Abstract

The objective was to evaluate the performance of a high‐definition multileaf collimator (MLC) of 2.5 mm leaf width (MLC2.5) and compare to standard 5 mm leaf width MLC (MLC5) for the treatment of intracranial lesions using dynamic conformal arcs (DCA) technique with a dedicated radiosurgery linear accelerator. Simulated cases of spherical targets were created to study solely the effect of target volume size on the performance of the two MLC systems independent of target shape complexity. In addition, 43 patients previously treated for intracranial lesions in our institution were retrospectively planned using DCA technique with MLC2.5 and MLC5 systems. The gross tumor volume ranged from 0.07 to 40.57 cm3 with an average volume of 5.9 cm3. All treatment parameters were kept the same for both MLC‐based plans. The plan evaluation was performed using figures of merits (FOM) for a rapid and objective assessment on the quality of the two treatment plans for MLC2.5 and MLC5. The prescription isodose surface was selected as the greatest isodose surface covering ≥95% of the target volume and delivering 95% of the prescription dose to 99% of target volume. A Conformity Index (CI) and conformity distance index (CDI) were used to quantifying the dose conformity to a target volume. To assess normal tissue sparing, a normal tissue difference (NTD) was defined as the difference between the volume of normal tissue receiving a certain dose utilizing MLC5 and the volume receiving the same dose using MLC2.5. The CI and normal tissue sparing for the simulated spherical targets were better with the MLC2.5 as compared to MLC5. For the clinical patients, the CI and CDI results indicated that the MLC2.5 provides better treatment conformity than MLC5 even at large target volumes. The CI's range was 1.15 to 2.44 with a median of 1.59 for MLC2.5 compared to 1.60–2.85 with a median of 1.71 for MLC5. Improved normal tissue sparing was also observed for MLC2.5 over MLC5, with the NTD always positive, indicating improvement, and ranging from 0.1 to 8.3 for normal tissue receiving 50% (NTV50), 70% (NTV70) and 90% (NTV90) of the prescription dose. The MLC2.5 has a dosimetric advantage over the MLC5 in Linac‐based radiosurgery using DCA method for intracranial lesions, both in treatment conformity and normal tissue sparing when target shape complexity increases.PACS number: 87.56J‐, 87.56 jk

Highlights

  • Stereotactic radiosurgery (SRS) is the process of delivering a high dose of external beam radiation to a small intracranial target in a single fraction using 60Co sources, medical linear accelerators, or charged particle beams guided by an external frame system

  • The plotted results show that, while Conformity Index (CI) for both MLCs decrease with the target volume, the CI for MLC2.5 for each TV volume is clearly lower than the corresponding CI of MLC5 across the entire range of volumes (Fig. 3)

  • Similar to our results with the simulated target study, these results indicate that the MLC2.5 provides better treatment conformity than MLC5 as judged by the CI values in a range of actual patient tumors

Read more

Summary

Introduction

Stereotactic radiosurgery (SRS) is the process of delivering a high dose of external beam radiation to a small intracranial target in a single fraction using 60Co sources, medical linear accelerators, or charged particle beams guided by an external frame system. As suggested by normal tissue complication probability modeling for radiosurgery,(1) a high degree of conformity of the prescription dose to target volume should be achieved to allow safe treatment of the target. Conformity Index (CI) is the ratio of the prescription volume to the target volume, as defined in the Radiation Therapy Oncology Group (RTOG) radiosurgery guidelines in 1993.(2-4) CI is useful for evaluating competing plans for the same patient or comparing different modalities. DVHs may be a preliminary step in evaluating statistics such as tumor control and normal tissue complication probabilities.[5,6] when comparing a large number of plans, DVHs contain large amounts of data and make the comparison difficult and cumbersome

Methods
Results
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.