Leveraging machine learning techniques for predicting pancreatic neuroendocrine tumor grades using biochemical and tumor markers  

作　　者：Rui-Quan Zhou Hong-Chen Ji Qu Liu, Chun-Yu Zhu Rong Liu 

机构地区：[1]School of Medicine, Nankai University, Tianjin 300071, China [2]The Second Department of Hepatobiliary Surgery, Chinese PLA General Hospital, Beijing 100853, China

出　　处：《World Journal of Clinical Cases》2019年第13期1611-1622,共12页世界临床病例杂志

基　　金：Supported by “Miaopu”Innovation Foundation of the Chinese PLA General Hospital,No.17KMM07

摘　　要：BACKGROUND The incidence of pancreatic neuroendocrine tumors (PNETs) is now increasing rapidly. The tumor grade of PNETs significantly affects the treatment strategy and prognosis. However, there is still no effective way to non-invasively classify PNET grades. Machine learning (ML) algorithms have shown potential in improving the prediction accuracy using comprehensive data. AIM To provide a ML approach to predict PNET tumor grade using clinical data. METHODS The clinical data of histologically confirmed PNET cases between 2012 and 2018 were collected. A method of minimum P for the Chi-square test was used to divide the continuous variables into binary variables. The continuous variables were transformed into binary variables according to the cutoff value, while the P value was minimum. Four classical supervised ML models, including logistic regression, support vector machine (SVM), linear discriminant analysis (LDA) and multi-layer perceptron (MLP) were trained by clinical data, and the models were labeled with the pathological tumor grade of each PNET patient. The performance of each model, including the weight of the different parameters, were evaluated. RESULTS In total, 91 PNET cases were included in this study, in which 32 were G1, 48 were G2 and 11 were G3. The results showed that there were significant differences among the clinical parameters of patients with different grades. Patients with higher grades tended to have higher values of total bilirubin, alpha fetoprotein, carcinoembryonic antigen, carbohydrate antigen 19-9 and carbohydrate antigen 72-4. Among the models we used, LDA performed best in predicting the PNET tumor grade. Meanwhile, MLP had the highest recall rate for G3 cases. All of the models stabilized when the sample size was over 70 percent of the total, except for SVM. Different parameters varied in affecting the outcomes of the models. Overall, alanine transaminase, total bilirubin, carcinoembryonic antigen, carbohydrate antigen 19-9 and carbohydrate antigen 72-4 affected the outcome gBACKGROUND The incidence of pancreatic neuroendocrine tumors(PNETs)is now increasing rapidly.The tumor grade of PNETs significantly affects the treatment strategy and prognosis.However,there is still no effective way to non-invasively classify PNET grades.Machine learning(ML)algorithms have shown potential in improving the prediction accuracy using comprehensive data.AIM To provide a ML approach to predict PNET tumor grade using clinical data ME THODS The clinical data of histologically confirmed PNET cases between 2012 and 2018 were collected.A method of minimum P for the Chi-square test was used to divide the continuous variables into binary variables.The continuous variables were transformed into binary variables according to the cutoff value,while the P value was minimum.Four classical supervised ML models,including logistic regression,support vector machine(SVM),linear discriminant analysis(LDA)and multi-layer perceptron(MLP)were trained by clinical data,and the models were labeled with the pathological tumor grade of each PNET patient.The performance of each model,including the weight of the different parameters,were evaluated.RES UL TS In total,91 PNET cases were included in this study,in which 32 were G1,48 were G2 and 11 were G3.The results showed that there were significant differences among the clinical parameters of patients with different grades.Patients with higher grades tended to have higher values of total bilirubin,alpha fetoprotein,carcinoembryonic antigen,carbohydrate antigen 19-9 and carbohydrate antigen72-4.Among the models we used,LDA performed best in predicting the PNET tumor grade.Meanwhile,MLP had the highest recall rate for G3 cases.All of the models stabilized when the sample size was over 70 percent of the total,except for SVM.Different parameters varied in affecting the outcomes of the models.Overall,alanine transaminase,total bilirubin,carcinoembryonic antigen,carbohydrate antigen 19-9 and carbohydrate antigen 72-4 affected the outcome greater than other parameters.CONCL USION ML c

关 键 词：Machine learning PANCREATIC NEUROENDOCRINE TUMORS TUMOR grade BIOCHEMICAL indexes TUMOR markers 

分 类 号：R[医药卫生]