Liu Chuanli, Yang Hongli, Feng Yuemin, Liu Cuihong, Rui Fajuan, Cao Yuankui, Hu Xinyu, Xu Jiawen, Fan Junqing, Zhu Qiang, Li Jie
Department of Infectious Disease, Shandong Provincial Hospital Affiliated to Shandong Frist Medical University, Ji'nan, Shandong, China.
Department of Infectious Disease, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Ji'nan, Shandong, China.
J Clin Transl Hepatol. 2022 Aug 28;10(4):600-607. doi: 10.14218/JCTH.2021.00348. Epub 2022 Jan 4.
Patients with hepatocellular carcinoma (HCC) surgically resected are at risk of recurrence; however, the risk factors of recurrence remain poorly understood. This study intended to establish a novel machine learning model based on clinical data for predicting early recurrence of HCC after resection.
A total of 220 HCC patients who underwent resection were enrolled. Classification machine learning models were developed to predict HCC recurrence. The standard deviation, recall, and precision of the model were used to assess the model's accuracy and identify efficiency of the model.
Recurrent HCC developed in 89 (40.45%) patients at a median time of 14 months from primary resection. In principal component analysis, tumor size, tumor grade differentiation, portal vein tumor thrombus, alpha-fetoprotein, protein induced by vitamin K absence or antagonist-II (PIVKA-II), aspartate aminotransferase, platelet count, white blood cell count, and HBsAg were positive prognostic factors of HCC recurrence and were included in the preoperative model. After comparing different machine learning methods, including logistic regression, decision tree, naïve Bayes, deep neural networks, and k-nearest neighbor (K-NN), we choose the K-NN model as the optimal prediction model. The accuracy, recall, precision of the K-NN model were 70.6%, 51.9%, 70.1%, respectively. The standard deviation was 0.020.
The K-NN classification algorithm model performed better than the other classification models. Estimation of the recurrence rate of early HCC can help to allocate treatment, eventually achieving safe oncological outcomes.
接受手术切除的肝细胞癌（HCC）患者有复发风险；然而，复发的危险因素仍知之甚少。本研究旨在基于临床数据建立一种新型机器学习模型，用于预测HCC切除术后的早期复发。
共纳入220例行切除手术的HCC患者。开发分类机器学习模型以预测HCC复发。使用模型的标准差、召回率和精确率来评估模型的准确性和识别效率。
89例（40.45%）患者出现复发性HCC，从初次切除起的中位时间为14个月。在主成分分析中，肿瘤大小、肿瘤分级分化、门静脉癌栓、甲胎蛋白、维生素K缺乏或拮抗剂-II诱导蛋白（PIVKA-II）、天冬氨酸转氨酶、血小板计数、白细胞计数和乙肝表面抗原是HCC复发的阳性预后因素，并被纳入术前模型。在比较包括逻辑回归、决策树、朴素贝叶斯、深度神经网络和k近邻（K-NN）在内的不同机器学习方法后，我们选择K-NN模型作为最佳预测模型。K-NN模型的准确率、召回率、精确率分别为70.6%、51.9%、70.1%。标准差为0.020。
K-NN分类算法模型的表现优于其他分类模型。估计早期HCC的复发率有助于分配治疗方案，最终实现安全的肿瘤学结局。
