UCLA

The introduction of image guided radiation therapy (IGRT), defined as regular patient imaging during the course of a radiation therapy treatment regimen, has resulted in significant improvements in both the quality and safety of radiotherapy treatments. Accurate visualization of the target and nearby normal tissue has allowed for the reduction of planning target volume (PTV) margins, leading to increased normal tissue sparing. Daily image guidance can also increase the safety of radiation therapy treatments, as the patient is imaged prior to each treatment fraction to verify target location and compensate for inter-fraction anatomical changes. However, the introduction of daily imaging into the clinical workflow has been coupled with a simultaneous introduction of so-called “IGRT errors.” IGRT errors arise from inaccuracies in registering the patient’s daily setup images with the simulation images acquired prior to treatment. Such errors could arise due to technical challenges with the image registration algorithms themselves, problems with applying the image registration algorithms to a particular set of patient images, or human mistakes made while interpreting the results of an image registration. While IGRT has increased the precision of radiotherapy treatments, it can lead to treatments that are “precisely wrong.” As a preliminary step to mitigate IGRT errors, we propose the development of novel tools for the automatic detection of IGRT errors. Specifically, we develop a convolutional neural network (CNN)-based model for detecting the rare but serious IGRT error of off-by-one vertebral body misalignments in radiation therapy treatments targeting the thoracic spine. We develop a second CNN-based model for detecting the more generic IGRT error of translational shifts of 1 cm from treatment isocenter in all anatomic regions. We apply both models to retrospective image data from patients aligned using daily image guidance in order to detect previously unreported IGRT errors and near miss events, and to understand where in the clinical workflow such incidents originated. Finally, we understand that new evidence-based tools can only be effective if they are successfully integrated into the clinical environment. A rigorous implementation science approach is a necessary step to integrating novel technologies and reducing the well-documented lag time from research to practice. We study the barriers and facilitators to use of both automated tools that are commercially available as well as automated tools still in development. We use a survey study to evaluate medical dosimetrists’ perceptions of auto-contouring and automated treatment planning tools and their perceived barriers to regular clinical use of such tools. To better understand how a new automated tool designed to assist in the IGRT review portion of weekly chart checks could be integrated clinically, we use a novel thematic analysis approach to analyze the current weekly chart check workflow from the perspective of the clinical medical physicist.