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Philips Research North America

2 ARTICLES PUBLISHED IN JoVE

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Medicine

Dual-phase Cone-beam Computed Tomography to See, Reach, and Treat Hepatocellular Carcinoma during Drug-eluting Beads Transarterial Chemo-embolization
Vania Tacher 1, MingDe Lin 2, Nikhil Bhagat 1, Nadine Abi Jaoudeh 3, Alessandro Radaelli 4, Niels Noordhoek 4, Bart Carelsen 4, Bradford J. Wood 3, Jean-François Geschwind 1
1Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, The Johns Hopkins Hospital, 2Clinical Informatics, Interventional, and Translational Solutions (CIITS), Philips Research North America, 3Center for Interventional Oncology, Interventional Radiology Section, National Institutes of Health, 4Interventional X-ray, Philips Healthcare

Dual-phase cone-beam computed tomography (DP-CBCT) is a useful intraprocedural imaging technique for transarterial chemo-embolization treatment with drug-eluting beads of hepatocellular carcinoma. DP-CBCT has been used to perform three major steps in oncologic interventional radiology: tumor localization (see), navigation and intraprocedural catheter guidance (reach), and intraprocedural evaluation of treatment success (treat).

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Medicine

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Aaron Abajian 1, Nikitha Murali 1, Lynn Jeanette Savic 1,2, Fabian Max Laage-Gaupp 1, Nariman Nezami 1, James S. Duncan 3, Todd Schlachter 1, MingDe Lin 4, Jean-François Geschwind 5, Julius Chapiro 1
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.

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