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Abstract #3993

Machine-learning-based treatment response stratification for trans-arterial chemoembolization in HCC patients.

Atilla Peter Kiraly1, Robert Grimm2, Mounes Aliyari Ghasebeh3, Li Pan4, David Liu1, Berthold Kiefer2, and Ihab Roushdy Kamel3

1Medical Imaging Technologies, Siemens Medical Solutions USA, Princeton, NJ, United States, 2MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany, 3The Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 4Siemens Healthcare, Baltimore, MD, United States

In determining the effectiveness of chemoembolization in HCC, functional MRI has been shown to differentiate responders and non-responders earlier than anatomical measurements such as RECIST or EASL criteria. In previous studies, multiparametric response criteria based on thresholds of changes in ADC and venous enhancement (VE) intensities were proposed. We present improved stratification based on machine learning and image-based features. On a set of 57 chemoembolization patients, the proposed approach achieved a mean classification accuracy of 84% versus 66% for the previous threshold-based approach. These results further demonstrate the incremental value of functional MRI over traditional anatomical measures.

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