Prediction Model for Pancreatic Cancer-A Population-Based Study from NHIRD.
Prediction Model for Pancreatic Cancer-A Population-Based Study from NHIRD.
复制标题
NHIRD的胰腺癌基于人群的研究的预测模型。
DOI：
10.3390/cancers14040882
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发表时间：
2022-02-10
期刊：
影响因子：
5.2
通讯作者：
Hsu CY
中科院分区：
文献类型：
--
作者：
Lee HA;Chen KW;Hsu CY
过去三年，胰腺癌在台湾十大癌症死亡率中排名第七。由于缺乏早期诊断工具，它是较难早期发现的癌症之一。这是NHIRD的一项基于人群的研究。建立了一种高性能的胰腺癌预测模型。这种预测模型可以提高人们对胰腺癌风险的认识，在胰腺癌仍可根治的黄金时期，为胰腺癌患者提供一种更简单的早期筛查工具。(1)背景：39年来，癌症一直是台湾最主要的死亡原因，其中胰腺癌在近三年的十大癌症死亡率中排名第七。虽然胰腺癌的发病率在十大癌症中排名垫底，但其存活率非常低。由于缺乏早期诊断工具，胰腺癌是较难早期发现的癌症之一。早期筛查对胰腺癌的治疗很重要。只有少数研究设计了胰腺癌的预测模型。(2)方法：本研究采用台湾健康保险数据库，覆盖台湾99%以上的人口。子集样本与原始NHIRD样本没有显著差异。使用机器学习方法开发胰腺癌疾病的预测模型。本研究采用了逻辑回归、深度神经网络、集成学习和投票集成四种模型。使用ROC曲线和混淆矩阵来评估胰腺癌预测模型的准确性。(3)结果：在三种因子组合的外部测试集中，LR模型的AUC均高于其他三种模型。对于第一种因子组合，用叠加模型最能测量灵敏度，对于第二种因子组合，用DNN模型最能测量特异性。仅使用9个因子（第三因子组合）的模型结果与其他两个因子组合相等。先前胰腺癌早期评估模型的AUC范围约为0.57 ~ 0.71。本研究的AUC高于以往的研究，范围在0.71 ~ 0.76之间，准确度更高。(4)结论：本研究比较了LR、DNN、stacking和voting模型在胰腺癌预测中的性能，构建了准确度高于以往研究的胰腺癌预测模型。这种预测模型将提高人们对胰腺癌风险的认识，并为胰腺癌患者提供一种更简单的工具，在疾病仍可根除的黄金时期进行早期筛查。
Pancreatic cancer has been ranked seventh in the top ten cancer mortality rates for the past three year in Taiwan. It is one of the more difficult cancers to detect early due to the lack of early diagnostic tools. This is a population-based study from NHIRD. A higher performance pancreatic cancer prediction model has been established. This predictive model can improve the awareness of the risk of pancreatic cancer and give patients with pancreatic cancer a simpler tool for early screening in the golden period when the disease can still be eradicated. (1) Background: Cancer has been the leading cause of death in Taiwan for 39 years, and among them, pancreatic cancer has been ranked seventh in the top ten cancer mortality rates for the past three years. While the incidence rate of pancreatic cancer is ranked at the bottom of the top 10 cancers, the survival rate is very low. Pancreatic cancer is one of the more difficult cancers to detect early due to the lack of early diagnostic tools. Early screening is important for the treatment of pancreatic cancer. Only a few studies have designed predictive models for pancreatic cancer. (2) Methods: The Taiwan Health Insurance Database was used in this study, covering over 99% of the population in Taiwan. The subset sample was not significantly different from the original NHIRD sample. A machine learning approach was used to develop a predictive model for pancreatic cancer disease. Four models, including logistic regression, deep neural networks, ensemble learning, and voting ensemble were used in this study. The ROC curve and a confusion matrix were used to evaluate the accuracy of the pancreatic cancer prediction models. (3) Results: The AUC of the LR model was higher than the other three models in the external testing set for all three of the factor combinations. Sensitivity was best measured by the stacking model for the first factor combinations, and specificity was best measured by the DNN model for the second factor combination. The result of the model that used only nine factors (third factor combinations) was equal to the other two factor combinations. The AUC of the previous models for the early assessment of pancreatic cancer ranged from approximately 0.57 to 0.71. The AUC of this study was higher than that of previous studies and ranged from 0.71 to 0.76, which provides higher accuracy. (4) Conclusions: This study compared the performances of LR, DNN, stacking, and voting models for pancreatic cancer prediction and constructed a pancreatic cancer prediction model with accuracy higher than that of previous studies. This predictive model will improve awareness of the risk of pancreatic cancer and give patients with pancreatic cancer a simpler tool for early screening in the golden period when the disease can still be eradicated.
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影响因子：
3.7
作者：
Baecker, Aileen;Kim, Sungjin;Jeon, Christie Y.
通讯作者：
Jeon, Christie Y.
影响因子：
25.7
作者：
Bergquist, A;Ekbom, A;Broomé, U
通讯作者：
Broomé, U
影响因子：
4.3
作者：
Ertz-Archambault N;Keim P;Von Hoff D
通讯作者：
Von Hoff D
影响因子：
24.5
作者：
Canto MI;Harinck F;Hruban RH;Offerhaus GJ;Poley JW;Kamel I;Nio Y;Schulick RS;Bassi C;Kluijt I;Levy MJ;Chak A;Fockens P;Goggins M;Bruno M;International Cancer of Pancreas Screening (CAPS) Consortium
通讯作者：
International Cancer of Pancreas Screening (CAPS) Consortium
影响因子：
5.6
作者：
Hujoel, PP;Drangsholt, M;Weiss, NS
通讯作者：
Weiss, NS