Evaluation of a new GPU-enabled VMAT multi-criteria optimisation plan generation algorithm

Spalding, Myles; Walsh, Alissa; Aland, Trent

doi:10.1016/j.meddos.2020.05.007

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…11,12 Until recently, the technical and logistical challenges of OART made it practically infeasible for most radiotherapy centers. The introduction of artificial intelligence (AI) 13,14 and graphics processing unit (GPU) based calculation engines [15][16][17] have allowed for the many steps in an online adaptive workflow to be performed in the timeframe required for OART. The Varian Ethos system (Varian Medical Systems, Palo Alto, CA) was recently developed as a completely self -contained online adaptive solution, and has been reported to be capable of performing adaptive treatments within 15-20 min.…”

Section: Introductionmentioning

confidence: 99%

Varian ethos online adaptive radiotherapy for prostate cancer: Early results of contouring accuracy, treatment plan quality, and treatment time

Byrne

Archibald-Heeren

et al. 2021

J Applied Clin Med Phys

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127

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The Varian Ethos system allows for online adaptive treatments through the utilization of artificial intelligence (AI) and deformable image registration which automates large parts of the anatomical contouring and plan optimization process. In this study, treatments of intact prostate and prostate bed, with and without nodes, were simulated for 182 online adaptive fractions, and then a further 184 clinical fractions were delivered on the Ethos system. Frequency and magnitude of contour edits were recorded, as well as a range of plan quality metrics. From the fractions analyzed, 11% of AI generated contours, known as influencer contours, required no change, and 81% required minor edits in any given fraction. The frequency of target and noninfluencer organs at risk (OAR) contour editing varied substantially between different targets and noninfluencer OARs, although across all targets 72% of cases required no edits. The adaptive plan was the preference in 95% of fractions. The adaptive plan met more goals than the scheduled plan in 78% of fractions, while in 15% of fractions the number of goals met was the same. The online adaptive recontouring and replanning process was carried out in 19 min on average. Significant improvements in dosimetry are possible with the Ethos online adaptive system in prostate radiotherapy.

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Section: Introductionmentioning

confidence: 99%

Varian ethos online adaptive radiotherapy for prostate cancer: Early results of contouring accuracy, treatment plan quality, and treatment time

Byrne

Archibald-Heeren

et al. 2021

J Applied Clin Med Phys

Self Cite

127

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“…An important consideration regarding deepPERFECT is its reliance on systems with on-line and off-line adaptive RT (ART) capabilities. ART can prolong the treatment time comparing to the conventional RT, however, recent advances in artificial intelligence (AI) applications [65] and graphical processing units (GPU) based dose calculation engines [66][67][68], have enabled the many steps of the online ART to be done in a reasonable timeframe [69]. For instance, the recently commercially available ART system Varian Ethos system (Varian Medical Systems, Palo Alto, CA, USA) can perform online ART, where new RT plan is adapted from initial RT plan based on simulation/planning CT while patient is lying on the RT treatment couch, in less than 20 minutes [70][71][72].…”

Section: Discussionmentioning

confidence: 99%

deepPERFECT: Novel Deep Learning CT Synthesis Method for Expeditious Pancreatic Cancer Radiotherapy

Hooshangnejad

Chen

Feng³

et al. 2023

Cancers

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Major sources of delay in the standard of care RT workflow are the need for multiple appointments and separate image acquisition. In this work, we addressed the question of how we can expedite the workflow by synthesizing planning CT from diagnostic CT. This idea is based on the theory that diagnostic CT can be used for RT planning, but in practice, due to the differences in patient setup and acquisition techniques, separate planning CT is required. We developed a generative deep learning model, deepPERFECT, that is trained to capture these differences and generate deformation vector fields to transform diagnostic CT into preliminary planning CT. We performed detailed analysis both from an image quality and a dosimetric point of view, and showed that deepPERFECT enabled the preliminary RT planning to be used for preliminary and early plan dosimetric assessment and evaluation.

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“…The advent of current advances in the application of artificial intelligence (AI) (28, 29), graphical processing unit (GPU)-based dose calculation engines (30)(31)(32), and automatic and semi-automatic segmentation and planning systems (33) have made the many steps of the online adaptive workflow to be performed faster and in the online ART timeframe (34), such as the recently developed ART systems such as ViewRay's MRIdian A3i (ViewRay, Oakwood Village, Ohio, USA) and Varian Ethos system (Varian Medical Systems, Palo Alto, CA). For instance, the Ethos system can perform online ART in less than 20 min (35)(36)(37).…”

Section: Introductionmentioning

confidence: 99%

DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer

Hooshangnejad

Chen

Feng³

et al. 2023

Front. Oncol.

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PurposeThe study aimed to implement a novel, deeply accelerated adaptive radiation therapy (DAART) approach for lung cancer radiotherapy (RT). Lung cancer is the most common cause of cancer-related death, and RT is the preferred medically inoperable treatment for early stage non-small cell lung cancer (NSCLC). In the current lengthy workflow, it takes a median of four weeks from diagnosis to RT treatment, which can result in complete restaging and loss of local control with delay. We implemented the DAART approach, featuring a novel deepPERFECT system, to address unwanted delays between diagnosis and treatment initiation.Materials and methodsWe developed a deepPERFECT to adapt the initial diagnostic imaging to the treatment setup to allow initial RT planning and verification. We used data from 15 patients with NSCLC treated with RT to train the model and test its performance. We conducted a virtual clinical trial to evaluate the treatment quality of the proposed DAART for lung cancer radiotherapy.ResultsWe found that deepPERFECT predicts planning CT with a mean high-intensity fidelity of 83 and 14 HU for the body and lungs, respectively. The shape of the body and lungs on the synthesized CT was highly conformal, with a dice similarity coefficient (DSC) of 0.91, 0.97, and Hausdorff distance (HD) of 7.9 mm, and 4.9 mm, respectively, compared with the planning CT scan. The tumor showed less conformality, which warrants acquisition of treatment Day1 CT and online adaptive RT. An initial plan was designed on synthesized CT and then adapted to treatment Day1 CT using the adapt to position (ATP) and adapt to shape (ATS) method. Non-inferior plan quality was achieved by the ATP scenario, while all ATS-adapted plans showed good plan quality.ConclusionDAART reduces the common online ART (ART) treatment course by at least two weeks, resulting in a 50% shorter time to treatment to lower the chance of restaging and loss of local control.

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Evaluation of a new GPU-enabled VMAT multi-criteria optimisation plan generation algorithm

Cited by 3 publications

References 4 publications

Varian ethos online adaptive radiotherapy for prostate cancer: Early results of contouring accuracy, treatment plan quality, and treatment time

Varian ethos online adaptive radiotherapy for prostate cancer: Early results of contouring accuracy, treatment plan quality, and treatment time

deepPERFECT: Novel Deep Learning CT Synthesis Method for Expeditious Pancreatic Cancer Radiotherapy

DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer

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