Browsing by Subject "Radiotherapy"

Radiation therapy (RT) has been a standard-of-care treatment for many localized cancers for decades. Despite being an effective treatment modality for many clinical presentations, the efficacy of RT against cancer can be limited due to local recurrence, metastatic spread, and radiation resistance from tumor hypoxia. These limitations provide opportunity for innovative approaches to enhance the overall efficacy of RT. This thesis explores the potential novel approach to enhancing RT through the paradigm changing approach of adding a phototherapeutic component initiated simultaneously with RT. X-ray Psoralen Activated Cancer Therapy (X-PACT) is one such approach, where diagnostics-energy kilovoltage (kV) x-ray coupled with energy modulators (phosphors) converts kV photon to ultraviolet (UV) light, which in turn activates psoralen. Radiotherapy Enhanced with Cherenkov photo-Activation (RECA) is another approach, where therapeutic megavoltage (MV) x-ray generates UV light via Cherenkov phenomenon. Both approaches could increase local control in RT, increase treatment effectiveness in hypoxic tumors, and amplify anti-cancer systemic response. The overarching hypothesis that drives this dissertation is that X-PACT and RECA can activate psoralen to enhance cytotoxicity in-vitro and tumor growth control in-vivo compared to RT alone. In line with this hypothesis, this work explores the feasibility of both X-PACT and RECA via in-vitro and in-vivo verification as well as optimization of radiation techniques to maximize the therapeutic benefit of the approach.

X-PACT and RECA in-vitro / in-vivo studies indicate radiotherapy enhancement is plausible with psoralens activated by secondary UV light production from radiation, though further investigation is required to establish feasibility of RECA in-vivo. For X-PACT in-vitro, a substantial reduction in cell viability and increase in apoptosis was observed in various murine cancer cells (4T1, KP-B, and CT2A) when treated with a combination of 50µg/mL phosphor, 10µM psoralen (8-MOP), and 1Gy of 80kVp x-ray (viability < 20%), compared to any of these components alone (viability > 70%). This suggests a synergistic interaction between the components congruent with the X-PACT scheme, where x-ray induces phosphor UV emission, which in turn activates psoralen. The X-PACT in-vivo mice study showed improved survival with X-PACT versus saline control with flank 4T1 tumors (30.7 days for X-PACT vs. 21.6 days for saline) for survival criteria of 1000, 1500, and 2000mm3, respectively. For RECA, in-vitro results seem promising, where reductions in viability of 20% and 9.5% were observed for 4T1 and B16 murine cancer cell lines treated with RECA (radiation + trioxsalen, a potent psoralen derivative) versus radiation alone. A substantial increase in MHC I expression was observed for B16 cells treated with RECA versus those treated with radiation alone. A small RECA in-vivo pilot study using 8-MOP was inconclusive. Further in-vivo trials with a greater number mice per arm of are required to establish the RECA feasibility to enhance radiotherapy.

Feasibility of treatment optimization for both X-PACT and RECA were demonstrated with kV and MV beams respectively, by optimization of optical output per radiation dose delivered. It was found that in both X-PACT and RECA scheme, the energy of the photon radiation beam (i.e. tube voltage and LINAC energy settings) affected optical output the most. With kV beams for X-PACT, accurate beam delivery within the target volume to reduce normal tissue damage typically expected of kV beams was demonstrated with a 3D-printing-based preclinical irradiation scheme, which is expected to help X-PACT translation into the clinics. In addition, for X-PACT, novel MV-responding phosphors were characterized under MV radiation beam, suggesting the possibility of MV-radiation-mediated X-PACT. Immediate future studies should investigate the efficacy of the optimized X-PACT and RECA, as well as MV X-PACT in-vitro and in-vivo. Studies beyond these immediate ones should investigate X-PACT and RECA efficacy against hypoxic and metastatic tumor sites, where radiation can traditionally fail.

Purpose: Traditional pre-treatment patient specific QA is known for its high workload for physicist, ineffectiveness at identifying clinically relevant dosimetric uncertainties of treatment plans, and incompatibility with on-line adaptive radiotherapy. Our purpose is to develop a trajectory file based PSQA procedure that allows for a virtual pre-treatment QA that can effectively evaluate the performance and robustness of a treatment plan via a DVH based analysis and can be carried out with online adaptive radiotherapy. For this purpose, we have developed a machine learning model that can predict discrepancy in machine parameters between delivery and treatment plan on a Varian TrueBeam linear accelerator.

Methods: Trajectory log files and DICOM-RT plan files of 30 IMRT plans and 75 VMAT plans from four Varian TrueBeam linear accelerators were collected for analysis. The discrepancy in machine parameters is divided into “conversion error” (from converting DICOM-RT to deliverable machine trajectory) and “delivery error” (difference in machine parameters recorded in trajectory files). Correlation matrices were obtained to determine the linear correlation between actual discrepancy and mechanical parameters, such as MLC velocity, MLC acceleration, control point, dose rate, gravity vector, gantry velocity, and gantry acceleration. Multiple regression algorithms were used to develop machine learning models to predict the total discrepancy in machine parameters and its components based on mechanical parameters. The fully trained models were validated with an independent validation dataset and treatment plans constructed with varying degrees of complexity approaching the limitations of the linear accelerator.

Results: For both IMRT and VMAT, the RMS of conversion error (0.1528 mm) was 4 times greater than the RMS of delivery error (0.0367 mm). A high correlation existed between MLC velocity and both components of discrepancies for IMRT (R2 ∈ [0.61, 0.75]) and VMAT [0.75, 0.85]). Final models trained by data from all linear accelerators can predict MLC delivery errors, conversion errors, and combined errors with a high degree of accuracy and correlation between predicted and actual errors for IMRT (R2 = 0.99, 0.86, 0.98) and VMAT (R2 = 0.84, 0.86, 0.87).

Conclusion: We developed an AI model that can predict total MLC discrepancy on Varian TrueBeam linear accelerator with high accuracy using mechanical parameters from trajectory log files and DICOM-RT plans. The software tool from our previous study has been updated to incorporate the discrepancy in planned position into the predictions of total delivery error. We have released the tool for public uses to enable researchers to simulate a treatment delivery without a physical delivery. The tool also has promise in clinical scenarios by allowing for a virtual pre-treatment QA and can be carried out with online adaptive radiotherapy, thereby increasing the effectiveness of pre-treatment patient specific QA.

With the development of medical linear accelerator technologies, the precision and complexity of external beam radiation therapy have increased tremendously over the years. The goal of radiation therapy has always been to push the limit to irradiate the target volume while preserving normal tissues. To achieve this goal, treatment planning for radiation therapy has become a labor-intensive and time-consuming task, which requires a high level of experience and knowledge from the planner. Therefore, automated treatment planning, or auto-planning, is of particular interest in radiation therapy research. The advantages of auto-planning are reduced planning time and increased plan quality consistency.Since the treatment planning workflow has multiple steps, auto-planning includes the automation of different planning procedures, such as contouring, beam placement, and inverse optimization, which can be achieved in different approaches. The main approaches are knowledge-based planning, automated rule implementation and reasoning, and multicriteria optimization. We can generally consider such novel auto-planning applications as artificial intelligence (AI). This study primarily focuses on treatment plan generation using knowledge-based planning and machine learning techniques. The study includes two main projects: automated beam setting for whole breast radiation therapy (WBRT) and fluence map prediction for intensity modulated radiation therapy (IMRT). In WBRT planning, tangential beams are used to irradiate the entire breast volume and avoid the organs-at-risk (OARs) (i.e., the lungs and the heart) as much as possible. The placement of the beams is vital in determining the planning target volume (PTV) coverage and normal tissue sparing. Furthermore, planners need to take multiple clinical considerations into account, e.g., avoiding the contralateral breast and the heart, and use a variety of techniques to meet the demands. Therefore, we developed an automated beam setting program which takes simple user settings and optimizes target coverage and OAR sparing. The program can be launched from the Eclipse Treatment Planning System (TPS) as a binary plug-in script, which generates a graphical user interface to accept user inputs. Several beam geometries are supported: tangential beams only (supine), tangential plus supraclavicular (SCV) beams (supine), and prone beams. For all geometries, the program calculates the optimal gantry angles, collimator angles, isocenter location, jaw sizes, and MLC shapes. The borders of the SCV beams are also matched to the tangential beams by using couch kicks on the main tangential fields. For the supine geometries, a coefficient was learned from existing clinical plans to balance between the PTV and lung coverages. The program searches from an initial setting based on breast wires and finds the optimal setting. For the prone geometry, the planner can set a margin to customize the coverage near the PTV-lung interface. The program has been implemented together with a WBRT fluence prediction program, which creates electronic compensation (ECOMP) plans from the given beam settings. This automated workflow can significantly reduce the workload of the forward planned ECOMP plans. The results showed that the AI plans achieved similar or better plan quality compared to the manual plans. In IMRT planning, inverse optimization is the standard practice to create treatment plans. Dose-volume histogram (DVH) constraints and priorities are set by the planner to start the optimization and often continuously tuned throughout the planning process until the optimal dose distribution is achieved. The actual parameters to be optimized are fluence map intensities of the IMRT beams. Numerous efforts have been devoted in KBP to predict either the DVH or the dose of the optimal plan. The rationale is that, given the patient anatomy and the physician’s prescription, the DVH or dose in the final plan can be predicted based on similar previous plans. The predicted DVH or dose can then be used as a reference to either evaluate the plan quality or generate new plans by converting them into inverse optimization objectives, which is a process also known as dose mimicking. However, most dose mimicking techniques are still in the development stage and not yet commercially available. We explored the feasibility to directly predict optimal fluence maps and generate IMRT plans without inverse optimization. In order to achieve fluence map prediction, we first investigated the correlation between patient anatomy and fluence maps. A database of patient anatomy and fluence maps was built with pancreas SBRT cases. Treatment planning was done on 2D axial slices with in-house dose calculation and fluence optimization algorithms. For a new slice, an atlas matching method was developed to search for the most anatomically similar slice in the database and initialize the optimization with the existing fluence. The atlas-guided fluence optimization reduced the optimization cost and offered a small dosimetric improvement compared to uniform initialization. With more training data, deep learning methods were experimented to predict fluence maps from patient anatomy. A deep learning framework consisting of two convolutional neural networks (CNN) was developed. As each plan has several beams, all beam doses must add up to the optimal plan’s total dose, while each beam dose is deposited only by said beam’s fluence map. Therefore, the BD-CNN predicts the individual beam doses (BD) for an IMRT plan, which tries to minimize the prediction error for both the beam doses and the total dose. Once the beam doses are available, each fluence map (FM) is generated separately by the FM-CNN. As the fluence maps exist in the beam’s eye view (BEV), a projection of the 3D beam dose onto the 2D BEV is necessary. The resulting dose map is used as the input to the FM-CNN, which predicts the fluence map as the output. The predicted fluence maps are imported into the TPS for leaf sequencing and dose calculation, generating a deliverable plan. These projects are retrospective studies using anonymized patient data for training and testing. The development of the deep learning framework was split into several stages: the initial test of the feasibility was conducted for pancreas stereotactic body radiation therapy (SBRT) with a single PTV, unified dose constraints, and a fixed 9-beam geometry; the networks were then modified to allow variable dose inputs and multiple PTVs for pancreas SBRT with simultaneous integrated boost (SIB); a transfer learning technique was applied to the training of the framework for adrenal SBRT plans with different beam settings and dose constraints, using the pancreas model as the base model. The framework has evolved to be more robust and support different sites and planning styles over time. The AI plans with predicted fluence maps achieved similar plan quality as manual plans for most cases. For some cases with particularly challenging patient anatomies, the AI plans can struggle to reach the high standard of the expert plans. Fluence map prediction is a viable way to directly generate IMRT plans without inverse optimization. This application may be especially useful for adaptive treatment planning.

Intensity-modulated radiotherapy (IMRT) remains the standard of care for external beam radiation therapy for head and neck cancers. Planning for IMRT requires a trial-and-error approach that is completely dependent on planner expertise and time available for multiple iterations of manual optimization adjustments. Knowledge-based radiation therapy planning utilizes a database of previously planned Duke University Medical Center patient plans to create clinically comparable treatment plans by comparing the geometrical two-dimensional projections of the planning target volume (PTV) and organs at risk (OAR). These 2D beam's eye view (BEV) images are first aligned with squared error registration, then the similarity is computed using the mutual information (MI) metric. After the closest match is found, computed constraints and deformed fluence maps are entered into Eclipse treatment planning system to generate the new knowledge-based treatment plan. For this study, 20 randomly selected cases were matched against a database of 103 head and neck cancer cases. The resulting new plans were compared to their clinically planned counterparts. For these 20 cases, 13 proved to be dosimetrically comparable by evaluation of the PTV dose-volume histogram. In 92% of cases planned, at least half of the OARs were also deemed comparable or better than the original plan. These cases were planned in less than 25 minutes with no manual constraint objective adjustments, as opposed to many hours needed in clinical planning.

Myxoid liposarcoma (MLPS) is a malignant soft tissue sarcoma characterized by a pathognomonic t(12;16)(q13;p11) translocation that produces a fusion oncoprotein, FUS-CHOP. This cancer is remarkably sensitive to radiotherapy and exhibits a unique pattern of extrapulmonary metastasis. However, the mechanism for its radiosensitivity is unknown. In order to further understand the biological mechanisms underlying MLPS response to radiotherapy we studied the fusion oncoprotein FUS-CHOP and linked its role in sarcomagenesis to the radiosensitivity phenotype.

Here we investigate a molecular mechanism of radiosensitization that couples ionizing radiation to inhibition of translocation-driven sarcomagenesis in myxoid liposarcoma. We performed co-immunoprecipitation (co-IP) to identify proteins interacting with FUS-CHOP. Incucyte assays measured cell proliferation after knockdown of interacting proteins fusion-negative and fusion-positive primary murine sarcoma cell lines from a novel FUS-CHOP genetically engineered mouse model (GEMM). ChIP-seq/CUT&RUN mapped genome-wide binding sites of FUS-CHOP and identified DNA-binding motifs for the fusion oncoprotein. Co-IP of irradiated human MLPS cell lines were performed to evaluate post-translational modification of FUS-CHOP after irradiation, and to investigate regulation of protein-protein interactions by these modifications.

We detected functionally important interactions between FUS-CHOP and multiple chromatin remodeling complexes via co-IP including a new interaction with SNF2H, the ATPase subunit of the imitation switch (ISWI) complex. Using knockdown systems, we demonstrated that these interacting chromatin remodelers are functionally important for proliferation specifically in FUS-CHOP-driven, but not Kras-driven murine sarcoma cells. ChIP-seq and CUT&RUN profiling of human MLPS cell lines identified DNA-binding motifs and genomic loci targeted by FUS-CHOP, which co-localized with SNF2H and H3K27ac marks of active chromatin. We further hypothesized that post-translational modification of the FUS-CHOP PrLD may regulate the protein-protein interactions between FUS-CHOP and chromatin remodelers. Using irradiated human MLPS cell lines, we show that FUS-CHOP is a target of phosphorylation by the DNA damage response kinases DNA-PK and ATM after irradiation. Finally, we show that phosphorylation of the PrLD of FUS-CHOP diminishes protein-protein interactions with chromatin remodeling complexes and the ability for FUS-CHOP to transform NIH-3T3 cells.

We also report the generation and characterization of a spatially and temporally restricted mouse model of sarcoma driven by FUS-CHOP. Using different Cre-drivers in the adipocyte lineage, we initiated in vivo tumorigenesis by expressing FUS-CHOP in Prrx1+ mesenchymal progenitor cells. In contrast, expression of FUS-CHOP in more differentiated cells does not form tumors in vivo, and early expression of the oncoprotein during embryogenesis is lethal. We also employ in vivo electroporation and CRISPR technology to rapidly generate spatially and temporally restricted mouse models and cell lines of high grade FUS-CHOP-driven sarcomas for preclinical studies.

Purpose: 4D cone-beam CT (CBCT) provides 4D localization and monitoring of moving targets for inter/intra-fraction target verification in lung radiotherapy. CBCT reconstruction with Feldkamp-Davis-Kress (FDK) algorithm requires retrospectively sorted full-angle (360° for half fan scan/ 180° plus n angle for full fan scan) cone-beam projections, leading to long acquisition time, high imaging dose and limited mechanical clearance. A prior knowledge-based 4D-CBCT estimation technique was developed to do fast and low-dose target verification by estimating 4D-CBCT images using limited-angle on-board kV or MV projections and information from planning CT images. The purposes of this thesis are to (1) Optimize the image acquisition parameters of this technique in reconstructing 4D-CBCT images using limited-angle kV projections; and (2) Evaluate the clinical efficacy of this technique through patient studies.

Methods: A digital anthropomorphic phantom (XCAT) and real patient 4D-CT images were used to optimize and evaluate the prior knowledge-based 4D-CBCT estimation technique. To optimize the imaging acquisition schemes, phantom studies were conducted to simulate eight different treatment scenarios. The image acquisition schemes were optimized by minimizing the scanning angle/time required for accurate image estimation. With the minimum scanning angle/time determined, the effect of scanning direction and imaging frame rate on estimation accuracy was also tested. To clinically evaluate this technique through patient studies, we employed patient data with multiple 4D-CT scans. For each patient, one 4D-CT scan was considered as planning CT images and another as on-board ground truth 4D images. Digital reconstructed radiographs (DRRs) were generated from the second 4D-CT scan to simulate on-board 4D-CBCT projections in a limited angle. Each phase of the 4D-CBCT was generated by deforming the prior CT volume based on Deformation Field Maps solved by motion modeling and free-form deformation in the data fidelity constraint. Patients with tumors at different locations were selected for evaluation. The estimated images (EIs) were quantitatively evaluated against ground truth images by calculating the Dice Coefficient and Center-of-Mass-Shift (COMS) of the tumor volume. The minimal total scan angle/time was also determined for all patients.

Results: The phantom studies showed accurate 4D-CBCT estimation requires 200 projections acquired in over 97.8 degree within a total scanning time of 20 seconds (with gantry rotation speed of 6°/s, respiratory period of 4s and frame rate of 10 frame/s). We found the technique was robust against different scanning directions and imaging frame rate was positively related to estimation accuracy with the same angle coverage. The scanning angle and time of the technique could be further reduced by increasing projection number without changing the projection angle coverage. Results of patient studies showed that the technique was able to accurately estimate patient 4D-CBCT using as fewer as 320 projections for 10 phases acquired in 32 seconds over scan angle of 169.8° (with gantry rotation speed of 6°/s and frame rate of 10 frame/s for breathing period of xxx). The estimation efficiency was affected by target location and contrast between target and background.

Conclusion: This technique estimates patient on-board 4D-CBCT with higher efficiency, reduced imaging dose and more mechanical clearance compared to conventional reconstruction techniques. Clinical implementation of this technique can provide an efficient tool for fast low dose inter- and intra-fractional 4D-localization to minimize the treatment errors in lung radiotherapy, which paves the way for further margin reduction and dose escalation.

Abstract

The goal of modern radiotherapy is to precisely deliver a prescribed radiation dose to delineated target volumes that contain a significant amount of tumor cells while sparing the surrounding healthy tissues/organs. Precise delineation of treatment and avoidance volumes is the key for the precision radiation therapy. In recent years, considerable clinical and research efforts have been devoted to integrate MRI into radiotherapy workflow motivated by the superior soft tissue contrast and functional imaging possibility. Dynamic contrast-enhanced MRI (DCE-MRI) is a noninvasive technique that measures properties of tissue microvasculature. Its sensitivity to radiation-induced vascular pharmacokinetic (PK) changes has been preliminary demonstrated. In spite of its great potential, two major challenges have limited DCE-MRI’s clinical application in radiotherapy assessment: the technical limitations of accurate DCE-MRI imaging implementation and the need of novel DCE-MRI data analysis methods for richer functional heterogeneity information.

This study aims at improving current DCE-MRI techniques and developing new DCE-MRI analysis methods for particular radiotherapy assessment. Thus, the study is naturally divided into two parts. The first part focuses on DCE-MRI temporal resolution as one of the key DCE-MRI technical factors, and some improvements regarding DCE-MRI temporal resolution are proposed; the second part explores the potential value of image heterogeneity analysis and multiple PK model combination for therapeutic response assessment, and several novel DCE-MRI data analysis methods are developed.

I. Improvement of DCE-MRI temporal resolution. First, the feasibility of improving DCE-MRI temporal resolution via image undersampling was studied. Specifically, a novel MR image iterative reconstruction algorithm was studied for DCE-MRI reconstruction. This algorithm was built on the recently developed compress sensing (CS) theory. By utilizing a limited k-space acquisition with shorter imaging time, images can be reconstructed in an iterative fashion under the regularization of a newly proposed total generalized variation (TGV) penalty term. In the retrospective study of brain radiosurgery patient DCE-MRI scans under IRB-approval, the clinically obtained image data was selected as reference data, and the simulated accelerated k-space acquisition was generated via undersampling the reference image full k-space with designed sampling grids. Two undersampling strategies were proposed: 1) a radial multi-ray grid with a special angular distribution was adopted to sample each slice of the full k-space; 2) a Cartesian random sampling grid series with spatiotemporal constraints from adjacent frames was adopted to sample the dynamic k-space series at a slice location. Two sets of PK parameters’ maps were generated from the undersampled data and from the fully-sampled data, respectively. Multiple quantitative measurements and statistical studies were performed to evaluate the accuracy of PK maps generated from the undersampled data in reference to the PK maps generated from the fully-sampled data. Results showed that at a simulated acceleration factor of four, PK maps could be faithfully calculated from the DCE images that were reconstructed using undersampled data, and no statistically significant differences were found between the regional PK mean values from undersampled and fully-sampled data sets. DCE-MRI acceleration using the investigated image reconstruction method has been suggested as feasible and promising.

Second, for high temporal resolution DCE-MRI, a new PK model fitting method was developed to solve PK parameters for better calculation accuracy and efficiency. This method is based on a derivative-based deformation of the commonly used Tofts PK model, which is presented as an integrative expression. This method also includes an advanced Kolmogorov-Zurbenko (KZ) filter to remove the potential noise effect in data and solve the PK parameter as a linear problem in matrix format. In the computer simulation study, PK parameters representing typical intracranial values were selected as references to simulated DCE-MRI data for different temporal resolution and different data noise level. Results showed that at both high temporal resolutions (<1s) and clinically feasible temporal resolution (~5s), this new method was able to calculate PK parameters more accurate than the current calculation methods at clinically relevant noise levels; at high temporal resolutions, the calculation efficiency of this new method was superior to current methods in an order of 102. In a retrospective of clinical brain DCE-MRI scans, the PK maps derived from the proposed method were comparable with the results from current methods. Based on these results, it can be concluded that this new method can be used for accurate and efficient PK model fitting for high temporal resolution DCE-MRI.

II. Development of DCE-MRI analysis methods for therapeutic response assessment. This part aims at methodology developments in two approaches. The first one is to develop model-free analysis method for DCE-MRI functional heterogeneity evaluation. This approach is inspired by the rationale that radiotherapy-induced functional change could be heterogeneous across the treatment area. The first effort was spent on a translational investigation of classic fractal dimension theory for DCE-MRI therapeutic response assessment. In a small-animal anti-angiogenesis drug therapy experiment, the randomly assigned treatment/control groups received multiple fraction treatments with one pre-treatment and multiple post-treatment high spatiotemporal DCE-MRI scans. In the post-treatment scan two weeks after the start, the investigated Rényi dimensions of the classic PK rate constant map demonstrated significant differences between the treatment and the control groups; when Rényi dimensions were adopted for treatment/control group classification, the achieved accuracy was higher than the accuracy from using conventional PK parameter statistics. Following this pilot work, two novel texture analysis methods were proposed. First, a new technique called Gray Level Local Power Matrix (GLLPM) was developed. It intends to solve the lack of temporal information and poor calculation efficiency of the commonly used Gray Level Co-Occurrence Matrix (GLCOM) techniques. In the same small animal experiment, the dynamic curves of Haralick texture features derived from the GLLPM had an overall better performance than the corresponding curves derived from current GLCOM techniques in treatment/control separation and classification. The second developed method is dynamic Fractal Signature Dissimilarity (FSD) analysis. Inspired by the classic fractal dimension theory, this method measures the dynamics of tumor heterogeneity during the contrast agent uptake in a quantitative fashion on DCE images. In the small animal experiment mentioned before, the selected parameters from dynamic FSD analysis showed significant differences between treatment/control groups as early as after 1 treatment fraction; in contrast, metrics from conventional PK analysis showed significant differences only after 3 treatment fractions. When using dynamic FSD parameters, the treatment/control group classification after 1st treatment fraction was improved than using conventional PK statistics. These results suggest the promising application of this novel method for capturing early therapeutic response.

The second approach of developing novel DCE-MRI methods is to combine PK information from multiple PK models. Currently, the classic Tofts model or its alternative version has been widely adopted for DCE-MRI analysis as a gold-standard approach for therapeutic response assessment. Previously, a shutter-speed (SS) model was proposed to incorporate transcytolemmal water exchange effect into contrast agent concentration quantification. In spite of richer biological assumption, its application in therapeutic response assessment is limited. It might be intriguing to combine the information from the SS model and from the classic Tofts model to explore potential new biological information for treatment assessment. The feasibility of this idea was investigated in the same small animal experiment. The SS model was compared against the Tofts model for therapeutic response assessment using PK parameter regional mean value comparison. Based on the modeled transcytolemmal water exchange rate, a biological subvolume was proposed and was automatically identified using histogram analysis. Within the biological subvolume, the PK rate constant derived from the SS model were proved to be superior to the one from Tofts model in treatment/control separation and classification. Furthermore, novel biomarkers were designed to integrate PK rate constants from these two models. When being evaluated in the biological subvolume, this biomarker was able to reflect significant treatment/control difference in both post-treatment evaluation. These results confirm the potential value of SS model as well as its combination with Tofts model for therapeutic response assessment.

In summary, this study addressed two problems of DCE-MRI application in radiotherapy assessment. In the first part, a method of accelerating DCE-MRI acquisition for better temporal resolution was investigated, and a novel PK model fitting algorithm was proposed for high temporal resolution DCE-MRI. In the second part, two model-free texture analysis methods and a multiple-model analysis method were developed for DCE-MRI therapeutic response assessment. The presented works could benefit the future DCE-MRI routine clinical application in radiotherapy assessment.

Radiation therapy is a frequently used treatment method for malignant tumors despite the heterogeneous response in tumors with a hypoxic microenvironment. Specific features in this microenvironment like poorly formed and inefficient vasculature contribute to chronic and cycling hypoxia. Notably, hypoxic tumor cells are three times more radioresistant than normoxic cells, which make hypoxia a key contributor to poor treatment outcome. There have been many previous attempts to re-oxygenate tumors either through increasing the supply or decreasing the demand of oxygen. However, no study has yet been performed to investigate the combined effect of increasing the oxygen supply and decrease the oxygen demand in vivo.

There are two main purposes of this study, which are tested in two individual rounds: 1) assessing the combined effect of oxygen micro-bubbles and papaverine in alleviating tumor hypoxia in murine sarcoma model and 2) assessing the combined effect of oxygen micro bubble and papaverine as radiosensitizers. Using nu-nu mice with subcutaneous sarcoma tumors, the change resulting from oxygen micro-bubbles and/or papaverine was evaluated by changes in hemoglobin saturation from baseline and control groups. By further monitoring tumor growth and percent hemoglobin saturation after administration of papaverine and oxygen micro-bubbles followed by a single fraction of 15 Gy of radiation, we also tested the effects of the combination of papaverine and oxygen micro-bubbles in tumor control and oxygenation.

The result of the non-irradiated study showed no significant improvement of the combination of oxygen micro-bubbles and papaverine group in the percent hemoglobin saturation level compared with other groups. Notably, percent hemoglobin saturation changes are rather heterogenous within each group. However, this unexpected result may be due to certain practical and theoretic limitations. The follow-up immunohistochemistry study may provide more information of the overall oxygenation of the tumors. For the irradiated study, the percent hemoglobin saturation measurement of the oxygen micro-bubbles and papaverine group is the only one showed improved level the day after the treatment compared with the day before treatment. The combination of oxygen micro-bubbles and papaverine did not show increased tumor control after radiotherapy compared with other groups. However, it should be noted that there are some practical and theoretical limitations in the study that may have contributed in this which is discussed in detail in the discussion chapter. Further studies might be needed to investigate the reasons for this unexpected result.

Surface guided radiation therapy (SGRT) is an emerging technology that uses non-ionizing methods for patient positioning and motion tracking during radiotherapy delivery. However, the use of radiotherapy boluses, which are tissue-equivalent materials placed on the skin to increase surface dose, has been shown to interfere with SGRT systems due to reflections from the bolus surface. This thesis presents the development and validation of an opaque and non-glossy radiotherapy bolus called the "Surface Guidance Optimized" (SGO), which is a variation of the previously developed transparent Clearsight bolus.The Surface Guidance Optimized bolus was rendered opaque by adding 0.6% titanium dioxide and given a matte finish using matte release paper. Spectroscopy measurements confirmed optimal opaqueness, while gloss meter readings verified a non-glossy surface. The bolus density was quantified to be 0.853 g/cm3 using water displacement and CT methods. Dosimetric characterization through direct surface dose measurements and Monte Carlo simulations demonstrated the SGO bolus mimics the dose deposition of water-equivalent materials when accounting for density differences. Compatibility testing with the AlignRT SGRT system showed the bolus allowed accurate surface reconstruction and submillimeter tracking (within 0.4 mm) under different lighting conditions. Overall, the SGO bolus mitigates issues of transparency and glossiness that interfered with SGRT systems, while maintaining desirable dosimetric properties for clinical use as a radiotherapy bolus compatible with modern surface guided techniques.

Purpose: Radiochromic film remains a useful and versatile clinical dosimetry tool. While simple to use, current film options are single use, with no forms of reusable film available commercially. Here we introduce a novel 2D radiochromic sheet, derived from Presage material, which optically clears after irradiation and can be reused. We evaluate the sheets for potential as an economic alternative to radiochromic film and also as a radiochromic bolus with capability for dose measurement.

Methods: A novel derivative of reusable Presage® was manufactured into thin sheets of 5mm thickness. The sheets contained 2% cumin-leucomalachitegreen-diethylamine (LMG-DEA) and plasticizer (up to 25% by weight). A series of radiation experiments were performed to characterize the radiation response of the sheets irradiated with megavoltage radiation from a Varian medical accelerator over time and in different settings. The local change in optical-density (OD), before and after radiation, was obtained by scanning the sheets with a flat-bed film scanner and extracting the red channel of the RGB image. Repeat sheet scanning enabled investigation of the temporal decay of OD. Additional studies investigated dose sensitivity, consistency of response through repeat irradiations, intra and inter-sheet reproducibility, multi-modality response (electrons and photons), and temperature sensitivity (temperature range 22°C to 36°C) of the Presage® sheets. Clinical utility of the sheets was investigated through application to IMRT treatment plans (prostate and a TG119 commissioning plan), and a chest wall electron boost treatment. In the latter test, the sheet performed as a radiochromic bolus.

Results: The radiation induced OD change in the sheets was found to be proportional to dose and to decay to baseline after ~24 hours with a decay constant of 6.0 hours-1 (standard deviation 0.33). After this time the sheet could be reused and had similar sensitivity (within 1% after the first irradiation) for at least 8 irradiations. Importantly, the sheets were not observed to carry any memory of previous irradiations within measurement uncertainty. The consistency of dose response from photons (6MV and 15MV) and electrons (6-20MeV) was found to be within 1%. The dose sensitivity of the sheets was observed to have a temperature dependence of 0.0012 ΔOD/°C. For the IMRT QA verification test, good agreement was observed between the Presage sheet and EBT film (gamma pass rate of 97% at 3% 3mm and 99% at 5% 3mm dose-difference and distance-to-agreement tolerance, with a 10% threshold). For the TG-119 tests the gamma agreement was 93% pass rate at 5% 3mm, 10% threshold, when compared with Eclipse. For the electron cutout treatment, both Presage and EBT agreed well (within 2% RMS difference) but differed from the Eclipse treatment plan (~7% RMS difference) indicating some limitations to the Eclipse modeling in this case.

Conclusion: The reusable Presage sheets show promise as an economic alternative for film applications and as a radiochromic bolus for in-vivo dose measurement. The preliminary work presented in this thesis indicates that these sheets have the capability to improve care in the most well-equipped clinics in the world, as well as provide a fast, inexpensive, and easy to use dosimeter to clinics in low-income countries in desperate need of versatile resources. This work is still a preliminary study of feasibility, where the central current limitations include the narrow nature of application testing and lack of inter-batch comparison. Further work is recommended to establish use in a wide variety of clinical applications, establish a material more closely reflecting flexible bolus, and push the extent of the potential for reusability in the sheets.

Purpose: To validate, assess, and utilize the Bugando Cancer Registry (BCR) of Bugando Medical Centre (BMC), Mwanza, Tanzania for radiotherapy (RT) services planning.

Methods: For BCR validation, we randomly sampled 63 total registry cases. Two reviewers not associated with the BCR manually collected data elements from medical records and compared them with BCR data to measure concordance and completeness.

For BCR assessment, we described the pattern of pediatric and adult malignancies from 2008 to 2016 and classified them by GLOBOCAN standards. Primary tumor sites, means of diagnosis, HIV status, and the number of AIDS-defining malignancies (cervix, non-Hodgkin lymphoma, and Kaposi sarcoma) were reported. The 2012 GLOBOCAN estimates for Tanzania were then scaled to the Lake Zone using 2012 national census data and adjusted for population growth to compare BCR cases in 2016.

Finally, for BCR utilization for RT planning, all notifiable cancer cases diagnosed in 2016 were used as the basis for analysis. RT utilization based on primary site was calculated using the latest version of the evidence-based estimation method from the Collaboration for Cancer Outcomes Research and Evaluation’s (CCORE-EBEST), a RT utilization model based on Australian incidence of notifiable cancers. CCORE-EBEST was modified to account for the high number of cases of Kaposi sarcoma seen at BMC. A time-driven activity-based costing (TD-ADC) model was used to compute the total resources and operational costs for three situations: the capacity to treat 500 patients per year, the required capacity to meet 100% of the clinical need, and the clinical need that could be met with maximal equipment and staff capacity (four teletherapy units, one orthovoltage unit, one brachytherapy afterloader, two simulators).

Results:

BCR validation: All 63 reviewed registry records had complete cancer site and morphology information included in the registry. For the majority (n=41), the basis of diagnosis was pathology. Of sampled registry cases, primary tumor site and morphology were 74% and 70% concordant, respectively. Of the fifteen parameters entered into the BRC, twelve (80%) were 100% complete and the overall completeness was 97%. The median concordance rate was 79% (interquartile range 72%-86%).

BCR assessment: A total of 2,772 cases were reported. 2,286 cases were adult (82.5%) and 486 cases were pediatric (17.5%). 2,522 patients (91%) lived in one of the six Lake Zone regions. Number of cancer cases reported in the registry by year increased from 19 in 2008 to 1,272 in 2016. Means of diagnosis were histologic or cytologic confirmation for 1,923 cases (85%), clinical investigations (e.g., radiologic studies) for 528 cases (19%), clinical diagnosis for 298 cases (11%), serologic tumor markers for 13 cases (0.5%), unknown for 7 (0.2%) cases, and death certificate for 3 (0.1%) cases. The most common adult cancers were cervix (n=520, 22.7%), breast (n=288, 12.6%), prostate (n=195, 8.5%), others (n=198, 8.7%), and Kaposi sarcoma (n=184, 8.0%). The most common pediatric cancers were non-Burkitt non-Hodgkin lymphoma (n=84, 17.3%), Burkitt lymphoma (n=80, 16.5%), Wilms tumor (n=71, 14.6%), soft tissue sarcoma (n=56, 11.5%), and leukemia (n=50, 10.3%). The number of unique BCR cases logged in 2016 was 12.2% of the expected number based on GLOBOCAN estimates (1,116 v. 9,165, p<0.001).

RT utilization: A total of 1,088 unique cancer cases were analyzed. Casemix was significantly different between BMC and the modified CCORE-EBEST model, with significantly higher cases of cervix (29.5% v. 1.0%, p<0.001) at BMC and fewer cases of breast cancer (9% v. 12.2%, p<0.001) and prostate cancer (10.1% v. 18.4%, p<0.001). The proportion of new cancer cases requiring RT at BMC was 56.4%, significantly higher than the original CCORE-EBEST expected rate of 48.4% (p<0.001). In 2016, had RT been available, 614 patients at BMC would have received RT for a total of 11,837 fractions. To treat 500 patients per year, BMC has sufficient teletherapy capacity with a single Co-60 teletherapy unit operating 12 hours per day, but a brachytherapy afterloader and C-arm are required. To scale up capacity to meet current clinical need in the least expensive fashion, an afterloader and C-arm are needed, as are 4 radiation oncologists, 3 medical physicists, 5 radiation therapists, and increasing the Co-60 workday from 8 to 14 hours. If BMC installed teletherapy equipment in every bunker, 1,900 patients could be treated a year. This would require 11 ROs, 6 MPs, 16 RTTs, 3 dosimetrists, 2 nurses, 0.3 information technologists, 0.5 mechanical engineers, and 1.4 electrical engineers.

Conclusions: Data quality metrics of the BCR indicate it is of sufficient quality for local planning. This first comprehensive report of the BCR shows a cancer diagnosis pattern typical of many hospital registries in low-income countries. For RT services planning. application of the CCORE-EBEST and TD-ADC models to a hospital-based cancer registry is feasible with minor modifications. The high RT utilization rate at BMC supports the clinical value of RT; however, current capacity at BMC is not sufficient to meet the clinical need. Scaling up RT services requires a combination of equipment and staffing to maximize efficiency and clinical outcomes.