Ultrasound-Based Radiomics Analysis for Preoperatively Predicting Different Histopathological Subtypes of Primary Liver Cancer - Peng, Yuting; Lin, Peng; Wu, Linyong; Wan, Da; Zhao, Yujia; Liang, Li; Ma, Xiaoyu; Qin, Hui; Liu, Yichen; Li, Xin; Wang, Xinrong; He, Yun; et al. (Frontiers in Oncology, 2020)
Background: Preoperative identification of hepatocellular carcinoma (HCC), combined hepatocellular-cholangiocarcinoma (cHCC-ICC), and intrahepatic cholangiocarcinoma (ICC) is essential for treatment decision making. We aimed to use ultrasound-based radiomics analysis to non-invasively distinguishing histopathological subtypes of primary liver cancer (PLC) before surgery. Methods: We retrospectively analysed ultrasound images of 668 PLC patients, comprising 531 HCC patients, 48 cHCC-ICC patients and 89 ICC patients. The boundary of a tumour was manually determined on the largest imaging slice of the ultrasound medicine image by ITK-SNAP software (version 3.8.0), and then, the high-throughput radiomics features were extracted from the obtained region of interest (ROI) of the tumour. The combination of different dimension-reduction technologies and machine learning approaches were used to identify important features and develop the moderate multi-class radiomics model. The comprehensive ability of the radiomics model by calculating the area under the receiver operating characteristic curve (AUC). Results: After digitally processing tumour ultrasound images, 5,234 high-throughput radiomics features were obtained. We used Spearman+ least absolute shrinkage and selection operator (LASSO) regression method for feature selection and logistics regression for modelling to develop HCC vs non-HCC radiomics model (composed of 16 features). Spearman +statistical test +random forest methods were used for feature selection and logistics regression were applied for modelling to develop ICC vs cHCC-ICC radiomics model (composed of 19 features). The overall performance of the radiomics model in identifying different histopathological types of PLC was moderate, with AUC values of 0.854 (training cohort) and 0.775 (test cohort) in HCC vs non-HCC radiomics model, and 0.920 (training cohort) and 0.728 (test cohort) in ICC vs cHCC-ICC radiomics model. Conclusions: Ultrasound-based radiomics models can help distinguish histopathological subtypes of PLC and provide effective clinical decision-making for the accurate diagnosis and treatment of PLC.