Five machine learning-based radiomics models for preoperative prediction of histological grade in hepatocellular carcinoma

• Published in: Journal of cancer research and clinical oncology 2023-11, Vol.149 (16), p.15103-15112
• Main Authors: Wu, Cuiyun, Du, Xingyue, Zhang, Yang, Zhu, Li, Chen, Junfa, Chen, Yuan, Wei, Yuguo, Liu, Yang
• Format: Article
• Language: English
• Subjects: Cancer Research; Computed tomography; Hematology; Hepatocellular carcinoma; Internal Medicine; Learning algorithms; Liver cancer; Machine learning; Medicine; Medicine & Public Health; Oncology; Radiomics; Support vector machines; Surgery
• ISSN: 0171-5216; 1432-1335
• DOI: 10.1007/s00432-023-05327-4

Purpose To compare the efficacy of radiomics models via five machine learning algorithms in predicting the histological grade of hepatocellular carcinoma (HCC) before surgery and to develop the most stable model to classify high-risk HCC patients. Methods Contrast-enhanced computed tomography (CECT) images of 175 HCC patients before surgery were analysed, and radiomics features were extracted from CECT images (including arterial and portal phases). Five machine learning models, including Bayes, random forest (RF), k-nearest neighbors (KNN), logistic regression (LR), and support vector machine (SVM), were applied to establish the model. The stability of the five models was weighed by the relative standard deviation (RSD), and the lowest RSD value was chosen as the most stable model to predict the histological grade of HCC. The area under the curve (AUC) and Delong tests were devoted to assessing the predictive efficacy of the models. Results High-grade HCC accounted for 28.57% (50/175) of the 175 patients. The RSD value of AUC via the RF machine learning model was the lowest (2.3%), followed by Bayes (3.2%), KNN (6.4%), SVM (8.7%) and LR (31.3%). In addition, the RF model (AUC = 0.995) was better than the other four models in the training set (p