Research on multi-model imaging machine learning for distinguishing early hepatocellular carcinoma

作者：Ya Ma, Yue Gong, Qingtao Qiu, Changsheng Ma, Shuang Yu · 发表于：BMC Cancer · 年份：2024 · DOI：10.1186/s12885-024-12109-9 · 被引用次数：19 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Hepatocellular Carcinoma Treatment and Prognosis、Liver Disease Diagnosis and Treatment

OBJECTIVE: To investigate the value of differential diagnosis of hepatocellular carcinoma (HCC) and non-hepatocellular carcinoma (non-HCC) based on CT and MR multiphase radiomics combined with different machine learning models and compare the diagnostic efficacy between different radiomics models. BACKGROUND: Primary liver cancer is one of the most common clinical malignancies, hepatocellular carcinoma (HCC) is the most common subtype of primary liver cancer, accounting for approximately 90% of cases. A clear diagnosis of HCC is important for the individualized treatment of patients with HCC. However, more sophisticated diagnostic modalities need to be explored. METHODS: This retrospective study included 211 patients with liver lesions: 97 HCC and 124 non-hepatocellular carcinoma (non-HCC) who underwent CT and MRI. Imaging data were used to obtain imaging features of lesions and radiomics regions of interest (ROI). The extracted imaging features were combined to construct different radiomics models. The clinical data and imaging features were then combined with radiomics features to construct the combined models. Support Vector Machine (SVM), K-nearest Neighbor (KNN), RandomForest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Multilayer Perceptron (MLP) six machine learning models were used for training. Five-fold cross-validation was used to train the models, and ROC curves were used to analyze the diagnostic efficacy of each model an...