Clinical features and Prognostic Factors of Patients with Primitive Neuroectodermal Tumors

Clinical features and Prognostic Factors of Patients with Primitive Neuroectodermal Tumors | 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 Clinical features and Prognostic Factors of Patients with Primitive Neuroectodermal Tumors Xiang Qu, Jie Yang, Ming Wu, Xiaoliang Yang, Bozhen Tian, Yu Qiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5233331/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 Background Primitive neuroectodermal tumors (PNET) are associated with poor prognosis, and their treatment remains a challenge. However, research on PNET patients is relatively limited. Therefore, we aim to investigate the prognosis of this specific cohort and identify independent prognostic factors. Methods This study screened a cohort of PNET patients from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute from 2000 to 2020. Prognostic analyses were performed using the Kaplan-Meier method and Cox proportional hazards regression model. Results A total of 941 eligible PNET patients were included, with the most common site of occurrence being the brain (57.07%). The 5-year overall survival (OS) and cancer-specific survival (CSS) rates for the entire study population were 51.1% and 54.2%, respectively, while the 10-year OS and CSS were 44.7% and 48.7%, respectively. In the univariate analysis, age, marital status, tumor stage, surgery, and chemotherapy had significant impacts on patient survival outcomes. In the multivariate analysis, age and the presence of metastasis at initial staging were identified as independent poor prognostic factors for both OS and CSS, while surgery and chemotherapy were independent prognostic factors for OS, with surgery also being an independent prognostic factor for CSS. Conclusion Surgery combined with chemotherapy showed survival benefits for PNET patients and is recommended. Radiotherapy did not improve patient survival, which requires further investigation in future studies. Health sciences/Oncology/Cancer/Cancer epidemiology Health sciences/Oncology/Cancer/Cancer therapy Primitive neuroectodermal tumors Clinical features prognostic factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Primitive Neuroectodermal Tumor (PNET) was first proposed by Hart in 1973 as a type of malignant small round cell tumor originating from the neuroectoderm 1 . In 1986, Dehner first classified PNET into central nervous system PNET (cPNET) and peripheral PNET (pPNET) 2 . cPNET primarily refers to malignant tumors arising from supratentorial brain tissue and the spinal cord, which have an extremely low incidence, accounting for only 0.03% of intracranial tumors 3 . pPNET can occur in various parts of the body, most commonly in the thoracic and abdominal regions, while it is less frequently found in the head and neck 4 , 5 . PNETs are morphologically and histologically similar to tumors in the Ewing sarcoma family and are therefore typically classified as part of this family 6 – 8 . PNET is characterized by low incidence, poor differentiation, and high malignancy, with very few patients achieving long-term survival 9 – 11 . Due to its rarity, randomized controlled studies on this disease are challenging to conduct. Currently, there are no clear guidelines or protocols for the treatment of PNET. Clinically, maximal tumor resection is typically performed, supplemented by postoperative local radiotherapy (RT) and systemic chemotherapy (CT). However, PNET patients still face a high risk of local recurrence and distant metastasis 3 , 12 . Therefore, the clinical features and optimal treatment strategies for PNET still require further research and clarification. To gain deeper insights into the clinical features and survival predictors of PNET patients, this study analyzed PNET patients from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute from 2000 to 2020. This represents the largest scale study conducted on this population, aiming to further clarify the clinical features and survival influencing factors of PNET. Methods Patient Selection and Variables The data for this study were sourced from the SEER database (version 8.4.3), maintained by the National Cancer Institute (NCI). This database covers approximately one-third of the U.S. population and provides detailed clinical, pathological, and survival outcome information. We extracted records of patients diagnosed with PNET (ICD-O-3; histologic type: 9473/3) between 2000 and 2020, collecting key clinical characteristics and survival outcomes, including age, race, marital status, household income, pathological grade, tumor stage, tumor size, and treatment methods (such as surgical status, CT, and RT). Marital status was categorized into married and unmarried, with unmarried including single (never married), living with a partner, separated, widowed, and divorced. To minimize the impact of other cancers on this study, we included only patients with PNET who did not have other primary malignant tumors, while excluding patients with unclear treatment methods. Statistical Analysis Overall survival (OS) was defined as the time from diagnosis to death from any cause, and cancer-specific survival (CSS) was defined as the time from diagnosis to death specifically due to cancer. Univariate Cox regression was employed to screen for potential risk factors associated with survival. Multivariate Cox regression analysis was used to determine independent predictors of OS and CSS. The statistical significance level was set at P ≤ 0.05. All statistical analyses were conducted using R statistical software (version 4.2.2). Results Demographic and Clinical Characteristics of PNET Patients From 2000 to 2020, a total of 941 eligible PNET patient data were collected from the SEER database. The clinical characteristics of the patients are shown in Table 1 . PNET is more prevalent in children and adolescents, with patients under 15 years old accounting for 46.3% of the total population, and the incidence decreases with increasing age (Fig. 1 A). In terms of tumor location, the most common site of occurrence was the brain (57.07%), followed by soft tissues (14.77%) and bones and joints (4.99%). Among the known pathological grades, PNET is primarily classified as grade IV (61.92%) and grade III (33.89%). Regarding tumor staging, PNET is most commonly found in the localized stage (52.71%), followed by the regional stage (19.55%) and distant metastatic stage (18.70%). In terms of treatment, most patients received surgery (78.64%) and CT (79.38%), while approximately half of the patients underwent RT (54.84%). Ultimately, 517 patients died, of which 461 died from PNET. The 5-year OS and CSS rates for the study population were 51.1% and 54.2%, respectively, while the 10-year OS and CSS rates were 44.7% and 48.7%, respectively (Fig. 1 B). Table 1 Demographic and clinical characteristics of 941patients with PNET identified in the SEER database from 2000 to 2020 Characteristics Overall n = 941(%) Age <15 years 436 (46.33) 15–29 years 235 (24.97) ≥30 years 270 (28.69) Sex Female 428 (45.48) Male 513 (54.52) Diagnosis 2000–2010 660 (70.14) 2011–2020 281 (29.86) Race Black and Other 180 (19.13) White 752 (79.91) Unknown 9 (0.96) Primary site Brain 537 (57.07) Soft Tissue 139 (14.77) Bones and Joints 47 (4.99) Cranial Nerves Other Nervous System 46 (4.89) Other 172 (18.28) Tumor size ≤45mm 193 (20.51) >45mm 312 (33.16) Unknown 436 (46.33) Marital status Married 195 (20.72) Unmarried 732 (77.79) Unknown 14 (1.49) Household income Low 243 (25.82) High 698 (74.18) Grade I 5 (0.53) II 5 (0.53) III 81 (8.61) IV 148 (15.73) Unknown 702 (74.60) Tumor stage Localized 496 (52.71) Regional 184 (19.55) Distant 176 (18.70) Unknown 85 (9.03) Surgical status No 201 (21.36) Yes 740 (78.64) Radiotherapy No 425 (45.16) Yes 516 (54.84) Chemotherapy No 194 (20.62) Yes 747 (79.38) Univariate Cox Analysis of OS and CSS in PNET Patients The univariate Cox analysis revealed (Table 2 ) that gender, race, time of diagnosis, tumor location, tumor size, and RT were not significantly associated with OS or CSS. However, age, tumor stage, and marital status showed significant differences with respect to OS and CSS, with older age (Fig. 2 A, Fig. 3 A) and initial staging with metastasis (Fig. 2 C, Fig. 3 C) indicating poorer prognosis. Being unmarried was associated with better OS and CSS, which may be due to unmarried individuals (Fig. 2 B, Fig. 3 B) typically being children and adolescents, a demographic with a more favorable prognosis. Both OS and CSS demonstrated significant differences based on surgical intervention (Fig. 2 D, Fig. 3 D) and CT (Fig. 2 E, Fig. 3 E), with patients receiving surgery and CT generally exhibiting better OS and CSS. Table 2 Univariate Cox regression analysis was used to screen prognostic factors in patients with PNET Characteristics OS CSS HR (95%CI) P HR (95%CI) P Age <15 years reference reference 15–29 years 1.15 (0.92–1.44) 0.230 1.15 (0.91–1.45) 0.256 ≥30 years 2.07 (1.70–2.52) <0.001 1.92 (1.55–2.37) <0.001 Sex Female reference reference Male 1.05 (0.88–1.24) 0.617 1.01 (0.84–1.21) 0.906 Diagnosis 2000–2010 reference reference 2011–2020 1.04 (0.85–1.27) 0.689 1.09 (0.89–1.34) 0.419 Race Black and Other reference reference White 0.84 (0.68–1.04) 0.117 0.84 (0.67–1.06) 0.138 Primary site Bones and Joints reference reference Brain 1.20 (0.80–1.80) 0.381 1.27 (0.82–1.96) 0.277 Soft Tissue 0.97 (0.62–1.54) 0.913 0.89 (0.54–1.46) 0.641 Cranial Nerves Other Nervous System 1.05 (0.60–1.83) 0.857 1.11 (0.62–1.99) 0.725 Other 1.04 (0.67–1.63) 0.859 0.99 (0.62–1.60) 0.983 Tumor size ≤45mm reference reference >45mm 1.17 (0.91–1.50) 0.229 1.22 (0.93–1.58) 0.145 Marital status Married reference reference Unmarried 0.64 (0.52–0.78) <0.001 0.67 (0.54–0.82) <0.001 Household income Low reference reference High 0.86 (0.71–1.05) 0.141 0.86 (0.70–1.06) 0.152 Grade I reference reference II 0.16 (0.02–1.54) 0.113 0.24 (0.02–2.68) 0.248 III 0.73 (0.23–2.33) 0.591 1.06 (0.26–4.38) 0.934 IV 0.84 (0.27–2.64) 0.761 1.14 (0.28–4.61) 0.859 Tumor stage Localized reference reference Regional 1.06 (0.84–1.34) 0.641 1.04 (0.81–1.33) 0.767 Distant 1.70 (1.36–2.12) <0.001 1.64 (1.30–2.07) <0.001 Surgical status No reference reference Yes 0.57 (0.47–0.70) <0.001 0.59 (0.48–0.73) <0.001 Radiotherapy No reference reference Yes 1.06 (0.89–1.27) 0.500 1.06 (0.88–1.27) 0.549 Chemotherapy No reference reference Yes 0.68 (0.55–0.83) <0.001 0.71 (0.57–0.88) 0.002 Multivariate Cox Analysis of OS and CSS in PNET Patients In the multivariate Cox regression analysis (Table 3 ), older age and the presence of metastasis at initial staging were identified as independent adverse prognostic factors for OS and CSS in PNET patients. Surgical intervention was considered an independent prognostic factor that could improve both OS and CSS, while CT was identified as an independent prognostic factor associated only with OS. Table 3 Multivariate Cox regression analysis was used to identify independent prognostic factors in patients with PNET. Characteristics OS CSS HR (95%CI) P HR (95%CI) P Age <15 years reference reference 15–29 years 1.08 (0.85–1.36) 0.528 1.08 (0.85–1.38) 0.521 ≥30 years 1.89 (1.46–2.44) <0.001 1.77 (1.34–2.32) <0.001 Marital status Married reference reference Unmarried 0.99 (0.77–1.27) 0.927 0.98 (0.75–1.29) 0.907 Tumor stage Localized reference reference Regional 1.03 (0.81–1.31) 0.809 1.01 (0.78–1.30) 0.941 Distant 1.60 (0.85–1.58) <0.001 1.54 (1.20–1.97) <0.001 Surgical status No reference reference Yes 0.69 (0.56–0.86) <0.001 0.71 (0.56–0.89) 0.003 Chemotherapy No reference reference Yes 0.79 (0.63–0.98) 0.03 0.81(0.64–1.03) 0.081 Subgroup Analysis Since the brain is the most common site of PNET occurrence, we conducted a separate analysis for this subgroup. Based on surgical coding, patients were divided into three groups: those who underwent no surgery, partial/subtotal resection (PR/STR), and gross total resection (GTR). The impact of subsequent treatment options, including RT, CT, chemoradiotherapy (CRT), and no chemoradiotherapy (no CRT), on survival rates was explored. Figures 4 A and 4 B show that patients who underwent GTR had significantly higher OS and CSS compared to those who had no surgery or underwent PR/STR. Additionally, the results indicated that there were no statistically significant survival differences among the treatment options of RT, CT, CRT, and No CRT, whether in the GTR (Figs. 4 C and 4 D), PR/STR (Figs. 4 E and 4 F), or no surgery groups (Figs. 4 G and 4 H). Conclusion PNET is a rare malignant tumor that has long lacked systematic understanding. To our knowledge, this study represents the largest cohort to date. Our findings primarily include two aspects: regarding clinical features, PNET predominantly occurs in the central nervous system, followed by soft tissues and bone/joint involvement. This contrasts with previous literature, which reported that intracranial PNETs are rarer than pPNET 3 , 13 , However, from a specific anatomical perspective, the brain is the most common site for PNET occurrence. Furthermore, while earlier studies suggested that the peak incidence of PNET is between the ages of 10 and 20 14 , our results indicate that the highest incidence occurs before the age of 5, with the majority of cases concentrated in individuals under 30, and the incidence declining with increasing age. The histopathological grading is primarily Grade III and Grade IV (poorly differentiated and undifferentiated). Overall, the prognosis for PNET patients is favorable, with 5-year OS and CSS rates of 51.1% and 54.2%, respectively, and 10-year OS and CSS rates of 44.7% and 48.7%. These rates are similar to the 45–55% reported in the literature 15 . However, it is noteworthy that the survival curve for PNET patients steeply declines within the first three years post-diagnosis, during which 370 patients (80.3%) died from PNET. This indicates that patients face significant treatment challenges and risks in the early stages of the disease, necessitating special attention to treatment and management during this period. Regarding prognosis, age is identified as an independent adverse prognostic factor for OS and CSS in PNET patients, particularly emphasizing the need to monitor the population over 30 years old, which experiences a significant decrease in survival, with a median OS of only 30 months. Cancer statistics from 2022 show that the 5-year survival rate for PNET patients under 15 years of age is 76%, while the rate for adolescents aged 15 to 19 is 59%, indicating a decline in survival with increasing age 16 . Additionally, the presence of metastasis at initial staging is also an independent adverse prognostic factor, consistent with prognostic trends seen in nearly all tumors. A retrospective study analyzing 117 patients with renal PNET found that the survival probability after 18 months for patients with metastatic disease was 60%, compared to 85% for those without metastatic disease 17 . Other studies have also indicated that older age and the presence of metastasis at initial staging are independent adverse prognostic factors 18 , aligning with our findings. In terms of treatment, surgery is an independent prognostic factor for both OS and CSS in PNET patients, while CT is an independent prognostic factor associated with OS. Due to the rarity of PNET, there is no standardized treatment protocol currently in place, but surgery has long been considered the cornerstone of PNET treatment 3 , 12 . CT is commonly used in combination with surgery or RT, or both, for local control 19 . Compared to surgery alone, combined surgery and CT often result in better outcomes (84% vs. 53%) 20 . Studies have shown that CT can improve the survival rate of PNET patients from 5%-10–70%-80% and reduce the recurrence rate 11 , 21 – 23 . Furthermore, induction CT can increase tumor resection rates, while surgery significantly improves patient survival 24 . Currently, early surgical resection of PNET is recommended, followed by high-dose cyclophosphamide CT and RT for residual lesions 25 . Although RT is also considered an important adjuvant treatment strategy 20 , 26 , its benefit in PNET remains controversial. A small sample study showed that postoperative RT could improve the median OS of patients with intracranial PNET (38 months vs. 13 months). Among these, three patients who underwent GTR, RT, and CT had the highest two-year survival rate (100%) and the longest OS (48 months) 12 . Another study also found that postoperative RT improved progression-free survival (PFS) and OS in patients with cranial PNET 26 . Regarding RT doses, one case reported using Gamma Knife radiosurgery to treat recurrent brain PNET, achieving disease control for up to seven years. It suggested that administering a high dose (≥ 20 Gy) in initial treatment might yield better survival benefits 10 . Subgroup analysis of brain PNET showed that complete surgical resection was the key factor for long-term survival, while no significant differences were observed among other postoperative treatment options. Since previous studies were mostly case reports or small sample studies, their conclusions may be biased, highlighting the need for more high-quality evidence-based studies to clarify the specific role of RT in PNET treatment. This study has several limitations. First, as a retrospective study, it is difficult to avoid selection bias. Second, although the SEER database provides a large number of cases and variables, it lacks detailed information on surgery, RT sites, dosages, and specific CT regimens. Patients who received RT may have had certain high-risk postoperative factors, which could affect our results. Finally, the database lacks information on patient recurrence and metastasis, preventing us from analyzing these aspects of the data. Conclusion This is the largest analysis of PNET patients to date. Although this tumor type generally has a favorable prognosis, with 5-year OS and CSS rates of 51.1% and 54.2%, respectively, we observed a high mortality risk within the first three years following diagnosis. Age, tumor stage, and surgery were identified as independent prognostic factors for both OS and CSS, while CT was an independent prognostic factor only for OS. For PNET patients, complete surgical resection should be pursued whenever possible, followed by routine CT to achieve favorable survival outcomes. Our study highlights the clinical features and prognostic factors of this specific cohort, laying a solid foundation for future research on standardized therapies. Abbreviations PNET Primitive Neuroectodermal Tumor cPNET central nervous system PNET pPNET peripheral PNET OS Overall Survival CSS Cancer-Specific Survival SEER Surveillance, Epidemiology, and End Results NCI National Cancer Institute cPNET Central Nervous System Primitive Neuroectodermal Tumor pPNET Peripheral Primitive Neuroectodermal Tumor GTR Gross Total Resection PR/STR Partial/Subtotal Resection PFS Progression-Free Survival NS no surgery S Surgery RT radiotherapy CT chemotherapy NCT no chemotherapy CRT chemoradiotherapy no CRT no chemoradiotherapy Declarations Consent for publication Not applicable. Conflict of Interest All authors of this study declared that there were no conflicts of interest regarding this research. Funding Shaanxi Natural Science Foundation (2024JC-YBMS-734); Baoji Municipal Health Commission Research Project (2024-008) Author Contribution XQ, JY and MW contributed to the writing of the manuscript and the creation of figures and tables. X Y were responsible for data collation and statistical analysis. BT and YQ were responsible for project design. All authors have read and approved the final manuscript. Acknowledgements We are grateful to the collaborators of the SEER for providing the data used in this study. Data Availability The original data presented in this study are included in the article. Further inquiries can be directed to the corresponding author. References Hart, M. N. & Earle, K. M. Primitive neuroectodermal tumors of the brain in children. Cancer . 32 (4), 890–897 (1973). Dehner, L. P. Peripheral and central primitive neuroectodermal tumors. A nosologic concept seeking a consensus. Arch. Pathol. Lab. Med. 110 (11), 997–1005 (1986). Yim, J. et al. 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Cranial Ewing Sarcoma/Peripheral Primitive Neuroectodermal Tumors: A Retrospective Study Focused on Prognostic Factors and Long-Term Outcomes. Front. Oncol. 9 , 1023 (2019). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5233331","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":376559247,"identity":"bc121b1d-6baf-4f78-ae02-93de800d36a3","order_by":0,"name":"Xiang Qu","email":"","orcid":"","institution":"Xi'an Daxing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Qu","suffix":""},{"id":376559248,"identity":"434f11e7-7e9f-4de0-87af-2311538e93a6","order_by":1,"name":"Jie Yang","email":"","orcid":"","institution":"Baoji Gaoxin Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Yang","suffix":""},{"id":376559249,"identity":"4c439395-5378-40da-a856-c2aaf0edb9be","order_by":2,"name":"Ming Wu","email":"","orcid":"","institution":"Xi'an Daxing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Wu","suffix":""},{"id":376559250,"identity":"b3f893b7-72ac-4a12-b44d-197f2afc4d7e","order_by":3,"name":"Xiaoliang Yang","email":"","orcid":"","institution":"987th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army","correspondingAuthor":false,"prefix":"","firstName":"Xiaoliang","middleName":"","lastName":"Yang","suffix":""},{"id":376559253,"identity":"db46e7c6-dca0-40d9-967b-d0c6dc1b3960","order_by":4,"name":"Bozhen Tian","email":"","orcid":"","institution":"Xi'an Daxing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bozhen","middleName":"","lastName":"Tian","suffix":""},{"id":376559256,"identity":"2e71d3a5-8c14-4d3f-b732-431e7b664490","order_by":5,"name":"Yu Qiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACNvnDBx8kVEjYycs/BzFqCGvhk2BLNnhwxibZsCEHxDhGWIucBI+Z5MO2NMaGAwlARgszEQ6TbkuTSGA7zAzUk1aR2MDGwN/enYBfi8zhwxYJPIf52Bkbj91I3CHDIHHm7Ab8WhjSEm8kSABtaWZIu5F4ho3BQCKXkJYcA4kEg8OMDccYzAoS25iJ0CKRYySRkAD0/hkGMwbitPAcSzZIOAAM5Bk8yRIJZ47xEPSLfHvzwYc//wGjUoL94McfFTVy/O29+LVgAB7SlI+CUTAKRsEowAoAklFLgrpeyOEAAAAASUVORK5CYII=","orcid":"","institution":"Baoji Traditional Chinese Medicine Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Qiao","suffix":""}],"badges":[],"createdAt":"2024-10-09 14:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5233331/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5233331/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69460758,"identity":"0879d8da-8346-4e5c-89cc-9c80d6ff5c9b","added_by":"auto","created_at":"2024-11-20 14:52:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":114629,"visible":true,"origin":"","legend":"\u003cp\u003eA. Age-related incidence of PNET patients. B. OS and CSS of the entire PNET patient cohort. \u003cstrong\u003eOS\u003c/strong\u003e - Overall Survival; \u003cstrong\u003eCSS\u003c/strong\u003e - Cancer-Specific Survival.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5233331/v1/42749b791a8b338935f1faa3.png"},{"id":69460762,"identity":"33ed4869-8cf8-4e67-b539-607217683328","added_by":"auto","created_at":"2024-11-20 14:52:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":987809,"visible":true,"origin":"","legend":"\u003cp\u003eOS curves based on age, marital status, tumor stage, surgery, and chemotherapy status. NS-no surgery; S-Surgery; CT-chemotherapy; NCT-no chemotherapy.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5233331/v1/2e8e8440528c0c1b6ab5d417.png"},{"id":69460761,"identity":"bc5bd705-07b6-48a9-bf2d-335165646f3e","added_by":"auto","created_at":"2024-11-20 14:52:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":533736,"visible":true,"origin":"","legend":"\u003cp\u003eCSS curves based on age, marital status, tumor stage, surgery, and chemotherapy status. NS-no surgery; S-Surgery; CT-chemotherapy; NCT-no chemotherapy.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5233331/v1/b97565340fa61cb77fc0e0f5.png"},{"id":69460759,"identity":"d2c02435-14ae-4537-b8bf-438e94dc2a0d","added_by":"auto","created_at":"2024-11-20 14:52:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":932226,"visible":true,"origin":"","legend":"\u003cp\u003eCSS curves based on age, marital status, tumor stage, surgery, and chemotherapy status. NS-no surgery; S-Surgery; CT-chemotherapy; NCT-no chemotherapy.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5233331/v1/33cf069b34b3ea5db73a0f42.png"},{"id":70899546,"identity":"ede38238-5279-42ba-a7cd-96bd1b0a51f0","added_by":"auto","created_at":"2024-12-09 05:32:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4920416,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5233331/v1/ef1c0a8f-0929-45a4-8ff9-35078854eb85.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical features and Prognostic Factors of Patients with Primitive Neuroectodermal Tumors","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrimitive Neuroectodermal Tumor (PNET) was first proposed by Hart in 1973 as a type of malignant small round cell tumor originating from the neuroectoderm\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In 1986, Dehner first classified PNET into central nervous system PNET (cPNET) and peripheral PNET (pPNET)\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. cPNET primarily refers to malignant tumors arising from supratentorial brain tissue and the spinal cord, which have an extremely low incidence, accounting for only 0.03% of intracranial tumors\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. pPNET can occur in various parts of the body, most commonly in the thoracic and abdominal regions, while it is less frequently found in the head and neck\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. PNETs are morphologically and histologically similar to tumors in the Ewing sarcoma family and are therefore typically classified as part of this family\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePNET is characterized by low incidence, poor differentiation, and high malignancy, with very few patients achieving long-term survival\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Due to its rarity, randomized controlled studies on this disease are challenging to conduct. Currently, there are no clear guidelines or protocols for the treatment of PNET. Clinically, maximal tumor resection is typically performed, supplemented by postoperative local radiotherapy (RT) and systemic chemotherapy (CT). However, PNET patients still face a high risk of local recurrence and distant metastasis\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Therefore, the clinical features and optimal treatment strategies for PNET still require further research and clarification.\u003c/p\u003e \u003cp\u003eTo gain deeper insights into the clinical features and survival predictors of PNET patients, this study analyzed PNET patients from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute from 2000 to 2020. This represents the largest scale study conducted on this population, aiming to further clarify the clinical features and survival influencing factors of PNET.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Selection and Variables\u003c/h2\u003e \u003cp\u003eThe data for this study were sourced from the SEER database (version 8.4.3), maintained by the National Cancer Institute (NCI). This database covers approximately one-third of the U.S. population and provides detailed clinical, pathological, and survival outcome information. We extracted records of patients diagnosed with PNET (ICD-O-3; histologic type: 9473/3) between 2000 and 2020, collecting key clinical characteristics and survival outcomes, including age, race, marital status, household income, pathological grade, tumor stage, tumor size, and treatment methods (such as surgical status, CT, and RT).\u003c/p\u003e \u003cp\u003eMarital status was categorized into married and unmarried, with unmarried including single (never married), living with a partner, separated, widowed, and divorced. To minimize the impact of other cancers on this study, we included only patients with PNET who did not have other primary malignant tumors, while excluding patients with unclear treatment methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eOverall survival (OS) was defined as the time from diagnosis to death from any cause, and cancer-specific survival (CSS) was defined as the time from diagnosis to death specifically due to cancer. Univariate Cox regression was employed to screen for potential risk factors associated with survival. Multivariate Cox regression analysis was used to determine independent predictors of OS and CSS. The statistical significance level was set at P\u0026thinsp;\u0026le;\u0026thinsp;0.05. All statistical analyses were conducted using R statistical software (version 4.2.2).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and Clinical Characteristics of PNET Patients\u003c/h2\u003e \u003cp\u003eFrom 2000 to 2020, a total of 941 eligible PNET patient data were collected from the SEER database. The clinical characteristics of the patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. PNET is more prevalent in children and adolescents, with patients under 15 years old accounting for 46.3% of the total population, and the incidence decreases with increasing age (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In terms of tumor location, the most common site of occurrence was the brain (57.07%), followed by soft tissues (14.77%) and bones and joints (4.99%). Among the known pathological grades, PNET is primarily classified as grade IV (61.92%) and grade III (33.89%). Regarding tumor staging, PNET is most commonly found in the localized stage (52.71%), followed by the regional stage (19.55%) and distant metastatic stage (18.70%).\u003c/p\u003e \u003cp\u003eIn terms of treatment, most patients received surgery (78.64%) and CT (79.38%), while approximately half of the patients underwent RT (54.84%). Ultimately, 517 patients died, of which 461 died from PNET. The 5-year OS and CSS rates for the study population were 51.1% and 54.2%, respectively, while the 10-year OS and CSS rates were 44.7% and 48.7%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of 941patients with PNET identified in the SEER database from 2000 to 2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall n = 941(%)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;15 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e436 (46.33)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15–29 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e235 (24.97)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≥30 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e270 (28.69)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e428 (45.48)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e513 (54.52)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000–2010\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e660 (70.14)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011–2020\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281 (29.86)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack and Other\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180 (19.13)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e752 (79.91)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (0.96)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary site\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e537 (57.07)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoft Tissue\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139 (14.77)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBones and Joints\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47 (4.99)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCranial Nerves Other Nervous System\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 (4.89)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172 (18.28)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≤45mm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e193 (20.51)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;45mm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e312 (33.16)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e436 (46.33)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e195 (20.72)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e732 (77.79)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (1.49)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e243 (25.82)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e698 (74.18)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (0.53)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (0.53)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81 (8.61)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e148 (15.73)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e702 (74.60)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocalized\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e496 (52.71)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e184 (19.55)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176 (18.70)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85 (9.03)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e201 (21.36)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e740 (78.64)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e425 (45.16)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e516 (54.84)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e194 (20.62)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e747 (79.38)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUnivariate Cox Analysis of OS and CSS in PNET Patients\u003c/h3\u003e\n\u003cp\u003eThe univariate Cox analysis revealed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) that gender, race, time of diagnosis, tumor location, tumor size, and RT were not significantly associated with OS or CSS. However, age, tumor stage, and marital status showed significant differences with respect to OS and CSS, with older age (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and initial staging with metastasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) indicating poorer prognosis. Being unmarried was associated with better OS and CSS, which may be due to unmarried individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) typically being children and adolescents, a demographic with a more favorable prognosis. Both OS and CSS demonstrated significant differences based on surgical intervention (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) and CT (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), with patients receiving surgery and CT generally exhibiting better OS and CSS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\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 Cox regression analysis was used to screen prognostic factors in patients with PNET\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCSS\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;15 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003e15–29 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0.92–1.44)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15 (0.91–1.45)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≥30 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.07 (1.70–2.52)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.92 (1.55–2.37)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\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\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003e1.05 (0.88–1.24)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.84–1.21)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnosis\u003c/b\u003e\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\u003e2000–2010\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003e2011–2020\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.85–1.27)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.89–1.34)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\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 and Other\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003eWhite\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84 (0.68–1.04)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84 (0.67–1.06)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary site\u003c/b\u003e\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\u003eBones and Joints\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003eBrain\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20 (0.80–1.80)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27 (0.82–1.96)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoft Tissue\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.62–1.54)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89 (0.54–1.46)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCranial Nerves Other Nervous System\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (0.60–1.83)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (0.62–1.99)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.725\u003c/p\u003e \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\u003e1.04 (0.67–1.63)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.62–1.60)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor size\u003c/b\u003e\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≤45mm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003e\u0026gt;45mm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17 (0.91–1.50)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22 (0.93–1.58)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\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\u003eMarried\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003eUnmarried\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64 (0.52–0.78)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.54–0.82)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold income\u003c/b\u003e\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\u003eLow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86 (0.71–1.05)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86 (0.70–1.06)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrade\u003c/b\u003e\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\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003e0.16 (0.02–1.54)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24 (0.02–2.68)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.248\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\u003e0.73 (0.23–2.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.26–4.38)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.934\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\u003e0.84 (0.27–2.64)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14 (0.28–4.61)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor stage\u003c/b\u003e\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\u003eLocalized\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003eRegional\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.84–1.34)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.81–1.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70 (1.36–2.12)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64 (1.30–2.07)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical status\u003c/b\u003e\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\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57 (0.47–0.70)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59 (0.48–0.73)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiotherapy\u003c/b\u003e\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\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.89–1.27)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.88–1.27)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\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\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68 (0.55–0.83)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71 (0.57–0.88)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMultivariate Cox Analysis of OS and CSS in PNET Patients\u003c/b\u003eIn the multivariate Cox regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), older age and the presence of metastasis at initial staging were identified as independent adverse prognostic factors for OS and CSS in PNET patients. Surgical intervention was considered an independent prognostic factor that could improve both OS and CSS, while CT was identified as an independent prognostic factor associated only with OS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Cox regression analysis was used to identify independent prognostic factors in patients with PNET.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCSS\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;15 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003e15–29 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (0.85–1.36)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.85–1.38)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≥30 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.89 (1.46–2.44)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77 (1.34–2.32)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\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\u003eMarried\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003eUnmarried\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.77–1.27)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.75–1.29)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor stage\u003c/b\u003e\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\u003eLocalized\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003eRegional\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.81–1.31)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.78–1.30)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60 (0.85–1.58)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.54 (1.20–1.97)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical status\u003c/b\u003e\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\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69 (0.56–0.86)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71 (0.56–0.89)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\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\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\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\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.63–0.98)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81(0.64–1.03)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup Analysis\u003c/h2\u003e \u003cp\u003eSince the brain is the most common site of PNET occurrence, we conducted a separate analysis for this subgroup. Based on surgical coding, patients were divided into three groups: those who underwent no surgery, partial/subtotal resection (PR/STR), and gross total resection (GTR). The impact of subsequent treatment options, including RT, CT, chemoradiotherapy (CRT), and no chemoradiotherapy (no CRT), on survival rates was explored. Figures\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB show that patients who underwent GTR had significantly higher OS and CSS compared to those who had no surgery or underwent PR/STR. Additionally, the results indicated that there were no statistically significant survival differences among the treatment options of RT, CT, CRT, and No CRT, whether in the GTR (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), PR/STR (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF), or no surgery groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\n "},{"header":"Conclusion","content":"\u003cp\u003ePNET is a rare malignant tumor that has long lacked systematic understanding. To our knowledge, this study represents the largest cohort to date. Our findings primarily include two aspects: regarding clinical features, PNET predominantly occurs in the central nervous system, followed by soft tissues and bone/joint involvement. This contrasts with previous literature, which reported that intracranial PNETs are rarer than pPNET\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, However, from a specific anatomical perspective, the brain is the most common site for PNET occurrence. Furthermore, while earlier studies suggested that the peak incidence of PNET is between the ages of 10 and 20\u003csup\u003e14\u003c/sup\u003e, our results indicate that the highest incidence occurs before the age of 5, with the majority of cases concentrated in individuals under 30, and the incidence declining with increasing age. The histopathological grading is primarily Grade III and Grade IV (poorly differentiated and undifferentiated). Overall, the prognosis for PNET patients is favorable, with 5-year OS and CSS rates of 51.1% and 54.2%, respectively, and 10-year OS and CSS rates of 44.7% and 48.7%. These rates are similar to the 45–55% reported in the literature\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, it is noteworthy that the survival curve for PNET patients steeply declines within the first three years post-diagnosis, during which 370 patients (80.3%) died from PNET. This indicates that patients face significant treatment challenges and risks in the early stages of the disease, necessitating special attention to treatment and management during this period.\u003c/p\u003e\u003cp\u003eRegarding prognosis, age is identified as an independent adverse prognostic factor for OS and CSS in PNET patients, particularly emphasizing the need to monitor the population over 30 years old, which experiences a significant decrease in survival, with a median OS of only 30 months. Cancer statistics from 2022 show that the 5-year survival rate for PNET patients under 15 years of age is 76%, while the rate for adolescents aged 15 to 19 is 59%, indicating a decline in survival with increasing age\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Additionally, the presence of metastasis at initial staging is also an independent adverse prognostic factor, consistent with prognostic trends seen in nearly all tumors. A retrospective study analyzing 117 patients with renal PNET found that the survival probability after 18 months for patients with metastatic disease was 60%, compared to 85% for those without metastatic disease\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Other studies have also indicated that older age and the presence of metastasis at initial staging are independent adverse prognostic factors\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, aligning with our findings. In terms of treatment, surgery is an independent prognostic factor for both OS and CSS in PNET patients, while CT is an independent prognostic factor associated with OS. Due to the rarity of PNET, there is no standardized treatment protocol currently in place, but surgery has long been considered the cornerstone of PNET treatment\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. CT is commonly used in combination with surgery or RT, or both, for local control\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Compared to surgery alone, combined surgery and CT often result in better outcomes (84% vs. 53%)\u003csup\u003e20\u003c/sup\u003e. Studies have shown that CT can improve the survival rate of PNET patients from 5%-10–70%-80% and reduce the recurrence rate\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Furthermore, induction CT can increase tumor resection rates, while surgery significantly improves patient survival\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Currently, early surgical resection of PNET is recommended, followed by high-dose cyclophosphamide CT and RT for residual lesions\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough RT is also considered an important adjuvant treatment strategy\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, its benefit in PNET remains controversial. A small sample study showed that postoperative RT could improve the median OS of patients with intracranial PNET (38 months vs. 13 months). Among these, three patients who underwent GTR, RT, and CT had the highest two-year survival rate (100%) and the longest OS (48 months)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Another study also found that postoperative RT improved progression-free survival (PFS) and OS in patients with cranial PNET\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Regarding RT doses, one case reported using Gamma Knife radiosurgery to treat recurrent brain PNET, achieving disease control for up to seven years. It suggested that administering a high dose (≥ 20 Gy) in initial treatment might yield better survival benefits\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Subgroup analysis of brain PNET showed that complete surgical resection was the key factor for long-term survival, while no significant differences were observed among other postoperative treatment options. Since previous studies were mostly case reports or small sample studies, their conclusions may be biased, highlighting the need for more high-quality evidence-based studies to clarify the specific role of RT in PNET treatment.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, as a retrospective study, it is difficult to avoid selection bias. Second, although the SEER database provides a large number of cases and variables, it lacks detailed information on surgery, RT sites, dosages, and specific CT regimens. Patients who received RT may have had certain high-risk postoperative factors, which could affect our results. Finally, the database lacks information on patient recurrence and metastasis, preventing us from analyzing these aspects of the data.\u003c/p\u003e\n\u003ch3\u003eConclusion\u003c/h3\u003e\n\u003cp\u003eThis is the largest analysis of PNET patients to date. Although this tumor type generally has a favorable prognosis, with 5-year OS and CSS rates of 51.1% and 54.2%, respectively, we observed a high mortality risk within the first three years following diagnosis. Age, tumor stage, and surgery were identified as independent prognostic factors for both OS and CSS, while CT was an independent prognostic factor only for OS. For PNET patients, complete surgical resection should be pursued whenever possible, followed by routine CT to achieve favorable survival outcomes. Our study highlights the clinical features and prognostic factors of this specific cohort, laying a solid foundation for future research on standardized therapies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePNET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrimitive Neuroectodermal Tumor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecPNET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecentral nervous system PNET\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epPNET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eperipheral PNET\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOverall Survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCancer-Specific Survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSurveillance, Epidemiology, and End Results\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Cancer Institute\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecPNET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentral Nervous System Primitive Neuroectodermal Tumor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epPNET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePeripheral Primitive Neuroectodermal Tumor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGross Total Resection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePR/STR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePartial/Subtotal Resection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProgression-Free Survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eno surgery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eradiotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echemotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eno chemotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echemoradiotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eno CRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eno chemoradiotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict of Interest\u003c/strong\u003e \u003cp\u003eAll authors of this study declared that there were no conflicts of interest regarding this research.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eShaanxi Natural Science Foundation (2024JC-YBMS-734); Baoji Municipal Health Commission Research Project (2024-008)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXQ, JY and MW contributed to the writing of the manuscript and the creation of figures and tables. X Y were responsible for data collation and statistical analysis. BT and YQ were responsible for project design. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe are grateful to the collaborators of the SEER for providing the data used in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe original data presented in this study are included in the article. Further inquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHart, M. N. \u0026amp; Earle, K. M. Primitive neuroectodermal tumors of the brain in children. \u003cem\u003eCancer\u003c/em\u003e. \u003cb\u003e32\u003c/b\u003e (4), 890\u0026ndash;897 (1973).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDehner, L. P. Peripheral and central primitive neuroectodermal tumors. 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Oncol.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 1023 (2019).\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":"Primitive neuroectodermal tumors, Clinical features, prognostic factors","lastPublishedDoi":"10.21203/rs.3.rs-5233331/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5233331/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrimitive neuroectodermal tumors (PNET) are associated with poor prognosis, and their treatment remains a challenge. However, research on PNET patients is relatively limited. Therefore, we aim to investigate the prognosis of this specific cohort and identify independent prognostic factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study screened a cohort of PNET patients from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute from 2000 to 2020. Prognostic analyses were performed using the Kaplan-Meier method and Cox proportional hazards regression model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 941 eligible PNET patients were included, with the most common site of occurrence being the brain (57.07%). The 5-year overall survival (OS) and cancer-specific survival (CSS) rates for the entire study population were 51.1% and 54.2%, respectively, while the 10-year OS and CSS were 44.7% and 48.7%, respectively. In the univariate analysis, age, marital status, tumor stage, surgery, and chemotherapy had significant impacts on patient survival outcomes. In the multivariate analysis, age and the presence of metastasis at initial staging were identified as independent poor prognostic factors for both OS and CSS, while surgery and chemotherapy were independent prognostic factors for OS, with surgery also being an independent prognostic factor for CSS.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSurgery combined with chemotherapy showed survival benefits for PNET patients and is recommended. Radiotherapy did not improve patient survival, which requires further investigation in future studies.\u003c/p\u003e","manuscriptTitle":"Clinical features and Prognostic Factors of Patients with Primitive Neuroectodermal Tumors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 14:52:03","doi":"10.21203/rs.3.rs-5233331/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":"a1f653c5-a296-4d98-9677-2fa27f96ae31","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40081002,"name":"Health sciences/Oncology/Cancer/Cancer epidemiology"},{"id":40081003,"name":"Health sciences/Oncology/Cancer/Cancer therapy"}],"tags":[],"updatedAt":"2024-12-09T05:23:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 14:52:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5233331","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5233331","identity":"rs-5233331","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}