Clinical value of machine learning model combining test data and enhanced CT images to predict primary hepatocellular carcinoma

"Objective To investigate the clinical value of machine learning methods for the detection of primary hepatocellular carcinoma (HCC) using laboratory data and enhanced CT imaging. Methods The enhanced CT images and test data of 654 patients with liver diseases admitted in Shiyan Taihe Hospital from 2013 to 2021 were retrospectively analyzed. These consisted of 199 patients with primary HCC, 223 patients with hepatitis, and 232 patients with liver cirrhosis. They were randomly allocated to the training set and testing set at 7∶3. Five machine learning algorithms (including logistic regression, support vector machine, random forest, decision tree, and AdaBoost) were implemented to train the model using test data, CT enhanced images, and combined test data/CT images. Receiver operating characteristic curve was used to calculate the area under curve (AUC), accuracy, sensitivity and specificity to verify the model. Results From the analysis of data modality, the use of a multimodal method combining test data and e