Craniospinal Irradiation: A Dosimetric Comparison Between O-Ring Linac and Conventional C-arm Linac

Stroubinis, Theodoros; Psarras, Michalis; Zygogianni, Anna; Protopapa, Maria Lucia; Kouloulias, Vassilis; Platoni, Kalliopi

doi:10.1016/j.adro.2022.101139

Cited by 3 publications

(4 citation statements)

References 28 publications

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“…We utilized this multi-isocenter approach routinely in our clinic on the TrueBeam LINAC. Similar to our VMAT CSI results, comparisons between the Halcyon and TrueBeam plans have demonstrated identical dosimetric results as previous studies [17][18][19].…”

Section: Discussionsupporting

confidence: 89%

“…In addition, the Halcyon plans were able to achieve a lower value of homogeneity index (HI) than the TrueBeam plans. As previous studies have shown, Halcyon was able to provide an improvement in target dose homogeneity and conformity while also having a lower dose spillage [18,19]. This is further backed up when V 10Gy and V 18Gy were analyzed in our study, in which…”

Section: Discussionsupporting

confidence: 67%

“…In addition, Halcyon offers translation but no rotational table correction and has no manual table shift option, so the localization accuracy of the entire craniospinal axis is of the highest importance. To combat this, a planning technique in which no junction shifts are utilized and the shifts between the isocenters are only along the longitudinal direction has been shown to be feasible and able to provide outstanding coverage with acceptable doses to OARs [17,18]. Compared with TrueBeam's VMAT plans, Halcyon demonstrated superiority in terms of lower OAR doses and lower dose spillage [19].…”

Section: Introductionmentioning

confidence: 99%

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Development and Quality Assurance of Multileaf Collimator (MLC) Auto-Feathering Junctions for Multi-Isocenter Supine Volumetric Modulated Arc Therapy (VMAT) Craniospinal Axis Irradiation on Halcyon

Misa,

McCarthy,

St. Clair

et al. 2023

Cureus

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Currently, there is a lack of methods and tools that efficiently evaluate the auto-feathering junctions created by multileaf collimator (MLCs) for supine volumetric modulated arc therapy (VMAT) craniospinal irradiation (CSI) plans. We have investigated the feasibility of stitching together multi-isocenter fluence maps to then analyze the feathered junctions for patient-specific quality assurance (QA). Furthermore, we investigated the capability of Halcyon for the treatment of CSI patients. Three patients, who previously underwent VMAT CSI treatment on TrueBeam (6-MV flattening filter-free (FFF)) for 36 Gy in 20 fractions were replanned for Halcyon. A multi-isocenter approach with only translational superior-inferior shifts was used for both platforms. Each isocenter consists of two full arcs with anterior avoidance sectors, ±5° collimator rotations between arcs, and 5-8 cm of overlapping MLC auto-feathering junctions. All plans were QA'd via electronic portal imaging device (EPID) portal dosimetry and analyzed with a gamma criteria of 3%/3 mm. A variety of plan quality metrics were analyzed to evaluate dose distributions to the target, doses to organs at risk (OARs), and integral dose to the patient. A MATLAB script was developed to stitch the calculated and measured fluence maps in order to perform patient-specific QA for the composite fluence. The Halcyon plans provided highly conformal and homogenous dose distributions to the entire CSI target, superior to the clinical TrueBeam plans, while sparing critical organs with significantly lower values of V10Gy and V18Gy by up to 2% and 2.5%, respectively. Qualitative depictions of vertical dose profiles from the stitched DICOM of the entire CSI target for both planned and delivered fluence maps demonstrated equivalency, with slightly lower average pass rates with Halcyon (97%) compared to TrueBeam (99.9%). This approach to stitch multiple measured versus calculated EPID fluence maps has shown to be a feasible and accurate method and will be helpful for comprehensive VMAT CSI QA on both platforms. Further implementation of this script will be used in examining dosimetric impacts of daily patient positioning errors at MLC auto-feathering junctions.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 67%

Section: Introductionmentioning

confidence: 99%

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Development and Quality Assurance of Multileaf Collimator (MLC) Auto-Feathering Junctions for Multi-Isocenter Supine Volumetric Modulated Arc Therapy (VMAT) Craniospinal Axis Irradiation on Halcyon

Misa,

McCarthy,

St. Clair

et al. 2023

Cureus

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“…Previous works by Varian's Halcyon and C-arm LINACs for spinal irradiation, finding that the modalities yielded dosimetrically similar plans but cautioning that Halcyon's couch has limited DOF which can complicate patient setup. 26,27 Though these studies offer generalized conclusions for a sample of the population, we posit that selection of the optimal EBRT dose delivery technique can benefit from patient-specific assessment rather than sole reliance on broad recommendations. Factors like a patient's specific RT target volume, organ geometry, or individual risk factors could uniquely influence their optimal dose distribution.…”

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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Dosimetric comparison of TrueBeam and Halcyon-based VMAT in craniospinal irradiation

Buranavanitvong,

Plangpleng,

Sanghangthum

2024

Health Technol.

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Craniospinal Irradiation: A Dosimetric Comparison Between O-Ring Linac and Conventional C-arm Linac

Cited by 3 publications

References 28 publications

Development and Quality Assurance of Multileaf Collimator (MLC) Auto-Feathering Junctions for Multi-Isocenter Supine Volumetric Modulated Arc Therapy (VMAT) Craniospinal Axis Irradiation on Halcyon

Development and Quality Assurance of Multileaf Collimator (MLC) Auto-Feathering Junctions for Multi-Isocenter Supine Volumetric Modulated Arc Therapy (VMAT) Craniospinal Axis Irradiation on Halcyon

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

Dosimetric comparison of TrueBeam and Halcyon-based VMAT in craniospinal irradiation

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