| 3 | A K-nearest Neighbor Model to Predict Early Recurrence of Hepatocellular Carcinoma After Resection显示文摘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,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,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. | Chuanli Liu Hongli Yang Yuemin Feng Cuihong Liu Fajuan Rui Yuankui Cao Xinyu Hu Jiawen Xu Junqing Fan Qiang Zhu Jie Li | 2022 | Journal of Clinical and Translational Hepatology2022，10，4： | 0 |
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