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J Vis Exp. 2018 Oct 10;(140). doi: 10.3791/58382.

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

Author information

Department of Radiology and Biomedical Imaging, Yale School of Medicine
Department of Radiology and Biomedical Imaging, Yale School of Medicine.
Department of Radiology and Biomedical Imaging, Yale School of Medicine; Department of Diagnostic and Interventional Radiology, Universitätsmedizin Charité Berlin.
Department of Biomedical Engineering, Yale School of Engineering and Applied Science.
Philips Research North America.
Prescience Labs.
Department of Radiology and Biomedical Imaging, Yale School of Medicine;


Intra-arterial therapies are the standard of care for patients with hepatocellular carcinoma who cannot undergo surgical resection. The objective of this study was to develop a method to predict response to intra-arterial treatment prior to intervention. The method provides a general framework for predicting outcomes prior to intra-arterial therapy. It involves pooling clinical, demographic and imaging data across a cohort of patients and using these data to train a machine learning model. The trained model is applied to new patients in order to predict their likelihood of response to intra-arterial therapy. The method entails the acquisition and parsing of clinical, demographic and imaging data from N patients who have already undergone trans-arterial therapies. These data are parsed into discrete features (age, sex, cirrhosis, degree of tumor enhancement, etc.) and binarized into true/false values (e.g., age over 60, male gender, tumor enhancement beyond a set threshold, etc.). Low-variance features and features with low univariate associations with the outcome are removed. Each treated patient is labeled according to whether they responded or did not respond to treatment. Each training patient is thus represented by a set of binary features and an outcome label. Machine learning models are trained using N - 1 patients with testing on the left-out patient. This process is repeated for each of the N patients. The N models are averaged to arrive at a final model. The technique is extensible and enables inclusion of additional features in the future. It is also a generalizable process that may be applied to clinical research questions outside of interventional radiology. The main limitation is the need to derive features manually from each patient. A popular modern form of machine learning called deep learning does not suffer from this limitation, but requires larger datasets.

[Available on 2020-10-10]
[Indexed for MEDLINE]

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