Reduction of setup uncertainties and tumor motion in the radiation therapy of lung tumors

Because the goal of radiation therapy is to deliver a lethal dose to the tumor, accurate information on the location of the tumor needs to be known. Margins are placed around the tumor to account for variations in the daily position of the tumor. If tumor motion and patient setup uncertainties can be reduced, margins that account for such uncertainties in tumor location in can be reduced allowing dose escalation, which in turn could potentially improve survival rates. ^ In the first part of this study, we monitor the location of fiducials implanted in the periphery of lung tumors to determine the extent of non-gated and gated fiducial motion, and to quantify patient setup uncertainties. In the second part we determine where the tumor is when different methods of image-guided patient setup and respiratory gating are employed. In the final part we develop, validate, and implement a technique in which patient setup uncertainties are reduced by aligning patients based upon fiducial locations in projection images. ^ Results from the first part indicate that respiratory gating reduces fiducial motion relative to motion during normal respiration and setup uncertainties when the patients were aligned each day using externally placed skin marks are large. The results from the second part indicate that current margins that account for setup uncertainty and tumor motion result in less than 2% of the tumor outside of the planning target volume (PTV) when the patient is aligned using skin marks. In addition, we found that if respiratory gating is going to be used, it is most effective if used in conjunction with image-guided patient setup. From the third part, we successfully developed, validated, and implemented on a patient a technique for aligning a moving target prior to treatment to reduce the uncertainties in tumor location. ^ In conclusion, setup uncertainties and tumor motion are a significant problem when treating tumors located within the thoracic region. Image-guided patient setup in conjunction with treatment delivery using respiratory gating reduces these uncertainties in tumor locations. In doing so, margins around the tumor used to generate the PTV can be reduced, which may allow for dose escalation to the tumor. ^

Slope estimation in a parametric measurement error model: A simulation study

In regression analysis, covariate measurement error occurs in many applications. The error-prone covariates are often referred to as latent variables. In this proposed study, we extended the study of Chan et al. (2008) on recovering latent slope in a simple regression model to that in a multiple regression model. We presented an approach that applied the Monte Carlo method in the Bayesian framework to the parametric regression model with the measurement error in an explanatory variable. The proposed estimator applied the conditional expectation of latent slope given the observed outcome and surrogate variables in the multiple regression models. A simulation study was presented showing that the method produces estimator that is efficient in the multiple regression model, especially when the measurement error variance of surrogate variable is large.^

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.

Analyzing left-truncated right-censored data with uncertain onset time with parametric models

Prevalent sampling is an efficient and focused approach to the study of the natural history of disease. Right-censored time-to-event data observed from prospective prevalent cohort studies are often subject to left-truncated sampling. Left-truncated samples are not randomly selected from the population of interest and have a selection bias. Extensive studies have focused on estimating the unbiased distribution given left-truncated samples. However, in many applications, the exact date of disease onset was not observed. For example, in an HIV infection study, the exact HIV infection time is not observable. However, it is known that the HIV infection date occurred between two observable dates. Meeting these challenges motivated our study. We propose parametric models to estimate the unbiased distribution of left-truncated, right-censored time-to-event data with uncertain onset times. We first consider data from a length-biased sampling, a specific case in left-truncated samplings. Then we extend the proposed method to general left-truncated sampling. With a parametric model, we construct the full likelihood, given a biased sample with unobservable onset of disease. The parameters are estimated through the maximization of the constructed likelihood by adjusting the selection bias and unobservable exact onset. Simulations are conducted to evaluate the finite sample performance of the proposed methods. We apply the proposed method to an HIV infection study, estimating the unbiased survival function and covariance coefficients. ^