Gamma Knife Dosimetric Differences, TMR 10 Versus Convolution Algorithm

Summary

Gamma Knife Radiosurgery (GKR) is a well established treatment modality for brain tumors and functional disorders of the brain. It relies on mathematical algorithms to predict dose distribution and to calculate the dose at arbitrary points in the head. For the last 25 years, doses applied using Gamma Knife Radiosurgery have been calculated using a simple algorithm, called the Tissue Maximum Ratio algorithm (TMR). Dose planning using this algorithm, relies on a number of approximations to enable fast isodose computation during treatment planning. One of the most significant of these is the approximation of the head to water-equivalent density. The increased electron density of brain and bone (relative to water) and the near-zero density of air cavities in the skull may make significant perturbations to isodose and beam-on time calculations.

With the advent of faster workstations, the effect of tissue in-homogeneities can finally be calculated in reasonable time during the treatment planning process; a newer, more modern algorithm known as convolution algorithm is now commercially available. It uses the values of density indicated in the CT scan to predict the dose distribution and is expected to more accurately calculate radiation dose, although it needs further investigation before clinical implementation. Inter- and intra-indication differences between the old and new algorithms need to be understood before this method can be confidently employed in a clinical setting. It is the aim of this study to understand the dosimetric differences between these dose calculation algorithms and to evaluate the implications of using the convolution algorithm for GKR. A large number of treatments will be re-planned using the convolution algorithm and compared to the TMR plans used to treat the patients. Beam-on-time, which is proportional to dose and a number of commonly used metrics for the targets such as coverage, selectivity, gradient index, and mean and maximum dose, will be estimated with both algorithms. Subgroup analysis will be done to assess whether any factor such as diagnosis, size of the head or location of the target could impact on the relative difference between the methods. The treatment plans will be compared and the potential implications on treatment planning will be elucidated.

Conditions

• Gamma Knife Radiosurgery

Interventions

OTHER

Gamma knife radiosurgery re-planning with convolution algorithm

The convolution algorithm, which uses the correlation between CT imaging density in Hounsfield units (HU) and electron density (ρe) of the tissues as input to predict dose distribution, can provide a better simulation of real delivered dose for GKR. By more accurately predicting the dose delivered, a better prediction of clinical effects can be made, increasing the potential clinical efficacy of treatment. Convolution algorithm is now available in Leksell GammaPlan® 10 but there is not enough clinical data to support its use over TMR 10, which is the current clinical standard. Using convolution algorithm to recalculate the dose for the otherwise unaltered TMR 10 plan will provide valuable insight and understanding of the dosimetric differences between these planning algorithms.

Sponsors & Collaborators

• University College London Hospitals

  collaborator OTHER

• University College, London

  lead OTHER

Principal Investigators

• Neil Kitchen · The National Hospital for Neurology and Neurosurgery

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2013-10-31

Primary Completion

2015-10-31

Completion

2016-10-31

Countries

• United Kingdom

Study Locations