CT radiomics based on different machine learning models for classifying gross tumor volume and normal liver tissue in hepatocellular carcinoma

• Published in: Cancer imaging Vol. 24; no. 1; pp. 20 - 9
• Main Authors: Zhang, Huai-wen, Huang, De-long, Wang, Yi-ren, Zhong, Hao-shu, Pang, Hao-wen
• Format: Journal Article
• Language: English
• Published: London BioMed Central 26.01.2024; BioMed Central Ltd; BMC
• Subjects: Accuracy; Algorithms; Automatic classification; Calibration; Cancer Research; Chemotherapy; Classification; Computed tomography; Correlation coefficients; CT imaging; CT radiomics; Data mining; Deep learning; Diagnostic imaging; Diagnostic systems; Discrimination; Hepatocellular carcinoma; Hepatoma; Imaging; Liver; Liver cancer; Machine learning; Medical imaging; Medical research; Medicine; Medicine & Public Health; Medicine, Experimental; Nuclear Medicine; Oncology; Patients; Radiology; Radiomics; Regression; Research Article; Software; Statistical analysis; Support vector machines; Tumors; Hepatocellular carcinoma; Automatic classification; CT radiomics; Machine learning
• ISSN: 1470-7330; 1740-5025; 1470-7330
• DOI: 10.1186/s40644-024-00652-4

Background & aims 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. Methods We recruited 104 pathologically confirmed hepatocellular carcinoma patients for this study. 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. Results 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). Conclusion CT radiomics based on machine learning algorithms can accurately classify GTV and normal liver tissue, while the Xgboost and SVM algorithms served as the best complementary algorithms.