Department of Physics, Florida Atlantic University, Boca Raton, FL, USA.
Department of Radiology, Medical Physics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Acta Oncol. 2023 May;62(5):495-502. doi: 10.1080/0284186X.2023.2213445. Epub 2023 May 21.
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.
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.
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.
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.
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。原发性肝癌最常见的类型是肝细胞癌（HCC），占病例的 75-85%。HCC 是一种恶性疾病，具有侵袭性进展和有限的治疗选择。虽然肝癌的确切原因尚不清楚，但习惯/生活方式可能会增加患该病的风险。
本研究旨在通过基于包括习惯/生活方式在内的基本健康数据的多参数人工神经网络（ANN）来量化肝癌风险。除输入和输出层外，我们的 ANN 模型还有三个隐藏层，分别具有 12、13 和 14 个神经元。我们使用来自国家健康访谈调查（NHIS）和前列腺癌、肺癌、结直肠癌和卵巢癌（PLCO）数据集的健康数据来训练和测试我们的 ANN 模型。
我们发现 ANN 模型的最佳性能为训练队列的受试者工作特征曲线下面积为 0.80，测试队列为 0.81。
我们的结果表明，一种可以使用基本健康数据和习惯/生活方式预测肝癌风险的方法。这种新方法可以通过早期检测使高危人群受益。
