A Systematic Approach to Statistical Analysis in Dosimetry and Patient Specific IMRT Plan Verification Measurements
S Qin1, M Zhang2, S Kim2, T Chen2, L Kim2, B Haffty2, N Yue2*, (1) The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, (2) The Cancer Institute of New Jersey, New Brunswick, NJSU-E-T-44 Sunday 3:00:00 PM - 6:00:00 PM Room: Exhibit Hall
Purpose: Due to existence of uncertainties, the delivered treatment dose likely exhibits a statistical distribution. The expected dose and variance of this distribution are unknown and are most likely not equal to the planned values since the current treatment planning systems cannot exactly model and simulate treatment machine. The questions are 1) how to quantitatively estimate the expected delivered dose, and relate the expected dose to the treatment dose over a treatment course, and 2) how to evaluate the treatment dose relative to the corresponding planned dose. This study is to present a systematic approach to address the questions and to apply the approach to patient specific IMRT (PSIMRT) plan verifications.
Methods: The expected delivered dose and variance are quantitatively estimated using Student T distribution and Chi Distribution, respectively, based on pre-treatment QA measurements. Relationships between the expected dose and the delivered dose over a treatment course, and between the expected dose and the planned dose are quantified with mathematical formalisms. The requirement and evaluation of the pre-treatment QA measurement results is also quantitatively related to the desired treatment accuracy and the to-be-delivered treatment course itself. This methodology was applied to PSIMRT plan verification procedures.
Results: Statistically, the pre-treatment QA measurement process was dictated not only by the corresponding plan but also by the measurement deviation, number of measurements, and treatment fractions and tolerance. For the PSIMRT QA procedures, more than one measurement had to be performed to evaluate whether the to-be-delivered treatment course would meet the desired dose coverage and treatment tolerance.
Conclusions: Beyond qualitative intuition, we quantitatively derive that not only the statistical parameters associated with the QA measurement but also treatment course itself need to be taken into account to evaluate the QA outcome. The result from a single QA measurement without statistical analysis can be misleading.