首页|A K-nearest Neighbor Model to Predict Early Recurrence of Hepatocellular Carcinoma After Resection

A K-nearest Neighbor Model to Predict Early Recurrence of Hepatocellular Carcinoma After Resection

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Background and Aims: Patients with hepatocellular carci-noma (HCC) surgically resected are at risk of recurrence;however, the risk factors of recurrence remain poorly un-derstood. This study intended to establish a novel machine learning model based on clinical data for predicting early re-currence of HCC after resection. Methods: A total of 220 HCC patients who underwent resection were enrolled. Clas-sification machine learning models were developed to predict HCC recurrence. The standard deviation, recall, and preci-sion of the model were used to assess the model's accura-cy and identify efficiency of the model. Results: Recurrent HCC developed in 89 (40.45％) patients at a median time of 14 months from primary resection. In principal compo-nent analysis, tumor size, tumor grade differentiation, por-tal 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 compar-ing different machine learning methods, including logistic re-gression, decision tree, na?ve Bayes, deep neural networks, and k-nearest neighbor (K-NN), we choose the K-NN model as the optimal prediction model. The accuracy, recall, preci-sion of the K-NN model were 70.6％, 51.9％, 70.1％, respec-tively. The standard deviation was 0.020. Conclusions: 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.

Hepatocellular carcinomaSurgical resectionRecurrenceMachine learningPrognostic model.

Chuanli Liu、Hongli Yang、Yuemin Feng、Cuihong Liu、Fajuan Rui、Yuankui Cao、Xinyu Hu、Jiawen Xu、Junqing Fan、Qiang Zhu、Jie Li

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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

Department of Gastroenterology,Shandong Provincial Hospital Affiliated to Shandong Frist Medical University,Ji'nan,Shandong,China

Department of Ultrasound Diagnosis and Treatment,Shandong Provincial Hospital Affiliated to Shandong Frist Medical University,Ji'nan,Shandong,China

School of Computer Science,China Uni-versity of Geosciences,Wuhan,Hubei,China

Department of Pathology,Shandong Provincial Hospital Affiliated to Shan-dong Frist Medical University,Ji'nan,Shandong,China

Department of Infectious Diseases,Nanjing Drum Tower Hospital,The Affiliated Hospital of Nanjing University Medical School,Nanjing,Jiangsu,China

Institute of Viruses and InfectiousDiseases,Nanjing University,Nanjing,Jiangsu,China

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国家自然科学基金国家自然科学基金Natural Science Foundation of Shandong Province(Major Project)Ji'nan Science and Tech-nology Development Project

8197054582170609ZR2020KH0062020190790

2022

10.14218/JCTH.2021.00348

临床与转化肝病杂志(英文版)

临床与转化肝病杂志(英文版)

ISSN：
年,卷(期)：2022.10(4)
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