No abstract

Objective: To explore the possible relationship between single nucleotide polymorphisms (SNP)

Backscatter from the asymmetric collimators of a linac into the beam monitor chamber (BMC) has been investigated for two accelerators having different BMC configurations. The effect has been quantified as a function of field size and collimator jaw position for 6 and 18 MV beams. The results indicate a maximum 2.5% (6 MV) and 4% (18 MV) decrease in output in one case and a negligible effect in the other case. The experiments indicate that the difference can be attributed to the different construction of the BMC's for the two accelerators.

The present study evaluates the performance of a newly released photon‐beam dose calculation algorithm that is incorporated into an established treatment planning system (TPS). We compared the analytical anisotropic algorithm (AAA) factory‐commissioned with “golden beam data” for Varian linear accelerators with measurements performed at two institutions using 6‐MV and 15‐MV beams. The TG‐53 evaluation regions and criteria were used to evaluate profiles measured in a water phantom for a wide variety of clinically relevant beam geometries. The total scatter factor (TSF) for each of these geometries was also measured and compared against the results from the AAA.At one institute, TLD measurements were performed at several points in the neck and thoracic regions of a Rando phantom; at the other institution, ion chamber measurements were performed in a CIRS inhomogeneous phantom. The phantoms were both imaged using computed tomography (CT), and the dose was calculated using the AAA at corresponding detector locations. Evaluation of measured relative dose profiles revealed that 97%, 99%, 97%, and 100% of points at one institute and 96%, 88%, 89%, and 100% of points at the other institution passed TG‐53 evaluation criteria in the outer beam, penumbra, inner beam, and buildup regions respectively. Poorer results in the inner beam regions at one institute are attributed to the mismatch of the measured profiles at shallow depths with the “golden beam data.”For validation of monitor unit (MU) calculations, the mean difference between measured and calculated TSFs was less than 0.5%; test cases involving physical wedges had, in general, differences of more than 1%. The mean difference between point measurements performed in inhomogeneous phantoms and Eclipse was 2.1% (5.3% maximum) and all differences were within TG‐53 guidelines of 7%. By intent, the methods and evaluation techniques were similar to those in a previous investigation involving another convolution–superposition photon‐beam dose calculation algorithm in another TPS, so that the current work permitted an independent comparison between the two algorithms for which results have been provided.PACS number: 87.53.Dq

Radiotherapy treatment plan evaluation relies on an implicit estimation of the tumor control probability (TCP) and normal tissue complication probability (NTCP) arising from a given dose distribution. A potential application of radiobiological modeling to radiotherapy is the ranking of treatment plans via a more explicit determination of TCP and NTCP values. Although the limited predictive capabilities of current radiobiological models prevent their use as a primary evaluative tool, radiobiological modeling predictions may still be a valuable complement to clinical experience. A convenient computational module has been developed for estimating the TCP and the NTCP arising from a dose distribution calculated by a treatment planning system, and characterized by differential (frequency) dose‐volume histograms (DDVHs). The radiobiological models included in the module are sigmoidal dose response and Critical Volume NTCP models, a Poisson TCP model, and a TCP model incorporating radiobiological parameters describing linear‐quadratic cell kill and repopulation. A number of sets of parameter values for the different models have been gathered in databases. The estimated parameters characterize the radiation response of several different normal tissues and tumor types. The system also allows input and storage of parameters by the user, which is particularly useful because of the rapidly increasing number of parameter estimates available in the literature. Potential applications of the system include the following: comparing radiobiological predictions of outcome for different treatment plans or types of treatment; comparing the number of observed outcomes for a cohort of patient DVHs to the predicted number of outcomes based on different models/parameter sets; and testing of the sensitivity of model predictions to uncertainties in the parameter values. The module thus helps to amalgamate and make more accessible current radiobiological modeling knowledge, and may serve as a useful aid in the prospective and retrospective analysis of radiotherapy treatment plans.PACS number: 87.53.Tf