Characterization of robust optimization for VMAT plan for liver cancer

• Published in: Reports of practical oncology and radiotherapy Vol. 25; no. 3; pp. 376 - 381
• Main Authors: Miura, Hideharu, Ozawa, Shuichi, Kusaba, Hayate, Doi, Yoshiko, Kenjo, Masahiko, Yamada, Kiyoshi, Nagata, Yasushi
• Format: Journal Article
• Language: English
• Published: Poland Elsevier B.V 01.05.2020; Elsevier
• Subjects: Dosimetry comparison; Liver; Original research article; Plan complexity; Robust optimization; VMAT; Dosimetry comparison; VMAT; Robust optimization; Liver; Plan complexity
• ISSN: 1507-1367; 2083-4640
• DOI: 10.1016/j.rpor.2020.03.012

We investigated the feasibility of robust optimization for volumetric modulated arc therapy (VMAT) stereotactic body radiation therapy (SBRT) for liver cancer in comparison with planning target volume (PTV)-based optimized plans. Treatment plan quality, robustness, complexity, and accuracy of dose delivery were assessed. Ten liver cancer patients were selected for this study. PTV-based optimized plans with an 8-mm PTV margin and robust optimized plans with an 8-mm setup uncertainty were generated. Plan perturbed doses were evaluated using a setup error of 8mm in all directions from the isocenter. The dosimetric comparison parameters were clinical target volume (CTV) doses (D98%, D50%, and D2%), liver doses, and monitor unit (MU). Plan complexity was evaluated using the modulation complexity score for VMAT (MCSv). There was no significant difference between the two optimizations with respect to CTV doses and MUs. Robust optimized plans had a higher liver dose than did PTV-based optimized plans. Plan perturbed dose evaluations showed that doses to the CTV for the robust optimized plans had small variations. Robust optimized plans were less complex than PTV-based optimized plans. Robust optimized plans had statistically significant fewer leaf position errors than did PTV-based optimized plans. Comparison of treatment plan quality, robustness, and plan complexity of both optimizations showed that robust optimization could be feasibile for VMAT of liver cancer.