Evaluation of Polymer Gel Dosimeters for Measurements of Dose and LET in Proton Beams

This project assessed the effectiveness of polymer gel dosimeters as tools for measuring the dose deposited by and LET of a proton beam. A total of three BANG® dosimeter formulations were evaluated: BANG®-3-Pro-2 BANGkits™ for dose measurement and two BANG®-3 variants, the LET-Baseline and LET-Meter dosimeters, for LET measurement. All dosimeters were read out using an OCT scanner. The basic characteristics of the BANGkits™ were assessed in a series of photon and electron irradiations. The dose-response relationship was found to be sigmoidal with a threshold for response of approximately 15 cGy. The active region of the dosimeter, the volume in which dosimeter response is not inhibited by oxygen, was found to make up roughly one fourth of the total dosimeter volume. Delivering a dose across multiple fractions was found to yield a greater response than delivering the same dose in a single irradiation. The dosimeter was found to accurately measure a dose distribution produced by overlapping photon fields, yielding gamma pass rates of 95.4% and 93.1% from two planar gamma analyses. Proton irradiations were performed for measurements of proton dose and LET. Initial irradiations performed through the side of a dosimeter led to OCT artifacts. Gamma pass rates of 85.7% and 89.9% were observed in two planar gamma analyses. In irradiations performed through the base of a dosimeter, gel response was found to increase with height in the dosimeter, even in areas of constant dose. After a correction was applied, gamma pass rates of 94.6% and 99.3% were observed in two planar gamma analyses. Absolute dose measurements were substantially higher (33%-100%) than the delivered doses for proton irradiations. Issues encountered while calibrating the LET-Meter gel restricted analysis of the LET measurement data to the SOBP of a proton beam. LET-Meter overresponse was found to increase linearly with track-average LET across the LET range that could be investigated (1.5 keV/micron – 3.5 keV/micron).

Application of the general linear model to assess the effect of missing data on bone marrow mononuclear cell therapy with infusion timing and follow-up MRI

With most clinical trials, missing data presents a statistical problem in evaluating a treatment's efficacy. There are many methods commonly used to assess missing data; however, these methods leave room for bias to enter the study. This thesis was a secondary analysis on data taken from TIME, a phase 2 randomized clinical trial conducted to evaluate the safety and effect of the administration timing of bone marrow mononuclear cells (BMMNC) for subjects with acute myocardial infarction (AMI).^ We evaluated the effect of missing data by comparing the variance inflation factor (VIF) of the effect of therapy between all subjects and only subjects with complete data. Through the general linear model, an unbiased solution was made for the VIF of the treatment's efficacy using the weighted least squares method to incorporate missing data. Two groups were identified from the TIME data: 1) all subjects and 2) subjects with complete data (baseline and follow-up measurements). After the general solution was found for the VIF, it was migrated Excel 2010 to evaluate data from TIME. The resulting numerical value from the two groups was compared to assess the effect of missing data.^ The VIF values from the TIME study were considerably less in the group with missing data. By design, we varied the correlation factor in order to evaluate the VIFs of both groups. As the correlation factor increased, the VIF values increased at a faster rate in the group with only complete data. Furthermore, while varying the correlation factor, the number of subjects with missing data was also varied to see how missing data affects the VIF. When subjects with only baseline data was increased, we saw a significant rate increase in VIF values in the group with only complete data while the group with missing data saw a steady and consistent increase in the VIF. The same was seen when we varied the group with follow-up only data. This essentially showed that the VIFs steadily increased when missing data is not ignored. When missing data is ignored as with our comparison group, the VIF values sharply increase as correlation increases.^