胰腺神经内分泌肿瘤生存预测模型的开发与验证及风险分层
Development and validation of a survival prediction model and risk stratification for pancreatic neuroendocrine neoplasms.
作者信息
Lu Z, Li T, Liu C, Zheng Y, Song J
机构信息
Department of General Surgery, Department of Hepato-Bilio-Pancreatic Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, NO. 1 DaHua Road, Dong Dan, Beijing, 100730, People's Republic of China.
National Center for Clinical Laboratories, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 Dahua Road, Dongcheng District, Beijing, 100730, People's Republic of China.
出版信息
J Endocrinol Invest. 2023 May;46(5):927-937. doi: 10.1007/s40618-022-01956-7. Epub 2022 Nov 17.
PURPOSE
We explored risk variables associated with cancer-specific survival (CSS) in patients with pancreatic neuroendocrine neoplasms (PNENs) and created a network dynamic nomogram model to predict patient survival time.
METHODS
A total of 7750 patients with PNENs were included in this analysis, including 134 with functional PNENs and 7616 with nonfunctional PNENs. Clinical feature and prognosis differences between functional and nonfunctional PNENs were compared. Independent prognostic factors affecting CSS were analyzed by univariate and multifactorial Cox regression. Nomogram and web-based prognosis prediction of PNENs were developed and validated by C indices, decision curve analysis, and calibration plots.
RESULTS
Patients with functional PNENs were younger at diagnosis than those with nonfunctional PNENs. Functional PNENs had better prognoses than nonfunctional PNENs (5-year survival rates: 78.55% and 71.10%, respectively). Univariate and multifactorial Cox regression analyses showed that tumor infiltration (T), nodal metastasis (N), metastasis (M), tumor site, differentiation grade, age, marital status, and surgical treatment were independent prognostic risk factors for CSS, which were included in the prognostic nomogram and web-based prognosis calculator. The calibration plots and decision curve analysis showed that the nomogram had excellent prediction and clinical practical ability. The C indices for CSS in the training and validation cohorts were 0.848 (95% CI 0.838-0.8578) and 0.823 (95% CI 0.807-0.839), respectively. We scored all patients according to the nomogram and divided patients into three different risk groups. The prognosis of the low-risk population was significantly better than those of the middle- and high-risk populations based on Kaplan-Meier survival curve.
CONCLUSION
We analyzed the clinical features of PNENs and developed a convenient and web dynamic nomogram to predict CSS.
目的
我们探讨了胰腺神经内分泌肿瘤（PNENs）患者癌症特异性生存（CSS）相关的风险变量，并创建了一个网络动态列线图模型来预测患者生存时间。
方法
本分析共纳入7750例PNENs患者，其中功能性PNENs患者134例，非功能性PNENs患者7616例。比较功能性和非功能性PNENs的临床特征及预后差异。采用单因素和多因素Cox回归分析影响CSS的独立预后因素。通过C指数、决策曲线分析和校准图对PNENs的列线图和基于网络的预后预测进行开发和验证。
结果
功能性PNENs患者诊断时年龄比非功能性PNENs患者年轻。功能性PNENs的预后优于非功能性PNENs（5年生存率分别为78.55%和71.10%）。单因素和多因素Cox回归分析显示，肿瘤浸润（T）、淋巴结转移（N）、远处转移（M）、肿瘤部位、分化程度、年龄、婚姻状况和手术治疗是CSS的独立预后危险因素，这些因素被纳入预后列线图和基于网络的预后计算器中。校准图和决策曲线分析显示，列线图具有出色的预测能力和临床实用性。训练队列和验证队列中CSS的C指数分别为0.848（95%CI 0.838 - 0.8578）和0.823（95%CI 0.807 - 0.839）。我们根据列线图对所有患者进行评分，并将患者分为三个不同风险组。根据Kaplan-Meier生存曲线，低风险人群的预后明显优于中、高风险人群。
结论
我们分析了PNENs的临床特征，并开发了一种方便的网络动态列线图来预测CSS。
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