u0026thinsp;50 years) with PanNETs tend to have fewer comorbidities, higher tumor staging, and larger tumors, but they have better overall survival after radical surgical resection\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Our study indicates that younger elderly patients benefit more from surgery, consistent with previous research. For younger elderly patients, more aggressive surgical treatment may be considered. A meta-analysis showed that compared to non-surgical treatments, surgical resection provides better overall survival, even for small non-functional neuroendocrine tumors. Studies have also shown that enucleation has shorter operation time and less intraoperative bleeding compared to standard surgery, but a higher incidence of postoperative pancreatic fistula\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Another study comparing endoscopic ultrasound-guided radiofrequency ablation (EUS-RFA) with surgical resection for insulinomas found that EUS-RFA is safer and more effective, potentially becoming a first-line therapy\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. For elderly patients with locally growing neuroendocrine tumors, EUS-RFA may replace surgery and see broader clinical application.\u003c/p\u003e \u003cp\u003eOur research indicates that gender is an independent prognostic factor. A retrospective study from Italy revealed that compared to male patients, female patients have a higher incidence and earlier onset of disease, and when combined with type 2 diabetes, they present with more advanced tumor staging\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Another study confirmed that female patients have a longer overall survival, suggesting that prognosis and treatment should consider gender factors\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Regarding underlying mechanisms, a study investigated the correlation between female estrogen exposure and clinical characteristics of pancreatic neuroendocrine tumors, finding that estrogen exposure correlates with tumor size and may inhibit tumor growth\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Patients with better prognosis showed higher expression of ERβ in tumor tissues\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. These studies collectively support the influence of gender on the prognosis of pancreatic neuroendocrine tumors.\u003c/p\u003e \u003cp\u003eThis study shows that N stage is an independent risk factor for prognosis. A meta-analysis indicated that lymph node metastasis is common even in small, well-differentiated neuroendocrine tumors and is associated with poor prognosis, suggesting that the wait-and-see strategy for small tumors should be reconsidered, and lymph node dissection should be performed during surgery\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Another retrospective study found that tumors larger than 1.5 cm, primary tumors in the pancreatic head, Ki-67 index greater than 20%, and lymphovascular invasion are associated with a higher likelihood of lymph node metastasis, and patients with lymph node metastasis have a shorter median disease-free survival. The study recommends lymph node dissection during surgery even if preoperative variables do not reliably predict a low probability of lymph node involvement\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStudies have shown that the size of non-functional PanNETs is closely related to malignant phenotype, and incidentally discovered tumors smaller than 2 cm are recommended for non-surgical management\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Our study found that in univariate Cox analysis, tumor size and T stage were related to prognosis, but in multivariate Cox analysis, tumor size was not associated with prognosis, suggesting an indirect or spurious relationship rather than a true independent risk factor. As one study showed, tumor size is positively correlated with the Ki-67 index in well-differentiated neuroendocrine tumors\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, but not necessarily in poorly differentiated or undifferentiated tumors. Therefore, making treatment decisions based solely on tumor size may delay appropriate treatment.\u003c/p\u003e \u003cp\u003eThis study also shows that tumor differentiation is an independent risk factor for prognosis. Studies have indicated that patients with G3 neuroendocrine tumors have significantly better overall survival if the tumors are well-differentiated compared to poorly differentiated ones\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. In clinical practice, there can be discrepancies between mitotic count and Ki-67 index-based grading in PanNETs, with clinical outcomes being worse when the mitotic count is G2 and the Ki-67 index is G3. More importantly, G3 well-differentiated PanNETs are significantly less aggressive than true poorly differentiated neuroendocrine carcinomas (NECs)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Tumor differentiation plays a crucial role in prognosis. A limitation of our study is the lack of Ki-67 index grading data. The Ki-67 index grading in well-differentiated neuroendocrine tumors is important for predicting prognosis, and our prognostic model needs further improvement.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAge is an independent prognostic factor for elderly patients with non-functional PanNETs. Younger elderly patients may benefit more from surgical treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch2\u003eConflicts of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this paper\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work received support from the following funds:\u003c/p\u003e\n\u003cp\u003e1 Natural Science Foundation of Hubei Provincial，China（2023AFB347）.\u003c/p\u003e\n\u003cp\u003e2 Major Project of Wuhan Municipal Health Commission，China（WZ21A04）.\u003c/p\u003e\n\u003cp\u003e3 Knowledge Innovation Program of Wuhan-Shuguang Project of Wuhan Science and Technology Bureau，China（2022020801020584）.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eZhengqiang Wang and Chaoqun Huang conceived and designed the research. Zhengqiang Wang collected and analyzed the data, interpreted the results, and wrote the manuscript. Chaoqun Huang reviewed and edited the manuscript and secured funding. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data supporting the findings of this study are freely available from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute at https://seer.cancer.gov/. These data were accessed under license for the current study and are publicly available with permission from the SEER Program.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDasari, A., Shen, C., Halperin, D., Zhao, B., Zhou, S., Xu, Y., Shih, T., \u0026amp; Yao, J. C. (2017). Trends in the Incidence, Prevalence, and Survival Outcomes in Patients With Neuroendocrine Tumors in the United States. 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The American Journal of Surgical Pathology, \u003cem\u003e39\u003c/em\u003e(5), 683\u0026ndash;690. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/PAS.0000000000000408\u003c/span\u003e\u003cspan address=\"10.1097/PAS.0000000000000408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pancreatic Neuroendocrine Tumors, elderly patients, Surgery, Age","lastPublishedDoi":"10.21203/rs.3.rs-4729493/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4729493/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e \u003cp\u003ePancreatic neuroendocrine tumors (PanNETs) account for about 7% of pancreatic tumors and are increasing in incidence. Non-functional PanNETs in the elderly often present asymptomatically, leading to more conservative treatment due to age bias. The impact of age on prognosis and management needs further clarification.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eClinical data for patients over 65 with non-functional PanNETs were collected from the SEER database. Kaplan-Meier curves analyzed overall survival between younger elderly (age 65\u0026ndash;75 years) and older elderly (age\u0026thinsp;\u0026ge;\u0026thinsp;75 years) groups. Competing risk models assessed cancer-specific mortality, and Cox regression models identified independent survival risk factors. A prognostic model was constructed and evaluated for performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 2,157 patients, older elderly patients (n\u0026thinsp;=\u0026thinsp;695) had larger tumors, poorer differentiation, worse M stage, and lower surgery rates compared to younger elderly patients (n\u0026thinsp;=\u0026thinsp;1,462). Younger elderly had better overall survival (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and lower cancer-specific mortality. Surgery improved survival in younger elderly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with no age group survival difference in non-surgical patients. Cox regression identified age\u0026thinsp;\u0026gt;\u0026thinsp;75, male, higher grades, advanced TNM stage, N1 stage, and lack of surgery as significant risk factors. A predictive model with a C-index of 0.77 was developed.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAge is an independent prognostic factor for elderly patients with non-functional PanNETs. Younger elderly patients benefit more from surgical treatment.\u003c/p\u003e","manuscriptTitle":"Age Influence on the Prognosis and Management of Non-Functional Pancreatic Neuroendocrine Tumors in the Elderly","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-11 12:34:01","doi":"10.21203/rs.3.rs-4729493/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae937c1c-e4a1-4920-ab7f-6391ad1fd5a6","owner":[],"postedDate":"August 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35499237,"name":"Biological sciences/Cancer"},{"id":35499238,"name":"Health sciences/Endocrinology"},{"id":35499239,"name":"Health sciences/Medical research"},{"id":35499240,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-08-19T03:11:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-11 12:34:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4729493","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4729493","identity":"rs-4729493","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}