Abstract：
Ovarian neuroendocrine neoplasm is a rare and highly heterogeneous neoplasm. This study is aimed to describe its demographic and clinicopathological features and identify its prognostic factors.
Clinical data of 399 patients diagnosed with ovarian neuroendocrine neoplasms between 2004 and 2016 in the Surveillance, Epidemiology, and End Results (SEER) database were analysed retrospectively. Survival curves were drawn using the Kaplan-Meier method, comparisons among different subgroups were evaluated using log-rank tests, and multivariate Cox regression analyses identified independent prognostic factors.
The five-year survival rates of patients with different histological types (carcinoid tumour, neuroendocrine carcinoma and special type of carcinoid tumour) were 25.5%, 96.1% and 75.0%, respectively (P < 0.001). Multivariate Cox analysis revealed that in carcinoid tumours, advanced FIGO stage was the only predictor. Additionally, no significant difference was observed among stages II, III and IV using the log-rank test. In neuroendocrine carcinoma, an advanced FIGO stage and high-grade differentiation were risk factors, while chemotherapy was a protective factor. Among all ovarian neuroendocrine neoplasms with a known histological differentiation status, no significant difference was observed among different histological types; only high-grade differentiation was an independent risk factor, and chemotherapy was a protective factor.
Patients with neuroendocrine carcinomas and carcinoid tumours of an advanced FIGO stage have a poor prognosis. Poor differentiation of neuroendocrine carcinomas indicates a short survival time, and adjuvant chemotherapy appears to be effective. Histological differentiation of ovarian neuroendocrine neoplasms is the most potent prognostic factor comparing to other known factors. Taken together, ovarian neuroendocrine neoplasms might be better classified as low- or high-grade ones rather than the currently used classification based on histological types in the future.
Clinical data of 399 patients diagnosed with ovarian neuroendocrine neoplasms between 2004 and 2016 in the Surveillance, Epidemiology, and End Results (SEER) database were analysed retrospectively. Survival curves were drawn using the Kaplan-Meier method, comparisons among different subgroups were evaluated using log-rank tests, and multivariate Cox regression analyses identified independent prognostic factors.
The five-year survival rates of patients with different histological types (carcinoid tumour, neuroendocrine carcinoma and special type of carcinoid tumour) were 25.5%, 96.1% and 75.0%, respectively (P < 0.001). Multivariate Cox analysis revealed that in carcinoid tumours, advanced FIGO stage was the only predictor. Additionally, no significant difference was observed among stages II, III and IV using the log-rank test. In neuroendocrine carcinoma, an advanced FIGO stage and high-grade differentiation were risk factors, while chemotherapy was a protective factor. Among all ovarian neuroendocrine neoplasms with a known histological differentiation status, no significant difference was observed among different histological types; only high-grade differentiation was an independent risk factor, and chemotherapy was a protective factor.
Patients with neuroendocrine carcinomas and carcinoid tumours of an advanced FIGO stage have a poor prognosis. Poor differentiation of neuroendocrine carcinomas indicates a short survival time, and adjuvant chemotherapy appears to be effective. Histological differentiation of ovarian neuroendocrine neoplasms is the most potent prognostic factor comparing to other known factors. Taken together, ovarian neuroendocrine neoplasms might be better classified as low- or high-grade ones rather than the currently used classification based on histological types in the future.
摘要：
卵巢神经内分泌肿瘤是一种罕见且高度异质性的肿瘤。本研究旨在描述其人口统计学和临床病理特征，并确定其预后因素。
2004年至2016年间诊断为卵巢神经内分泌肿瘤的399例患者的临床数据，流行病学,和最终结果（SEER）数据库进行回顾性分析。使用Kaplan-Meier方法绘制生存曲线，使用对数秩检验评估不同亚组之间的比较，和多变量Cox回归分析确定了独立的预后因素。
不同组织学类型（类癌，神经内分泌癌和特殊类型类癌）占25.5％，96.1%和75.0%，分别(P<0.001)。多变量Cox分析显示，在类癌肿瘤中，高级FIGO阶段是唯一的预测因子。此外,在第二阶段之间没有观察到显著差异，III和IV使用对数秩检验。在神经内分泌癌中，高级FIGO阶段和高级分化是危险因素，而化疗是一个保护因素。在所有已知组织学分化状态的卵巢神经内分泌肿瘤中，不同组织学类型间无显著差异;只有高度分化是独立的危险因素,化疗是一个保护因素。
FIGO晚期神经内分泌癌和类癌患者预后不良。神经内分泌癌的分化差表明生存期短，辅助化疗似乎是有效的。与其他已知因素相比，卵巢神经内分泌肿瘤的组织学分化是最有效的预后因素。一起来看,卵巢神经内分泌肿瘤未来可能比目前使用的基于组织学类型的分类更好地分为低级别或高级别。
2004年至2016年间诊断为卵巢神经内分泌肿瘤的399例患者的临床数据，流行病学,和最终结果（SEER）数据库进行回顾性分析。使用Kaplan-Meier方法绘制生存曲线，使用对数秩检验评估不同亚组之间的比较，和多变量Cox回归分析确定了独立的预后因素。
不同组织学类型（类癌，神经内分泌癌和特殊类型类癌）占25.5％，96.1%和75.0%，分别(P<0.001)。多变量Cox分析显示，在类癌肿瘤中，高级FIGO阶段是唯一的预测因子。此外,在第二阶段之间没有观察到显著差异，III和IV使用对数秩检验。在神经内分泌癌中，高级FIGO阶段和高级分化是危险因素，而化疗是一个保护因素。在所有已知组织学分化状态的卵巢神经内分泌肿瘤中，不同组织学类型间无显著差异;只有高度分化是独立的危险因素,化疗是一个保护因素。
FIGO晚期神经内分泌癌和类癌患者预后不良。神经内分泌癌的分化差表明生存期短，辅助化疗似乎是有效的。与其他已知因素相比，卵巢神经内分泌肿瘤的组织学分化是最有效的预后因素。一起来看,卵巢神经内分泌肿瘤未来可能比目前使用的基于组织学类型的分类更好地分为低级别或高级别。
