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.

A MC-based anthropomorphic test case for commissioning model-based dose calculation in interstitial breast 192-Ir HDR brachytherapy.

PURPOSE: To provide the first clinical test case for commissioning of 192 Ir brachytherapy model-based dose calculation algorithms (MBDCAs) according to the AAPM TG-186 report workflow. ACQUISITION AND VALIDATION METHODS: A computational patient phantom model was generated from a clinical multi-catheter 192 Ir HDR breast brachytherapy case. Regions of interest (ROIs) were contoured and digitized on the patient CT images and the model was written to a series of DICOM CT images using MATLAB. The model was imported into two commercial treatment planning systems (TPSs) currently incorporating an MBDCA. Identical treatment plans were prepared using a generic 192 Ir HDR source and the TG-43-based algorithm of each TPS. This was followed by dose to medium in medium calculations using the MBDCA option of each TPS. Monte Carlo (MC) simulation was performed in the model using three different codes and information parsed from the treatment plan exported in DICOM radiation therapy (RT) format. Results were found to agree within statistical uncertainty and the dataset with the lowest uncertainty was assigned as the reference MC dose distribution. DATA FORMAT AND USAGE NOTES: The dataset is available online at http://irochouston.mdanderson.org/rpc/BrachySeeds/BrachySeeds/index.html,https://doi.org/10.52519/00005. Files include the treatment plan for each TPS in DICOM RT format, reference MC dose data in RT Dose format, as well as a guide for database users and all files necessary to repeat the MC simulations. POTENTIAL APPLICATIONS: The dataset facilitates the commissioning of brachytherapy MBDCAs using TPS embedded tools and establishes a methodology for the development of future clinical test cases. It is also useful to non-MBDCA adopters for intercomparing MBDCAs and exploring their benefits and limitations, as well as to brachytherapy researchers in need of a dosimetric and/or a DICOM RT information parsing benchmark. Limitations include specificity in terms of radionuclide, source model, clinical scenario, and MBDCA version used for its preparation.

Dose prescription and reporting in stereotactic body radiotherapy: A multi-institutional study.

BACKGROUND AND PURPOSE: Radiation dose prescriptions are foundational for optimizing treatment efficacy and limiting treatment-related toxicity. We sought to assess the lack of standardization of SBRT dose prescriptions across institutions. MATERIALS & METHODS: Dosimetric data from 1298 patients from 9 academic institutions treated with IMRT and VMAT were collected. Dose parameters D100, D98, D95, D50, and D2 were used to assess dosimetric variability. RESULTS: Disease sites included lung (48.3 %) followed by liver (29.7 %), prostate (7.5 %), spine (6.8 %), brain (4.1 %), and pancreas (2.5 %). The PTV volume in lung varied widely with bimodality into two main groups (22.0-28.7 cm3) and (48.0-67.1 cm3). A hot spot ranging from 120-150 % was noted in nearly half of the patients, with significant variation across institutions. A D50 ≥ 110 % was found in nearly half of the institutions. There was significant dosimetric variation across institutions. CONCLUSIONS: The SBRT prescriptions in the literature or in treatment guidelines currently lack nuance and hence there is significant variation in dose prescriptions across academic institutions. These findings add greater importance to the identification of dose parameters associated with improved clinical outcome comparisons as we move towards more hypofractionated treatments. There is a need for standardized reporting to help institutions in adapting treatment protocols based on the outcome of clinical trials. Dosimetric parameters are subsequently needed for uniformity and thereby standardizing planning guidelines to maximize efficacy, mitigate toxicity, and reduce treatment disparities are urgently needed.

Development and Clinical Implementation of an Automated Virtual Integrative Planner for Radiation Therapy of Head and Neck Cancer.

Purpose: Head and neck (HN) radiation (RT) treatment planning is complex and resource intensive. Deviations and inconsistent plan quality significantly affect clinical outcomes. We sought to develop a novel automated virtual integrative (AVI) knowledge-based planning application to reduce planning time, increase consistency, and improve baseline quality. Methods and Materials: An in-house write-enabled script was developed from a library of 668 previously treated HN RT plans. Prospective hazard analysis was performed, and mitigation strategies were implemented before clinical release. The AVI-planner software was retrospectively validated in a cohort of 52 recent HN cases. A physician panel evaluated planning limitations during initial deployment, and feedback was enacted via software refinements. A final second set of plans was generated and evaluated. Kolmogorov-Smirnov test in addition to generalized evaluation metric and weighted experience score were used to compare normal tissue sparing between final AVI planner versus respective clinically treated and historically accepted plans. A t test was used to compare the interactive time, complexity, and monitor units for AVI planner versus manual optimization. Results: Initially, 86% of plans were acceptable to treat, with 10% minor and 4% major revisions or rejection recommended. Variability was noted in plan quality among HN subsites, with high initial quality for oropharynx and oral cavity plans. Plans needing revisions were comprised of sinonasal, nasopharynx, P-16 negative squamous cell carcinoma unknown primary, or cutaneous primary sites. Normal tissue sparing varied within subsites, but AVI planner significantly lowered mean larynx dose (median, 18.5 vs 19.7 Gy; P < .01) compared with clinical plans. AVI planner significantly reduced interactive optimization time (mean, 2 vs 85 minutes; P < .01). Conclusions: AVI planner reliably generated clinically acceptable RT plans for oral cavity, salivary, oropharynx, larynx, and hypopharynx cancers. Physician-driven iterative learning processes resulted in favorable evolution in HN RT plan quality with significant time savings and improved consistency using AVI planner.

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.

Randomized Phase II Study of Physiologic MRI-Directed Adaptive Radiation Boost in Poor Prognosis Head and Neck Cancer.

PURPOSE: We conducted a randomized phase II multicenter clinical trial to test the hypothesis that physiologic MRI-based radiotherapy (RT) dose escalation would improve the outcome of patients with poor prognosis head and neck cancer. PATIENTS AND METHODS: MRI was acquired at baseline and at RT fraction 10 to create low blood volume/apparent diffusion coefficient maps for RT boost subvolume definition in gross tumor volume. Patients were randomized to receive 70 Gy (standard RT) or 80 Gy to the boost subvolume (RT boost) with concurrent weekly platinum. The primary endpoint was disease-free survival (DFS) with significance defined at a one-sided 0.1 level, and secondary endpoints included locoregional failure (LRF), overall survival (OS), comparison of adverse events and patient reported outcomes (PRO). RESULTS: Among 81 randomized patients, neither the primary endpoint of DFS (HR = 0.849, P = 0.31) nor OS (HR = 1.19, P = 0.66) was significantly improved in the RT boost arm. However, the incidence of LRF was significantly improved with the addition of the RT boost (HR = 0.43, P = 0.047). Two-year estimates [90% confidence interval (CI)] of the cumulative incidence of LRF were 40% (27%-53%) in the standard RT arm and 18% (10%-31%) in the RT boost arm. Two-year estimates (90% CI) for DFS were 48% (34%-60%) in the standard RT arm and 57% (43%-69%) in the RT boost arm. There were no significant differences in toxicity or longitudinal differences seen in EORTC QLQ30/HN35 subscales between treatment arms in linear mixed-effects models. CONCLUSIONS: Physiologic MRI-based RT boost decreased LRF without a significant increase in grade 3+ toxicity or longitudinal PRO differences, but did not significantly improve DFS or OS. Additional improvements in systemic therapy are likely necessary to realize improvements in DFS and OS.

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.

Early HPV ctDNA Kinetics and Imaging Biomarkers Predict Therapeutic Response in p16+ Oropharyngeal Squamous Cell Carcinoma.

PURPOSE: In locally advanced p16+ oropharyngeal squamous cell carcinoma (OPSCC), (i) to investigate kinetics of human papillomavirus (HPV) circulating tumor DNA (ctDNA) and association with tumor progression after chemoradiation, and (ii) to compare the predictive value of ctDNA to imaging biomarkers of MRI and FDG-PET. EXPERIMENTAL DESIGN: Serial blood samples were collected from patients with AJCC8 stage III OPSCC (n = 34) enrolled on a randomized trial: pretreatment; during chemoradiation at weeks 2, 4, and 7; and posttreatment. All patients also had dynamic-contrast-enhanced and diffusion-weighted MRI, as well as FDG-PET scans pre-chemoradiation and week 2 during chemoradiation. ctDNA values were analyzed for prediction of freedom from progression (FFP), and correlations with aggressive tumor subvolumes with low blood volume (TVLBV) and low apparent diffusion coefficient (TVLADC), and metabolic tumor volume (MTV) using Cox proportional hazards model and Spearman rank correlation. RESULTS: Low pretreatment ctDNA and an early increase in ctDNA at week 2 compared with baseline were significantly associated with superior FFP (P < 0.02 and P < 0.05, respectively). At week 4 or 7, neither ctDNA counts nor clearance were significantly predictive of progression (P = 0.8). Pretreatment ctDNA values were significantly correlated with nodal TVLBV, TVLADC, and MTV pre-chemoradiation (P < 0.03), while the ctDNA values at week 2 were correlated with these imaging metrics in primary tumor. Multivariate analysis showed that ctDNA and the imaging metrics performed comparably to predict FFP. CONCLUSIONS: Early ctDNA kinetics during definitive chemoradiation may predict therapy response in stage III OPSCC.

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.

Early MRI Blood Volume Changes in Constrictor Muscles Correlate With Postradiation Dysphagia.

PURPOSE: Predicting individual patient sensitivity to radiation therapy (RT) for tumor control or normal tissue toxicity is necessary to individualize treatment planning. In head and neck cancer, radiation doses are limited by many nearby critical structures, including structures involved in swallowing. Previous efforts showed that imaging parameters correlate with RT dose; here, we investigate the role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) blood volume (BV) changes in predicting dysphagia. METHODS AND MATERIALS: This study included 32 patients with locally advanced oropharyngeal squamous cell carcinoma treated with definitive chemoradiation on an institutional protocol incorporating baseline and early midtreatment DCE-MRI. BV maps of the pharyngeal constrictor muscles (PCM) were created, and BV increases midtreatment were correlated with the following parameters at 3 and 12 months post-RT: RT dose, Dynamic Imaging Grade of Swallowing Toxicity swallow score, aspiration frequency, European Organisation for Research and Treatment of Cancer HN35 patient-reported outcomes, physician-reported dysphagia, and feeding tube (FT) dependence. RESULTS: The mean BV to the PCMs increased from baseline to fraction 10, which was significant for the superior PCM (P = .006) and middle PCM (P < .001), with a trend in the inferior PCM where lower mean doses were seen (P = .077). The factors associated with FT dependence at 3 months included BV increases in the total PCM (correlation, 0.48; P = .006) and middle PCM (correlation, 0.50; P = .004). A post-RT increase in aspiration was associated with a BV increase in the superior PCM (correlation, 0.44; P = .013),and the increase in the total PCMs was marginally significant (correlation, 0.34; P = .06). The best-performing models of FT dependence (area under the receiver operating curve [AUC] = 0.84) and aspiration increases (AUC = 0.78) included BV increases as well as a mean RT dose to middle PCM. CONCLUSIONS: Our results suggest that midtreatment BV increases derived from DCE-MRI are an early predictor of dysphagia. Further investigation of these promising imaging markers to assess individual patient sensitivity to treatment and the patient's subsequent risk of toxicities is warranted to improve personalization of RT planning.

A dose voxel kernel method for rapid reconstruction of out-of-field neutron dose of patients in pencil beam scanning (PBS) proton therapy.

Monte Carlo (MC) radiation transport methods are used for dose calculation as 'gold standard.' However, the method is computationally time-consuming and thus impractical for normal tissue dose reconstructions for the large number of proton therapy patients required for epidemiologic investigations of late health effects. In the present study, we developed a new dose calculation method for the rapid reconstruction of out-of-field neutron dose to patients undergoing pencil beam scanning (PBS) proton therapy. The new dose calculation method is based on neutron dose voxel kernels (DVKs) generated by MC simulations of a proton pencil beam irradiating a water phantom (60 × 60 × 300 cm3), which was conducted using a MC proton therapy simulation code, TOPAS. The DVKs were generated for 19 beam energies (from 70 to 250 MeV with the 10 MeV interval) and three range shifter thicknesses (1, 3, and 5 cm). An in-house program was written in C++ to superimpose the DVKs onto a patient CT images according to proton beam characteristics (energy, position, and direction) available in treatment plans. The DVK dose calculation method was tested by calculating organ/tissue-specific neutron doses of 1- and 5-year-old whole-body computational phantoms where intracranial and craniospinal irradiations were simulated. The DVK-based doses generally showed reasonable agreement with those calculated by direct MC simulations with a detailed PBS model that were previously published, with differences mostly less than 30% and 10% for the intracranial and craniospinal irradiations, respectively. The computation time of the DVK method for one patient ranged from 1 to 30 min on a single CPU core of a personal computer, demonstrating significant improvement over the direct MC dose calculation requiring several days on high-performance computing servers. Our DVK-based dose calculation method will be useful when dosimetry is needed for the large number of patients such as for epidemiologic or clinical research.

Practice Patterns for the Treatment of Uveal Melanoma with Iodine-125 Plaque Brachytherapy: Ocular Oncology Study Consortium Report 5.

BACKGROUND: Treatment planning for I-125 plaque therapy for uveal melanoma has advanced significantly since the Collaborative Ocular Melanoma Study trial, with more widely available image-guided planning and improved dosimetry. OBJECTIVE: We evaluated real-world practice patterns for I-125 plaque brachytherapy in the United States by studying practice patterns at centers that comprise the Ocular Oncology Study Consortium (OOSC). METHODS: The OOSC database and responses to a treatment practice survey were evaluated. The database contains treatment information from 9 institutions. Patients included in the database were treated between 2010 and 2014. The survey was conducted in 2018 and current treatment planning methods and prescriptions were queried. RESULTS: Examination of the OOSC database revealed that average doses to critical structures were highly consistent, with the exception of one institution. Survey responses indicated that most centers followed published guidelines regarding dose and prescription point. Dose rate ranged from 51 to 118 cGy/h. As of 2018, most institutions use pre-loaded plaques and fundus photographs and/or computed tomography or magnetic resonance imaging in planning. CONCLUSIONS: While there were differences in dosimetric practices, overall agreement in plaque brachytherapy practices was high among OOSC institutions. Clinical margins and planning systems were similar among institutions, while prescription dose, dose rates, and dosimetry varied.

Development and comprehensive commissioning of an automated brachytherapy plan checker.

PURPOSE: To present on the commissioning of an automated brachytherapy plan checker (BPC) for the evaluation of high-dose-rate brachytherapy treatment plans in support of standardized workflows and patient safety. METHODS AND MATERIALS: A BPC was developed using an applications programming interface in a commercial treatment planning system based on different inputs (e.g., regulations, professional society recommendations, and user feedback) and leveraged our experience with an in-house developed external beam plan checker. The BPC was commissioned using a comprehensive suite of test plans with known errors and anonymized clinical plans. RESULTS: During commissioning, the BPC was successfully executed on a total of 87 test plans. Commissioning tests spanned a range of treatment sites and evaluated that pass and fail states were correct. Administration settings were changed in a nonclinical database to evaluate tests involving the source and afterloader. Clinical testing of the BPC was then performed in parallel with a manual review process before clinical implementation. CONCLUSIONS: To commission the BPC for clinical use, a comprehensive suite of test plans was developed and used to ensure the BPC correctly detected and reported errors. A summary of the test plans is presented to help guide users developing similar automated tools. The BPC represents a process-improvement initiative designed to reduce errors and improve safety for brachytherapy patients. By using a comprehensive test suite for commissioning, tests are available for periodic quality assurance and after software upgrades.

A Monte Carlo model for organ dose reconstruction of patients in pencil beam scanning (PBS) proton therapy for epidemiologic studies of late effects.

Significant efforts such as the Pediatric Proton/Photon Consortium Registry (PPCR) involving multiple proton therapy centers have been made to conduct collaborative studies evaluating outcomes following proton therapy. As a groundwork dosimetry effort for the late effect investigation, we developed a Monte Carlo (MC) model of proton pencil beam scanning (PBS) to estimate organ/tissue doses of pediatric patients at the Maryland Proton Treatment Center (MPTC), one of the proton centers involved in the PPCR. The MC beam modeling was performed using the TOPAS (TOol for PArticle Simulation) MC code and commissioned to match measurement data within 1% for range, and 0.3 mm for spot sizes. The established MC model was then tested by calculating organ/tissue doses for sample intracranial and craniospinal irradiations on whole-body pediatric computational human phantoms. The simulated dose distributions were compared with the treatment planning system dose distributions, showing the 3 mm/3% gamma index passing rates of 94%-99%, validating our simulations with the MC model. The calculated organ/tissue doses per prescribed doses for the craniospinal irradiations (1 mGy Gy-1 to 1 Gy Gy-1) were generally much higher than those for the intracranial irradiations (2.1 µGy Gy-1 to 0.1 Gy Gy-1), which is due to the larger field coverage of the craniospinal irradiations. The largest difference was observed at the adrenal dose, i.e. ∼3000 times. In addition, the calculated organ/tissue doses were compared with those calculated with a simplified MC model, showing that the beam properties (i.e. spot size, spot divergence, mean energy, and energy spread) do not significantly influence dose calculations despite the limited irradiation cases. This implies that the use of the MC model commissioned to the MPTC measurement data might be dosimetrically acceptable for patient dose reconstructions at other proton centers particularly when their measurement data are unavailable. The developed MC model will be used to reconstruct organ/tissue doses for MPTC pediatric patients collected in the PPCR.

Predictive Values of MRI and PET Derived Quantitative Parameters for Patterns of Failure in Both p16+ and p16- High Risk Head and Neck Cancer.

Purpose: FDG-PET adds to clinical factors, such tumor stage and p16 status, in predicting local (LF), regional (RF), and distant failure (DF) in poor prognosis locally advanced head and neck cancer (HNC) treated with chemoradiation. We hypothesized that MRI-based quantitative imaging (QI) metrics could add to clinical predictors of treatment failure more significantly than FDG-PET metrics. Materials and methods: Fifty four patients with poor prognosis HNCs who were enrolled in an IRB approved prospective adaptive chemoradiotherapy trial were analyzed. MRI-derived gross tumor volume (GTV), blood volume (BV), and apparent diffusion coefficient (ADC) pre-treatment and mid-treatment (fraction 10), as well as pre-treatment FDG PET metrics, were analyzed in primary and individual nodal tumors. Cox proportional hazards models for prediction of LRF and DF free survival were used to test the additional value of QI metrics over dominant clinical predictors. Results: The mean ADC pre-RT and its change rate mid-treatment were significantly higher and lower in p16- than p16+ primary tumors, respectively. A Cox model identified that high mean ADC pre-RT had a high hazard for LF and RF in p16- but not p16+ tumors (p = 0.015). Most interesting, persisting subvolumes of low BV (TVbv) in primary and nodal tumors mid-treatment had high-risk for DF (p < 0.05). Also, total nodal GTV mid-treatment, mean/max SUV of FDG in all nodal tumors, and total nodal TLG were predictive for DF (p < 0.05). When including clinical stage (T4/N3) and total nodal GTV in the model, all nodal PET parameters had a p-value of >0.3, and only TVbv of primary tumors had a p-value of 0.06. Conclusion: MRI-defined biomarkers, especially persisting subvolumes of low BV, add predictive value to clinical variables and compare favorably with FDG-PET imaging markers. MRI could be well-integrated into the radiation therapy workflow for treatment planning, response assessment, and adaptive therapy.

A feasibility study to reduce misclassification error in occupational dose estimates for epidemiological studies using body size-dependent computational phantoms.

In the epidemiological study on the health effects of participants in the United States Radiologic Technologists (USRT) study, organ dosimetry was performed based on surveys and literature reviews. To convert dosimeter readings to organ doses, organ dose coefficients were adopted. However, the existing dose coefficients were derived from computational human phantoms with ICRP reference height and weight not accounting for the variation in body size. We first calculated preliminary body size-dependent organ dose coefficients using selected body size-dependent phantoms combined with Monte Carlo radiation transport method. We then tested the accuracy of these body-size dependent coefficients against the ICRP 74 reference size coefficients in comparison with five individual-specific organ dose coefficients computed from computed tomography (CT) image-based anatomical models of five adult males with different body sizes also using Monte Carlo methods. The reference size dose coefficients overall underestimate the patient-specific dose coefficients by up to 51%. Body size-dependent phantoms overall provided more accurate organ dose coefficients for the five patients. In case of the esophagus, the dose underestimation of 51% in the comparison with the reference phantom was reduced to 7%. The results confirm that potential dosimetric misclassification caused by using reference size phantom-based dose coefficients can be resolved by using the body size-dependent dose coefficients.

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).

Functional Adaptation in Radiation Therapy.

The promise of adaptive therapy to improve outcomes in radiation oncology has been an area of interest and research in the community for many years. One of the sources of data that can be used to drive adaptive therapy is functional information about the tumor or normal tissues. This avenue of adaptation includes many potential sources of data including global markers and functional imaging. Global markers can be assessments derived from blood measurements, patient functional testing, and circulating tumor material and functional imaging data comprises spatial physiological information from various imaging studies such as positron emission tomography, magnetic resonance imaging, and single photon emission computed tomography. The goal of functional adaptation is to use these functional data to adapt radiation therapy to improve patient outcomes. While functional adaptation holds a lot of promise, there are challenges such as quantifying and minimizing uncertainties, streamlining clinical implementation, determining the ideal way to incorporate information within treatment plan optimization, and proving the clinical benefit through trials. This paper will discuss the types of functional information currently being used for adaptation, highlight several areas where functional adaptation has been studied, and introduce some of the barriers to more widespread clinical implementation.