Radiation therapy plays an important role in the treatment of cancer, and it is used as a treatment for more than half of all the cancer patients world wide (IAEA, 2004). The radiation treatment design is simulated in a treatment planning system (TPS) prior delivery to patient. The dose distribution is calculated and optimized in the TPS.
Intensity-‐modulated radiation therapy (IMRT) is a radiation technique where the fluence of the beam is non-‐uniform (Kahn, 2010). With IMRT the fluence in a beam at a static gantry angle can be adjusted during radiation delivery based on user-‐defined constrains. This enables higher and more conformal absorbed dose distributions to the tumor with less absorbed dose to OAR. Another radiation technique is the volumetric modulated arc therapy (VMAT). For VMAT the treatment is delivered at the same time as the gantry is rotating around the patients and with a continuously changing MLC shape, dose rate and angle speed of the gantry (O´Daniel et al, 2012).
VMAT has the potential to deliver a conformal dose distribution with less monitor units (MU) and shorter treatment time compared to IMRT technique (Younge et al, 2012).
Shorter treatment time reduces the risk of patient movement during treatment and gives the patient an increased comfort.
Unlike conventional radiation therapy, IMRT and VMAT often have a greater ability to increase the dose to the target with a reduced dose to healthy tissue (McNiven et al, 2010). However, the characteristics of the IMRT/VMAT treatment make higher demands on treatment planning, dose delivery and quality assurance.
An IMRT/VMAT treatment plan is created using an optimization algorithm in the TPS.
This optimization procedure creates a treatment plan based on user-‐defined constrains that tells the system what the prescribed absorbed dose should be for the target and maximum permissible absorbed dose for the OAR. This generates a treatment plan composed of unique MLC openings with different sizes and shapes (Kahn, 2010).
During optimizing of VMAT or IMRT plans the TPS creating more small and irregular shaped MLC openings compared to conventional treatment planning (Younge et al, 2012). Small or irregular MLC openings make it more difficult for the TPS to calculate an accurate dose distribution due to regions lacking charge particle equilibrium (CPE). Fog et al (2011) showed that apertures consisting of small subfields or large fields containing isolated MLC leaves could give rise to significant dose calculation errors.
Small MLC openings also lead to a more pronounced dependence on the position of the MLC leafs during delivery and the MLC modeling in the TPS (LoSasso et al, 1998).
dose delivery from the treatment machine are in tolerance. This is done by quality assurance (QA) (Nelms et al, 2011). The QA includes IMRT quality control (QC). A common way for IMRT QC is to compare the TPS calculated dose distribution with corresponding measured dose distribution in a phantom. Common QC methods that are in use nowadays have been found to miss relevant clinical differences between calculated and delivered dose distributions (Nelms et al, 2011; Götstedt et al, 2015;
Nilsson et al, 2013).
By taking advantage of complexity metrics that describes the calculation and delivery complexity by calculating a complexity score in the dose calculation process, complex MLC openings can be avoided before the treatment plans are approved (Götstedt et al, 2015). Complexity metrics can also be used as a supplement and simplification of the QA process by giving a signal to the physicist of which QA method that may be required and which treatment plans that need detailed examination. QA measurements require a lot of staff time and demands machine time.
Younge et al (2012) showed that highly complex MLC openings are not always needed to create a clinically accepted VMAT plan, instead it is an unwanted effect of the optimizing process in the TPS. Oliver et al (2011) concluded that decreasing the MU:s and creating MLC openings with increased opening area should lead to more stable treatment plans with preserved quality.
Complexity metrics can also be used in the optimization procedure to force the optimizer to create treatment plans with less complex apertures (Younge et al, 2012).
Younge at el (2012) developed a function that was integrated in the optimization process. The function penalized small and irregular MLC openings. The study verified that penalizing small and irregular MLC openings could give an increased agreement between calculated and measured absorbed dose with negligible changes in the planed dose to target and OAR.
The two aperture-‐based metrics evaluated in this study is the converted aperture metric based on the distance between the MLC leave positions and the edge area metric based on the circumference of the MLC opening in relation to the area of the opening. These metrics were developed in a previously study (Götstedt et al, 2015). The study examined the correlation between the metric scores and the difference between calculated and measured absorbed dose. The evaluations were performed with three electronic portal imaging device (EPID) measurements and one film measurement and the calculations were performed with the anisotropic analytical algorithm (AAA). The results indicated a correlation between the metrics and the percentage of pixels that did not deviate more than 3 % or 5 % normalized to the maximum calculated absorbed dose.
The correlations were calculated with pearsons´s r-‐value. The pearsons´s r-‐value gives the linear dependence i.e. correlation between two variables by giving a value between
-‐1 and 1 (Djurfeldt et al, 2010). -‐1 och 1 means a negative or positive correlation respectively. A value close to 0 means that no correlation exists.
This project is a complement and an extension of the study by Götstedt et al (2015) for the converted aperture and the edge are metric. The influence on the correlation between the metric scores and the difference between measured and calculated dose distribution were investigated when different dose calculation algorithms and grid sizes were used.
1.1 Aim
§ Reproduce the film measurements from Götstedt el at (2015) to study the precision for the measurement procedure.
§ Evaluate the correlation between the two different complexity metrics, edge area and converted aperture metric, and the dose difference (i.e. difference between measured and calculated dose distributions) between measured and calculated dose distributions for the pencil beam convolution (PBC), collapsed cone (CC) and acuros XB (AXB) algorithms.
§ Study the impact of the dose calculation grid size on the correlation between the dose differences and the two complexity metrics when using the AAA algorithm.