Geometrical tracking accuracy and appropriate PTV margins for robotic radiosurgery of liver lesions by SBRT

Liu, Ming; Cygler, Joanna; Vandervoort, Eric

doi:10.1080/0284186x.2019.1578896

Cited by 10 publications

(40 citation statements)

References 52 publications

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“…7,25,26 However, adjusting PTV margins requires an estimate of motion tracking errors for the patient in advance. In Liu et al, 27 using data for 72 liver patients treated with robotic SBRT, we showed that interfractional standard deviations for motion tracking errors were small (<1 mm in any anatomical direction). We found that the standard 5 mm PTV margin for some patients greatly exceeded the observed motion tracking errors consistently throughout the treatment courses.…”

Section: Introductionmentioning

confidence: 65%

“…36,37 In this work, the Synchrony error is calculated as the square root of a sum of the motion tracking residual error, the error compensating for system latency in beam positioning, and the global beam positioning error. 27,38 The CT image resolution used for these treatments is typically ≤1.0 9 1.0 9 1.0 mm 3 and adjusted based on the patient size. Therefore, margin expansion or contraction is rounded up to the nearest mm considering partial volume effects in the planning process.…”

Section: B Synchrony Respiratory Tracking Systemmentioning

confidence: 99%

“…Therefore, margin expansion or contraction is rounded up to the nearest mm considering partial volume effects in the planning process. 27…”

Section: B Synchrony Respiratory Tracking Systemmentioning

confidence: 99%

“…In our previous study, we found that 4 mm isotropic PTV margins were sufficient to encompass the motion tracking residual errors as well as target deformations for 95% of the patient population. 27 However, the standard deviations across different motion tracking models for the same patient were relatively small and many patients always had RMS tracking errors per model below 2 mm. In this study, we explore using SVC to predict which patients will have maximum RMS motion tracking errors per model <2 mm for all subsequent treatments based on dry-run session data.…”

Section: Introductionmentioning

confidence: 99%

“…They also concluded that a 5 mm PTV margin could compensate for the observed random error components, which was similar to our previous findings. 27 To our knowledge, there has been no literature report on posttreatment delivery log file data to facilitate patient-specific PTV margins in SBRT liver treatment with motion tracking. Our study aims to identify patients who could be assigned smaller PTV margins for robotic SBRT liver treatments, based on a small subset of CyberKnifeâ (Accuray Inc., Sunnyvale, CA) system log file data retrospectively emulating a dry-run session (mock) prior to planning.…”

Section: Introductionmentioning

confidence: 99%

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Patient‐specific PTV margins for liver stereotactic body radiation therapy determined using support vector classification with an early warning system for margin adaptation

2020

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Purpose: An adaptive planning target volume (PTV) margin strategy incorporating a volumetric tracking error assessment after each fraction is proposed for robotic stereotactic body radiation therapy (SBRT) liver treatments. Methods and materials: A supervised machine learning algorithm employing retrospective data, which emulates a dry-run session prior to planning, is used to investigate if motion tracking errors are <2 mm, and consequently, planning target volume (PTV) margins can be reduced. A fraction of data collected during the beginning of a treatment course emulates a dry-run session (mock) before planning. Twenty features are calculated using mock data and used for support vector classification (SVC). A treatment course is labeled as Class 1 if the maximum root-mean-square radial tracking error for all remaining fractions is below 2 mm, or Class 2 otherwise. We evaluate the classification using fivefold cross-validation, leave-one-out cross-validation, 500 repeated random subsampling cross-validation, and the receiver operating characteristic (ROC) metric. The classification is independently cross-validated on a cohort of 48 treatment plans for other anatomical sites. A per fraction assessment of volumetric tracking errors is performed for the standard 5 mm PTV margin (PTV std) for courses predicted as Class 2; or for a margin reduced by 2 mm (PTV std-2mm) for those predicted as Class 1. We perturb the gross tumor volume (GTV) by the tracking errors for each x-ray image acquisition and calculate the fractional GTV voxel occupancy probability (P i) inside the PTV for each treatment fraction i. For treatment courses classified as Class 1, an early warning system flags treatment courses having any P i < 0.99, and the subsequent treatments are proposed to be replanned using PTV std. Results: The classification accuracies are 0.84 AE 0.06 using fivefold cross-validation, and 0.77 when validated using an independent testing set (other anatomical sites). Eighty percent of treatment courses are correctly classified using leave-one-out cross-validation. The sensitivity, precision, specificity, F1 score, and accuracy are 0.81 AE 0.09, 0.85 AE 0.08, 0.80 AE 0.11, 0.83 AE 0.06, and 0.80 AE 0.07, respectively, using 500 repeated random subsampling cross-validation. The area under the curve for the ROC metric is 0.87 AE 0.05. The four most important features for classification are related to standard deviations of motion tracking errors, the linearity between the target location and external LED marker positions, and marker radial motion amplitudes. Eleven of 64 cases predicted to be of Class 1 have 0.96 < P i < 0.99 for each treatment fraction, and require replanning using PTV std. In comparison, the PTV std always covers the perturbed GTVs with P i > 0.99 for all patients. Conclusions: Support vector classification is proposed for the classification of different motion tracking errors for patient courses based on a mock session before planning for SBRT liver treatments. It is feasible to implement patient-specific P...

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Section: Introductionmentioning

confidence: 65%

Section: B Synchrony Respiratory Tracking Systemmentioning

confidence: 99%

“…Therefore, margin expansion or contraction is rounded up to the nearest mm considering partial volume effects in the planning process. 27…”

Section: B Synchrony Respiratory Tracking Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Patient‐specific PTV margins for liver stereotactic body radiation therapy determined using support vector classification with an early warning system for margin adaptation

2020

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A dose perturbation tool for robotic radiosurgery: Experimental validation and application to liver lesions

Liu

Cygler

Dennis

et al. 2022

J Applied Clin Med Phys

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Background: An analytical tool is empirically validated and used to assess the delivered dose to liver lesions accounting for different types of errors in robotic radiosurgery treatment. Material and methods: A tool is proposed to estimate the target doses taking into account the translation, rotation, and deformation of a target. Translational errors are modeled as a spatial convolution of the planned dose with a probability distribution function derived from treatment data. Rotations are modeled by rotating the target volume about the imaging isocenter. Target deformation is simulated as an isotropic target expansion or contraction based on changes in inter-fiducial spacing. The estimated dose is validated using radiochromic film measurements in nine experimental conditions, including in-phase and out-of -phase internal-and-external breathing motion patterns, with and without uncorrectable rotations, and for homogenous and heterogeneous phantoms. The measured dose is compared to the perturbed and planned doses using gamma analyses. This proposed tool is applied to assess the dose coverage for liver treatments using D99/Rx where D99 and Rx are the minimum target and prescription doses, respectively. These metrics are used to evaluate plan robustness to different magnitudes of rotational errors. Case studies are presented to illustrate how to improve plan robustness against delivery errors. Results: In the experimental validations, measured dose agrees with the estimated dose at the 2%/2 mm level. When accounting for translational and rotational tracking residual errors using this tool, approximately one-fifth of targets are considered underdosed (D99/Rx < 1.0). If target expansion or contraction is modeled, approximately one-third of targets are underdosed. The dose coverage can be improved if treatments are planned following proposed guidelines. Conclusion:The dose perturbation model can be used to assess dose delivery accuracy and investigate plan robustness to different types of errors.

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A retrospective multi‐center feasibility study of a new PTV margin estimation approach for moving targets using CyberKnife log files

Miandoab

Saramad

Setayeshi

et al. 2023

J Applied Clin Med Phys

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Purpose This study investigates a new approach for estimating the planning target volume (PTV) margin for moving tumors treated with robotic stereotactic body radiotherapy (SBRT). Methods In this new approach, the covariance of modeling and prediction errors was estimated using error propagation and implemented in the Van Herk formula to form a Modified Van Herk formula (MVHF). To perform a retrospective multi‐center analysis, the MVHF was studied using 163 patients treated with different system versions of robotic SBRT (G3 version 6.2.3, VSI version 8.5, and VSI version 9.5) and compared with two established PTV margins estimation methods: The original Van Herk Formula (VHF) and the Uncertainty Estimation Method (UEM). Results Overall, the PTV margins provided by the three formalisms are similar with 4–5 mm in the lung region and 4 mm in abdomen region to the PTV margins used in clinical. Furthermore, when analyzing individual patients, a difference of up to 1 mm was found between the PTV margin estimations using MVHF and VHF. The corresponding average discrepancies for the superior‐inferior (SI) direction ranged between −0.19 mm to 0.38 mm in CK G3 version 6.2.3, −0.36 mm to 0.33 mm in CK VSI version 8.5, and −0.34 mm to 0.40 mm in CK VSI version 9.5. Conclusions It was found that for the lower left lung, upper left lung, lower right lung, upper right lung, central liver, and upper liver, the effect of covariance between model and prediction errors in SI direction was around 20%, 30%, 25%, 25%, 25%, and 30%, respectively. Notable covariance effects between model and prediction errors can be considered in PTV margin estimation using a modified VHF, which allowed for more precise target localization in robotic SBRT for moving tumors. Overall, in each of the three directions, the difference between MVHF and utilized clinical margins is 0.65 mm in the lung and abdominal region. Therefore, to improve the clinical PTV margins with the new approach, it is suggested to use the adaptive PTV margins in the next fractions.

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Geometrical tracking accuracy and appropriate PTV margins for robotic radiosurgery of liver lesions by SBRT

Cited by 10 publications

References 52 publications

Patient‐specific PTV margins for liver stereotactic body radiation therapy determined using support vector classification with an early warning system for margin adaptation

Patient‐specific PTV margins for liver stereotactic body radiation therapy determined using support vector classification with an early warning system for margin adaptation

A dose perturbation tool for robotic radiosurgery: Experimental validation and application to liver lesions

A retrospective multi‐center feasibility study of a new PTV margin estimation approach for moving targets using CyberKnife log files

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