Modeling the impact of out‐of‐phase ventilation on normal lung tissue response to radiation dose

Wallat, Eric M.; Flakus, Mattison J.; Wuschner, Antonia E.; Shao, Wei; Christensen, Gary E.; Reinhardt, Joseph M.; Baschnagel, Andrew M.; Bayouth, John E.

doi:10.1002/mp.14146

Cited by 13 publications

(31 citation statements)

References 22 publications

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“…al and Wallet et al and was chosen for consistency. Additionally, it is the threshold used for evaluation of damage in the clinical trial the human patients were taken from 6 , 39 . Dose binned analysis was performed using the same contours created for the HU analysis.…”

Section: Methodsmentioning

confidence: 99%

Radiation-induced Hounsfield unit change correlates with dynamic CT perfusion better than 4DCT-based ventilation measures in a novel-swine model

Wuschner

Wallat

Flakus

et al. 2021

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To analyze radiation induced changes in Hounsfield units and determine their correlation with changes in perfusion and ventilation. Additionally, to compare the post-RT changes in human subjects to those measured in a swine model used to quantify perfusion changes and validate their use as a preclinical model. A cohort of 5 Wisconsin Miniature Swine (WMS) were studied. Additionally, 19 human subjects were recruited as part of an IRB approved clinical trial studying functional avoidance radiation therapy for lung cancer and were treated with SBRT. Imaging (a contrast enhanced dynamic perfusion CT in the swine and 4DCT in the humans) was performed prior to and post-RT. Jacobian elasticity maps were calculated on all 4DCT images. Contours were created from the isodose lines to discretize analysis into 10 Gy dose bins. B-spline deformable image registration allowed for voxel-by-voxel comparative analysis in these contours between timepoints. The WMS underwent a research course of 60 Gy in 5 fractions delivered locally to a target in the lung using an MRI-LINAC system. In the WMS subjects, the dose-bin contours were copied onto the contralateral lung, which received < 5 Gy for comparison. Changes in HU and changes in Jacobian were analyzed in these contours. Statistically significant (p < 0.05) changes in the mean HU value post-RT compared to pre-RT were observed in both the human and WMS groups at all timepoints analyzed. The HU increased linearly with dose for both groups. Strong linear correlation was observed between the changes seen in the swine and humans (Pearson coefficient > 0.97, p < 0.05) at all timepoints. Changes seen in the swine closely modeled the changes seen in the humans at 12 months post RT (slope = 0.95). Jacobian analysis showed between 30 and 60% of voxels were damaged post-RT. Perfusion analysis in the swine showed a statistically significant (p < 0.05) reduction in contrast inside the vasculature 3 months post-RT compared to pre-RT. The increases in contrast outside the vasculature was strongly correlated (Pearson Correlation 0.88) with the reduction in HU inside the vasculature but were not correlated with the changes in Jacobians. Radiation induces changes in pulmonary anatomy at 3 months post-RT, with a strong linear correlation with dose. The change in HU seen in the non-vessel lung parenchyma suggests this metric is a potential biomarker for change in perfusion. Finally, this work suggests that the WMS swine model is a promising pre-clinical model for analyzing radiation-induced changes in humans and poses several benefits over conventional swine models.

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Section: Methodsmentioning

confidence: 99%

Radiation-induced Hounsfield unit change correlates with dynamic CT perfusion better than 4DCT-based ventilation measures in a novel-swine model

Wuschner

Wallat

Flakus

et al. 2021

Sci Rep

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“…To do this, detailed dose response models are required. In recent years, multiple groups have begun developing these models and some have tested their efficacy in clinical trials ( McDonald et al, 1995 ) ( Mah and Dyk, 1988 ) ( Mehta, 2005 ) ( Graves et al, 2010 ) ( Patton et al, 2018 ) ( Koike et al, 2015 ) ( Hopkins et al, 2012 ) ( Zhang et al, 2010 ) ( Vinogradskiy et al, 2013 ) ( Wallat et al, 2020 ) ( Wallat et al, 2021 ) ( Bates et al, 2009 ) ( Hoover et al, 2014 ) ( Ireland et al, 2016 ) ( Vicente et al, 2020 ). To date, the only prospective clinical trials using these techniques have been ventilation-based ( Bayouth et al, 2019 ) and all non-contrast CT-derived methods have been exclusively ventilation based ( Patton et al, 2018 ) ( Vinogradskiy et al, 2013 ) ( Vicente et al, 2020 ) ( Castillo et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

CT-derived vessel segmentation for analysis of post-radiation therapy changes in vasculature and perfusion

et al. 2022

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Vessel segmentation in the lung is an ongoing challenge. While many methods have been able to successfully identify vessels in normal, healthy, lungs, these methods struggle in the presence of abnormalities. Following radiotherapy, these methods tend to identify regions of radiographic change due to post-radiation therapytoxicities as vasculature falsely. By combining texture analysis and existing vasculature and masking techniques, we have developed a novel vasculature segmentation workflow that improves specificity in irradiated lung while preserving the sensitivity of detection in the rest of the lung. Furthermore, radiation dose has been shown to cause vascular injury as well as reduce pulmonary function post-RT. This work shows the improvements our novel vascular segmentation method provides relative to existing methods. Additionally, we use this workflow to show a dose dependent radiation-induced change in vasculature which is correlated with previously measured perfusion changes (R2 = 0.72) in both directly irradiated and indirectly damaged regions of perfusion. These results present an opportunity to extend non-contrast CT-derived models of functional change following radiation therapy.

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“…By excluding the full inhale volume of the second scan, the new TV (80IN-0EX) may more closely match the TV of the first scan and be used for ensuing calculation of CT-ventilation biomarkers. Selecting inhale volumes to achieve ETV between scans accommodates for differences in breathing effort that affect lung volumes; this method of effort correction has previously been described in depth ( Wallat et al., 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Quantifying robustness of CT-ventilation biomarkers to image noise

et al. 2023

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Purpose: To quantify the impact of image noise on CT-based lung ventilation biomarkers calculated using Jacobian determinant techniques.Methods: Five mechanically ventilated swine were imaged on a multi-row CT scanner with acquisition parameters of 120 kVp and 0.6 mm slice thickness in static and 4-dimensional CT (4DCT) modes with respective pitches of 1 and 0.09. A range of tube current time product (mAs) values were used to vary image dose. On two dates, subjects received two 4DCTs: one with 10 mAs/rotation (low-dose, high-noise) and one with CT simulation standard of care 100 mAs/rotation (high-dose, low-noise). Additionally, 10 intermediate noise level breath-hold (BHCT) scans were acquired with inspiratory and expiratory lung volumes. Images were reconstructed with and without iterative reconstruction (IR) using 1 mm slice thickness. The Jacobian determinant of an estimated transformation from a B-spline deformable image registration was used to create CT-ventilation biomarkers estimating lung tissue expansion. 24 CT-ventilation maps were generated per subject per scan date: four 4DCT ventilation maps (two noise levels each with and without IR) and 20 BHCT ventilation maps (10 noise levels each with and without IR). Biomarkers derived from reduced dose scans were registered to the reference full dose scan for comparison. Evaluation metrics were gamma pass rate (Γ) with 2 mm distance-to-agreement and 6% intensity criterion, voxel-wise Spearman correlation (ρ) and Jacobian ratio coefficient of variation (CoVJR).Results: Comparing biomarkers derived from low (CTDIvol = 6.07 mGy) and high (CTDIvol = 60.7 mGy) dose 4DCT scans, mean Γ, ρ and CoVJR values were 93% ± 3%, 0.88 ± 0.03 and 0.04 ± 0.009, respectively. With IR applied, those values were 93% ± 4%, 0.90 ± 0.04 and 0.03 ± 0.003. Similarly, comparisons between BHCT-based biomarkers with variable dose (CTDIvol = 1.35–7.95 mGy) had mean Γ, ρ and CoVJR of 93% ± 4%, 0.97 ± 0.02 and 0.03 ± 0.006 without IR and 93% ± 4%, 0.97 ± 0.03 and 0.03 ± 0.007 with IR. Applying IR did not significantly change any metrics (p>0.05).Discussion: This work demonstrated that CT-ventilation, calculated using the Jacobian determinant of an estimated transformation from a B-spline deformable image registration, is invariant to Hounsfield Unit (HU) variation caused by image noise. This advantageous finding may be leveraged clinically with potential applications including dose reduction and/or acquiring repeated low-dose acquisitions for improved ventilation characterization.

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Modeling the impact of out‐of‐phase ventilation on normal lung tissue response to radiation dose

Cited by 13 publications

References 22 publications

Radiation-induced Hounsfield unit change correlates with dynamic CT perfusion better than 4DCT-based ventilation measures in a novel-swine model

Radiation-induced Hounsfield unit change correlates with dynamic CT perfusion better than 4DCT-based ventilation measures in a novel-swine model

CT-derived vessel segmentation for analysis of post-radiation therapy changes in vasculature and perfusion

Quantifying robustness of CT-ventilation biomarkers to image noise

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