Dosimetric quality and delivery efficiency of robotic radiosurgery for brain metastases: Comparison with C‐arm linear accelerator based plans

Zhang, Shuming; Yang, Renjie; Wang, Xin

doi:10.1002/acm2.12746

Cited by 11 publications

(3 citation statements)

References 35 publications

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“…It was largely because AutoMBM was a AP solution to linac-based radiosurgery using MLC that shows broader dosimetric penumbra compared with conical collimators used in GammaKnife and CyberKnife. GammaPlan and MultiPlan also used a large number of isocenters and non-isocenter confocal beams which could significantly protect the normal brain and other OARs than other TPS for non-coplanar radiosurgery on linacs [31][32][33][34]. Nonetheless, the composite plan scores of GammaPlan, Mul-tiPlan and AutoMBM were statistically equal although AutoMBM showed slightly worse dose statistics in other OARs in general.…”

Section: Overall Plan Quality Between Autombm and Other Non-autoplan Tpsmentioning

confidence: 99%

Multi-center planning study of radiosurgery for intracranial metastases through Automation (MC-PRIMA) by crowdsourcing prior web-based plan challenge study

Chan

Gevaert

Dörr³

et al. 2022

Physica Medica

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Background: Planning radiosurgery to multiple intracranial metastases is complex and shows large variability in dosimetric quality among planners and treatment planning systems (TPS). This project aimed to determine whether autoplanning using the Muliple Brain Mets (AutoMBM) software can improve plan quality and reduce inter-planner variability by crowdsourcing results from prior international planning study. Methods: Twenty-four institutions autoplanned with AutoMBM on a five metastases case from a prior international planning competition from which population statistics (means and variances) of 23 dosimetric metrics and resulting composite plan score (maximum score = 150) of other TPS (Eclipse, Monaco, RayStation, iPlan, GammaPlan, MultiPlan) were crowdsourced. Plan results of AutoMBM and each of the other TPS were compared using two sample t-tests for means and Levene's tests for variances. Plan quality of AutoMBM was correlated with the planner' experience and compared between academic and non-academic centers. Results: AutoMBM produced plans with comparable composite plan score to GammaPlan, MultiPlan, Eclipse and iPlan (127.6 vs. 131.7 vs. 127.3 vs. 127.3 and 126.7; all p > 0.05) and superior to Monaco and RayStation (118.3 and 108.6; both p < 0.05). Inter-planner variability of overall plan quality was lowest for AutoMBM among all TPS (all p < 0.05). AutoMBM's plan quality did not differ between academic and non-academic centers and uncorrelated with planning experience (all p > 0.05).

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Section: Overall Plan Quality Between Autombm and Other Non-autoplan Tpsmentioning

confidence: 99%

Multi-center planning study of radiosurgery for intracranial metastases through Automation (MC-PRIMA) by crowdsourcing prior web-based plan challenge study

Chan

Gevaert

Dörr³

et al. 2022

Physica Medica

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“…and Zhang et al. contrasted CK plans with conventional LINAC VMAT plans, respectively concluding that VMAT was dosimetrically preferential for the prostate and CK for the brain 24,25 . Stroubinis et al.…”

Section: Introductionmentioning

confidence: 99%

Multimodal radiotherapy dose prediction using a multi‐task deep learning model

Maniscalco,

Mathew,

Parsons

et al. 2024

Medical Physics

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BackgroundIn radiation therapy (RT), accelerated partial breast irradiation (APBI) has emerged as an increasingly preferred treatment modality over conventional whole breast irradiation due to its targeted dose delivery and shorter course of treatment. APBI can be delivered through various modalities including Cobalt‐60‐based systems and linear accelerators with C‐arm, O‐ring, or robotic arm design. Each modality possesses distinct features, such as beam energy or the degrees of freedom in treatment planning, which influence their respective dose distributions. These modality‐specific considerations emphasize the need for a quantitative approach in determining the optimal dose delivery modality on a patient‐specific basis. However, manually generating treatment plans for each modality across every patient is time‐consuming and clinically impractical.PurposeWe aim to develop an efficient and personalized approach for determining the optimal RT modality for APBI by training predictive models using two different deep learning‐based convolutional neural networks. The baseline network performs a single‐task (ST), predicting dose for a single modality. Our proposed multi‐task (MT) network, which is capable of leveraging shared information among different tasks, can concurrently predict dose distributions for various RT modalities. Utilizing patient‐specific input data, such as a patient's computed tomography (CT) scan and treatment protocol dosimetric goals, the MT model predicts patient‐specific dose distributions across all trained modalities. These dose distributions provide patients and clinicians quantitative insights, facilitating informed and personalized modality comparison prior to treatment planning.MethodsThe dataset, comprising 28 APBI patients and their 92 treatment plans, was partitioned into training, validation, and test subsets. Eight patients were dedicated to the test subset, leaving 68 treatment plans across 20 patients to divide between the training and validation subsets. ST models were trained for each modality, and one MT model was trained to predict doses for all modalities simultaneously. Model performance was evaluated across the test dataset in terms of Mean Absolute Percent Error (MAPE). We conducted statistical analysis of model performance using the two‐tailed Wilcoxon signed‐rank test.ResultsTraining times for five ST models ranged from 255 to 430 min per modality, totaling 1925 min, while the MT model required 2384 min. MT model prediction required an average of 1.82 s per patient, compared to ST model predictions at 0.93 s per modality. The MT model yielded MAPE of 1.1033 ± 0.3627% as opposed to the collective MAPE of 1.2386 ± 0.3872% from ST models, and the differences were statistically significant (p = 0.0003, 95% confidence interval = [–0.0865, –0.0712]).ConclusionOur study highlights the potential benefits of a MT learning framework in predicting RT dose distributions across various modalities without notable compromises. This MT architecture approach offers several advantages, such as flexibility, scalability, and streamlined model management, making it an appealing solution for clinical deployment. With such a MT model, patients can make more informed treatment decisions, physicians gain more quantitative insight for pre‐treatment decision‐making, and clinics can better optimize resource allocation. With our proposed goal array and MT framework, we aim to expand this work to a site‐agnostic dose prediction model, enhancing its generalizability and applicability.

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“…The QA program goal is to assure that the machine characteristics do not deviate significantly from their baseline values acquired at the time of machine acceptance and commissioning because the deviation from baseline values could result in suboptimal treatment of patients. Nowadays many new advanced technologies 3 , 4 , 5 , 6 and various imaging devices 7 , 8 , 9 , 10 are integrated as common parts of the medical linear accelerator. Therefore, the QA test methods become more and more complex and needed to spend a lot of time executing.…”

Section: Introductionmentioning

confidence: 99%

The performance of a new type accelerator uRT‐linac 506c evaluated by a quality assurance automation system

Sun,

Shi,

Yang

et al. 2023

J Applied Clin Med Phys

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PurposeThe purpose of this study was to evaluate the performance of our quality assurance (QA) automation system and to evaluate the machine performance of a new type linear accelerator uRT‐linac 506c within 6 months using this system.MethodsThis QA automation system consists of a hollow cylindrical phantom with 18 steel balls in the phantom surface and an analysis software to process electronic portal imaging device (EPID) measurement image data and report the results. The performance of the QA automation system was evaluated by the tests of repeatability, archivable precision, detectability of introduced errors, and the impact of set‐up errors on QA results. The performance of this linac was evaluated by 31 items using this QA system over 6 months.ResultsThis QA system was able to automatically deliver QA plan, EPID image acquisition, and automatic analysis. All images acquiring and analysis took approximately 4.6 min per energy. The preset error of 0.1 mm in multi‐leaf collimator (MLC) leaf were detected as 0.12 ± 0.01 mm for Bank A and 0.10 ± 0.01 mm in Bank B. The 2 mm setup error was detected as −1.95 ± 0.01 mm, −2.02 ± 0.01 mm, 2.01 ± 0.01 mm for X, Y, Z directions, respectively. And data from the tests of repeatability and detectability of introduced errors showed the standard deviation were all within 0.1 mm and 0.1°. and data of the machine performance were all within the tolerance specified by AAPM TG‐142.ConclusionsThe QA automation system has high precision and good performance, and it can improve the QA efficiency. The performance of the new accelerator has also performed very well during the testing period.

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Dosimetric quality and delivery efficiency of robotic radiosurgery for brain metastases: Comparison with C‐arm linear accelerator based plans

Cited by 11 publications

References 35 publications

Multi-center planning study of radiosurgery for intracranial metastases through Automation (MC-PRIMA) by crowdsourcing prior web-based plan challenge study

Multi-center planning study of radiosurgery for intracranial metastases through Automation (MC-PRIMA) by crowdsourcing prior web-based plan challenge study

Multimodal radiotherapy dose prediction using a multi‐task deep learning model

The performance of a new type accelerator uRT‐linac 506c evaluated by a quality assurance automation system

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