Use of Web-Based Calculator for the Implementation of ACR TI-RADS Risk-Stratification System.

In this article, we demonstrate the use of a software-based radiologist reporting tool for the implementation of American College of Radiology Thyroid Imaging, Reporting and Data System thyroid nodule risk-stratification. The technical details are described with emphasis on addressing the information security and patient privacy issues while allowing it to integrate with the electronic health record and radiology reporting dictation software. Its practical implementation is assessed in a quality improvement project in which guideline adherence and recommendation congruence were measured pre and post implementation. The descriptions of our solution and the release of the open-sourced codes may be helpful in future implementation of similar web-based calculators.

Deep Learning Convolutional Neural Networks for the Estimation of Liver Fibrosis Severity from Ultrasound Texture.

Diagnosis and staging of liver fibrosis is a vital prognostic marker in chronic liver diseases. Due to the inaccuracies and risk of complications associated with liver core needle biopsy, the current standard for diagnosis, other less invasive methods are sought for diagnosis. One such method that has been shown to correlate well with liver fibrosis is shear wave velocity measured by ultrasound (US) shear wave elastography; however, this technique requires specific software, hardware, and training. A current perspective in the radiology community is that the texture pattern from an US image may be predictive of the stage of liver fibrosis. We propose the use of convolutional neural networks (CNNs), a framework shown to be well suited for real world image interpretation, to test whether the texture pattern in gray scale elastography images (B-mode US with fixed, subject-agnostic acquisition settings) is predictive of the shear wave velocity (SWV). In this study, gray scale elastography images from over 300 patients including 3,500 images with corresponding SWV measurements were preprocessed and used as input to 100 different CNN architectures that were trained to regress shear wave velocity. In this study, even the best performing CNN explained only negligible variation in the shear wave velocity measures. These extensive test results suggest that the gray scale elastography image texture provides little predictive information about shear wave velocity and liver fibrosis.