Liver cancer risk quantification through an artificial neural network based on personal health data
Liver cancer risk quantification through an artificial neural network based on personal health data
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DOI：
10.1080/0284186x.2023.2213445
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发表时间：
2023-05
期刊：
影响因子：
3.1
通讯作者：
A. Ataei;Jun Deng;Wazir Muhammad
中科院分区：
文献类型：
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作者：
A. Ataei;Jun Deng;Wazir Muhammad
### 摘要
**背景** 肝癌是最常见的癌症类型之一，也是全球癌症相关死亡的第三大主要原因。最常见的原发性肝癌类型是肝细胞癌（HCC），占病例的75% - 85%。HCC是一种恶性疾病，进展迅速且治疗选择有限。虽然肝癌的确切病因尚不清楚，但一些习惯/生活方式可能会增加患该病的风险。
**材料与方法** 本研究旨在通过基于基本健康数据（包括习惯/生活方式）的多参数人工神经网络（ANN）对肝癌风险进行量化。除输入层和输出层外，我们的ANN模型有三个隐藏层，分别包含12、13和14个神经元。我们使用了来自美国国家健康访谈调查（NHIS）以及前列腺、肺、结肠直肠和卵巢癌（PLCO）数据集的健康数据来训练和测试我们的ANN模型。
**结果** 我们发现ANN模型表现最佳，训练队列和测试队列的受试者工作特征曲线下面积分别为0.80和0.81。
**结论** 我们的研究结果展示了一种利用基本健康数据以及习惯/生活方式来预测肝癌风险的方法。这种新方法能够实现早期检测，可能会让高危人群受益。
Abstract Background Liver cancer is one of the most common types of cancer and the third leading cause of cancer-related deaths globally. The most common type of primary liver cancer is called hepatocellular carcinoma (HCC) which accounts for 75–85% of cases. HCC is a malignant disease with aggressive progression and limited therapeutic options. While the exact cause of liver cancer is not known, habits/lifestyles may increase the risk of developing the disease. Material and methods This study is designed to quantify the liver cancer risk through a multi-parameterized artificial neural network (ANN) based on basic health data including habits/lifestyles. In addition to input and output layers, our ANN model has three hidden layers having 12, 13, and 14 neurons, respectively. We have used the health data from the National Health Interview Survey (NHIS) and Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) datasets to train and test our ANN model. Results We have found the best performance of the ANN model with an area under the receiver operating characteristic curve of 0.80 and 0.81 for training and testing cohorts, respectively. Conclusion Our results demonstrate a method that can predict liver cancer risk with basic health data and habits/lifestyles. This novel method could be beneficial to high-risk populations by enabling early detection.