首页|Southwest Medical University Reports Findings in Liver Cancer (CT radiomics based on different machine learning models for classifying gross tumor volume and normal liver tissue in hepatocellular carcinoma)
Southwest Medical University Reports Findings in Liver Cancer (CT radiomics based on different machine learning models for classifying gross tumor volume and normal liver tissue in hepatocellular carcinoma)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Springer Nature
New research on Oncology - Liver Cancer is the subject of a report. According to news originating from Luzhou, People’s Republic of China, by NewsRx correspondents, research stated, “The present study utilized extracted computed tomography radiomics features to classify the gross tumor volume and normal liver tissue in hepatocellular carcinoma by mainstream machine learning methods, aiming to establish an automatic classification model. We recruited 104 pathologically confirmed hepatocellular carcinoma patients for this study.” Funders for this research include The Open Fund for Scientific Research of Jiangxi Cancer Hospital, The Sichuan Provincial Medical Research Project Plan. Our news journalists obtained a quote from the research from Southwest Medical University, “GTV and normal liver tissue samples were manually segmented into regions of interest and randomly divided into five-fold cross-validation groups. Dimensionality reduction using LASSO regression. Radiomics models were constructed via logistic regression, support vector machine (SVM), random forest, Xgboost, and Adaboost algorithms. The diagnostic efficacy, discrimination, and calibration of algorithms were verified using area under the receiver operating characteristic curve (AUC) analyses and calibration plot comparison. Seven screened radiomics features excelled at distinguishing the gross tumor area. The Xgboost machine learning algorithm had the best discrimination and comprehensive diagnostic performance with an AUC of 0.9975 [95% confidence interval (CI): 0.9973-0.9978] and mean MCC of 0.9369. SVM had the second best discrimination and diagnostic performance with an AUC of 0.9846 (95% CI: 0.9835-0.9857), mean Matthews correlation coefficient (MCC)of 0.9105, and a better calibration. All other algorithms showed an excellent ability to distinguish between gross tumor area and normal liver tissue (mean AUC 0.9825, 0.9861,0.9727,0.9644 for Adaboost, random forest, logistic regression, naivem Bayes algorithm respectively).”
LuzhouPeople’s Republic of ChinaAsiaAlgorithmsCancerCarcinomasCyborgsDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineLiver CancerMachine LearningOncologySupport Vector Machines
NETL
NSTL
Springer Nature
