Browsing by Author "Das, Shiva K"

Over the course of a patient’s radiotherapy treatment, there are often changes of internal anatomy relative to the planning CT scan. Because patients are typically treated with a treatment plan created based on one planning CT, dose is likely deposited in healthy tissue locations not targeted by the initial treatment plan, thus leading to less dose given in the target region. To combat this problem, adaptive radiation therapy (ART) was introduced where a new treatment plan is created based on the anatomy of the patient imaged on the treatment day. However, ART often cannot be feasibly routinely applied due to the involved time-intensive workflow required for implementation. For patients treated with VMAT and IMRT, part of this workflow is delivery quality assurance (QA) to verify the accuracy of radiation fields. This study introduces a new approach to remove the need for delivery QA for adapted plans in an attempt to shorten the ART clinical workflow. The implemented approach is a partial re-optimization of the initial plan maintaining the segment shapes from the original plan and minimum monitor unit (MU) constraints. Dose will be delivered through aperture shapes that have already passed QA protocol before the patient’s initial treatment, eliminating the DQA step from the ART workflow.

CT images from 20 prostate cancer patients were retrospectively chosen for this study. Each of these patients had 5 CT-on-rails (CTOR) images taken, one from each week of their treatment, in addition to their original planning CT. Partially re-optimized VMAT and IMRT plans were created for all 100 images and compared to dosimetry directly calculated onto the treatment CT from the original plan. Goals for treatment adaption included regaining coverage lost on the treatment day CT as well as sparing organs at risk. Dosimetric benefits of this adaption method could be seen in many treatment plans.

Successful ART plans, with significant change of anatomy between initial plan and ART plans, were delivered and measured on treatment machine. They show similar DQA quality as original plans, demonstrating that partial re-optimization can be performed and delivered to a patient without the need for delivery QA. With the shortened workflow of this technique, adaptive therapy may become a viable option for patients that typically may not qualify for a full adapted plan.

The use of positron emission tomography (PET) in radiation therapy has continued to grow, especially since the development of combined computed tomography (CT) and PET imaging system in the early 1990s. Today, the biggest use of PET-CT is in oncology, where a glucose analog radiotracer is rapidly incorporated into the metabolic pathways of a variety of cancers. Images representing the in-vivo distribution of this radiotracer are used for the staging, delineation and assessment of treatment response of patients undergoing chemotherapy or radiation therapy. While PET offers the ability to provide functional information, the imaging quality of PET is adversely affected by its lower spatial resolution. It also has unfavorable image noise characteristics due to radiation dose concerns and patient compliance. These factors result in PET images having less detail and lower signal-to-noise (SNR) properties compared to images produced by CT. This complicates the use of PET within many areas of radiation oncology, but particularly the delineation of targets for radiation therapy and the assessment of patient response to therapy. The development of segmentation methods that can provide accurate object identification in PET images under a variety of imaging conditions has been a goal of the imaging community for years. The goal of this thesis are to: (1) investigate the effect of filtering on segmentation methods; (2) investigate whether combining individual segmentation methods can improve segmentation accuracy; (3) investigate whether the consensus volumes can be useful in aiding physicians of different experience in defining gross tumor volumes (GTV) for head-and-neck cancer patients; and (4) to investigate whether consensus volumes can be useful in assessing early treatment response in head-and-neck cancer patients.

For this dissertation work, standard spherical objects of volumes ranging from 1.15 cc to 37 cc and two irregularly shaped objects of volume 16 cc and 32 cc formed by deforming high density plastic bottles were placed in a standardized image quality phantom and imaged at two contrasts (4:1 or 8:1 for spheres, and 4.5:1 and 9:1 for irregular) and three scan durations (1, 2 and 5 minutes). For the work carried out into the comparison of images filters, Gaussian and bilateral filters matched to produce similar image signal to noise (SNR) in background regions were applied to raw unfiltered images. Objects were segmented using thresholding at 40% of the maximum intensity within a region-of-interest (ROI), an adaptive thresholding method which accounts for the signal of the object as well as background, k-means clustering, and a seeded region-growing method adapted from the literature. Quality of the segmentations was assessed using the Dice Similarity Coefficient (DSC) and symmetric mean absolute surface distance (SMASD). Further, models describing how DSC varies with object size, contrast, scan duration, filter choice and segmentation method were fitted using generalized estimating equations (GEEs) and standard regression for comparison. GEEs accounted for the bounded, correlated and heteroscedastic nature of the DSC metric. Our analysis revealed that object size had the largest effect on DSC for spheres, followed by contrast and scan duration. In addition, compared to filtering images with a 5 mm full-width at half maximum (FWHM) Gaussian filter, a 7 mm bilateral filter with moderate pre-smoothing (3 mm Gaussian (G3B7)) produced significant improvements in 3 out of the 4 segmentation methods for spheres. For the irregular objects, time had the biggest effect on DSC values, followed by contrast.

For the study of applying consensus methods to PET segmentation, an additional gradient based method was included into the collection individual segmentation methods used for the filtering study. Objects in images acquired for 5 minute scan durations were filtered with a 5 mm FWHM Gaussian before being segmented by all individual methods. Two approaches of creating a volume reflecting the agreement between the individual methods were investigated. First, a simple majority voting scheme (MJV), where individual voxels segmented by three or more of the individual methods are included in the consensus volume, and second, the Simultaneous Truth and Performance Level Estimation (STAPLE) method which is a maximum likelihood methodology previously presented in the literature but never applied to PET segmentation. Improvements in accuracy to match or exceed the best performing individual method were observed, and importantly, both consensus methods provided robustness against poorly performing individual methods. In fact, the distributions of DSC and SMASD values for the MJV and STAPLE closely match the distribution that would result if the best individual method result were selected for all objects (the best individual method varies by objects). Given that the best individual method is dependent on object type, size, contrast, and image noise and the best individual method is not able to be known before segmentation, consensus methods offer a marked improvement over the current standard of using just one of the individual segmentation methods used in this dissertation.

To explore the potential application of consensus volumes to radiation therapy, the MJV consensus method was used to produce GTVs in a population of head and neck cancer patients. This GTV and one created using simple 40% thresholding were then available to be used as a guidance volume for an attending head and neck radiation oncologist and a resident who had completed their head and neck rotation. The task for each physician was to manually delineate GTVs using the CT and PET images. Each patient was contoured three times by each physician- without guidance and with guidance using either the MJV consensus volume or 40% thresholding. Differences in GTV volumes between physicians were not significant, nor were differences between the GTV volumes regardless of the guidance volume available to the physicians. However, on average, 15-20% of the provided guidance volume lay outside the final physician-defined contour.

In the final study, the MJV and STAPLE consensus volumes were used to extract maximum, peak and mean SUV measurements in two baseline PET scans and one PET scan taken during patients' prescribed radiation therapy treatments. Mean SUV values derived from consensus volumes showed smaller variability compared to maximum SUV values. Baseline and intratreatment variability was assessed using a Bland-Altman analysis which showed that baseline variability in SUV was lower than intratreatment changes in SUV.

The techniques developed and reported in this thesis demonstrate how filter choice affects segmentation accuracy, how the use of GEEs more appropriately account for the properties of a common segmentation quality metric, and how consensus volumes not only provide an accuracy on par with the single best performing individual method in a given activity distribution, but also exhibit a robustness against variable performance of individual segmentation methods that make up the consensus volume. These properties make the use of consensus volumes appealing for a variety of tasks in radiation oncology.

Oropharyngeal Squamous Cell Carcinoma (OPSCC) is by far the most predominant form of head and neck cancer in the United States. The survival rate for OPSCC is very high, which, while fortunate, yields many patients who are left with the late term toxicities consequent of their treatment. This project aimed to use patient-reported outcome (PRO) data from two sources – the PRO-CTCAE and the QLQ-C30 – along with the dosimetric data of patients that have already been treated, in order to characterize retrospectively a relationship between patient dosimetric data and the severity of response of PRO data. In particular, PRO data was used as a way to characterize the severity of patient-experienced xerostomia and dysphagia. Additionally, this data was used to fit the radiobiological parameters for two normal tissue complication probability (NTCP) models: the Lyman-Kutcher-Burman (LKB) model, and the Relative Seriality (RS) model. Overall, it was found that the PRO-CTCAE data was more robust than the QLQ-C30 data in its characterization. Based on the PRO-CTCAE data, the V52 (volume which receives at least 52 Gy) of the combined constrictors and the V59 of the superior pharyngeal constrictor show the strongest relationship with patient-reported dysphagia. Additionally, the V27 of the contralaterals and the V12 of the contralateral parotid show the strongest relationship with patient-reported xerostomia. Furthermore, it was found that the dose response curves for both NTCP models fit the data with similar accuracy.

Intensity Modulated Radiation Therapy (IMRT) has allowed a large degree of healthy tissue sparing while delivering therapeutic dose to tumors. However, the treatment planning process for IMRT is iterative and time consuming and the resultant plan quality is dependent on the skill and experience of the planner.

Following the work of Chanyavanich, a knowledge-based approach to IMRT treatment planning was used to generate high quality IMRT plans for patients from another hospital, using previously treated Duke plans as a reference library. An image-similarity metric was used to identify the patient from our database with the most similar anatomy to each new patient. Parameters from the Duke plan were then modified and applied to the new patient, resulting in quality dose distributions.

In conclusion, the treatment planning time was reduced to approximately ten minutes for all cases, and the resultant plans were frequently of higher quality than the original, manually produced plans. The quality of the Duke treatment plans was preserved as the plans were adapted to new patient anatomy.

Knowledge based radiation therapy planning was developed as a way to use a knowledge base of clinically approved radiation therapy plans to shorten the amount of time necessary to create a new treatment plan. The method has been tested using plans from a database of approved plans from Duke University and it was shown that the quality of the new plan is at least comparable to the original plan. When a match is found from the database for a query anatomy, the fluences of the match plan are deformed to fit the query planning target volume (PTV) and brought into the new query plan. The deformation is currently done using several two dimensional deformation registrations of the beam's eye vies (BEV) projection of the match PTV to the BEV of the query PTV for each beam in the plan. We investigated whether using information from a three dimensional deformation of the PTVs to deform the fluences would yield higher quality plans than the two dimensional method. Using Velocity AI's three dimensional deformation capabilities we deformed several match query pairs and projected the deformation field down into two dimensions for each beam angle. Using the projected fields we deformed each beam's fluence and imported the new fluences into a radiation therapy plan. After evaluating the DVHs of both pre- and post-optimized plans we concluded that there is no benefit to performing the three dimensional deformation method over the two dimensional method for prostate patients.

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.

Intensity-modulated radiotherapy (IMRT) has become an effective tool for cancer treatment with radiation. However, even expert radiation planners still need to spend a substantial amount of time, approximately 4 hours, manually adjusting IMRT optimization parameters such as dose limits and costlet weights in order to obtain a clinically acceptable plan. Also, the quality of the treatment plan generated is solely based on the experience and training of the planning. In comparing the geometries of the planning target volume (PTV), bladder, rectum, right and left femoral heads, a knowledge-based approach to IMRT treatment planning may reduce the time needed to generate a clinically acceptable prostate plan. The knowledge-based approach uses the clinically acceptable plans of previously irradiated patients which are adapted to the new patient. Patient selection is done by using mutual information (MI). Having selected the best matched patient, Elastix (a toolkit for rigid and deformable registration) is used to deform the treatment plan of the previously irradiated patient to the new patient's geometry. The Eclipse treatment planning system is used to generate both pre-optimized and post optimized plans for the new patients. The knowledge-based treatment plans require no manual intervention. For the 101 patient data, it was shown that the newly generated plans were of similar or slightly worse dosimetric quality and were only generated in less than 30 minutes. Given the large size of this data set, the results are likely to be robust in representing treatment planning efficacy over a diverse range of patient anatomy. The results also show that this work has the potential to automatically provide high quality treatment plans while dramatically reducing the dependence of the expertise of the planner and the treatment planning time.

Purpose: HNC IMRT treatment planning is a challenging process that relies heavily on the planner's experience. Previously, we used the single, best match from a library of manually planned cases to semi-automatically generate IMRT plans for a new patient. The current multi-case Knowledge Based Radiation Therapy (MC-KBRT) study utilized different matching cases for each of six individual organs-at-risk (OARs), then combined those six cases to create the new treatment plan.

Methods: From a database of 103 patient plans created by experienced planners, MC-KBRT plans were created for 40 (17 unilateral and 23 bilateral) HNC "query" patients. For each case, 2D beam's-eye-view images were used to find similar geometric "match" patients separately for each of 6 OARs. Dose distributions for each OAR from the 6 matching cases were combined and then warped to suit the query case's geometry. The dose-volume constraints were used to create the new query treatment plan without the need for human decision-making throughout the IMRT optimization. The optimized MC-KBRT plans were compared against the clinically approved plans and Version 1 (original KBRT) using the dose metrics: mean, median, and maximum (brainstem and cord+5mm) doses.

Results: Compared to Version 1, MC-KBRT had no significant reduction of the dose to any of the OARs in either unilateral/bilateral cases. Compared to the manually-planned unilateral cases, there was significant reduction of the oral cavity mean/median dose (>2Gy) at the expense of the contralateral parotid. Compared to the manually-planned bilateral cases, reduction of dose was significant in the ipsilateral parotid, larynx, and oral cavity (>3Gy mean/median) while maintaining PTV coverage.

Conclusion: MC-KBRT planning in head and neck cancer generates IMRT plans with equivalent dose sparing to manually created plans. MC-KBRT using multiple case matches does not show significant dose reduction compared to using a single match case with dose warping.

Purpose

Methods and materials

Results

Conclusions

Pulmonary fibrosis is the process of lung tissue becoming damaged and scarred, losing its elastic and diffusive properties needed for proper lung function. This change in tissue structure can make it difficult to draw in a breath (ventilation) and cause a decrease in the amount of oxygen and carbon dioxide that can transfer between the alveoli and blood vessels (gas exchange). Therefore, the most common symptom of progressive pulmonary fibrosis is shortness of breath, or dyspnea. Pulmonary fibrosis can be caused by environmental pollutants, treatment-related toxicity from a drug or therapy, or interstitial lung diseases.

Regardless of its origin, pulmonary fibrosis can have devastating outcomes for patients. For example, the median survival for patients with idiopathic pulmonary fibrosis (IPF), an interstitial lung disease of unknown origin, is historically less than 3 years. For patients with IPF, the path of clinical decline is often sporadic and plagued with acute exacerbations and hospitalizations. Idiopathic pulmonary fibrosis currently affects between 100,000-200,000 people in the United States alone, and over a million worldwide. Unfortunately, the tools currently available to classify disease severity, determine prognosis, and assess disease progression or treatment response are simply inadequate. Patients with IPF exhibit distinct and unpredictable clinical trajectories, and a tool with the ability to predict these trajectories could improve targeted interventions. One such set of tools, pulmonary function tests (PFTs) can measure global ventilation and gas exchange, but have high variability and no spatial information. Another, high resolution computed tomography (HRCT), provides a 3D image, but function must be inferred from tissue density or structure, which comes with a number of limitations. A new tool that can spatially resolve and quantify regional pulmonary function could be invaluable in improving the clinical management of IPF.

In addition to fibrotic lung diseases, such as IPF, pulmonary fibrosis may also transpire as a treatment-related toxicity. Over 100,000 people per year in the U.S. will receive thoracic radiation therapy (RT) as treatment for cancer, putting them at risk for radiation-induced lung injury (RILI). Approximately 5-25% of patients that receive conventional thoracic RT develop clinically significant symptomatic radiation pneumonitis (RP), the acute form of RILI, causing patients to experience dyspnea, persistent coughing, pain, and fever. Radiation pneumonitis can lead to chronic radiation pulmonary fibrosis (RPF), or even result in death for an estimated 1-2% of patients. Current methods to assess RILI and grade RP rely on a clinical diagnosis and patient- and physician-reported symptoms. This leads to large variability in toxicity grading for thoracic RT clinical trials, hampering the effort to design treatments that reduce side effects. Further, a recently proposed treatment planning technique, “functional avoidance”, was designed to preserve pulmonary function and reduce the incidence or severity of RP by minimizing radiation dose to areas with high pulmonary function. However, clinically available tools can only measure ventilation or perfusion, neither of which are a true representation of end-to-end pulmonary gas exchange, a fact that may be limiting the potential effectiveness of this technique. Once again, a tool that can spatially resolve and quantify regional pulmonary function could offer improvements to functional avoidance treatment planning and the prevention of RP. If such a tool was sensitive enough to detect radiation-induced changes in function, this could reduce the variability in the current guidelines for the toxicity grading of RP.

In this work, we investigated non-invasive hyperpolarized-129Xe gas exchange magnetic resonance imaging (MRI), which acquires 3D maps of lung ventilation, alveolar barrier uptake, and capillary red blood cell (RBC) transfer. This unique tool can quantify and spatially resolve the gas exchange capabilities of a human lung in a single 15-second breath-hold MRI acquisition. Currently limited to research studies, the clinical utility of this new technique is yet to be firmly established. Therefore, the objectives of this dissertation are to: 1) identify metrics from baseline 129Xe gas exchange MRI that are predictive of clinical outcomes in IPF; 2) quantify the extent to which ventilation and gas exchange distributions are spatially correlated, and the effect that this may have on functional avoidance treatment planning; and 3) establish a relationship between regional changes in gas exchange and local radiation therapy dose in RT patients.

First, we sought to identify 129Xe gas exchange MRI features in IPF patients, and establish 129Xe-based imaging metrics to be used for classifying patients into groups, as detailed in Chapter 3. As previously mentioned, subjects with IPF exhibit distinct clinical trajectories that are difficult to predict prospectively using currently available means. We acquired baseline 129Xe MRI for 12 newly diagnosed IPF patients, and prospectively grouped these subjects based on percentage-volumes of abnormal barrier uptake and RBC transfer, using thresholds we derived from 129Xe MRI of a healthy subject cohort (N=13). We then followed these subjects for 36 months and analyzed the clinically acquired PFT and outcome data. We examined the differences in clinical outcomes and temporal changes in PFTs based on these groupings. We also observed changes in 129Xe metrics over time for those subjects with serial time-point imaging. Our results indicated that 129Xe MRI characteristics appear to group disease in a way that was distinct from traditional clinical or radiographic approaches; in particular, excessive volumes of lung with elevated barrier uptake and reduced RBC transfer were associated with poor clinical outcome. This study provided preliminary evidence that IPF patients can be classified by 129Xe MRI, and that this classification may predict clinical outcomes. These results open the door for larger, prospective studies using 129Xe MRI in IPF. More generally, and perhaps most importantly, this work established 129Xe gas exchange MRI as a prognostic biomarker in fibrotic lung disease.

Our results from Chapter 3 established that, when accompanied by an increase in 129Xe MRI barrier signal, which is a hallmark characteristic of IPF, a reduction in RBC transfer signal is associated with clinical decline in IPF patients. Extending this work to fibrotic lung processes beyond IPF, we hypothesize that the RBC signal is an important marker for regional lung function, and preserving and protecting the volumes of lung exhibiting “healthy” RBC transfer could translate to preservation of overall pulmonary function. In RT treatment planning, the concept of avoiding excess radiation dose to highly functioning areas of lung is not new; “functional avoidance” (or “functional guidance”) has previously been proposed and implemented using both perfusion and ventilation imaging. In Chapter 4, however, we establish 129Xe gas exchange MRI as a unique marker of regional lung function compared to ventilation, which is the most popular functional avoidance planning technique due to its “free” derivation from the 4 dimensional (4D) CT acquired during the RT planning process. In this chapter, we examined the correlation of ventilation and RBC signals in a healthy volunteer cohort and a handful of thoracic RT patients. Our results indicated a weak-to-moderate correlation, which determined that the RBC signal was indeed spatially unique from the ventilation signal, but did not explore the extent to which this affects functional plans created using one or the other (ventilation or RBC gas exchange) for guidance. Therefore, Chapter 5 details our study of 11 patients that received RT for treatment of lung cancer in which we re-planned these patients’ clinically approved plans using ventilation and RBC gas exchange functional information. This study established a methodology for 129Xe gas exchange MRI functional avoidance planning, and the results showed that, for some RP-predictive metrics, gas exchange-guided planning produced significantly different dose distributions than ventilation-guided planning.

Finally, in Chapter 6 we focused on furthering our understanding of RILI in RT patients, and examined the sensitivity of 129Xe MRI for detecting pulmonary radiation damage. In this study, we quantified changes in regional gas exchange as a function of radiation dose for six patients undergoing conventional radiotherapy for lung cancer. As briefly described earlier, RT of tumors in or around the thorax is known to cause regional lung injury, with the acute injury phase symptoms of RP typically emerging 1-6 months after RT. Previous studies using SPECT have established that perfusion changes are dose-dependent and evident at 3-6 months after RT. Therefore, we acquired 129Xe MRI scans before RT and at 3- and 6-months after RT to evaluate the progression of the acute inflammatory phase of RILI, as it relates to changes in regional gas exchange. We co-registered the MRI data to the RT treatment planning data, to evaluate regional changes in ventilation, barrier uptake, and RBC transfer, as a function of delivered radiation dose. Our results indicated that the barrier uptake signal increased with radiation doses above 20 Gy, and that the magnitude of change was dose-dependent. This potentially confirms increased barrier uptake as a marker of regional inflammation. In addition, we observed that the RBC transfer signal decreased with radiation doses above 35 Gy, possibly quantifying a reduction in overall gas exchange properties of the tissue at these high doses. Our observations of this dose-dependent relationship are consistent with historic ventilation and perfusion data, and gives rise to the idea that 129Xe MRI may be a powerful tool in furthering understanding of the subclinical progression of RILI and potentially other causes of lung fibrosis.

Overall, we have demonstrated the potential of 129Xe-MRI gas exchange to 1) improve disease classification in IPF, 2) add unique functional information to the planning of thoracic radiation treatments, and 3) assess RT-associated subclinical changes in regional lung function. We have established a strong foundation for this non-invasive technology, enabling further development and validation of these MRI biomarkers in larger studies. The work presented herein marks the beginning of a journey to advance our understanding of fibrotic progression in IPF, RILI, and all other causes of pulmonary fibrosis.

Purpose: The goal of this planning study is to determine which sectors of the gantry rotation are most and least important in the treatment of head-and-neck carcinomas with Intensity Modulated Arc Therapy, and then use this knowledge to optimize the arc arrangement by adding arcs to reinforce the sectors that are most significant. Materials and Methods: Ten patients with head-and-neck cancer involving bilateral lymph nodes were selected for this planning study. Baseline RapidArc plans comprising two full gantry rotation arcs (RA2) were generated. Avoidance sectors and partial gantry rotations were used to produce RapidArc plans with various sectors removed: posterior (RA_post-), anterior (RA_ant-), or lateral sections (RA_lat-). Based on the results of these two-arc plans, two different resulting three-arc plans were created, with the third arc used to reinforce the important sectors. Results:The posterior sector was the least important contributor to overall plan quality. Removal of the lateral sector increased the dose to all critical structures with a resultant decrease in the median dose to the parotids. Removal of the anterior portion increased the dose to the larynx and parotids. The first three-arc plan produced from these results removed the posterior and lateral section and reinforced the anterior sectors (RA3_ant+). The second three-arc plan removed the posterior and one lateral sector, while reinforcing the anterior sector (RA3_ant+lat+). Both three arc plans provided better sparing to the parotids and spinal cord over RA2. Doses to the oral cavity, larynx, and brainstem were larger than RA2. RapidArc always produced plans with lower MUs than the corresponding IMRT plans while integral dose was lower for IMRT. Conclusions: For the class of tumors investigated in this report, RA3_ant+lat+ produced the most optimal plan in terms of target coverage and critical structure sparing while also being the simplest to develop treatment plans for.

Purpose: Our primary goal was to examine the effect of PET image reconstruction parameters on baseline and early-treatment FDG-PET/CT quantitative imaging. Early-treatment changes in tumor metabolism in primary tumor and nodes can potentially determine if the patient is responding to therapy, but this assessment can change based on the reconstruction parameters. We investigated the effect of the following reconstruction parameters: number of Ordered-Subset-Expectation-Maximization (OSEM) iterations, post-reconstruction smoothing, and quantitative metrics (SUV-max, SUV-mean, SUV-peak).

A concurrent investigation explored in detail the sampling parameters of SUV-peak by way of a Monte Carlo digital phantom study. SUV-peak was proposed as a compromise between SUV-max and SUV-mean, in hope to retain key attractive features of these two metrics (inter-physician independence of SUV-max, noise-averaging of SUV-mean) but reduce unwanted errors (noise dependence of SUV-max, contour-dependence of SUV-mean). Sampling parameters have vaguely been defined, in particular, the scanning resolution (i.e. 1 voxel, 1/2 voxel, 1/4 voxel, etc.) of the SUV-peak spherical ROI . We examined the role that partial-voxel scanning plays in tumor SUV recovery in both noise-free and realistic OS-EM noise environments.

Materials and Methods: The response assessment investigation involved 19 patients on an IRB-approved study who underwent 2 baseline PET scans (mean-separation = 11 days) prior to chemoradiotherapy (70 Gy, 2 Gy/fraction). An intra-treatment PET scan was performed early in the course of therapy (10-20 Gy, mean = 14 Gy). The images were reconstructed with varying OS-EM iterations (1-12) and Gaussian post-smoothing (0-7 mm). Patients were analyzed in two separate groups, distinguished by the PET/CT scanner used to acquire data: (1) GE Discovery STE; and (2) Siemens Biograph mCT. For each combination of iterations and smoothing, Bland-Altman analysis was applied to quantitative metrics (SUV-max, SUV-mean, SUV-peak) from the baseline scans to evaluate metabolic variability (repeatability, R = 1.96&sigma). The number and extent of early treatment changes that were significant, i.e., exceeding repeatability, was assessed.

An original SUV-peak algorithm was developed, which measures SUV-max and SUV-peak for as small as 1/32 voxel scanning. Two rounds of digital phantoms were generated for the SUV-peak investigation. First, 10,000 spherical tumors were generated at a random matrix location for each diameter 1-4 cm and smoothed with an isotropic Gaussian, FWHM = 0.8 cm, then evaluated using the SUV-peak algorithm. Next, realistic body-sized phantoms were generated with background activity, and 1,000 spherical tumors of activity 4 time the background for each diameter (1-4cm) were placed inside (8 tumors per phantom, location randomized within certain constraints). These images received realistic corrections in projection space for attenuation, spatial resolution, and noise, were reconstructed with an in-house OS-EM algorithm, and then assessed using the SUV-peak algorithm. The mean recovered activity above background and its coefficient of variation were calculated for all metrics for each tumor size, for both simulations. For the realistic noise simulation, various levels of Gaussian smoothing was applied post-reconstruction, the effects summarized in plots showing coefficient of variation vs. mean recovered activity above background - a comparison of the effectiveness of SUV-max and SUV-peak.

Results: For the GE Discovery STE 2D cases averaged over all metrics (SUV-max, SUV-mean, SUV-peak) and structures (GTV, LN), repeatability, R, improved with increasing smoothing and decreasing iterations. Individually, SUV-mean repeatability was less affected by the number of iterations, but demonstrated the same relationship with smoothing. SUV-mean outperformed SUV-max and SUV-peak with regards to the number of cases exceeding repeatability, N. Considering R, N, and the sum of relative metric change outside repeatability, &Omega, averaged over all metrics and all structures, and normalized, several combinations of reconstruction parameters produced five optimal combinations above set thresholds: 1 iteration with 0.1-3.0 mm smoothing; and 2 iterations with 2.0-3.0 mm smoothing. Current GE 2D reconstruction protocol for HN cases uses 2 iterations and 3.0 mm post-smoothing, which lies on the edge, but within these recommendations.

The relationship between repeatability and number of iterations for the 3D cases was more complex; SUV-max demonstrated the best repeatability with 2 iterations, with both SUV-mean and SUV-peak reaching the best repeatability with 4 iterations. The same dependence on smoothing was noted, i.e. increased smoothing gives lover (desirable) repeatability. SUV-mean once again outperformed SUV-max and SUV-peak with regards to the number of cases exceeding repeatability, N. The calculations of N and &Omega averaged over all metrics were limited severely by the low number of cases, damaging the statistical significance of the following recommendation. Three optimal combinations with averaged and normalized R, N, &Omega, above a set threshold are recommended as most effective reconstruction parameter combinations: 4 iterations with 2.0-4.0 mm smoothing. Current Siemens 3D reconstruction protocol for HN cases uses 4 iterations and 3.0 mm post-smoothing, which lies within these recommended parameters. However, no statistically significant conclusions could be drawn from this analysis for this scanner, and performing similar data analysis on a larger patient pool is proposed.

The minimum spherical tumor diameter required for full recovery was 3.0-3.5 cm for SUV-peak, and 2.5-3.0 cm for SUV-max. SUV-max was found to overestimate the recovered value of tumors by up to 46% (vs. 10% for SUV-peak); above the minimum diameter for full recovery, SUV-peak values were significantly closer to actual tumor activity. Considering only the realistic noise tumors, the coefficient of variation for SUV-max ranged from 5.5-17.7%, whereas for SUV-peak these values were lower, 2.7-13.2%. Partial-voxel scanning did not substantially affect the coefficient of variation (<0.2%). Comparison of coefficient of variation vs. mean recovered value demonstrated that SUV-max with additional Gaussian smoothing outperforms SUV-peak by up to 0.8% for 1 cm tumors and 0.2% for 4 cm tumors. Other tumor sizes showed little difference between the two metrics.

Conclusion: For patients scanned on the GE Discovery STE using the HN protocol (2D acquisition mode), images reconstructed for quantitative analysis may benefit from a low number of OS-EM iterations (≤ 2). Some post-reconstruction smoothing proved to be beneficial (1.0 mm ≤ FWHM ≤ 3.0 mm), however, over-smoothing for the sake of more qualitatively appealing images or improved image quality metric (e.g. SNR, CNR) may prove detrimental to quantitative response assessment analysis. Our results for the Siemens Biograph mCT using the HN protocol (3D acquisition mode) demonstrated favor towards 4 iterations and limited range of smoothing (2.0 mm ≤ FWHM ≤ 4.0 mm). These results are statistically limited, further cases are necessary for any conclusive recommendations on reconstruction parameters.

SUV-peak was shown to reduce uncertainties associated with quantitative PET image analysis when compared directly to SUV-max. Above the minimum tumor diameter required for full recovery, SUV-peak also provides a better estimate of the actual tumor activity. However, initial comparisons of SUV-peak and SUV-max over various levels of additional Gaussian smoothing found SUV-max more favorable. Partial-voxel scanning of SUV-peak did not reduce the metric's coefficient of variation in images with realistic noise. Therefore, a phantom investigation is proposed to compare SUV-peak and SUV-max of real scanned images with various levels of post-smoothing, which may conclusively eliminate the need for SUV-peak.

Abstract

Purpose: The aim of this study is to quantify the effectiveness of adaptive radiation therapy (ART) when anatomical changes to the tumor and/or the organs are observed for head and neck patients during the course of intensity modulated radiation therapy (IMRT).

Methods and Materials: In this study, ART was retrospectively studied in 10 head and neck cancer treated patients after examining the 2nd CT, obtained after the first several fractions of radiotherapy, to see if anatomical changes had taken place. The adaptive treatment plan (ATP) was generated on the 2nd CT to mimic the relative dose-volume histograms of the spinal cord, brainstem, parotid glands, larynx, and oral cavity from the original treatment plan. The total ATP was generated as the sum of the original treatment plan delivered to the initial CT for the first several fractions and the ATP delivered to the 2nd CT for the remaining fractions. The delivered treatment plan (DTP) was generated as the sum of the original treatment plan delivered to the initial CT for the first several fractions and the original treatment plan delivered to the 2nd CT for the remaining fractions. For quantification of the effectiveness of ART, planning target volume (PTV) coverage and homogeneity, maximum dose to the brainstem and spinal cord, and median, mean doses, and D1% (highest dose to 1% volume) for the parotid glands, the oral cavity, and the larynx were compared between the adaptive treatment plan (ATP) and delivered treatment plan (DTP) using the Wilcoxon signed-rank test, a non-parametric comparison test. For a total of 15 comparisons, significance was set at p = 0.0033 accounting for Bonferroni correction.

Results: For ATP compared to DTP, PTV44/70 homogeneity was improved by 10.38% (p = 0.0234) and 7.96% (p = 0.04922) respectively. PTV44/70 coverage (%volume covered by prescription dose) were improved by 7.27% (p = 0.0078) and 12.00% (p = 0.0020) respectively. Maximum dose to the spinal cord and brainstem were reduced by 6.47% (p = 0.0195) and 8.24% (p = 0.0098), respectively. Median and mean doses for the parotid glands were reduced by 6.01% (p = 0.0029) and 4.14% (p = 0.0043) respectively whereas D1% remained approximately the same with a reduction of 0.78% (p = 0.8789). Median dose to oral cavity was reduced by 0.20%, but mean dose and D1% increased by 0.43% and 2.12%, respectively; however all oral cavity changes were insignificant (p = 0.9102, p = 0.7344, and p = 0.2031 respectively). Similarly, larynx mean dose was reduced by 3.54%, median dose was reduced by 2.15%, and D1% was reduced by 7.11%, but all reductions were statistically insignificant (p = 0.5625, p = 0.6875, and p = 0.8789 respectively).

Conclusions: In cases where anatomical changes are observed during therapy, ART can be applied to significantly reduce median parotid glands dose and improve target coverage. However, these changes are small and may not be clinically significant, implying that adaptive radiotherapy may not provide benefit for head-and-neck cases, on average.

Purpose: The goal of this study is to investigate the necessity of adaptive radiation therapy (ART) for lung cancer patients treated with intensity modulated radiation therapy (IMRT) by quantifying the change in the tumor volume and its associate impacts on the target, lungs and esophagus.

Materials and Methods: Fifteen patients enrolled on an IRB-approved lung dose escalation phase I study were treated with IMRT (58-72 Gy, 2Gy/fraction), along with concurrent cisplatin and etoposide. Contrasted CT scans were acquired prior to RT and in the 2nd and 5th weeks of treatment. Tumor, lung and esophagus volumes were segmented on all CT datasets. The clinical target volumes were enlarged by 3 - 5 mm for planning target volume (PTV) expansions. The original plan (generated on pre-RT CT set) was recomputed on the subsequent CT sets and doses were accumulated by deformable registration to approximate the actual delivery. Five patients with the largest tumor shrinkage were selected and their original plans were re-optimized on the 2nd and 5th week CT sets. The plans on the 3 CT sets were summed to simulate ART. Comparisons were made between the original plan, approximated actual treatment and ART plan. Comparison metrics included QUANTEC dose parameters (lungs: V5, V20, and mean dose; esophagus: V35, V50, V70), equivalent uniform dose (EUD), maximum dose to the highest 1% of volume, and target volume covered by the prescription dose. Dosimetric and volumetric changes were tested for significance (Wilcoxon signed-rank test).

Results: Compared to the original plan, the approximated actual delivery had significantly increased lung dose and volume metrics: V5 = 8.10%, V20 = 4.08% (p < 0.05), and EUD (5.42%, p < 0.05). Tumor shrinkage-induced esophageal and lung volume motion outside the originally segmented volume was significant, ranging from 67.2%- 185%, and 16%-49.7% of the original volume (p < 0.05), respectively. The correlation between the original GTV volume and esophageal EUD increase was significant (ρ = 0.83, p < 0.005). Elevated esophagus EUD and spinal cord maximum dose were observed in most patients, with averages of 7.19% and 4.39% (p > 0.05), respectively. PTV/GTV volumes receiving 100% of prescription dose decreased (week 2/5 PTV = -10.0%/-6.88%, week 2/5 GTV = -6.7%/-4.1%), along with slightly increased dose to the highest 1% of volume. Compared to the approximated actual delivery, ART plans overall were superior in lowering dose to the lungs (V5=-4.42% (p=0.3125), V20=-7.52% (p=0.625)), esophagus (V35=-25.98% (p=0.3125), EUD =-13.18% (p=0.1094)), and spinal cord (Dmax=-15.82% (p=0.0625)).

Conclusions: RT-induced esophageal volume displacement and increased lung dose-volume metrics during treatment are significant. Adaptive plan re-optimization may be warranted in cases with larger tumors, where sizeable changes are expected during radiotherapy.