Construction and evaluation of a liver cancer risk prediction model based on machine learning  

作　　者：Ying-Ying Wang Wan-Xia Yang Qia-Jun Du Zhen-Hua Liu Ming-Hua Lu Chong-Ge You 

机构地区：[1]Laboratory Medicine Center,The Second Hospital&Clinical Medical School,Lanzhou University,Lanzhou 730030,Gansu Province,China

出　　处：《World Journal of Gastrointestinal Oncology》2024年第9期3839-3850,共12页世界胃肠肿瘤学杂志（英文版）（电子版）

基　　金：Cuiying Scientific and Technological Innovation Program of the Second Hospital,No.CY2021-BJ-A16 and No.CY2022-QN-A18;Clinical Medical School of Lanzhou University and Lanzhou Science and Technology Development Guidance Plan Project,No.2023-ZD-85.

摘　　要：BACKGROUND Liver cancer is one of the most prevalent malignant tumors worldwide,and its early detection and treatment are crucial for enhancing patient survival rates and quality of life.However,the early symptoms of liver cancer are often not obvious,resulting in a late-stage diagnosis in many patients,which significantly reduces the effectiveness of treatment.Developing a highly targeted,widely applicable,and practical risk prediction model for liver cancer is crucial for enhancing the early diagnosis and long-term survival rates among affected individuals.AIM To develop a liver cancer risk prediction model by employing machine learning techniques,and subsequently assess its performance.METHODS In this study,a total of 550 patients were enrolled,with 190 hepatocellular carcinoma(HCC)and 195 cirrhosis patients serving as the training cohort,and 83 HCC and 82 cirrhosis patients forming the validation cohort.Logistic regression(LR),support vector machine(SVM),random forest(RF),and least absolute shrinkage and selection operator(LASSO)regression models were developed in the training cohort.Model performance was assessed in the validation cohort.Additionally,this study conducted a comparative evaluation of the diagnostic efficacy between the ASAP model and the model developed in this study using receiver operating characteristic curve,calibration curve,and decision curve analysis(DCA)to determine the optimal predictive model for assessing liver cancer risk.RESULTS Six variables including age,white blood cell,red blood cell,platelet counts,alpha-fetoprotein and protein induced by vitamin K absence or antagonist II levels were used to develop LR,SVM,RF,and LASSO regression models.The RF model exhibited superior discrimination,and the area under curve of the training and validation sets was 0.969 and 0.858,respectively.These values significantly surpassed those of the LR(0.850 and 0.827),SVM(0.860 and 0.803),LASSO regression(0.845 and 0.831),and ASAP(0.866 and 0.813)models.Furthermore,calibration and DCA indicated that

关 键 词：Hepatocellular carcinoma CIRRHOSIS Prediction model Machine learning Random forest 

分 类 号：R73-31[医药卫生—肿瘤]