Online Adaptive Radiation Therapy

Lim‐Reinders, Stephanie; Keller, Brian; Al-Ward, Shahad M.; Sahgal, Arjun; Kim, Anthony

doi:10.1016/j.ijrobp.2017.04.023

Cited by 167 publications

(142 citation statements)

References 93 publications

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“…68,69 These issues lie at the heart of the general adaptive RT framework. 70,71 Radiomic approaches could therefore represent significant components of this process. 72 Improvements in machine learning algorithms 73,74 -through the huge amount of data they can process-have made it possible to investigate the hypothesis that OAR radio-induced toxicity is associated with the OAR structural organisation as captured by image analysis.…”

Section: Bjr|openmentioning

confidence: 99%

Radiomics for radiation oncologists: are we ready to go?

et al. 2020

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Radiomics have emerged as an exciting field of research over the past few years, with very wide potential applications in personalised and precision medicine of the future. Radiomics-based approaches are still however limited in daily clinical practice in oncology. This review focus on how radiomics could be incorporated into the radiation therapy pipeline, and globally help the radiation oncologist, from the tumour diagnosis to follow-up after treatment. Radiomics could impact on all steps of the treatment pipeline, once the limitations in terms of robustness and reproducibility are overcome. Major ongoing efforts should be made to collect and share data in the most standardised manner possible.

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Section: Bjr|openmentioning

confidence: 99%

Radiomics for radiation oncologists: are we ready to go?

et al. 2020

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“…29 The annealing schedule for T was identical to the schedule used for the QTA annealing variable, C, and is defined in Eq. (6). Note that while the formalism of QTA shares many similarities with SA, the probability of accepting a worse solution in QTA differs from SA in two key respects: (a) reduced dependence on the potential energy difference between the current and new solution and (b) the presence of an additional dynamic parameter in the barrier width.…”

Section: B Simulated Annealing (Sa)mentioning

confidence: 99%

“…The ability to quickly perform robust optimizations is particularly significant in online adaptive radiotherapy, in which a patient's plan may be reoptimized several times during the treatment course to account for changes such as tumor shrinkage or organ deformations. 6 Quantum computing research is believed to hold promise for achieving computational speedup for certain types of problems. 7 In quantum computing, classical bits (whose two states are often represented by 0 and 1) are replaced by quantum bits (qubits) which may exist in any linear superposition of 0 and 1.…”

Section: Introductionmentioning

confidence: 99%

Quantum‐inspired algorithm for radiotherapy planning optimization

et al. 2019

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Purpose: Modern inverse radiotherapy treatment planning requires nonconvex, large-scale optimizations that must be solved within a clinically feasible timeframe. We have developed and tested a quantum-inspired, stochastic algorithm for intensity-modulated radiotherapy (IMRT): quantum tunnel annealing (QTA). By modeling the likelihood probability of accepting a higher energy solution after a particle tunneling through a potential energy barrier, QTA features an additional degree of freedom (the barrier width, w) not shared by traditional stochastic optimization methods such as Simulated Annealing (SA). This additional degree of freedom can improve convergence rates and achieve a more efficient and, potentially, effective treatment planning process. Methods: To analyze the character of the proposed QTA algorithm, we chose two stereotactic body radiation therapy (SBRT) liver cases of variable complexity. The "easy" first case was used to confirm functionality, while the second case, with a more challenging geometry, was used to characterize and evaluate the QTA algorithm performance. Plan quality was assessed using dose-volume histogram-based objectives and dose distributions. Due to the stochastic nature of the solution search space, extensive tests were also conducted to determine the optimal smoothing technique, ensuring balance between plan deliverability and the resulting plan quality. QTA convergence rates were investigated in relation to the chosen barrier width function, and QTA and SA performances were compared regarding sensitivity to the choice of solution initializations, annealing schedules, and complexity of the dose-volume constraints. Finally, we investigated the extension from beamlet intensity optimization to direct aperture optimization (DAO). Influence matrices were calculated using the Eclipse scripting application program interface (API), and the optimizations were run on the University of Michigan's high-performance computing cluster, Flux. Results: Our results indicate that QTA's barrier-width function can be tuned to achieve faster convergence rates. The QTA algorithm reached convergence up to 46.6% faster than SA for beamlet intensity optimization and up to 26.8% faster for DAO. QTA and SA were ultimately found to be equally insensitive to the initialization process, but the convergence rate of QTA was found to be more sensitive to the complexity of the dose-volume constraints. The optimal smoothing technique was found to be a combination of a Laplace-of-Gaussian (LOG) edge-finding filter implemented as a penalty within the objective function and a two-dimensional Savitzky-Golay filter applied to the final iteration; this achieved total monitor units more than 20% smaller than plans optimized by commercial treatment planning software. Conclusions: We have characterized the performance of a stochastic, quantum-inspired optimization algorithm, QTA, for radiotherapy treatment planning. This proof of concept study suggests that QTA can be tuned to achieve faster convergence than SA; therefore, ...

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“…Adaptive radiotherapy (ART) has the potential to correct for interfractional variations during radiotherapy (1,2) by imaging the patients with technologies such as the CT on-rails or cone beam computed tomography (CBCT). However, online ART has not been widely used.…”

Section: Introductionmentioning

confidence: 99%

A Fast Online Replanning Algorithm Based on Intensity Field Projection for Adaptive Radiotherapy

Liu

Liang²,

Zhu

et al. 2020

Front. Oncol.

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The purpose of this work was to propose an online replanning algorithm, named intensity field projection (IFP), that directly adjusts intensity distributions for each beam based on the deformation of structures. IFP can be implemented within a reasonably acceptable time frame. Methods and Materials: The online replanning method is based on the gradient-based free form deformation (GFFD) algorithm, which we have previously proposed. The method involves the following steps: The planning computed tomography (CT) and cone-beam CT image are registered to generate a three-dimensional (3-D) deformation field. According to the 3-D deformation field, the registered image and a new delineation are generated. The two-dimensional (2-D) deformation field of ray intensity in each beam direction is determined based on the 3-D deformation field in the region of interest. The 2-D ray intensity distribution in the corresponding beam direction is deformed to generate a new 2-D ray intensity distribution. According to the new 2-D ray intensity distribution, corresponding multi-leaf collimator (MLC), and jaw motion data are generated. The feasibility and advantages of our method have been demonstrated in 20 lung cancer intensity modulated radiation therapy (IMRT) cases. Results: Substantial underdosing in the CTV is seen in the original and the repositioning plans. The average prescription dose coverage (V100%) and D95 for CTV were 100% and 60.3 Gy for the IFP plans compared to 82.6% (P < 0.01) and 44.0 Gy (P < 0.01) for original plans, 86.7% (P < 0.01), and 58.5 Gy (P < 0.01) for repositioning plans. On average, the mean total lung doses were 12.2 Gy for the IFP plan compared to the 12.4 Gy (P < 0.01) and 12.6 Gy (P < 0.01) for the original and the repositioning plans. The entire process of IFP can be completed within 3 min. Conclusions: We proposed an online replanning strategy for automatically correcting interfractional anatomy variations. The preliminary results indicate that the IFP method substantially increased planning speed for online adaptive replanning.

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Online Adaptive Radiation Therapy

Cited by 167 publications

References 93 publications

Radiomics for radiation oncologists: are we ready to go?

Radiomics for radiation oncologists: are we ready to go?

Quantum‐inspired algorithm for radiotherapy planning optimization

A Fast Online Replanning Algorithm Based on Intensity Field Projection for Adaptive Radiotherapy

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