A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization

Some patients with hepatocellular carcinoma (HCC) are more likely to experience disease progression despite transcatheter arterial chemoembolization (TACE) treatment, and thus would benefit from early switching to other therapeutic regimens. We sought to evaluate a fully automated machine learning a...

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Bibliographic Details
Published in:Radiology. Artificial intelligence 2019-09, Vol.1 (5), p.e180021
Main Authors: Morshid, Ali, Elsayes, Khaled M, Khalaf, Ahmed M, Elmohr, Mohab M, Yu, Justin, Kaseb, Ahmed O, Hassan, Manal, Mahvash, Armeen, Wang, Zhihui, Hazle, John D, Fuentes, David
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Language:English
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Summary:Some patients with hepatocellular carcinoma (HCC) are more likely to experience disease progression despite transcatheter arterial chemoembolization (TACE) treatment, and thus would benefit from early switching to other therapeutic regimens. We sought to evaluate a fully automated machine learning algorithm that uses pre-therapeutic quantitative computed tomography (CT) image features and clinical factors to predict HCC response to TACE. Outcome information from 105 patients receiving first-line treatment with TACE was evaluated retrospectively. The primary clinical endpoint was time to progression (TTP) based on follow-up CT radiological criteria (mRECIST). A 14-week cutoff was used to classify patients as TACE-susceptible (TTP ≥14 weeks) or TACE-refractory (TTP
ISSN:2638-6100
2638-6100
DOI:10.1148/ryai.2019180021