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预测神经内分泌肿瘤人群的生存概率:开发和评估新的预测列线图。

Predicting the Survival Probability of Neuroendocrine Tumor Populations: Developing and Evaluating a New Predictive Nomogram.

机构信息

Department of Medical Oncology, Guangxi Medical University Cancer Hospital, Nanning 530021, China.

Department of Oncology, Baise People's Hospital, Baise 533000, China.

出版信息

Biomed Res Int. 2021 Jan 28;2021:9126351. doi: 10.1155/2021/9126351. eCollection 2021.

DOI:10.1155/2021/9126351
PMID:33575356
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7864749/
Abstract

PURPOSE

The purpose of this study was to develop and initially validate a nomogram model in order to predict the 3-year and 5-year survival rates of neuroendocrine tumor patients.

METHODS

Accordingly, 348 neuroendocrine tumor patients were enrolled as study objects, of which 244 (70%) patients were included in the training set to establish the nomogram model, while 104 (30%) patients were included in the validation set to verify the robustness of the model. First, the variables related to the survival rate were determined by univariable analysis. In addition, variables that were sufficiently significant were selected for constructing the nomogram model. Furthermore, the concordance index (C-index), receiver operating characteristic (ROC), and calibration curve analysis were used to evaluate the performance of the proposed nomogram model. The survival analysis was then used to evaluate the return to survival probability as well as the indicators of constructing the nomogram model.

RESULTS

According to the multivariable analysis, lymphatic metastasis, international normalized ratio (INR), prothrombin time (PT), tumor differentiation, and the number of tumor metastases were found to be independent predictors of survival rate. Moreover, the C-index results demonstrated that the model was robust in both the training set (0.891) and validation set (0.804). In addition, the ROC results further verified the robustness of the model either in the training set (AUC = 0.823) or training set (AUC = 0.768). Furthermore, the calibration curve results showed that the model can be used to predict the 3-year and 5-year survival probability of neuroendocrine tumor patients. Meaningfully, five variables were found: lymphatic metastasis ( = 0.0095), international standardized ratio ( = 0.024), prothrombin time ( = 0.0036), tumor differentiation ( = 0.0026), and the number of tumor metastases ( = 0.00096), which were all significantly related to the 3-year and 5-year survival probability of neuroendocrine tumor patients.

CONCLUSION

In summary, a nomogram model was constructed in this study based on five variables (lymphatic metastasis, international normalized ratio (INR), prothrombin time (PT), tumor differentiation, and number of tumor metastases), which was shown to predict the survival probability of patients with neuroendocrine tumors. Additionally, the proposed nomogram exhibited good ability in predicting survival probability, which may be easily adopted for clinical use.

摘要

目的

本研究旨在开发并初步验证一种列线图模型,以预测神经内分泌肿瘤患者的 3 年和 5 年生存率。

方法

纳入 348 例神经内分泌肿瘤患者作为研究对象,其中 244 例(70%)患者纳入训练集以建立列线图模型,104 例(30%)患者纳入验证集以验证模型的稳健性。首先,通过单变量分析确定与生存率相关的变量。此外,选择具有足够显著性的变量构建列线图模型。进一步采用一致性指数(C 指数)、受试者工作特征(ROC)和校准曲线分析评估所提出的列线图模型的性能。生存分析用于评估回归生存率概率以及构建列线图模型的指标。

结果

多变量分析显示,淋巴转移、国际标准化比值(INR)、凝血酶原时间(PT)、肿瘤分化和肿瘤转移数量是生存率的独立预测因子。此外,C 指数结果表明,该模型在训练集(0.891)和验证集(0.804)中均稳健。ROC 结果进一步验证了模型在训练集(AUC=0.823)和验证集(AUC=0.768)中的稳健性。此外,校准曲线结果表明,该模型可用于预测神经内分泌肿瘤患者的 3 年和 5 年生存率。有意义的是,发现了五个变量:淋巴转移( = 0.0095)、国际标准化比值( = 0.024)、凝血酶原时间( = 0.0036)、肿瘤分化( = 0.0026)和肿瘤转移数量( = 0.00096),这些变量均与神经内分泌肿瘤患者的 3 年和 5 年生存率显著相关。

结论

综上所述,本研究构建了一个基于五个变量(淋巴转移、国际标准化比值(INR)、凝血酶原时间(PT)、肿瘤分化和肿瘤转移数量)的列线图模型,可预测神经内分泌肿瘤患者的生存概率。此外,所提出的列线图模型在预测生存概率方面表现出良好的能力,可能易于在临床中应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9a/7864749/ee0d8b407738/BMRI2021-9126351.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9a/7864749/87be57c17ee4/BMRI2021-9126351.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9a/7864749/2a31eb49b106/BMRI2021-9126351.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9a/7864749/88b0ba8debee/BMRI2021-9126351.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9a/7864749/4a54e8c69d59/BMRI2021-9126351.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9a/7864749/da1a2dbdbd4d/BMRI2021-9126351.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9a/7864749/317ebd1c2c22/BMRI2021-9126351.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9a/7864749/ee0d8b407738/BMRI2021-9126351.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9a/7864749/87be57c17ee4/BMRI2021-9126351.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9a/7864749/2a31eb49b106/BMRI2021-9126351.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9a/7864749/88b0ba8debee/BMRI2021-9126351.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9a/7864749/4a54e8c69d59/BMRI2021-9126351.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9a/7864749/da1a2dbdbd4d/BMRI2021-9126351.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9a/7864749/317ebd1c2c22/BMRI2021-9126351.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9a/7864749/ee0d8b407738/BMRI2021-9126351.007.jpg

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