Practical Clinical Workflows for Online and Offline Adaptive Radiation Therapy

Green, Olga L.; Henke, Lauren E.; Hugo, Geoffrey D.

doi:10.1016/j.semradonc.2019.02.004

Cited by 118 publications

(109 citation statements)

References 72 publications

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“…Detailed descriptions of the specific workflows and planning strategies for adaptive MRgRT using the ViewRay linac system (Viewray, Cleveland, OH) have been presented previously. 12,13,21,[38][39][40][41] A brief summary, with particular focus on the details related to the online adaptive process, follows. Only if target coverage can clearly be improved or any critical OAR dose constraint is not met will a new plan be created.…”

Section: B | Treatment Plan Adaptation Techniquesmentioning

confidence: 99%

“…As the use of online adaptive MRgRT has grown recently across a variety of clinics, both the potential benefits and practical difficulties of its utilization are beginning to be understood. [12][13][14][15][16][17][18][19][20][21] But ample opportunity remains for assessment and improvement of online adaptive RT as this unique treatment method continues to develop clinically. Analysis of a collection of patient data and their treatment plans in order to better understand and improve a particular RT process can proceed in a variety of ways.…”

Section: Introductionmentioning

confidence: 99%

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Development and evaluation of machine learning models for voxel dose predictions in online adaptive magnetic resonance guided radiation therapy

Thomas

Yang

2020

J Applied Clin Med Phys

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Purpose: Daily online adaptive plan quality in magnetic resonance imaging guided radiation therapy (MRgRT) is difficult to assess in relation to the fully optimized, high quality plans traditionally established offline. Machine learning prediction models developed in this work are capable of predicting 3D dose distributions, enabling the evaluation of online adaptive plan quality to better inform adaptive decisionmaking in MRgRT. Methods: Artificial neural networks predicted 3D dose distributions from input variables related to patient anatomy, geometry, and target/organ-at-risk relationships in over 300 treatment plans from 53 patients receiving adaptive, linac-based MRgRT for abdominal cancers. The models do not include any beam related variables such as beam angles or fluence and were optimized to balance errors related to raw dose and specific plan quality metrics used to guide daily online adaptive decisions. Results: Averaged over all plans, the dose prediction error and the absolute error were 0.1 ± 3.4 Gy (0.1 ± 6.2%) and 3.5 ± 2.4 Gy (6.4 ± 4.3%) respectively. Plan metric prediction errors were −0.1 ± 1.5%, −0.5 ± 2.1%, −0.9 ± 2.2 Gy, and 0.1 ± 2.7 Gy for V95, V100, D95, and D mean respectively. Plan metric prediction

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Section: B | Treatment Plan Adaptation Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and evaluation of machine learning models for voxel dose predictions in online adaptive magnetic resonance guided radiation therapy

Thomas

Yang

2020

J Applied Clin Med Phys

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“…The use of diagnostic MR imaging in staging, treatment planning, and surveillance is rapidly becoming integral to the standard of care of an increasing number of disease sites, necessitating a shift in resident education to inculcate facility in interpreting and using these images. Additionally, online adaptive radiotherapy presents new skills for trainees to master [ 7 ]. Much like the chief resident service described by Jeans et al [ 8 ], we believe this unique curricular offering addresses a need as we look to continually modernize our educational curricula to account for new technologies.…”

Section: Discussionmentioning

confidence: 99%

Novel MR-Guided Radiotherapy Elective Rotation for Radiation Oncology Trainees

Sim

Frakes

Hoffe

et al. 2020

Cureus

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MR-guided adaptive radiation therapy (RT) is emerging as an integral treatment modality for certain applications and is poised to become an exciting opportunity for greater treatment precision and personalization. However, this is still a relatively nascent technology and only a few institutions and programs have access to this technology for clinical use and trainee education. To increase the diversity of elective offerings and improve the understanding of an MR-guided radiotherapy program, we initiated a unique MR-guided radiotherapy elective rotation for radiation oncology residents. During a representative four-week rotation, 21 simulations were completed by the resident on service. A plurality of simulations were for pancreas stereotactic body radiation therapy (SBRT; 48%) and a majority (71%) of simulations were for adaptive treatments. Additionally, 74 adaptive fractions were completed during this month, of which a significant majority (74%) were for pancreas SBRT. Of the non-adaptive fractions, the majority were for prostate SBRT and intensity-modulated radiation therapy (IMRT). Although many programs may offer training in some aspects of MR-guided radiotherapy as trainees rotate through certain disease sites, we hope this may serve as a blueprint to encourage programs with this technology to fully embrace training in essential competencies related to MR-guided radiotherapy. MR-guided radiotherapy has unique challenges that trainees need to understand to deliver treatment safely: geometric uncertainty, MRI to RT isocenter, and uncertainties with voxel size/tracking.

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“…[3][4][5] Based on daily pretreatment CBCT imaging, online (i.e., with a patient in the treatment position) ART is performed to improve daily dose delivery accuracy through tracking the interfraction anatomical changes of tumors during the course of RT. 6,7 However, due to the limited detection sensitivity to functional/molecular events within tumor cells that influence the radiosensitivity of tumor cells to radiation dose, conventional CBCT used in ART does not fully account for interfraction changes. In addition, on-board CBCT based on energy integrating FPD can hardly address the material-specific and energy-dependent x-ray spectral attenuation, which indicates the material compositions of tissues related to functional-and molecular-level changes within tumors.…”

Section: Introductionmentioning

confidence: 99%

“…An ART workflow streamlines multiple RT components, including pretreatment imaging, assessment, re-contouring, plan re-optimization, among others. 7 Offline ART processes are usually implemented in a timescale of hours to days, thereby difficult to address the frequent changes between fractions or within a fraction. Although inline or real-time ART adapts the plan in real time during delivery responding to rapid intrafraction changes in patients, no real-time functional/molecular modalities are currently in clinical use due to the challenges of highly efficient and specialized tools needed.…”

Section: Introductionmentioning

confidence: 99%

A Monte Carlo study to investigate the feasibility of an on‐board SPECT/spectral‐CT/CBCT imager for medical linear accelerator

et al. 2020

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The on-board flat-panel cone-beam computed tomography (CBCT) lacks molecular/functional information for current online image-guided radiation therapy (IGRT). It might not be adequate for adaptive radiation therapy (ART), particularly for biologically guided tumor delineation and targeting which might be shifted and/or distorted during the course of RT. A linear accelerator (Linac) gantry-mounted on-board imager (OBI) was proposed using a single photon counting detector (PCD) panel to achieve single photon emission computed tomography (SPECT), energy-resolved spectral CT, and conventional CBCT triple on-board imaging, which might facilitate online ART with an addition of volumetric molecular/functional imaging information. Methods: The system was designed and evaluated in the GATE Monte Carlo platform. The OBI system including a kV-beam source and a pixelated cadmium zinc telluride (CZT) detector panel mounted on a medical Linac orthogonally to the MV beam direction was designed to obtain online CBCT, spectral CT, and SPECT tri-modal imaging of patients in the treatment room. The spatial resolutions of the OBI system were determined by imaging simulated phantoms. The CBCT imaging was evaluated by a simulated contrast phantom. A PMMA phantom containing gadolinium was imaged to demonstrate quantitative imaging of spectral-CT/CBCT of the system. The capability of tri-modal imaging of the OBI was demonstrated using three different spectral CT imaging methods to differentiate gadolinium, gold, calcium within simulated PMMA and the SPECT to image radioactive 99m Tc distribution. The dual-isotope SPECT imaging of the system was also evaluated by imaging a phantom containing 99m Tc and 123 I. The radiotherapy-related parameters of iodine contrast fraction and virtual non-contrast (VNC) tissue electron density in the Kidney1 inserts of a simulated phantom were decomposed using the Bayesian eigentissue decomposition method for contrastenhanced CBCT/spectral-CT of the OBI in a single scan. Results: The spatial resolutions of CBCT and SPECT of the OBI were determined to be 15.1 lp/cm at 10% MTF and 4.8-12 mm for radii of rotation of 10-40 cm, respectively. In CBCT image of the contrast phantom, most of the soft-tissue inserts were visible with sufficient spatial structure details. As compared to the CBCT image of gadolinium, the spectral CT image provided higher image contrasts. Calcium, gadolinium, and gold were separated well by using the spectral CT material imaging methods. The reconstructed distribution of 99m Tc agreed with the spatial position within the phantom. The two isotopes were separated from each other in dual-isotope SPECT imaging of the OBI. The iodine fractions and the VNC electron densities were estimated in the iodine-enhanced Kidney1 tissue inserts with reasonable RMS errors. The main procedures of the tri-modal imaging guided online ART workflow were presented with new functional features included. Conclusions: Using a single photon counting CZT detector panel, an on-board SPECT, spectral CT, and CBCT tr...

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Practical Clinical Workflows for Online and Offline Adaptive Radiation Therapy

Cited by 118 publications

References 72 publications

Development and evaluation of machine learning models for voxel dose predictions in online adaptive magnetic resonance guided radiation therapy

Development and evaluation of machine learning models for voxel dose predictions in online adaptive magnetic resonance guided radiation therapy

Novel MR-Guided Radiotherapy Elective Rotation for Radiation Oncology Trainees

A Monte Carlo study to investigate the feasibility of an on‐board SPECT/spectral‐CT/CBCT imager for medical linear accelerator

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