DEVELOPMENT OF A BEAM-SPECIFIC PLANNING TARGET VOLUME AND A ROBUST PLAN ANALYSIS TOOL FOR PROTON THERAPY

Proton therapy is growing increasingly popular due to its superior dose characteristics compared to conventional photon therapy. Protons travel a finite range in the patient body and stop, thereby delivering no dose beyond their range. However, because the range of a proton beam is heavily dependent on the tissue density along its beam path, uncertainties in patient setup position and inherent range calculation can degrade thedose distribution significantly. Despite these challenges that are unique to proton therapy, current management of the uncertainties during treatment planning of proton therapy has been similar to that of conventional photon therapy. The goal of this dissertation research was to develop a treatment planning method and a planevaluation method that address proton-specific issues regarding setup and range uncertainties. Treatment plan designing method adapted to proton therapy: Currently, for proton therapy using a scanning beam delivery system, setup uncertainties are largely accounted for by geometrically expanding a clinical target volume (CTV) to a planning target volume (PTV). However, a PTV alone cannot adequately account for range uncertainties coupled to misaligned patient anatomy in the beam path since it does not account for the change in tissue density. In order to remedy this problem, we proposed a beam-specific PTV (bsPTV) that accounts for the change in tissue density along the beam path due to the uncertainties. Our proposed method was successfully implemented, and its superiority over the conventional PTV was shown through a controlled experiment.. Furthermore, we have shown that the bsPTV concept can be incorporated into beam angle optimization for better target coverage and normal tissue sparing for a selected lung cancer patient. Treatment plan evaluation method adapted to proton therapy: The dose-volume histogram of the clinical target volume (CTV) or any other volumes of interest at the time of planning does not represent the most probable dosimetric outcome of a given plan as it does not include the uncertainties mentioned earlier. Currently, the PTV is used as a surrogate of the CTV’s worst case scenario for target dose estimation. However, because proton dose distributions are subject to change under these uncertainties, the validity of the PTV analysis method is questionable. In order to remedy this problem, we proposed the use of statistical parameters to quantify uncertainties on both the dose-volume histogram and dose distribution directly. The robust plan analysis tool was successfully implemented to compute both the expectation value and its standard deviation of dosimetric parameters of a treatment plan under the uncertainties. For 15 lung cancer patients, the proposed method was used to quantify the dosimetric difference between the nominal situation and its expected value under the uncertainties.

Improving Cervical Cancer Nodal Boost Radiation Therapy by Quantifying Uncertainties and Exploring Advanced Radiation Therapy Modalities

Radiation therapy for patients with intact cervical cancer is frequently delivered using primary external beam radiation therapy (EBRT) followed by two fractions of intracavitary brachytherapy (ICBT). Although the tumor is the primary radiation target, controlling microscopic disease in the lymph nodes is just as critical to patient treatment outcome. In patients where gross lymphadenopathy is discovered, an extra EBRT boost course is delivered between the two ICBT fractions. Since the nodal boost is an addendum to primary EBRT and ICBT, the prescription and delivery must be performed considering previously delivered dose. This project aims to address the major issues of this complex process for the purpose of improving treatment accuracy while increasing dose sparing to the surrounding normal tissues. Because external beam boosts to involved lymph nodes are given prior to the completion of ICBT, assumptions must be made about dose to positive lymph nodes from future implants. The first aim of this project was to quantify differences in nodal dose contribution between independent ICBT fractions. We retrospectively evaluated differences in the ICBT dose contribution to positive pelvic nodes for ten patients who had previously received external beam nodal boost. Our results indicate that the mean dose to the pelvic nodes differed by up to 1.9 Gy between independent ICBT fractions. The second aim is to develop and validate a volumetric method for summing dose of the normal tissues during prescription of nodal boost. The traditional method of dose summation uses the maximum point dose from each modality, which often only represents the worst case scenario. However, the worst case is often an exaggeration when highly conformal therapy methods such as intensity modulated radiation therapy (IMRT) are used. We used deformable image registration algorithms to volumetrically sum dose for the bladder and rectum and created a voxel-by-voxel validation method. The mean error in deformable image registration results of all voxels within the bladder and rectum were 5 and 6 mm, respectively. Finally, the third aim explored the potential use of proton therapy to reduce normal tissue dose. A major physical advantage of protons over photons is that protons stop after delivering dose in the tumor. Although theoretically superior to photons, proton beams are more sensitive to uncertainties caused by interfractional anatomical variations, and must be accounted for during treatment planning to ensure complete target coverage. We have demonstrated a systematic approach to determine population-based anatomical margin requirements for proton therapy. The observed optimal treatment angles for common iliac nodes were 90° (left lateral) and 180° (posterior-anterior [PA]) with additional 0.8 cm and 0.9 cm margins, respectively. For external iliac nodes, lateral and PA beams required additional 0.4 cm and 0.9 cm margins, respectively. Through this project, we have provided radiation oncologists with additional information about potential differences in nodal dose between independent ICBT insertions and volumetric total dose distribution in the bladder and rectum. We have also determined the margins needed for safe delivery of proton therapy when delivering nodal boosts to patients with cervical cancer.

Data quality in trauma transfusion studies and the impact of missing data on predicting massive transfusion

Maximizing data quality may be especially difficult in trauma-related clinical research. Strategies are needed to improve data quality and assess the impact of data quality on clinical predictive models. This study had two objectives. The first was to compare missing data between two multi-center trauma transfusion studies: a retrospective study (RS) using medical chart data with minimal data quality review and the PRospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study with standardized quality assurance. The second objective was to assess the impact of missing data on clinical prediction algorithms by evaluating blood transfusion prediction models using PROMMTT data. RS (2005-06) and PROMMTT (2009-10) investigated trauma patients receiving ≥ 1 unit of red blood cells (RBC) from ten Level I trauma centers. Missing data were compared for 33 variables collected in both studies using mixed effects logistic regression (including random intercepts for study site). Massive transfusion (MT) patients received ≥ 10 RBC units within 24h of admission. Correct classification percentages for three MT prediction models were evaluated using complete case analysis and multiple imputation based on the multivariate normal distribution. A sensitivity analysis for missing data was conducted to estimate the upper and lower bounds of correct classification using assumptions about missing data under best and worst case scenarios. Most variables (17/33=52%) had <1% missing data in RS and PROMMTT. Of the remaining variables, 50% demonstrated less missingness in PROMMTT, 25% had less missingness in RS, and 25% were similar between studies. Missing percentages for MT prediction variables in PROMMTT ranged from 2.2% (heart rate) to 45% (respiratory rate). For variables missing >1%, study site was associated with missingness (all p≤0.021). Survival time predicted missingness for 50% of RS and 60% of PROMMTT variables. MT models complete case proportions ranged from 41% to 88%. Complete case analysis and multiple imputation demonstrated similar correct classification results. Sensitivity analysis upper-lower bound ranges for the three MT models were 59-63%, 36-46%, and 46-58%. Prospective collection of ten-fold more variables with data quality assurance reduced overall missing data. Study site and patient survival were associated with missingness, suggesting that data were not missing completely at random, and complete case analysis may lead to biased results. Evaluating clinical prediction model accuracy may be misleading in the presence of missing data, especially with many predictor variables. The proposed sensitivity analysis estimating correct classification under upper (best case scenario)/lower (worst case scenario) bounds may be more informative than multiple imputation, which provided results similar to complete case analysis.^