Ultrasound-Based Radiomics Analysis for Preoperatively Predicting Different Histopathological Subtypes of Primary Liver Cancer

Yuting Peng; Peng Lin; Linyong Wu; Da Wan; Yujia Zhao; Li Liang; Xiaoyu Ma; Hui Qin; Yichen Liu; Xin Li; Xinrong Wang; Yun He; Hong Yang

doi:10.3389/fonc.2020.01646

Ultrasound-Based Radiomics Analysis for Preoperatively Predicting Different Histopathological Subtypes of Primary Liver Cancer

Front Oncol. 2020 Sep 24:10:1646. doi: 10.3389/fonc.2020.01646. eCollection 2020.

Authors

Yuting Peng¹, Peng Lin¹, Linyong Wu¹, Da Wan¹, Yujia Zhao¹, Li Liang¹, Xiaoyu Ma¹, Hui Qin¹, Yichen Liu¹, Xin Li², Xinrong Wang², Yun He¹, Hong Yang¹

Affiliations

¹ Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
² GE Healthcare, Shanghai, China.

Abstract

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 distinguish histopathological subtypes of primary liver cancer (PLC) before surgery.

Methods: We retrospectively analyzed ultrasound images of 668 PLC patients, comprising 531 HCC patients, 48 cHCC-ICC patients, and 89 ICC patients. The boundary of a tumor 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 tumor. The combination of different dimension-reduction technologies and machine learning approaches was used to identify important features and develop the moderate radiomics model. The comprehensive ability of the radiomics model can be evaluated by the area under the receiver operating characteristic curve (AUC).

Results: After digitally processing tumor ultrasound images, 5,234 high-throughput radiomics features were obtained. We used the Spearman + least absolute shrinkage and selection operator (LASSO) regression method for feature selection and logistics regression for modeling to develop the HCC-vs-non-HCC radiomics model (composed of 16 features). The Spearman + statistical test + random forest methods were used for feature selection, and logistics regression was applied for modeling to develop the 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 the HCC-vs-non-HCC radiomics model and 0.920 (training cohort) and 0.728 (test cohort) in the ICC-vs-cHCC-ICC radiomics model.

Conclusion: 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.

Keywords: histopathological subtype; identification; primary liver cancer; radiomics; ultrasound.