Clinical adoption of deep learning target auto-segmentation for radiation therapy: challenges, clinical risks, and mitigation strategies

Radiation therapy is a localized cancer treatment that relies on precise delineation of the target to be treated and healthy tissues to guarantee optimal treatment effect. This step, known as contouring or segmentation, involves identifying both target volumes and organs at risk on imaging modalities like CT, PET, and MRI to guide radiation delivery. Manual segmentation, however, is time-consuming and highly subjective, despite the presence of contouring guidelines. In recent years, automated segmentation methods, particularly deep learning models, have shown promise in addressing this task. However, challenges persist in their clinical use, including the need for robust quality assurance (QA) processes and addressing clinical risks associated with the use of the models. This review examines the challenges and considerations of the clinical adoption of deep learning target auto-segmentation in radiotherapy, focused on the target volume. We discuss potential clinical risks (eg, over- and under-segmentation, automation bias, and appropriate trust), mitigation strategies (eg, human oversight, uncertainty quantification, and education of clinical professionals), and we highlight the importance of expanding QA to include geometric, dose-volume, and outcome-based performance monitoring. While deep learning target auto-segmentation offers significant potential benefits, careful attention to clinical risks and rigorous QA measures are essential for its successful integration in clinical practice.