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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