Yale School of Engineering and Applied Science 1 article published in JoVE Medicine Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma Aaron Abajian1, Nikitha Murali1, Lynn Jeanette Savic1,2, Fabian Max Laage-Gaupp1, Nariman Nezami1, James S. Duncan3, Todd Schlachter1, MingDe Lin4, Jean-François Geschwind5, Julius Chapiro1 1Department of Radiology and Biomedical Imaging, Yale School of Medicine, 2Department of Diagnostic and Interventional Radiology, Universitätsmedizin Charité Berlin, 3Department of Biomedical Engineering, Yale School of Engineering and Applied Science, 4Philips Research North America, 5Prescience Labs Intra-arterial therapies are the standard of care for patients with hepatocellular carcinoma who cannot undergo surgical resection. A method for predicting response to these therapies is proposed. The technique uses pre-procedural clinical, demographic, and imaging information to train machine learning models capable of predicting response prior to treatment.
