Measuring Indirect Radiation-Induced Perfusion Change in Fed Vasculature Using Dynamic Contrast CT

Wuschner, Antonia E.; Flakus, Mattison J.; Wallat, Eric M.; Reinhardt, Joseph M.; Shanmuganayagam, Dhanansayan; Christensen, Gary E.; Bayouth, John E.

doi:10.3390/jpm12081254

Cited by 9 publications

(21 citation statements)

References 36 publications

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“…Vascular atrophy has already been observed in the Group A swine and the direct effects of this atrophy on perfusion were reported previously ( Wuschner et al, 2021a ). Pathological analysis on these swine has confirmed a loss of structure in the vascular wall in addition to reporting additional mechanisms of constriction as well as confirming the intraparenchymal hemorrhage hypotheses reported in ( Wuschner et al, 2021a ), ( Wuschner et al, 2022 ), and ( Marks et al, 2003 ) where vascular leakage was observed as a result of radiation dose delivered ( Wuschner et al, 2021b ).…”

Section: Discussionsupporting

confidence: 72%

“…Frontiers in Physiology frontiersin.org 10 of structure in the vascular wall in addition to reporting additional mechanisms of constriction as well as confirming the intraparenchymal hemorrhage hypotheses reported in (Wuschner et al, 2021a), (Wuschner et al, 2022), and (Marks et al, 2003) where vascular leakage was observed as a result of radiation dose delivered (Wuschner et al, 2021b).…”

Section: Figure 11supporting

confidence: 80%

“…Figure 11 and Table 3 show the relation between the percent change in vessel volume and the percent change in perfusion with each analysis contour labeled. The percent change in perfusion values were analyzed on the same subjects and same contours and were previously reported in Wuschner et al ( Wuschner et al, 2022 ). The perfusion study showed an indirect effect where the fed vessels, regardless of dose, experienced large, statistically significant compared to pre-RT, perfusion reductions.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally for these subjects, the perfusion change in each of the seven contours analyzed in the group B subjects was calculated from the contrast-CTs using the methodology described in Wuschner et al ( Wuschner et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

“…Entries are written as the average (standard deviation) of the five subjects. Perfusion results are as reported in Wuschner et al(Wuschner et al, 2022). Statistically significant values (p < 0.05) are denoted with a *.…”

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confidence: 96%

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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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Section: Discussionsupporting

confidence: 72%

Section: Figure 11supporting

confidence: 80%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 96%

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

et al. 2022

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Predicting pulmonary ventilation damage after radiation therapy for nonsmall cell lung cancer using a ResNet generative adversarial network

et al. 2023

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Background: Functional lung avoidance radiation therapy (RT) is a technique being investigated to preferentially avoid specific regions of the lung that are predicted to be more susceptible to radiation-induced damage. Reducing the dose delivered to high functioning regions may reduce the occurrence radiationinduced lung injuries (RILIs) and toxicities. However, in order to develop effective lung function-sparing plans, accurate predictions of post-RT ventilation change are needed to determine which regions of the lung should be spared. Purpose: To predict pulmonary ventilation change following RT for nonsmall cell lung cancer using machine learning. Methods: A conditional generative adversarial network (cGAN) was developed with data from 82 human subjects enrolled in a randomized clinical trial approved by the institution's IRB to predict post-RT pulmonary ventilation change. The inputs to the network were the pre-RT pulmonary ventilation map and radiation dose distribution. The loss function was a combination of the binary cross-entropy loss and an asymmetrical structural similarity index measure (aSSIM) function designed to increase penalization of underprediction of ventilation damage. Network performance was evaluated against a previously developed polynomial regression model using a paired sample t-test for comparison. Evaluation was performed using eight-fold crossvalidation. Results: From the eight-fold cross-validation, we found that relative to the polynomial model, the cGAN model significantly improved predicting regions of ventilation damage following radiotherapy based on true positive rate (TPR), 0.14±0.15 to 0.72±0.21, and Dice similarity coefficient (DSC), 0.19±0.16 to 0.46±0.14, but significantly declined in true negative rate, 0.97±0.05 to 0.62±0.21, and accuracy, 0.79±0.08 to 0.65±0.14. Additionally, the average true positive volume increased from 104±119 cc in the POLY model to 565±332 cc in the cGAN model, and the average false negative volume decreased from 654±361 cc in the POLY model to 193±163 cc in the cGAN model. Conclusions:The proposed cGAN model demonstrated significant improvement in TPR and DSC. The higher sensitivity of the cGAN model can improve the clinical utility of functional lung avoidance RT by identifying larger volumesThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Integrating local and distant radiation‐induced lung injury: Development and validation of a predictive model for ventilation loss

Vicente,

Grande Gutierrez,

Oakes

et al. 2024

Medical Physics

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BackgroundInvestigations on radiation‐induced lung injury (RILI) have predominantly focused on local effects, primarily those associated with radiation damage to lung parenchyma. However, recent studies from our group and others have revealed that radiation‐induced damage to branching serial structures such as airways and vessels may also have a substantial impact on post‐radiotherapy (RT) lung function. Furthermore, recent results from multiple functional lung avoidance RT trials, although promising, have demonstrated only modest toxicity reduction, likely because they were primarily focused on dose avoidance to lung parenchyma. These observations emphasize the critical need for predictive dose‐response models that effectively incorporate both local and distant RILI effects.PurposeWe develop and validate a predictive model for ventilation loss after lung RT. This model, referred to as P+A, integrates local (parenchyma [P]) and distant (central and peripheral airways [A]) radiation‐induced damage, modeling partial (narrowing) and complete (collapse) obstruction of airways.MethodsIn an IRB‐approved prospective study, pre‐RT breath‐hold CTs (BHCTs) and pre‐ and one‐year post‐RT 4DCTs were acquired from lung cancer patients treated with definitive RT. Up to 13 generations of airways were automatically segmented on the BHCTs using a research virtual bronchoscopy software. Ventilation maps derived from the 4DCT scans were utilized to quantify pre‐ and post‐RT ventilation, serving, respectively, as input data and reference standard (RS) in model validation. To predict ventilation loss solely due to parenchymal damage (referred to as P model), we used a normal tissue complication probability (NTCP) model. Our model used this NTCP‐based estimate and predicted additional loss due radiation‐induced partial or complete occlusion of individual airways, applying fluid dynamics principles and a refined version of our previously developed airway radiosensitivity model. Predictions of post‐RT ventilation were estimated in the sublobar volumes (SLVs) connected to the terminal airways. To validate the model, we conducted a k‐fold cross‐validation. Model parameters were optimized as the values that provided the lowest root mean square error (RMSE) between predicted post‐RT ventilation and the RS for all SLVs in the training data. The performance of the P+A and the P models was evaluated by comparing their respective post‐RT ventilation values with the RS predictions. Additional evaluation using various receiver operating characteristic (ROC) metrics was also performed.ResultsWe extracted a dataset of 560 SLVs from four enrolled patients. Our results demonstrated that the P+A model consistently outperformed the P model, exhibiting RMSEs that were nearly half as low across all patients (13 ± 3 percentile for the P+A model vs. 24 ± 3 percentile for the P model on average). Notably, the P+A model aligned closely with the RS in ventilation loss distributions per lobe, particularly in regions exposed to doses ≥13.5 Gy. The ROC analysis further supported the superior performance of the P+A model compared to the P model in sensitivity (0.98 vs. 0.07), accuracy (0.87 vs. 0.25), and balanced predictions.ConclusionsThese early findings indicate that airway damage is a crucial factor in RILI that should be included in dose‐response modeling to enhance predictions of post‐RT lung function.

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Measuring Indirect Radiation-Induced Perfusion Change in Fed Vasculature Using Dynamic Contrast CT

Cited by 9 publications

References 36 publications

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

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

Predicting pulmonary ventilation damage after radiation therapy for nonsmall cell lung cancer using a ResNet generative adversarial network

Integrating local and distant radiation‐induced lung injury: Development and validation of a predictive model for ventilation loss

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