A deep learning segmentation method to assess dose to organs at risk during breast radiotherapy.

Background and purpose: Contouring of organs at risk is important for studying health effects following breast radiotherapy. However, manual contouring is time-consuming and subject to variability. The purpose of this study was to develop a deep learning-based method to automatically segment multiple structures on breast radiotherapy planning computed tomography (CT) images. Materials and methods: We used data from 118 patients, including 90 diagnostic CT scans with expert structure delineations for training and 28 breast radiotherapy planning CT images for testing. The radiotherapy CT images also had expert delineations for evaluating performance. We targeted a total of eleven organs at risk including five heart substructures. Segmentation performance was evaluated using the metrics of Dice similarity coefficient (DSC), overlap fraction, volume similarity, Hausdorff distance, mean surface distance, and dose. Results: The average DSC achieved on the radiotherapy planning images was 0.94 ± 0.02 for the whole heart, 0.96 ± 0.02 and 0.97 ± 0.01 for the left and right lung, 0.61 ± 0.10 for the esophagus, 0.81 ± 0.04 and 0.86 ± 0.04 for left and right atrium, 0.91 ± 0.02 and 0.84 ± 0.04 for left and right ventricle, and 0.21 ± 0.11 for the left anterior descending artery (LAD), respectively. Except for the LAD, the median difference in mean dose to these structures was small with absolute (relative) differences < 0.1 Gy (6 %). Conclusions: Except for the LAD, our method demonstrated excellent performance and can be generalized to segment additional structures of interest.

The Pediatric Proton and Photon Therapy Comparison Cohort: Study Design for a Multicenter Retrospective Cohort to Investigate Subsequent Cancers After Pediatric Radiation Therapy.

Purpose: The physical properties of protons lower doses to surrounding normal tissues compared with photons, potentially reducing acute and long-term adverse effects, including subsequent cancers. The magnitude of benefit is uncertain, however, and currently based largely on modeling studies. Despite the paucity of directly comparative data, the number of proton centers and patients are expanding exponentially. Direct studies of the potential risks and benefits are needed in children, who have the highest risk of radiation-related subsequent cancers. The Pediatric Proton and Photon Therapy Comparison Cohort aims to meet this need. Methods and Materials: We are developing a record-linkage cohort of 10,000 proton and 10,000 photon therapy patients treated from 2007 to 2022 in the United States and Canada for pediatric central nervous system tumors, sarcomas, Hodgkin lymphoma, or neuroblastoma, the pediatric tumors most frequently treated with protons. Exposure assessment will be based on state-of-the-art dosimetry facilitated by collection of electronic radiation records for all eligible patients. Subsequent cancers and mortality will be ascertained by linkage to state and provincial cancer registries in the United States and Canada, respectively. The primary analysis will examine subsequent cancer risk after proton therapy compared with photon therapy, adjusting for potential confounders and accounting for competing risks. Results: For the primary aim comparing overall subsequent cancer rates between proton and photon therapy, we estimated that with 10,000 patients in each treatment group there would be 80% power to detect a relative risk of 0.8 assuming a cumulative incidence of subsequent cancers of 2.5% by 15 years after diagnosis. To date, 9 institutions have joined the cohort and initiated data collection; additional centers will be added in the coming year(s). Conclusions: Our findings will affect clinical practice for pediatric patients with cancer by providing the first large-scale systematic comparison of the risk of subsequent cancers from proton compared with photon therapy.

Prompt gamma ray activation analysis for determining chemical composition of 3D printing and casting materials used in biomedical applications.

Three-dimensional printing and casting materials were analyzed by prompt gamma-ray activation analysis (PGAA) to determine their suitability as human tissue surrogates for the fabrication of phantoms for medical imaging and radiation dosimetry applications. Measured elemental compositions and densities of five surrogate materials simulating soft tissue and bone were used to determine radiological properties (x-ray mass attenuation coefficient and electron stopping power). When compared with radiological properties of International Commission on Radiation Units and Measurements (ICRU) materials, it was determined that urethane rubber and PLA plastic yielded the best match for soft tissue, while silicone rubber and urethane resin best simulated the properties of bone.

Extension of the RADTriage Colorimetric Dosimeter to Low-Dose Gamma-Ray Exposure Using Scanning Densitometry.

ABSTRACT: There is a need for an instantly indicating, easy-to-read, and inexpensive ionizing radiation dosimeter for first responders and members of the general public. One commercially available option is the RADTriage50 TM colorimetric dosimeter. However, existing literature has not adequately addressed the accuracy of RADTriage50 dosimeters at low doses of ionizing radiation (<50 mSv) or the need for methods to quantitatively read the RADTriage50 dosimeters after they are exposed. In this paper, we use digital scanning methods to read the RADTriage50 dosimeters. The performance of the dosimeters was evaluated by irradiation with a gamma irradiator traceable to national standards. Experiments covered a range of deep dose equivalents (50 mSv to 2,000 mSv) within the manufacturer's specified range (50 mSv to 4,000 mSv) and also below 50 mSv to determine if the digital scanning densitometry method allowed for a quantitative readout with a greater dynamic range. We also conducted tests using different gamma energies, 137 Cs (662 keV) and 60 Co (1.17 and 1.33 MeV), and different dose rates to evaluate the dependency of the RADTriage50 dosimeters on these parameters. Modeling of our measurements suggests that the dose-response of the RADTriage50 dosimeter is linear at low doses with strong non-linearity beginning at ~750 mSv and the dosimeter response appearing to plateau at ~2,000 mSv, although additional measurements at doses beyond 2,000 mSv are needed to confirm this finding. We also found that the RadTriage50 dosimeter response varied with gamma energy, but not with dose rate.

Comparison of out-of-field normal tissue dose estimates for pencil beam scanning proton therapy: MCNP6, PHITS, and TOPAS.

Monte Carlo (MC) methods are considered the gold-standard approach to dose estimation for normal tissues outside the treatment field (out-of-field) in proton therapy. However, the physics of secondary particle production from high-energy protons are uncertain, particularly for secondary neutrons, due to challenges in performing accurate measurements. Instead, various physics models have been developed over the years to reenact these high-energy interactions based on theory. It should thus be acknowledged that MC users must currently accept some unknown uncertainties in out-of-field dose estimates. In the present study, we compared three MC codes (MCNP6, PHITS, and TOPAS) and their available physics models to investigate the variation in out-of-field normal tissue dosimetry for pencil beam scanning proton therapy patients. Total yield and double-differential (energy and angle) production of two major secondary particles, neutrons and gammas, were determined through irradiation of a water phantom at six proton energies (80, 90, 100, 110, 150, and 200 MeV). Out-of-field normal tissue doses were estimated for intracranial irradiations of 1-, 5-, and 15-year-old patients using whole-body computational phantoms. Notably, the total dose estimates for each out-of-field organ varied by approximately 25% across the three codes, independent of its distance from the treatment volume. Dose discrepancies amongst the codes were linked to the utilized physics model, which impacts the characteristics of the secondary radiation field. Using developer-recommended physics, TOPAS produced both the highest neutron and gamma doses to all out-of-field organs from all examined conditions; this was linked to its highest yields of secondary particles and second hardest energy spectra. Subsequent results when using other physics models found reduced yields and energies, resulting in lower dose estimates. Neutron dose estimates were the most impacted by physics model choice, and thus the variation in out-of-field dose estimates may be even larger than 25% when considering biological effectiveness.

Treatment-related thoracic soft tissue sarcomas in US breast cancer survivors: a retrospective cohort study.

BACKGROUND: Soft tissue sarcoma is a rare but serious side-effect of radiotherapy to treat breast cancer, and rates are increasing in the USA. We evaluated potential co-factors in two complimentary cohorts of US breast cancer survivors. METHODS: In this retrospective cohort study, we sourced data from the Kaiser Permanente (KP) cohort and the Surveillance, Epidemiology, and End Results (SEER) 13 registries cohort, both in the USA. The KP cohort included 15 940 women diagnosed with breast cancer from Jan 1, 1990, to Dec 31, 2016, in KP Colorado, KP Northwest (which serves Oregon and Southwest Washington state), or KP Washington, with detailed treatment data and comorbidities (including hypertension and diabetes at or before breast cancer diagnosis) from electronic medical records. The SEER cohort included 457 300 women diagnosed with breast cancer from Jan 1, 1992, to Dec 31, 2016, within the 13 SEER registries across the USA, with initial treatment data (yes vs no or unknown). Eligibility criteria in both cohorts were female breast cancer survivors (stage I-III) aged 20-84 years at diagnosis who had breast cancer surgery, and had survived at least 1 year after breast cancer diagnosis. The outcome of interest was any second thoracic soft tissue sarcoma (angiosarcomas and other subtypes) that developed at least 1 year after breast cancer diagnosis. Risk factors for thoracic soft tissue sarcoma were assessed using multivariable Poisson regression models. FINDINGS: In the KP cohort, median follow-up was 9·3 years (IQR 5·7-13·9) and 19 (0·1%) of 15 940 eligible, evaluable women developed a thoracic soft tissue sarcoma (11 angiosarcomas, eight other subtypes). Most (94·7%; 18 of 19) thoracic soft tissue sarcomas occurred in women treated with radiotherapy; thus, radiotherapy was associated with a significantly increased risk of developing a thoracic soft tissue sarcoma (relative risk [RR] 8·1 [95% CI 1·1-60·4]; p=0·0052), but there was no association with prescribed dose, fractionation, or boost. The RR of angiosarcoma after anthracyclines was 3·6 (95% CI 1·0-13·3; p=0·058). Alkylating agents were associated with an increased risk of developing other sarcomas (RR 7·7 [95% CI 1·2-150·8]; p=0·026). History of hypertension (RR 4·8 [95% CI 1·3-17·6]; p=0·017) and diabetes (5·3 [1·4-20·8]; p=0·036) were each associated with around a five-times increased risk of angiosarcoma. In the SEER cohort, 430 (0·1%) of 457 300 patients had subsequent thoracic soft tissue sarcomas (268 angiosarcomas and 162 other subtypes) after a median follow-up of 8·3 years (IQR 4·3-13·9). Most (77·9%; 335 of 430) cases occurred after radiotherapy; thus, radiotherapy was associated with a significantly increased risk of developing a thoracic soft tissue sarcoma (RR 3·0 [95% CI 2·4-3·8]; p<0·0001) and, for angiosarcomas, the RR for breast-conserving surgery plus radiotherapy versus mastectomy plus radiotherapy was 1·9 (1·1-3·3; p=0·012). By 10 years after radiotherapy, the cumulative incidence of thoracic soft tissue sarcoma was 0·21% (95% CI 0·12-0·34) in the KP cohort and 0·15% (95% CI 0·13-0·17) in SEER. INTERPRETATION: Radiotherapy was the strongest risk factor for thoracic soft tissue sarcoma in both cohorts. This finding, along with the novel findings for diabetes and hypertension as potential risk factors for angiosarcomas, warrant further investigation as potential targets for prevention strategies and increased surveillance. FUNDING: US National Cancer Institute and National Institutes of Health.

Validation and Comparison of Radiograph-Based Organ Dose Reconstruction Approaches for Wilms Tumor Radiation Treatment Plans.

Purpose: Our purpose was to validate and compare the performance of 4 organ dose reconstruction approaches for historical radiation treatment planning based on 2-dimensional radiographs. Methods and Materials: We considered 10 patients with Wilms tumor with planning computed tomography images for whom we developed typical historic Wilms tumor radiation treatment plans, using anteroposterior and posteroanterior parallel-opposed 6 MV flank fields, normalized to 14.4 Gy. Two plans were created for each patient, with and without corner blocking. Regions of interest (lungs, heart, nipples, liver, spleen, contralateral kidney, and spinal cord) were delineated, and dose-volume metrics including organ mean and minimum dose (Dmean and Dmin) were computed as the reference baseline for comparison. Dosimetry for the 20 plans was then independently reconstructed using 4 different approaches. Three approaches involved surrogate anatomy, among which 2 used demographic-matching criteria for phantom selection/building, and 1 used machine learning. The fourth approach was also machine learning-based, but used no surrogate anatomies. Absolute differences in organ dose-volume metrics between the reconstructed and the reference values were calculated. Results: For Dmean and Dmin (average and minimum point dose) all 4 dose reconstruction approaches performed within 10% of the prescribed dose (≤1.4 Gy). The machine learning-based approaches showed a slight advantage for several of the considered regions of interest. For Dmax (maximum point dose), the absolute differences were much higher, that is, exceeding 14% (2 Gy), with the poorest agreement observed for near-beam and out-of-beam organs for all approaches. Conclusions: The studied approaches give comparable dose reconstruction results, and the choice of approach for cohort dosimetry for late effects studies should still be largely driven by the available resources (data, time, expertise, and funding).

Fetal dose from proton pencil beam scanning craniospinal irradiation during pregnancy: a Monte Carlo study.

Objective. We conducted a Monte Carlo study to comprehensively investigate the fetal dose resulting from proton pencil beam scanning (PBS) craniospinal irradiation (CSI) during pregnancy.Approach. The gestational-age dependent pregnant phantom series developed at the University of Florida (UF) were converted into DICOM-RT format (CT images and structures) and imported into a treatment planning system (TPS) (Eclipse v15.6) commissioned to a IBA PBS nozzle. A proton PBS CSI plan (prescribed dose: 36 Gy) was created on the phantoms. The TOPAS MC code was used to simulate the proton PBS CSI on the phantoms, for which MC beam properties at the nozzle exit (spot size, spot divergence, mean energy, and energy spread) were matched to IBA PBS nozzle beam measurement data. We calculated mean absorbed doses for 28 organs and tissues and whole body of the fetus at eight gestational ages (8, 10, 15, 20, 25, 30, 35, and 38 weeks). For contextual purposes, the fetal organ/tissue doses from the treatment planning CT scan of the mother's head and torso were estimated using the National Cancer Institute dosimetry system for CT (NCICT, Version 3) considering a low-dose CT protocol (CTDIvol: 8.97 mGy).Main results. The majority of the fetal organ/tissue doses from the proton PBS CSI treatment fell within a range of 3-6 mGy. The fetal organ/tissue doses for the 38 week phantom showed the largest variation with the doses ranging from 2.9 mGy (adrenals) to 8.2 mGy (eye lenses) while the smallest variation ranging from 3.2 mGy (oesophagus) to 4.4 mGy (brain) was observed for the doses for the 20 week phantom. The fetal whole-body dose ranged from 3.7 mGy (25 weeks) to 5.8 mGy (8 weeks). Most of the fetal doses from the planning CT scan fell within a range of 7-13 mGy, approximately 2-to-9 times lower than the fetal dose equivalents of the proton PBS CSI treatment (assuming a quality factor of 7).Significance. The fetal organ/tissue doses observed in the present work will be useful for one of the first clinically informative predictions on the magnitude of fetal dose during proton PBS CSI during pregnancy.

Development of whole-body representation and dose calculation in a commercial treatment planning system.

For the epidemiological evaluation of long-term side effects of radiotherapy patients, it is important to know the doses to organs and tissues everywhere in the patient. Computed tomography (CT) images of the patients which contain the anatomical information are sometimes available for each treated patient. However, the available CT scans usually cover only the treated volume of the patient including the target and surrounding anatomy. To overcome this limitation, in this work we describe the development of a software tool using the Varian Eclipse Scripting API for extending a partial-body CT to a whole-body representation in the treatment planning system for dose calculation. The whole-body representation is created by fusing the partial-body CT with a similarly sized whole-body computational phantom selected from a library containing 64 phantoms of different heights, weights, and genders. The out-of-field dose is calculated with analytical models from the literature and merged with the treatment planning system-calculated dose. To test the method, the out-of-field dose distributions on the computational phantoms were compared to dose calculations on whole-body patient CTs. The mean doses, D2% and D98% were compared in 26 organs and tissues for 14 different treatment plans in 5 patients using 3D-CRT, IMRT, VMAT, coplanar and non-coplanar techniques. From these comparisons we found that mean relative differences between organ doses ranged from -10% and +20% with standard deviations of up to 40%. The developed method will help epidemiologists and researchers estimate organ doses outside the treated volume when only limited treatment planning CT information is available.

Application of an automatic segmentation method for evaluating cardiac structure doses received by breast radiotherapy patients.

BACKGROUND AND PURPOSE: Quantifying radiation dose to cardiac substructures is important for research on the etiology and prevention of complications following radiotherapy; however, segmentation of substructures is challenging. In this study we demonstrate the application of our atlas-based automatic segmentation method to breast cancer radiotherapy plans for generating radiation doses in support of late effects research. MATERIAL AND METHODS: We applied our segmentation method to contour heart substructures on the computed tomography (CT) images of 70 breast cancer patients who received external photon radiotherapy. Two cardiologists provided manual segmentation of the whole heart (WH), left/right atria, left/right ventricles, and left anterior descending artery (LAD). The automatically contours were compared with manual delineations to evaluate similarity in terms of geometry and dose. RESULTS: The mean Dice similarity coefficient between manual and automatic segmentations was 0.96 for the WH, 0.65 to 0.82 for the atria and ventricles, and 0.06 for the LAD. The mean average surface distance was 1.2 mm for the WH, 3.4 to 4.1 mm for the atria and ventricles, and 6.4 mm for the LAD. We found the dose to the cardiac substructures based on our automatic segmentation agrees with manual segmentation within expected observer variability. For left breast patients, the mean absolute difference in mean dose was 0.1 Gy for the WH, 0.2 to 0.7 Gy for the atria and ventricles, and 1.8 Gy for the LAD. For right breast patients, these values were 0.0 Gy, 0.1 to 0.4 Gy, and 0.4 Gy, respectively. CONCLUSION: Our automatic segmentation method will facilitate the development of radiotherapy prescriptive criteria for mitigating cardiovascular complications.

Fabrication of a pediatric torso phantom with multiple tissues represented using a dual nozzle thermoplastic 3D printer.

PURPOSE: To demonstrate an on-demand and nearly automatic method for fabricating tissue-equivalent physical anthropomorphic phantoms for imaging and dosimetry applications using a dual nozzle thermoplastic three-dimensional (3D) printer and two types of plastic. METHODS: Two 3D printing plastics were investigated: (a) Normal polylactic acid (PLA) as a soft tissue simulant and (b) Iron PLA (PLA-Fe), a composite of PLA and iron powder, as a bone simulant. The plastics and geometry of a 1-yr-old computational phantom were combined with a dual extrusion 3D printer to fabricate an anthropomorphic imaging phantom. The volumetric fill density of the 3D-printed parts was varied to approximate tissues of different radiographic density using a calibration curve relating the printer infill density setting to measured CT number. As a demonstration of our method we printed a 10 cm axial cross-section of the computational phantom's torso at full scale. We imaged the phantom on a CT scanner and compared HU values to those of a 1-yr-old patient and a commercial 5-yr-old physical phantom. RESULTS: The phantom was printed in six parts over the course of a week. The printed phantom included 30 separate anatomical regions including soft tissue remainder, lungs (left and right), heart, esophagus, rib cage (left and right ribs 1 to 10), clavicles (left and right), scapulae (left and right), thoracic vertebrae (one solid object defining thoracic vertebrae T1 to T9). CT scanning of the phantom showed five distinct radiographic regions (heart, lung, soft tissue remainder, bone, and air cavity) despite using only two types of plastic. The 3D-printed phantom demonstrated excellent similarity to commercially available phantoms, although key limitations in the printer and printing materials leave opportunity for improvement. CONCLUSION: Patient-specific anthropomorphic phantoms can be 3D printed and assembled in sections for imaging and dosimetry applications. Such phantoms will be useful for dose verification purposes when commercial phantoms are unavailable for purchase in the specific anatomies of interest.

Conversion of computational human phantoms into DICOM-RT for normal tissue dose assessment in radiotherapy patients.

Radiotherapy (RT) treatment planning systems (TPS) are designed for the fast calculation of dose to the tumor bed and nearby organs at risk using x-ray computed tomography (CT) images. However, CT images for a patient are typically available for only a small portion of the body, and in some cases, such as for retrospective epidemiological studies, no images may be available at all. When dose to organs that lie out-of-scan must be estimated, a convenient alternative for the unknown patient anatomy is to use a matching whole-body computational phantom as a surrogate. The purpose of the current work is to connect such computational phantoms to commercial RT TPS for retrospective organ dose estimation. A custom software with graphical user interface (GUI), called the DICOM-RT Generator, was developed in MATLAB to convert voxel computational phantoms into the digital imaging and communications in medicine radiotherapy (DICOM-RT) format, compatible with commercial TPS. DICOM CT image sets for the phantoms are created via a density-to-Hounsfield unit (HU) conversion curve. Accompanying structure sets containing the organ contours are automatically generated by tracing binary masks of user-specified organs on each phantom CT slice. The software was tested on a library of body size-dependent phantoms, the International Commission on Radiological Protection reference phantoms, and a canine voxel phantom, taking only a few minutes per conversion. The resulting DICOM-RT files were tested on several commercial TPS. As an example application, a library of converted phantoms was used to estimate organ doses for members of the National Wilms Tumor Study (NWTS) cohort. The converted phantom library, in DICOM format, and a standalone MATLAB-compiled executable of the DICOM-RT Generator are available for others to use for research purposes (http://ncidose.cancer.gov).

Determination of proton stopping power ratio with dual-energy CT in 3D-printed tissue/air cavity surrogates.

PURPOSE: To study the accuracy with which proton stopping power ratio (SPR) can be determined with dual-energy computed tomography (DECT) for small structures and bone-tissue-air interfaces like those found in the head or in the neck. METHODS: Hollow cylindrical polylactic acid (PLA) plugs (3 cm diameter, 5 cm height) were 3D printed containing either one or three septa with thicknesses tsepta  = 0.8, 1.6, 3.2, and 6.4 mm running along the length of the plug. The cylinders were inserted individually into a tissue-equivalent head phantom (16 cm diameter, 5 cm height). First, DECT scans were obtained using a Siemens SOMATOM Definition Edge CT scanner. Effective atomic number (Zeff ) and electron density (ρe ) images were reconstructed from the DECT to produce SPR-CT images of each plug. Second, independent elemental composition analysis of the PLA plastic was used to determine the Zeff and ρe for calculating the theoretical SPR (SPR-TH) using the Bethe-Bloch equation. Finally, for each plug, a direct measurement of SPR (SPR-DM) was obtained in a clinical proton beam. The values of SPR-CT, SPR-TH, and SPR-DM were compared. RESULTS: The SPR-CT for PLA agreed with SPR-DM for tsepta  ≥ 3 mm (for CT slice thicknesses of 0.5, 1.0, and 3.0 mm). The density of PLA was found to decrease with thickness when tsepta  < 3 mm. As tsepta (and density) decreased, the SPR-CT values also decreased, in good agreement with SPR-DM and SPR-TH. CONCLUSION: Overall, the DECT-based SPR-CT was within 3% of SPR-TH and SPR-DM in the high-density gradient regions of the 3D-printed plugs for septa greater than ~ 3mm in thickness.

Automatic segmentation of cardiac structures for breast cancer radiotherapy.

BACKGROUND AND PURPOSE: We developed an automatic method to segment cardiac substructures given a radiotherapy planning CT images to support epidemiological studies or clinical trials looking at cardiac disease endpoints after radiotherapy. MATERIAL AND METHODS: We used a most-similar atlas selection algorithm and 3D deformation combined with 30 detailed cardiac atlases. We cross-validated our method within the atlas library by evaluating geometric comparison metrics and by comparing cardiac doses for simulated breast radiotherapy between manual and automatic contours. We analyzed the impact of the number of cardiac atlas in the library and the use of manual guide points on the performance of our method. RESULTS: The Dice Similarity Coefficients from the cross-validation reached up to 97% (whole heart) and 80% (chambers). The Average Surface Distance for the coronary arteries was less than 10.3 mm on average, with the best agreement (7.3 mm) in the left anterior descending artery (LAD). The dose comparison for simulated breast radiotherapy showed differences less than 0.06 Gy for the whole heart and atria, and 0.3 Gy for the ventricles. For the coronary arteries, the dose differences were 2.3 Gy (LAD) and 0.3 Gy (other arteries). The sensitivity analysis showed no notable improvement beyond ten atlases and the manual guide points does not significantly improve performance. CONCLUSION: We developed an automated method to contour cardiac substructures for radiotherapy CTs. When combined with accurate dose calculation techniques, our method should be useful for cardiac dose reconstruction of a large number of patients in epidemiological studies or clinical trials.

A Novel Method to Extend a Partial-Body CT for the Reconstruction of Dose to Organs beyond the Scan Range.

Epidemiological investigation is an important approach to assessing the risk of late effects after radiotherapy, and organ dosimetry is a crucial part of such analysis. Computed tomography (CT) images, if available, can be a valuable resource for individualizing the dosimetry, because they describe the specific anatomy of the patient. However, CT images acquired for radiation treatment planning purposes cover only a portion of the body near the target volume, whereas for epidemiology, the interest lies in the more distant normal tissues, which may be located outside the scan range. To address this challenge, we developed a novel method, called the Anatomically Predictive Extension (APE), to extend a partial-body CT image stack using images of a computational human phantom matched to the patient based on their height and weight. To test our method, we created five APE phantoms from chest and abdominal images extracted from the chest-abdomen-pelvis (CAP) CT scans of five patients. Organ doses were calculated for simple chest and prostate irradiations that were planned on the reference computational phantom (assumed patient geometry if no CT images are available), APE phantoms (patient-phantom hybrid given a partial-body patient CT) and full patient CAP CT scans (ground truth). The APE phantoms and patient CAP CT scans resulted in nearly identical dosimetry for those organs that were fully included in the partial-body CT used to construct the APE. The calculated doses to these same organs in the reference phantoms differed by up to 20% and 52% for the chest and prostate cases, respectively. For organs outside the scan coverage, the reference phantom showed, on average, dose differences of 31% (chest case) and 41% (prostate case). For the APE phantoms, these values were 26% (chest) and 17% (prostate). The APE method combines patient and phantom images to improve organ dosimetry both inside and outside the scan range. We intend to use the APE method for estimating dose for organs peripheral to the treatment fields; however, this method is quite generalizable with many potential applications.

Comparison of normal tissue dose calculation methods for epidemiological studies of radiotherapy patients.

Radiation dosimetry is an essential input for epidemiological studies of radiotherapy patients aimed at quantifying the dose-response relationship of late-term morbidity and mortality. Individualised organ dose must be estimated for all tissues of interest located in-field, near-field, or out-of-field. Whereas conventional measurement approaches are limited to points in water or anthropomorphic phantoms, computational approaches using patient images or human phantoms offer greater flexibility and can provide more detailed three-dimensional dose information. In the current study, we systematically compared four different dose calculation algorithms so that dosimetrists and epidemiologists can better understand the advantages and limitations of the various approaches at their disposal. The four dose calculations algorithms considered were as follows: the (1) Analytical Anisotropic Algorithm (AAA) and (2) Acuros XB algorithm (Acuros XB), as implemented in the Eclipse treatment planning system (TPS); (3) a Monte Carlo radiation transport code, EGSnrc; and (4) an accelerated Monte Carlo code, the x-ray Voxel Monte Carlo (XVMC). The four algorithms were compared in terms of their accuracy and appropriateness in the context of dose reconstruction for epidemiological investigations. Accuracy in peripheral dose was evaluated first by benchmarking the calculated dose profiles against measurements in a homogeneous water phantom. Additional simulations in a heterogeneous cylinder phantom evaluated the performance of the algorithms in the presence of tissue heterogeneity. In general, we found that the algorithms contained within the commercial TPS (AAA and Acuros XB) were fast and accurate in-field or near-field, but not acceptable out-of-field. Therefore, the TPS is best suited for epidemiological studies involving large cohorts and where the organs of interest are located in-field or partially in-field. The EGSnrc and XVMC codes showed excellent agreement with measurements both in-field and out-of-field. The EGSnrc code was the most accurate dosimetry approach, but was too slow to be used for large-scale epidemiological cohorts. The XVMC code showed similar accuracy to EGSnrc, but was significantly faster, and thus epidemiological applications seem feasible, especially when the organs of interest reside far away from the field edge.

Extension of RPI-adult male and female computational phantoms to obese patients and a Monte Carlo study of the effect on CT imaging dose.

Although it is known that obesity has a profound effect on x-ray computed tomography (CT) image quality and patient organ dose, quantitative data describing this relationship are not currently available. This study examines the effect of obesity on the calculated radiation dose to organs and tissues from CT using newly developed phantoms representing overweight and obese patients. These phantoms were derived from the previously developed RPI-adult male and female computational phantoms. The result was a set of ten phantoms (five males, five females) with body mass indexes ranging from 23.5 (normal body weight) to 46.4 kg m(-2) (morbidly obese). The phantoms were modeled using triangular mesh geometry and include specified amounts of the subcutaneous adipose tissue and visceral adipose tissue. The mesh-based phantoms were then voxelized and defined in the Monte Carlo N-Particle Extended code to calculate organ doses from CT imaging. Chest-abdomen-pelvis scanning protocols for a GE LightSpeed 16 scanner operating at 120 and 140 kVp were considered. It was found that for the same scanner operating parameters, radiation doses to organs deep in the abdomen (e.g., colon) can be up to 59% smaller for obese individuals compared to those of normal body weight. This effect was found to be less significant for shallow organs. On the other hand, increasing the tube potential from 120 to 140 kVp for the same obese individual resulted in increased organ doses by as much as 56% for organs within the scan field (e.g., stomach) and 62% for those out of the scan field (e.g., thyroid), respectively. As higher tube currents are often used for larger patients to maintain image quality, it was of interest to quantify the associated effective dose. It was found from this study that when the mAs was doubled for the obese level-I, obese level-II and morbidly-obese phantoms, the effective dose relative to that of the normal weight phantom increased by 57%, 42% and 23%, respectively. This set of new obese phantoms can be used in the future to study the optimization of image quality and radiation dose for patients of different weight classifications. Our ultimate goal is to compile all the data derived from these phantoms into a comprehensive dosimetry database defined in the VirtualDose software.

Comparison of organ doses for patients undergoing balloon brachytherapy of the breast with HDR 192Ir or electronic sources using monte carlo simulations in a heterogeneous human phantom.

PURPOSE: Accelerated partial breast irradiation via interstitial balloon brachytherapy is a fast and effective treatment method for certain early stage breast cancers. The radiation can be delivered using a conventional high-dose rate (HDR) 192Ir gamma-emitting source or a novel electronic brachytherapy (eBx) source which uses lower energy x rays that do not penetrate as far within the patient. A previous study [A. Dickler, M. C. Kirk, N. Seif, K. Griem, K. Dowlatshahi, D. Francescatti, and R. A. Abrams, "A dosimetric comparison of MammoSite high-dose-rate brachytherapy and Xoft Axxent electronic brachytherapy," Brachytherapy 6, 164-168 (2007)] showed that the target dose is similar for HDR 192Ir and eBx. This study compares these sources based on the dose received by healthy organs and tissues away from the treatment site. METHODS: A virtual patient with left breast cancer was represented by a whole-body, tissue-heterogeneous female voxel phantom. Monte Carlo methods were used to calculate the dose to healthy organs in a virtual patient undergoing balloon brachytherapy of the left breast with HDR 192Ir or eBx sources. The dose-volume histograms for a few organs which received large doses were also calculated. Additional simulations were performed with all tissues in the phantom defined as water to study the effect of tissue inhomogeneities. RESULTS: For both HDR 192Ir and eBx, the largest mean organ doses were received by the ribs, thymus gland, left lung, heart, and sternum which were close to the brachytherapy source in the left breast, eBx yielded mean healthy organ doses that were more than a factor of approximately 1.4 smaller than for HDR 192Ir for all organs considered, except for the three closest ribs. Excluding these ribs, the average and median dose-reduction factors were approximately 28 and approximately 11, respectively. The volume distribution of doses in nearby soft tissue organs that were outside the PTV were also improved with eBx. However, the maximum dose to the closest rib with the eBx source was 5.4 times greater than that of the HDR 192Ir source. The ratio of tissue-to-water maximum rib dose for the eBx source was approximately 5. CONCLUSIONS: The results of this study indicate that eBx may offer lower toxicity to most healthy tissues, except nearby bone. TG-43 methods have a tendency to underestimate dose to bone, especially the ribs. Clinical studies evaluating the negative health effects caused by irradiating healthy organs are needed so that physicians can better understand when HDR 192Ir or eBx might best benefit a patient.