A method for optimizing planning target volume margins for patients receiving lung stereotactic body radiotherapy. (2nd October 2018)

• Record Type: Journal Article
• Authors: Wierzbicki, Marcin; Mathew, Lindsay; Swaminath, Anand
• Is Part Of: Physics in medicine & biology. Volume 63:Number 19(2018:Oct.)
• Publication Date: 2018-10-02
• Subjects: lung stereotactic body radiotherapy -- planning target volume margin -- deformable registration -- image guided radiotherapy; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aadf26
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Lung stereotactic-body radiotherapy (SBRT) places additional requirements on targeting accuracy over standard approaches. In treatment planning, a tumour volume is geometrically expanded and the resulting planning target volume (PTV) is covered with the prescribed dose. This ensures full dose delivery despite various uncertainties encountered during treatment. We developed a retrospective technique for optimizing the PTV expansion for a patient population. The method relies on deformable image registration (DIR) of the planning CT to a treatment cone-beam CT (CBCT). The resulting transformation is used to map the planned target onto the treatment geometry, allowing the computation of the achieved target/PTV overlap. Basic validation of the method was performed using an anthropomorphic respiratory motion phantom. A self-validation technique was also implemented to allow estimation of the DIR error for the data being analyzed. Our workflow was used to retrospectively optimize PTV margin for 25 patients treated over 93 fractions. Targets for these patients were contoured on 4D CT images. SBRT delivery followed CBCT acquisition and a couch correction. A post-treatment CBCT was also acquired in some cases. Our basic validation demonstrated that the DIR-based technique is capable of transforming target volumes from planning CTs to treatment CBCTs with sub-mm accuracy. Our clinical analysis showed that the minimum percentages of target volumes covered for 3, 4, and 5 mm PTV margins were 92.1, 97.6, and 99.2, respectively. Analyzing data acquired before and just after treatment demonstrated that margins exceeding 5 mm did not significantly improve coverage. Finally, a 5 mm PTV margin achieved ⩾95% target volume coverage with ⩾95% probability. Our technique is accurate, automated, self-validating, and incorporates complex ITV shapes/deformations to allow PTV margin optimization. The analysis of clinical data indicates a 5 mm PTV margin is optimal for our process. This approach is generalizable to other disease sites and treatment strategies.