Radiology: Artificial Intelligence ,

September 2019 Volume 1, Issue 5

Editorial

Special Feature   Magician's Corner 2: Optimizing a Simple Image Classifier Bradley J. Erickson

Original Research

A Machine Learning Model to Predict Hepatocellular Carcinoma Response to Transcatheter Arterial Chemoembolization   Prediction of hepatocellular carcinoma response to transcatheter arterial chemoembolization (TACE) using quantitative imaging and clinical measurements of pretreatment lesions is a potentially useful clinical tool that can assist in patient selection for TACE.     Ali Morshid, Khaled M. Elsayes... David Fuentes

Commentary: From Code to Bedside: Introducing Predictive Intelligence to Interventional Oncology Julius Chapiro, James S. Duncan

Automated Organ-Level Classification of Free-Text Pathology Reports to Support a Radiology Follow-up Tracking Engine   Neural network–based algorithms perform well in organ-level classification of multi-institution free-text pathology reports and learn features familiar to and understandable by humans.     Jackson M. Steinkamp, Charles M. Chambers... Tessa S. Cook

Commentary: Deep Learning Approaches Substantially Improve Automated Extraction of Information from Free-Text Medical Reports Tiffany Ting Liu

Neural Network–derived Perfusion Maps for the Assessment of Lesions in Patients with Acute Ischemic Stroke   In patients with acute ischemic stroke, convolutional neural networks can produce brain perfusion maps with clinical utility similar to that of standard deconvolution-based estimates.     Raphael Meier, Paula Lux... Johannes Kaesmacher