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

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

doi:10.1002/mp.16311

Cited by 7 publications

(1 citation statement)

References 54 publications

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“…This RT treatment planning technique redirects radiation dose away from high-functioning lung regions (as defined by pulmonary biomarkers) to lower-functioning regions with the goals of preserving overall lung function and mitigating radiation-induced toxicities post-RT. In this process, treatment plans avoiding dose to the high functioning lung are generated using developed models predicting the pulmonary function response to delivered radiation dose 29 , 30 . Using imaging biomarkers, several groups have demonstrated the feasibility of sparing highly functional lung from high radiation dose 31 – 37 .…”

Section: Introductionmentioning

confidence: 99%

Validation of CT-based ventilation and perfusion biomarkers with histopathology confirms radiation-induced pulmonary changes in a porcine model

Flakus

Wuschner

Wallat

et al. 2023

Sci Rep

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Imaging biomarkers can assess disease progression or prognoses and are valuable tools to help guide interventions. Particularly in lung imaging, biomarkers present an opportunity to extract regional information that is more robust to the patient’s condition prior to intervention than current gold standard pulmonary function tests (PFTs). This regional aspect has particular use in functional avoidance radiation therapy (RT) in which treatment planning is optimized to avoid regions of high function with the goal of sparing functional lung and improving patient quality of life post-RT. To execute functional avoidance, detailed dose–response models need to be developed to identify regions which should be protected. Previous studies have begun to do this, but for these models to be clinically translated, they need to be validated. This work validates two metrics that encompass the main components of lung function (ventilation and perfusion) through post-mortem histopathology performed in a novel porcine model. With these methods validated, we can use them to study the nuanced radiation-induced changes in lung function and develop more advanced models.

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Section: Introductionmentioning

confidence: 99%

Validation of CT-based ventilation and perfusion biomarkers with histopathology confirms radiation-induced pulmonary changes in a porcine model

Flakus

Wuschner

Wallat

et al. 2023

Sci Rep

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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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4DCT image artifact detection using deep learning

Carrizales,

Flakus,

Fairbourn

et al. 2024

Medical Physics

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BackgroundFour‐dimensional computed tomography (4DCT) is an es sential tool in radiation therapy. However, the 4D acquisition process may cause motion artifacts which can obscure anatomy and distort functional measurements from CT scans.PurposeWe describe a deep learning algorithm to identify the location of artifacts within 4DCT images. Our method is flexible enough to handle different types of artifacts, including duplication, misalignment, truncation, and interpolation.MethodsWe trained and validated a U‐net convolutional neural network artifact detection model on more than 23 000 coronal slices extracted from 98 4DCT scans. The receiver operating characteristic (ROC) curve and precision‐recall curve were used to evaluate the model's performance at identifying artifacts compared to a manually identified ground truth. The model was adjusted so that the sensitivity in identifying artifacts was equivalent to that of a human observer, as measured by computing the average ratio of artifact volume to lung volume in a given scan.ResultsThe model achieved a sensitivity, specificity, and precision of 0.78, 0.99, and 0.58, respectively. The ROC area‐under‐the‐curve (AUC) was 0.99 and the precision‐recall AUC was 0.73. Our model sensitivity is 8% higher than previously reported state‐of‐the‐art artifact detection methods.ConclusionsThe model developed in this study is versatile, designed to handle duplication, misalignment, truncation, and interpolation artifacts within a single image, unlike earlier models that were designed for a single artifact type.

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

Cited by 7 publications

References 54 publications

Validation of CT-based ventilation and perfusion biomarkers with histopathology confirms radiation-induced pulmonary changes in a porcine model

Validation of CT-based ventilation and perfusion biomarkers with histopathology confirms radiation-induced pulmonary changes in a porcine model

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

4DCT image artifact detection using deep learning

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