Clinical necessity of multi-image based (4D) optimization for targets affected by respiratory motion and treated with scanned particle therapy - A comprehensive review.

4D multi-image-based (4D) optimization is a form of robust optimization where different uncertainty scenarios, due to anatomy variations, are considered via multiple image sets (e.g., 4DCT). In this review, we focused on providing an overview of different 4D optimization implementations, introduced various frameworks to evaluate the robustness of scanned particle therapy affected by breathing motion and summarized the existing evidence on the necessity of using 4D optimization clinically. Expected potential benefits of 4D optimization include more robust and/or interplay-effect-resistant doses for the target volume and organs-at-risk for indications affected by anatomical variations (e.g., breathing, peristalsis, etc.). Although considerable literature is available on the research and technical aspects of 4D, clinical studies are rare and often contain methodological limitations, such as, limited patient number, motion amplitude, motion and delivery time structure considerations, number of repeat CTs, etc. Therefore, the data are not conclusive. In addition, multiple studies have found that robust 3D optimized plans result in dose distributions within the set clinical tolerances and, therefore, are suitable for a treatment of moving targets with scanned particle therapy. We, therefore, consider the clinical necessity of 4D optimization, when treating moving targets with scanned particle therapy, as still to be demonstrated.