Predicting hepatocellular carcinoma response to TACE: A machine learning study based on 2.5D CT imaging and deep features analysis.

Journal: European journal of radiology

PMID: 40158473

Abstract

OBJECTIVES: Prior to the commencement of treatment, it is essential to establish an objective method for accurately predicting the prognosis of patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE). In this study, we aimed to develop a machine learning (ML) model to predict the response of HCC patients to TACE based on CT images analysis.

Authors

Chong Lin
Ting Cao

Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Maowen Tang

Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
Wei Pu

School of Aeronautics and Astronautics, Sichuan University, Chengdu, China.
Pinggui Lei

Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, China.

Keywords

Adult Aged Carcinoma, Hepatocellular Chemoembolization, Therapeutic Female Humans Liver Neoplasms Machine Learning Male Middle Aged Prognosis Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Tomography, X-Ray Computed Treatment Outcome

External Resources

View on PubMed Access via DOI PubMed (40158473)

Predicting hepatocellular carcinoma response to TACE: A machine learning study based on 2.5D CT imaging and deep features analysis.

Abstract

Authors

Keywords

External Resources

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