Executive summary of AAPM Report Task Group 113: Guidance for the physics aspects of clinical trials

Moran, Jean M.; Molineu, A.; Kruse, Jon J.; Oldham, Mark; Jeraj, Robert; Galvin, James M.; Palta, Jatinder; Olch, Arthur J.

doi:10.1002/acm2.12384

Cited by 16 publications

(12 citation statements)

References 26 publications

(41 reference statements)

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“…These disparate considerations point to unmet needs specific to adaptive radiotherapy. While several reports have defined recommendations for clinical trial implementation (35), dose prescription and reporting (36,37), uncertainty margination (38)(39)(40), ROI and DVH nomenclature(41), commissioning of IGRT systems (42,43), implementation and reporting of image registration techniques (44), modeling (45) and reporting guidelines (46), as yet no single standardized reporting structure exists to allow the massive amounts and permutations of image, margination, dose, clinical, and relational data generated by even a small adaptive head and neck clinical trial to be reportable in an efficient manner, let alone consistent with FAIR Guiding Principles for scientific data management (47) (vide infra, Table 5). The use of radiation oncology specific ontology systems at clinical scale remains exciting, but nascent (41,(48)(49)(50).…”

Section: Cataloguing Artmentioning

confidence: 99%

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Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician

Heukelom

Fuller

2019

Seminars in Radiation Oncology

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For nearly two decades, adaptive radiation therapy (ART) has been proposed as a method to account for changes in head and neck tumor and normal tissue to enhance therapeutic ratios. While technical advances in imaging, planning and delivery have allowed greater capacity for ART delivery, and a series of dosimetric explorations have consistently shown capacity for improvement, there remains a paucity of clinical trials demonstrating the utility of ART. Furthermore, while ad hoc implementation of head and neck ART is reported, systematic full-scale head and neck ART remains an as yet unreached reality. To some degree, this lack of scalability may be related to not only the complexity of ART, but also variability in the nomenclature and descriptions of what is encompassed by ART. Consequently, we present an overview of the history, current status, and recommendations for the future of ART, with an eye towards improving the clarity and description of head and neck ART for interested clinicians, noting practical considerations for implementation of an ART program or clinical trial. Process level considerations for ART are noted, reminding the reader that, paraphrasing Elbert Hubbard, "Art is not a thing, it is a way." "When an artist uses a conceptual form of art, it means that all of the planning and decisions are made beforehand and the execution is a perfunctory affair. The idea becomes a machine that makes the art."-Sol LeWitt (1) The concept of "adaptive radiation therapy" (ART) has been widely praised, serially modeled in silico, and heavily discussed, but to date, at a practical level, remains rarely implemented in vivo outside the research setting. We aim to discuss the "state of the ART" in head and neck therapy, with an emphasis on specification of the intent with which ART is performed; the terms of ART used, or a disambiguation of the nomenclature; technical

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Section: Cataloguing Artmentioning

confidence: 99%

“…-"Improve local control probability by 15% via an isotoxic dose escalation of residual PET-derived high-risk regions on mid-therapy imaaina." (35), (36,37)…”

Section: Processmentioning

confidence: 99%

Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician

Heukelom

Fuller

2019

Seminars in Radiation Oncology

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“…Repeatability and reproducibility of MRI data significantly affect MRI‐guided quantitative analyses in clinical RT applications, especially for large‐scale, multi‐center prospective clinical studies, such as the Radiation Therapy Oncology Group (RTOG) clinical trials. Data with excellent repeatability and reproducibility will not only improve the statistical power and therefore the reliability of the scientific conclusions from the studies, but also potentially reduce the total number of enrollments and hence have possible economic impact.…”

Section: Discussionmentioning

confidence: 99%

Initial assessment of 3D magnetic resonance fingerprinting (MRF) towards quantitative brain imaging for radiation therapy

Chen

Shen

et al. 2019

Medical Physics

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Purpose Magnetic resonance fingerprinting (MRF) provides quantitative T1/T2 maps, enabling applications in clinical radiotherapy such as large‐scale, multi‐center clinical trials for longitudinal assessment of therapy response. We evaluated the feasibility of a quantitative three‐dimensional‐MRF (3D‐MRF) towards its radiotherapy applications of primary brain tumors. Methods A fast whole‐brain 3D‐MRF sequence initially developed for diagnostic radiology was optimized using flexible body coils, which is the typical MR imaging setup for radiotherapy treatment planning and for MR imaging (MRI)‐guided treatment delivery. Optimization criteria included the accuracy and the precision of T1/T2 quantifications of polyvinylpyrrolidone (PVP) solutions, compared to those from the 3D‐MRF using a 32‐channel head coil. The accuracy of T1/T2 quantifications from the optimized MRF was first examined in healthy volunteers with two different coil setups. The intra‐ and inter‐scanner variations of image intensity from the optimized sequence were quantified by longitudinal scans of the PVP solutions on two 3T scanners. Using a 3D‐printed MRI geometry phantom, susceptibility‐induced distortion with the optimized 3D‐MRF was quantified as the Dice coefficient of phantom contours, compared to those from CT images. By introducing intentional head motion during 10% of the scan, the robustness of the optimized 3D‐MRF towards motion was evaluated through visual inspection of motion artifacts and through quantitative analysis of image sharpness in brain MRF maps. Results The optimized sequence acquired whole‐brain T1, T2 and proton density maps and with a resolution of 1.2 × 1.2 × 3 mm3 in 10 min, similar to the total acquisition time of 3D T1‐ and T2‐weighted images of the same resolution. In vivo T1 and T2 values of the white and gray matter were consistent with literature. The intra‐ and inter‐scanner variability of the intensity‐normalized MRF T1 was 1.0% ± 0.7% and 2.3% ± 1.0% respectively, in contrast to 5.3% ± 3.8% and 3.2% ± 1.6% from the normalized T1‐weighted MRI. Repeatability and reproducibility of MRF T1 were independent of intensity normalization. Both phantom and human data demonstrated that the optimized 3D‐MRF is more robust to subject motion and artifacts from subject‐specific susceptibility difference. Compared to CT contours, the Dice coefficient of phantom contours from 3D‐MRF was 0.93, improved from 0.87 from the T1‐weighted MRI. Conclusion Compared to conventional MRI, the optimized 3D‐MRF demonstrated improved repeatability across time points and reproducibility across scanners for better tissue quantification, as well as improved robustness to subject‐specific susceptibility and motion artifacts under a typical MR imaging setup for radiotherapy. More importantly, quantitative MRF T1/T2 measurements lead to promising potentials towards longitudinal quantitative assessment of treatment response for better adaptive therapy and for large‐scale, multi‐center clinical trials.

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“…A risk assessment and consensus evaluation of the critical requirements is presented in AAPM Medical Physics Practice Guideline 8a on linear accelerator QA . Moreover, QA is a necessary process for credentialing institutions for multi‐institutional radiotherapy clinical trials such as the ones carried out by the NRG Oncology consortium and AAPM report TG113 . While these QA guidelines have focused on monitoring all functional aspects of radiotherapy equipment, recent efforts have been geared toward identifying failures in workflow and processes.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for automated quality assurance in radiotherapy: A proof of principle using EPID data description

et al. 2019

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Executive summary of AAPM Report Task Group 113: Guidance for the physics aspects of clinical trials

Cited by 16 publications

References 26 publications

Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician

Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician

Initial assessment of 3D magnetic resonance fingerprinting (MRF) towards quantitative brain imaging for radiation therapy

Machine learning for automated quality assurance in radiotherapy: A proof of principle using EPID data description

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