Machine-learning Approach for the Development of a Novel Predictive Model for the Diagnosis of Hepatocellular Carcinoma
Machine-learning Approach for the Development of a Novel Predictive Model for the Diagnosis of Hepatocellular Carcinoma
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DOI：
10.1038/s41598-019-44022-8
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发表时间：
2019-05-30
影响因子：
4.6
通讯作者：
Yatomi, Yutaka
中科院分区：
文献类型：
--
作者：
Sato, Masaya;Morimoto, Kentaro;Yatomi, Yutaka
由于其多因素的性质，使用单一生物标志物预测癌症的存在是困难的。我们的目的是建立一种新的机器学习模型，用于使用临床实践中获得的真实数据预测肝细胞癌（HCC）。为了建立预测模型，我们开发了一个机器学习框架，该框架使用网格搜索方法，根据数据的性质开发了优化的分类器及其各自的超参数。我们将目前的框架应用于539例和1043例有和没有HCC的患者，以开发HCC诊断的预测模型。使用最佳超参数，梯度增强为HCC的存在提供了最高的预测准确性（87.34%），并产生了0.940的曲线下面积（AUC）。以AFP为200 ng/mL、DCP为40 mAu/mL、AFP-L3为15%的临界值，AFP、DCP和AFP-L3预测HCC的准确率分别为70.67%（AUC，0.766）、74.91%（AUC，0.644）和71.05%（AUC，0.683）。与单一肿瘤标志物相比，使用机器学习方法的新型预测模型将错误分类率降低了约一半。本研究所使用的框架可以应用于各种类型的数据，从而有可能成为学术研究和临床实践之间的转换机制。
Because of its multifactorial nature, predicting the presence of cancer using a single biomarker is difficult. We aimed to establish a novel machine-learning model for predicting hepatocellular carcinoma (HCC) using real-world data obtained during clinical practice. To establish a predictive model, we developed a machine-learning framework which developed optimized classifiers and their respective hyperparameter, depending on the nature of the data, using a grid-search method. We applied the current framework to 539 and 1043 patients with and without HCC to develop a predictive model for the diagnosis of HCC. Using the optimal hyperparameter, gradient boosting provided the highest predictive accuracy for the presence of HCC (87.34%) and produced an area under the curve (AUC) of 0.940. Using cut-offs of 200 ng/mL for AFP, 40 mAu/mL for DCP, and 15% for AFP-L3, the accuracies of AFP, DCP, and AFP-L3 for predicting HCC were 70.67% (AUC, 0.766), 74.91% (AUC, 0.644), and 71.05% (AUC, 0.683), respectively. A novel predictive model using a machine-learning approach reduced the misclassification rate by about half compared with a single tumor marker. The framework used in the current study can be applied to various kinds of data, thus potentially become a translational mechanism between academic research and clinical practice.