Development of automated image analysis tools for verification of radiotherapy field accuracy with an electronic portal imaging device

The successful management of cancer with radiation relies on the accurate deposition of a prescribed dose to a prescribed anatomical volume within the patient. Treatment set-up errors are inevitable because the alignment of field shaping devices with the patient must be repeated daily up to eighty times during the course of a fractionated radiotherapy treatment. With the invention of electronic portal imaging devices (EPIDs), patient's portal images can be visualized daily in real-time after only a small fraction of the radiation dose has been delivered to each treatment field. However, the accuracy of human visual evaluation of low-contrast portal images has been found to be inadequate. The goal of this research is to develop automated image analysis tools to detect both treatment field shape errors and patient anatomy placement errors with an EPID. A moments method has been developed to align treatment field images to compensate for lack of repositioning precision of the image detector. A figure of merit has also been established to verify the shape and rotation of the treatment fields. Following proper alignment of treatment field boundaries, a cross-correlation method has been developed to detect shifts of the patient's anatomy relative to the treatment field boundary. Phantom studies showed that the moments method aligned the radiation fields to within 0.5mm of translation and 0.5$\sp\circ$ of rotation and that the cross-correlation method aligned anatomical structures inside the radiation field to within 1 mm of translation and 1$\sp\circ$ of rotation. A new procedure of generating and using digitally reconstructed radiographs (DRRs) at megavoltage energies as reference images was also investigated. The procedure allowed a direct comparison between a designed treatment portal and the actual patient setup positions detected by an EPID. Phantom studies confirmed the feasibility of the methodology. Both the moments method and the cross-correlation technique were implemented within an experimental radiotherapy picture archival and communication system (RT-PACS) and were used clinically to evaluate the setup variability of two groups of cancer patients treated with and without an alpha-cradle immobilization aid. The tools developed in this project have proven to be very effective and have played an important role in detecting patient alignment errors and field-shape errors in treatment fields formed by a multileaf collimator (MLC). ^

INDUCTION OF DNA STRAND BREAKS AND CROSS-LINKS BY THE NEW ANTICANCER AGENT 3,6-DIAZIRIDINYL-2,5-BIS(CARBOETHOXYAMINO) -1,4-BENZOQUINONE

The DNA breakage effect of the anticancer agent 3,6-diaziridinyl-2,5-bis(carboethoxyamino)-1,4-benzoquinone (AZQ, NSC-182986) on bacteriophage PM2 DNA was investigated using agarose gel electrophoresis. AZQ caused both single-stranded and double-stranded breaks after reduction with NaBH(,4), but it was not active in the native state. At 120 (mu)M, it degraded 50% of the closed circular form I DNA into 40% form II DNA (single-stranded break) and 10% form III DNA (double-stranded break). It produced a dose-response breakage between 1 (mu)M and 320 (mu)M. The DNA breakage exhibited a marked pH dependency. At 320 (mu)M, AZQ degraded 80% and 60% of form I DNA at pH 4 and 10 respectively, but none between pH 6 to 8. The DNA breakage at physiologic pH was greatly enhanced when 10 (mu)M cupric sulfate was included in the incubation mixture. The DNA strand scission was inhibited by catalase, glutathione, KI, histidine, Tiron, and DABCO. These results suggest that the DNA breakage may be caused by active oxygen metabolites including hydroxyl free radical. The bifunctional cross-linking activity of reduced AZQ on isolated calf thymus DNA was investigated by ethidium fluorescence assay. The cross-linking activity exhibited a similar pH dependency; highest in acidic and alkaline pH, inactive under neutral conditions. Using the alkaline elution method, we found that AZQ induced DNA single-stranded breaks in Chinese hamster ovary cells treated with 50 (mu)M of AZQ for 2 hr. The single-stranded break frequencies in rad equivalents were 17 with 50 (mu)M and 140 with 100 (mu)M of AZQ. In comparison, DNA cross-links appeared in cells treated with only 1 to 25 (mu)M of AZQ for 2 hr. The cross-linking frequencies in rad equivalents were 39 and 90 for 1 and 5 (mu)M of AZQ, respectively. Both DNA-DNA and DNa-protein cross-links were induced by AZQ in CHO cells as revealed by the proteinas K digestion assay. DNA cross-links increased within the first 4 hr of incubation in drug-free medium and slightly decreased by 12 hr, and most of the cross-links disappeared after cells were allowed to recovered for 24 hr.^ By electrochemical analysis, we found that AZQ was more readily reduced at acidic pH. However, incubation of AZQ with NaBH(,4) at pH 7.8 or 10, but not at 4, produced superoxide anion. The opening of the aziridinyl rings of AZQ at pH 4 was faster in the presence of NaBH(,4) than in its absence; no ring-opening was detected at pH 7.8 regardless of the inclusion of NaBH(,4). . . . (Author's abstract exceeds stipulated maximum length. Discontinued here with permission of author.) UMI ^

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^

Screening for anemia in Red Cross blood donor clinics

Accurate screening for anemia at Red Cross blood donor clinics is essential to maintain a safe national blood supply. Despite the importance of identifying anemia correctly by measurement of hemoglobin or hematocrit (hemoglobin/hematocrit) there is no consensus regarding the efficacy of the current two stage screening method which uses the Readacrit$\sp{\rm tm}$ microhematocrit in conjunction with copper sulfate.^ A cross-sectional study was implemented in which hemoglobin/hematocrit was measured, with the present method and four new devices, on 504 prospective blood donors at a Canadian Red Cross permanent blood donor clinic in London, Canada. Concurrently gathered, venous and capillary blood samples were tested by each device and compared to Coulter S IV$\sp{\rm tm}$ determined venous standard readings. Instrument hemoglobin/hematocrit means were statistically calibrated to the standard ones in order to appraise systematic deviations from the standard. Classification analysis was employed to assess concordance between each instrument and the standard when classifying prospective donors as anemic or non-anemic. This was done both when each instrument was used alone (single stage) and when copper sulfate was used as a preliminary screen (two stage) and simulated over a range of anemia prevalences. The Hemoximeter$\sp{\rm tm}$ and Compur M1000$\sp{\rm tm}$ devices had the highest correlations of hemoglobin measurements with the standard ones for both capillary (n.s.) and venous blood (p $<$.05). Analysis of variance (anova) also showed them to be the most accurate (p $<$.05), as did both single and two stage classification analysis, therefore, they are both recommended. There was a smaller difference between instruments for two stage than for single stage screening; therefore instrument choice is less crucial for the former. The present method was adequate for two stage screening as tested but simulations showed that it would discriminate poorly in populations with a higher prevalence of anemia. The Stat-crit and Readacrit, which measure hematocrit, became less accurate at crucial low hematocrit levels. In light of this finding and the introduction of new, effective and easy to use hemoglobin measuring instruments, the continued use of hematocrit as a surrogate for hemoglobin, is not recommended. ^

Advanced Protein Modeling Method: Benchmarking and Applications in Computer-Aided Drug Discovery

Development of homology modeling methods will remain an area of active research. These methods aim to develop and model increasingly accurate three-dimensional structures of yet uncrystallized therapeutically relevant proteins e.g. Class A G-Protein Coupled Receptors. Incorporating protein flexibility is one way to achieve this goal. Here, I will discuss the enhancement and validation of the ligand-steered modeling, originally developed by Dr. Claudio Cavasotto, via cross modeling of the newly crystallized GPCR structures. This method uses known ligands and known experimental information to optimize relevant protein binding sites by incorporating protein flexibility. The ligand-steered models were able to model, reasonably reproduce binding sites and the co-crystallized native ligand poses of the β2 adrenergic and Adenosine 2A receptors using a single template structure. They also performed better than the choice of template, and crude models in a small scale high-throughput docking experiments and compound selectivity studies. Next, the application of this method to develop high-quality homology models of Cannabinoid Receptor 2, an emerging non-psychotic pain management target, is discussed. These models were validated by their ability to rationalize structure activity relationship data of two, inverse agonist and agonist, series of compounds. The method was also applied to improve the virtual screening performance of the β2 adrenergic crystal structure by optimizing the binding site using β2 specific compounds. These results show the feasibility of optimizing only the pharmacologically relevant protein binding sites and applicability to structure-based drug design projects.