Limiting treatment plan complexity by applying a novel commercial tool. Issue 8 (21st May 2020)

• Record Type: Journal Article
• Authors: Scaggion, Alessandro; Fusella, Marco; Agnello, Giancarmelo; Bettinelli, Andrea; Pivato, Nicola; Roggio, Antonella; Rossato, Marco A.; Sepulcri, Matteo; Paiusco, Marta
• Is Part Of: Journal of applied clinical medical physics. Volume 21:Issue 8(2020)
• Page Start: 27
• Page End: 34
• Publication Date: 2020-05-21
• Subjects: aperture shape controller -- complexity metrics -- MLC -- plan complexity -- plan quality metric -- treatment planning; Medical physics -- Periodicals; Clinical medicine -- Periodicals; Health Physics; Clinical Medicine; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; 610.153
• DOI: 10.1002/acm2.12908
• Languages: English
• ISSNs: 1526-9914

Abstract

Abstract: Purpose: A recently introduced commercial tool is tested to assess whether it is able to reduce the complexity of a treatment plan and improve deliverability without compromising overall quality. Methods: Ten prostate and ten oropharynx plans of previously treated patients were reoptimized using the aperture shape controller (ASC) tool recently introduced in Eclipse TPS (Varian Medical Systems, Palo Alto, CA). The performance of ASC was assessed in terms of the overall plan quality using a plan quality metric, the reduction in plan complexity through the analysis of 14 of the most common plan complexity metrics, and the change in plan deliverability through 3D dosimetric measurements. Similarly, plans optimized limiting the total number of delivered monitor units was assessed and compared. The two strategies were also combined to assess their potential combination. Results: The plans optimized by exploiting the ASC generally show a reduced number of total Monitor Units, a more constant gantry rotation and a MLC modulation characterized by larger and less complicated shapes with leaves traveling shorter overall lengths. Conclusions: This first experience suggests that the ASC is an effective tool to reduce the unnecessary complexity of a plan. This turns into an increased plan deliverability with no loss of plan quality.