Monte Carlo calculated kilovoltage x-ray arc therapy plans for three lung cancer patients

Author:

Breitkreutz Dylan YamabeORCID,Renaud Marc-Andre,Weil Michael David,Zavgorodni Sergei,Han Jaeyoung,Baxter Henry,Seuntjens Jan,Song Samuel,Boyd Douglas,Bazalova-Carter Magdalena

Abstract

Abstract Purpose: The intent of this work was to evaluate the ability of our 200 kV kilovoltage arc therapy (KVAT) system to treat realistic lung tumors without exceeding dose constraints to organs-at-risk (OAR). Methods and Materials: Monte Carlo (MC) methods and the McO optimization framework generated and inversely optimized KVAT treatment plans for 3 SABR lung cancer patients. The KVAT system was designed to treat deep-seated lesions with kilovoltage photons. KVAT delivers dose to roughly spherical PTVs and therefore non-spherical PTVs were divided into spherical sub-volumes. A prescription dose of 12 Gy/fx × 4 fractions was planned to 90% of the PTV volume. KVAT plans were compared to VMC++ calculated, 6 MV stereotactic ablative radiotherapy (SABR) treatment plans. Dose distributions, dose volume histograms, gradient index (GI), planned mean doses and plan treatment times were calculated. Dose constraints for organs-at-risk (OAR) were taken from RTOG 101. Results: All plans, with the exception of the rib dose calculated in one of the KVAT plans for a peripheral lesion, were within dose-constraints. In general, KVAT plans had higher planned doses to OARs. KVAT GI values were 5.7, 7.2 and 8.9 and SABR values were 4.6, 4.1, and 4.7 for patient 1, 2 and 3, respectively. KVAT plan treatment times were 49, 65 and 17 min for patients 1, 2 and 3, respectively. Conclusions: Inverse optimization and MC methods demonstrated the ability of KVAT to produce treatment plans without exceeding TG 101 dose constraints to OARs for 2 out of 3 investigated lung cancer patients.

Funder

American Association of Physicists in Medicine

Natural Sciences and Engineering Research Council of Canada

Canada Research Chairs

Publisher

IOP Publishing

Subject

General Nursing

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