Validation of PTV margin for Gamma Knife Icon frameless treatment using a PseudoPatient® Prime anthropomorphic phantom

J Appl Clin Med Phys. 2020 Sep;21(9):278-285. doi: 10.1002/acm2.12997. Epub 2020 Aug 12.

Abstract

The Gamma Knife Icon allows the treatment of brain tumors mask-based single-fraction or fractionated treatment schemes. In clinic, uniform axial expansion of 1 mm around the gross tumor volume (GTV) and a 1.5 mm expansion in the superior and inferior directions are used to generate the planning target volume (PTV). The purpose of the study was to validate this margin scheme with two clinical scenarios: (a) the patient's head remaining right below the high-definition motion management (HDMM) threshold, and (b) frequent treatment interruptions followed by plan adaptation induced by large pitch head motion. A remote-controlled head assembly was used to control the motion of a PseudoPatient® Prime head phantom; for dosimetric evaluations, an ionization chamber, EBT3 films, and polymer gels were used. These measurements were compared with those from the Gamma Knife plan. For the absolute dose measurements using an ionization chamber, the percentage differences for both targets were less than 3.0% for all scenarios, which was within the expected tolerance. For the film measurements, the two-dimensional (2D) gamma index with a 2%/2 mm criterion showed the passing rates of ≥87% in all scenarios except the scenario 1. The results of Gel measurements showed that GTV (D100 ) was covered by the prescription dose and PTV (D95 ) was well above the planned dose by up to 5.6% and the largest geometric PTV offset was 0.8 mm for all scenarios. In conclusion, the current margin scheme with HDMM setting is adequate for a typical patient's intrafractional motion.

Keywords: Gamma Knife Icon; PTV; anthropomorphic phantom; gel.

MeSH terms

  • Brain Neoplasms* / radiotherapy
  • Brain Neoplasms* / surgery
  • Humans
  • Motion
  • Phantoms, Imaging
  • Radiometry
  • Radiosurgery*
  • Radiotherapy Dosage
  • Radiotherapy Planning, Computer-Assisted