predicting-treatment-response-to-image-guided-therapies-using-machine

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

Intra-arterial therapies are the standard of care for patients with hepatocellular carcinoma who cannot undergo surgical resection. The objective of this ...

Yale School of Medicine

There are two different types of Y Microspheres, glass and resin, in the market for Y radioembolization (Y-RE). This study aimed to investigate the dose of radiation delivered through glass vs. resin-based Y-RE to intrahepatic cholangiocarcinoma (ICC).

Y radioembolization dosimetry using a simple semi-quantitative method in intrahepatic cholangiocarcinoma: Glass versus resin microspheres.

To use magnetic resonance (MR) imaging and clinical patient data to create an artificial intelligence (AI) framework for the prediction of therapeutic outcomes of transarterial chemoembolization by applying machine learning (ML) techniques.

Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.

Nariman Nezami

Department of Radiology and Biomedical Imaging

Yale School of Medicine

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

Nariman Nezami

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Radiology and Biomedical Imaging

Yale School of Medicine

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

Department of Radiology and Biomedical Imaging