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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Article DOI: 10.3791/58382 04:09 min October 10th, 2018
October 10th, 2018



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.



Treatment Response Image-guided Therapies Machine Learning Trans-arterial Treatment Hepatocellular Carcinoma Interventional Oncology Liver Cancer Feature Generation Clinical Dataset Machine Learning Algorithm Clinical Features Natural Language Tool Kit Plain-text Clinic Notes Feature Extraction Non-binary Features Median Value Liver Enhancement Feature
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