Design and Implementation of Handheld and Desktop Software for the Structured Reporting of Hepatic Masses Using the LI-RADS Schema

Rationale and Objectives The Liver Imaging Reporting and Data System (LI-RADS) can enhance communication between radiologists and clinicians if applied consistently. We identified an institutional need to improve liver imaging report standardization and developed handheld and desktop software to serve this purpose. Materials and Methods We developed two complementary applications that implement the LI-RADS schema. A mobile application for iOS devices written in the Objective-C language allows for rapid characterization of hepatic observations under a variety of circumstances. A desktop application written in the Java language allows for comprehensive observation characterization and standardized report text generation. We chose the applications' languages and feature sets based on the computing resources of target platforms, anticipated usage scenarios, and ease of application installation, deployment, and updating. Results Our primary results are the publication of the core source code implementing the LI-RADS algorithm and the availability of the applications for use worldwide via our website, http://www.liradsapp.com/ . The Java application is free open-source software that can be integrated into nearly any vendor's reporting system. The iOS application is distributed through Apple's iTunes App Store. Observation categorizations of both programs have been manually validated to be correct. The iOS application has been used to characterize liver tumors during multidisciplinary conferences of our institution, and several faculty members, fellows, and residents have adopted the generated text of Java application into their diagnostic reports. Conclusions Although these two applications were developed for the specific reporting requirements of our liver tumor service, we intend to apply this development model to other diseases as well. Through semiautomated structured report generation and observation characterization, we aim to improve patient care while increasing radiologist efficiency.