Browsing by Subject "Radiation oncology"

Quality assurance (QA) is a critical component of radiation oncology medical physics for both effective treatment and patient safety, particularly as innovations in technology allow movement toward advanced treatment techniques that require increasingly higher accuracy in delivery. Comprehensive 3D dosimetry with PRESAGE® 3D dosimeters read out via optical CT has the potential to detect errors that would be missed by current systems of measurement, and thereby improve the rigor of current QA techniques through providing high-resolution, full 3D verification for a wide range of clinical applications. The broad objective of this dissertation research is to advance and strengthen the standards of QA for radiation therapy, both by driving the development and optimization of PRESAGE® 3D dosimeters for specific clinical and research applications and by applying the technique of high resolution 3D dosimetry toward addressing clinical needs in the current practice of radiation therapy. The specific applications that this dissertation focuses on address several topical concerns: (1) increasing the quality, consistency, and rigor of radiation therapy delivery through comprehensive 3D verification in remote credentialing evaluations, (2) investigating a reusable 3D dosimeter that could potentially facilitate wider implementation of 3D dosimetry through improving cost-effectiveness, and (3) validating deformable image registration (DIR) algorithms prior to clinical implementation in dose deformation and accumulation calculations.

3D Remote Dosimetry: The feasibility of remote high-resolution 3D dosimetry with the PRESAGE®/Optical-CT system was investigated using two nominally identical optical-CT scanners for 3D dosimetry were constructed and placed at the base (Duke University) and remote (IROC Houston) institutions. Two formulations of PRESAGE® (SS1, SS2) were investigated with four unirradiated PRESAGE® dosimeters imaged at the base institution, then shipped to the remote institution for planning and irradiation. After each dosimeter was irradiated with the same treatment plan and subsequently read out by optical CT at the remote institution, the dosimeters were shipped back to the base institution for remote dosimetry readout 3 days post-irradiation. Measured on-site and remote relative 3D dose distributions were registered to the Pinnacle dose calculation, which served as the reference distribution for 3D gamma calculations with passing criteria of 5%/2mm, 3%/3mm, and 3%/2mm with a 10% dose threshold. Gamma passing rates, dose profiles, and dose maps were used to assess and compare the performance of both PRESAGE® formulations for remote dosimetry. Both PRESAGE® formulations under study maintained high linearity of dose response (R2>0.996) over 14 days with response slope consistency within 4.9% (SS1) and 6.6% (SS2). Better agreements between the Pinnacle plan and dosimeter readout were observed in PRESAGE® formulation SS2, which had higher passing rates and consistency between immediate and remote results at all metrics. This formulation also demonstrated a relative dose distribution that remained stable over time. These results provide a foundation for future investigations using remote dosimetry to study the accuracy of advanced radiation treatments.

A Reusable 3D Dosimeter: New Presage-RU formulations made using a lower durometer polyurethane matrix (Shore hardness 30-50A) exhibit a response that optically clears following irradiation and opens up the potential for reirradiation and dosimeter reusability. This would have the practical benefit of improving cost-effectiveness and thereby facilitating the wider implementation of comprehensive, high resolution 3D dosimetry. Three formulations (RU-3050-1.7, RU-3050-1.5, and RU-50-1.5) were assessed with multiple irradiations of both small volume samples and larger volume dosimeters, then characterized and evaluated for dose response sensitivity, optical clearing, dose-rate independence, dosimetric accuracy, and the effects of reirradiation on dose measurement. The primary shortcoming of these dosimeters was the discovery of age-dependent gradients in dose response sensitivity, which varied dose response by as much as 30% and prevented accurate measurement. This is unprecedented in the standard formulations and presumably caused by diffusion of a desensitizing agent into the lower durometer polyurethane. The effect of prior irradiation on the dosimeters would also be a concern as it was seen that the relative amount of dose delivered to any given region of the dosimeter will affect subsequent sensitivity in that area, which would in effect create spatially-dependent variable dose sensitivities throughout the dosimeter based on the distributions of prior irradiations. While a successful reusable dosimeter may not have been realized from this work, these studies nonetheless contributed useful information that will affect future development, including in the area of deformable dosimetry, and provide a framework for future reusable dosimeter testing.

Validating Deformable Image Registration Algorithms: Deformable image registration (DIR) algorithms are used for multi-fraction dose accumulation and treatment response assessment for adaptive radiation therapy, but the accuracy of these methods must be investigated prior to clinical implementation. 12 novel deformable PRESAGE® 3D dosimeter formulations were introduced and characterized for potential use in validating DIR algorithms by providing accurate, ground-truth deformed dose measurement for comparison to DIR-predicted deformed dose distributions. Two commercial clinical DIR software algorithms were evaluated for dose deformation accuracy by comparison against a measured deformed dosimeter dose distribution. This measured distribution was obtained by irradiating a dosimeter under lateral compression, then releasing it from compression so that it could return to its original geometry. The dose distribution within the dosimeter deformed along with the dosimeter volume as it regained to its original shape, thus providing a measurable ground truth deformed dose distribution. Results showed that intensity-based DIR algorithms produce high levels of error and physically unrealistic deformations when deforming a homogeneous structure; this is expected as lack of internal structure is challenging for intensity-based DIR algorithms to deform accurately as they rely on matching fairly closely spaced heterogeneous intensity features. A biomechanical, intensity-independent DIR algorithm demonstrated substantially closer agreement to the measured deformed dose distribution with 3D gamma passing rates (3%/3mm) in the range of 90-91%. These results underscore the necessity and importance of validating DIR algorithms for specific clinical scenarios prior to clinical implementation.

Accurate delivery of external beam radiation therapy relies on localization of the treatment target. For this work, the accuracy of an electromagnetic localization system (ELS) was first verified. Next, prostate deformations and rotations occurring throughout therapy were analyzed. Motion studies were also conducted to investigate the use of the ELS during intensity modulated arc therapy (IMAT) for prostate cancer. Lastly, appropriate margins were determined and new plans utilizing smaller margins were tested for two patients who exhibited large transponder displacements. Electromagnetic alignments were accurate to within 1mm as compared to x-ray imaging. All patients fell within the default geometric residual limit (2mm) and most fell within the default rotation limit (10°). The ELS appeared to be suitable for use during IMAT with a 5mm margin. A 3mm margin was tested and was adequate when the main displacements were translational shifts; however, it was not adequate when large rotational displacements occurred.

Since 2010, Duke University Medical Center has used the single-isocenter technique to treat patients with multiple brain metastases. The purpose of this project is to compare treatment planning techniques for these patients who received treatment. First, we want to determine if volumetric modulated arc therapy (VMAT) or dynamic conformal arc therapy (DCAT) is the better method for treatment for incoming patients. Next, we want to know if using U-frame or frameless masks provide better plan quality. Lastly, we want to test the use of a stationary couch to simulate imaging while treating with the moving gantry. DCAT plans were created for each of the 40 single-isocenter patients who received VMAT at Duke University Medical Center from 2016 to 2018. These patients were randomly selected based only on the number of metastases, from 2 to 14. We created the DCAT plans using 5 couch positions, 2 collimator angles, and 100° arcs on BrainLab Elements. We modeled U-frame and frameless masks using 100° and 180° arcs, respectively. To simulate imaging, we kept the couch at 0° while using only 180° arcs. The clinical VMAT plans delivered to the 40 patients had an average conformity index of 1.47 and average gradient index of 8.57. Average whole-brain V3 Gy and V5 Gy were 14.07% and 5.80%, respectively. In comparison, using DCAT the conformity index was 1.75 and the gradient index was 6.87. Whole-brain V3 Gy and V5 Gy were 11.25% and 5.59%, respectively. The frameless mask plans had conformity and gradient indexes of 1.68 and 6.39 and V3 Gy and V5 Gy of 11.39% and 5.09%, respectively. Using VMAT for the imaging cases, we found conformity and gradient indexes of 1.59 and 11.99 and V3 Gy and V5 Gy of 18.04% and 8.41%. Using DCAT for the imaging cases had conformity and gradient indexes of 2.02 and 9.86 and V3 Gy and V5 Gy of 14.21% and 7.25%, respectively. Overall, VMAT plans had higher conformity index with lower gradient index at the cost of healthy brain protection compared to DCAT. Frameless masks also increased the conformity index and decreased the gradient index with no significant impact on low doses to the brain. The use of imaging while treating should be considered with the benefit when imaging on a case-by-case basis.

A tenet of modern radiotherapy (RT) is to identify the treatment target accurately, following which the high-dose treatment volume may be expanded into the surrounding tissues in order to create the clinical and planning target volumes. Respiratory motion can induce errors in target volume delineation and dose delivery in radiation therapy for thoracic and abdominal cancers. Historically, radiotherapy treatment planning in the thoracic and abdominal regions has used 2D or 3D images acquired under uncoached free-breathing conditions, irrespective of whether the target tumor is moving or not. Once the gross target volume has been delineated, standard margins are commonly added in order to account for motion. However, the generic margins do not usually take the target motion trajectory into consideration. That may lead to under- or over-estimate motion with subsequent risk of missing the target during treatment or irradiating excessive normal tissue. That introduces systematic errors into treatment planning and delivery. In clinical practice, four-dimensional (4D) imaging has been popular in For RT motion management. It provides temporal information about tumor and organ at risk motion, and it permits patient-specific treatment planning. The most common contemporary imaging technique for identifying tumor motion is 4D computed tomography (4D-CT). However, CT has poor soft tissue contrast and it induce ionizing radiation hazard. In the last decade, 4D magnetic resonance imaging (4D-MRI) has become an emerging tool to image respiratory motion, especially in the abdomen, because of the superior soft-tissue contrast. Recently, several 4D-MRI techniques have been proposed, including prospective and retrospective approaches. Nevertheless, 4D-MRI techniques are faced with several challenges: 1) suboptimal and inconsistent tumor contrast with large inter-patient variation; 2) relatively low temporal-spatial resolution; 3) it lacks a reliable respiratory surrogate. In this research work, novel 4D-MRI techniques applying MRI weightings that was not used in existing 4D-MRI techniques, including T2/T1-weighted, T2-weighted and Diffusion-weighted MRI were investigated. A result-driven phase retrospective sorting method was proposed, and it was applied to image space as well as k-space of MR imaging. Novel image-based respiratory surrogates were developed, improved and evaluated.

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.

The online adaptive radiation therapy for prostate cancer based on re-optimization has been shown to provide better daily target coverage through the treatment course, especially in treatment sessions with large anatomical deformation. However, the clinical implementation of such technique is still limited primarily due to two major challenges: the low efficiency of re-optimization and the lack of online quality assurance technique to verify delivery accuracy. This project aims at developing new techniques and understandings to address these two challenges.

The study was based on retrospective study on patient data following IRB-approved protocol, including both planning Computer Tomography (CT) and daily Cone-Beam Computer Tomography (CBCT) images. The project is divided in to three parts. The first two parts address primarily the efficiency challenge; and the third part of this project aims at validating the deliverability of the online re-optimized plans and developing an online delivery monitoring system.

I. Overall implementation scheme. In this part, an evidence-based scheme, named Adaptive Image-Guided Radiation Therapy (AIGRT), was developed to integrate the re-optimization technique with the current IGRT technique. The AIGRT process first searches for a best plan for the daily target from a plan pool, which consists the original CT plan and all previous re-optimized plans. If successful, the selected plan is used for the daily treatment with translational shifts. Otherwise, the AIGRT invokes re-optimization process of the CT plan for the anatomy-of-the-day, which is added to the plan pool afterwards as a candidate plan for future fractions. The AIGRT scheme is evaluated by comparisons with daily re-optimization and online repositioning techniques based on daily target coverage, Organ-at-Risk (OAR) sparing and implementation efficiency. Simulated treatment courses for 18 patients with re-optimization alone, re-positioning alone and AIGRT shows that AIGRT offers reliable daily target coverage that is highly comparable to re-optimization everyday and significantly improves compared to re-positioning. AIGRT is also seen to provide improved organs-at-risk (OARs) sparing compared to re-positioning. Apart from dosimetric benefits, AIGRT in addition offers an efficient scheme to integrate re-optimization to current re-positioning-based IGRT workflow.

II. Strategies for automatic re-optimization. This part aims at improving the efficiency of re-optimization through automation and strategic selections of optimization parameters. It investigates the strategies for performing fast (~2 min) automatic online re-optimization with a clinical treatment planning system; and explores the performance with different input parameters settings: the DVH objective settings, starting stage and iteration number (in the context of real time planning). Simulated treatments of 10 patients were re-optimized daily for the first week of treatment (5 fractions) using 12 different combinations of optimization strategies. Options for objective settings included guideline-based RTOG objectives, patient-specific objectives based on anatomy on the planning CT, and daily-CBCT anatomy-based objectives adapted from planning CT objectives. Options for starting stages involved starting re-optimization with and without the original plan's fluence map. Options for iteration numbers were 50 and 100. The adapted plans were then analysed by statistical modelling, and compared both in terms of dosimetry and delivery efficiency. The results show that all fast online re-optimized plans provide consistent coverage and conformity to the daily target. For OAR sparing however, different planning parameters led to different optimization results. The 3 input parameters, i.e. DVH objectives, starting stages and iteration numbers, contributed to the outcome of optimization nearly independently. Patient-specific objectives generally provided better OAR sparing compared to guideline-based objectives. The benefit in high-dose sparing from incorporating daily anatomy into objective settings was positively correlated with the relative change in OAR volumes from planning CT to daily CBCT. The use of the original plan fluence map as the starting stage reduced OAR dose at the mid-dose region, but increased 17% more monitor units. Only < 2cc differences in OAR V50% / V70Gy / V76Gy were observed between 100 and 50 iterations. Based on these results, it is feasible to perform automatic online re-optimization in ~2 min using a clinical treatment planning system. Selecting optimal sets of input parameters is the key to achieving high quality re-optimized plans, and should be based on the individual patient's daily anatomy, delivery efficiency and time allowed for plan adaptation.

III. Delivery accuracy evaluation and monitoring. This part of the project aims at validating the deliverability of the online re-optimized plans and developing an online delivery monitoring system. This system is based on input from Dynamic Machine Information (DMI), which continuously reports actual multi-leaf collimator (MLC) positions and machine monitor units (MUs) at 50ms intervals. Based on these DMI inputs, the QA system performed three levels of monitoring/verification on the plan delivery process: (1) Following each input, actual and expected fluence maps delivered up to the current MLC position were dynamically updated using corresponding MLC positions in the DMI. The difference between actual and expected fluence maps creates a fluence error map (FEM), which is used to assess the delivery accuracy. (2) At each control point, actual MLC positions were verified against the treatment plan for potential errors in data transfer between the treatment planning system (TPS) and the MLC controller. (3) After treatment, delivered dose was reconstructed in the treatment planning system based on DMI data during delivery, and compared to planned dose. FEMs from 210 prostate IMRT beams were evaluated for error magnitude and patterns. In addition, systematic MLC errors of ±0.5 and ±1 mm for both banks were simulated to understand error patterns in resulted FEMs. Applying clinical IMRT QA standard to the online re-optimized plans suggests the deliverability of online re-optimized plans are similar to regular IMRT plans. Applying the proposed QA system to online re-optimized plans also reveals excellent delivery accuracy: over 99% leaf position differences are < 0.5 mm, and the majority of pixels in FEMs are < 0.5 MU with errors exceeding 0.5 MU primarily located on the edge of the fields. All clinical FEMs observed in this study have positive errors on the left edges, and negative errors on the right. Analysis on a typical FEM reveals positive correlation between the magnitude of fluence errors and the corresponding leaf speed. FEMs of simulated erroneous delivery exhibit distinct patterns for different MLC error magnitudes and directions, indicating the proposed QA system is highly specific in detecting the source of errors. Based on these results, it can be concluded that the proposed online delivery monitoring system is very sensitive to leaf position errors, highly specific of the error types, and therefore meets the purpose for online delivery accuracy verification. Post-treatment dosimetric verification shows minimal difference between planned and actual delivered DVH, further confirming that the online re-optimized plans can be accurately delivered.

In summary, this project addressed two most important challenges for clinical implementation of online ART, efficiency and quality assurance, through innovative system design, technique development and validation with clinical data. The efficiencies of the overall treatment scheme and the re-optimization process have been improved significantly; and the proposed online quality assurance system is found to be effective in catching and differentiating leaf motion errors.