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Vicente EM, Grande Gutierrez N, Oakes JM, Cammin J, Gopal A, Kipritidis J, Modiri A, Mossahebi S, Mohindra P, Citron WK, Matuszak MM, Timmerman R, Sawant A. Integrating local and distant radiation-induced lung injury: Development and validation of a predictive model for ventilation loss. Med Phys 2024. [PMID: 38820385 DOI: 10.1002/mp.17187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/04/2024] [Accepted: 05/11/2024] [Indexed: 06/02/2024] Open
Abstract
BACKGROUND Investigations 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. PURPOSE We 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. METHODS In 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. RESULTS We 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. CONCLUSIONS These 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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Affiliation(s)
- Esther M Vicente
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Noelia Grande Gutierrez
- Mechanical Engineering Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Jessica M Oakes
- Department of Bioengineering, Northeastern University, Boston, Massachusetts, USA
| | - Jochen Cammin
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Arun Gopal
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - John Kipritidis
- Department of Radiotherapy, Northern Sydney Cancer Centre, Sydney, Australia
| | - Arezoo Modiri
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Sina Mossahebi
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Pranshu Mohindra
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Wendla K Citron
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Martha M Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Robert Timmerman
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Amit Sawant
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
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Lombardo J, Castillo E, Castillo R, Miller R, Jones B, Miften M, Kavanagh B, Dicker A, Boyle C, Leiby B, Banks J, Simone NL, Movsas B, Grills I, Guerrero T, Rusthoven CG, Vinogradskiy Y. Prospective Trial of Functional Lung Avoidance Radiation Therapy for Lung Cancer: Quality of Life Report. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00476-0. [PMID: 38614278 DOI: 10.1016/j.ijrobp.2024.03.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/26/2024] [Accepted: 03/29/2024] [Indexed: 04/15/2024]
Abstract
PURPOSE A novel form of lung function imaging has been developed that uses 4-dimensional computed tomography (4DCT) data to generate lung ventilation images (4DCT-ventilation). Functional avoidance uses 4DCT-ventilation to reduce doses to functional lung with the aim of reducing pulmonary side effects. A phase 2, multicenter 4DCT-ventilation functional avoidance clinical trial was completed. The purpose of this work was to quantify changes in patient-reported outcomes (PROs) for patients treated with functional avoidance and determine which metrics are predictive of PRO changes. MATERIALS AND METHODS Patients with locally advanced lung cancer receiving curative-intent radiation therapy were accrued. Each patient had a 4DCT-ventilation image generated using 4DCT data and image processing. PRO instruments included the Functional Assessment of Cancer Therapy-Lung (FACT-L) questionnaire administered pretreatment; at the end of treatment; and at 3, 6, and 12 months posttreatment. Using the FACT-Trial Outcome Index and the FACT-Lung Cancer Subscale results, the percentage of clinically meaningful declines (CMDs) were determined. A linear mixed-effects model was used to determine which patient, clinical, dose, and dose-function metrics were predictive of PRO decline. RESULTS Of the 59 patients who completed baseline PRO surveys. 83% had non-small cell lung cancer, with 75% having stage 3 disease. The median dose was 60 Gy in 30 fractions. CMD FACT-Trial Outcome Index decline was 46.3%, 38.5%, and 26.8%, at 3, 6, and 12 months, respectively. CMD FACT-Lung Cancer Subscale decline was 33.3%, 33.3%, and 29.3%, at 3, 6, and 12 months, respectively. Although an increase in most dose and dose-function parameters was associated with a modest decline in PROs, none of the results were significant (all P > .053). CONCLUSIONS The current work presents an innovative combination of use of functional avoidance and PRO assessment and is the first report of PROs for patients treated with prospective 4DCT-ventilation functional avoidance. Approximately 30% of patients had clinically significant decline in PROs at 12 months posttreatment. The study provides additional data on outcomes with 4DCT-ventilation functional avoidance.
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Affiliation(s)
- Joseph Lombardo
- Thomas Jefferson University, Radiation Oncology, Philadelphia, Pennsylvania
| | - Edward Castillo
- UT Austin, Department of Biomedical Engineering, Austin, Texas
| | - Richard Castillo
- Emory University School of Medicine, Radiation Oncology, Atlanta, Georgia
| | - Ryan Miller
- Thomas Jefferson University, Radiation Oncology, Philadelphia, Pennsylvania
| | - Bernard Jones
- University of Colorado, Radiation Oncology, Denver, Colorado
| | - Moyed Miften
- University of Colorado, Radiation Oncology, Denver, Colorado
| | - Brian Kavanagh
- University of Colorado, Radiation Oncology, Denver, Colorado
| | - Adam Dicker
- Thomas Jefferson University, Radiation Oncology, Philadelphia, Pennsylvania
| | - Cullen Boyle
- Thomas Jefferson University, Radiation Oncology, Philadelphia, Pennsylvania
| | - Benjamin Leiby
- Thomas Jefferson University, Department of Pharmacology, Physiology, and Cancer Biology, Philadelphia, Pennsylvania
| | - Joshua Banks
- Thomas Jefferson University, Department of Pharmacology, Physiology, and Cancer Biology, Philadelphia, Pennsylvania
| | - Nicole L Simone
- Thomas Jefferson University, Radiation Oncology, Philadelphia, Pennsylvania
| | - Benjamin Movsas
- Henry Ford Cancer Institute, Radiation Oncology, Detroit, Michigan
| | - Inga Grills
- Beaumont Health, Radiation Oncology, Royal Oak, Michigan
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Chen Y, Pahlavian SH, Jacobs P, Neupane T, Forghani-Arani F, Castillo E, Castillo R, Vinogradskiy Y. Systematic Evaluation of the Impact of Lung Segmentation Methods on 4-Dimensional Computed Tomography Ventilation Imaging Using a Large Patient Database. Int J Radiat Oncol Biol Phys 2024; 118:242-252. [PMID: 37607642 PMCID: PMC10842520 DOI: 10.1016/j.ijrobp.2023.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 08/24/2023]
Abstract
PURPOSE A novel form of lung functional imaging applied for functional avoidance radiation therapy has been developed that uses 4-dimensional computed tomography (4DCT) data and image processing techniques to calculate lung ventilation (4DCT-ventilation). Lung segmentation is a common step to define a region of interest for 4DCT-ventilation generation. The purpose of this study was to quantitatively evaluate the sensitivity of 4DCT-ventilation imaging using different lung segmentation methods. METHODS AND MATERIALS The 4DCT data of 350 patients from 2 institutions were used. Lung contours were generated using 3 methods: (1) reference segmentations that removed airways and pulmonary vasculature manually (Lung-Manual), (2) standard lung contours used for planning (Lung-RadOnc), and (3) artificial intelligence (AI)-based contours that removed the airways and pulmonary vasculature (Lung-AI). The AI model was based on a residual 3-dimensional U-Net and was trained using the Lung-Manual contours of 279 patients. We compared the Lung-RadOnc or Lung-AI with Lung-Manual contours for the entire 4DCT-ventilation functional avoidance process including lung segmentation (surface Dice similarity coefficient [Surface DSC]), 4DCT-ventilation generation (correlation), and subanalysis of 10 patients on a dosimetric endpoint (percentage of high functional volume of lung receiving ≥20 Gy [fV20{%}]). RESULTS Surface DSC comparing Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI contours was 0.40 ± 0.06 and 0.86 ± 0.04, respectively. The correlation between 4DCT-ventilation images generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI were 0.48 ± 0.14 and 0.85 ± 0.14, respectively. The difference in fV20[%] between 4DCT-ventilation generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI was 2.5% ± 4.1% and 0.3% ± 0.5%, respectively. CONCLUSIONS Our work showed that using standard planning lung contours can result in significantly variable 4DCT-ventilation images. The study demonstrated that AI-based segmentations generate lung contours and 4DCT-ventilation images that are similar to those generated using manual methods. The significance of the study is that it characterizes the lung segmentation sensitivity of the 4DCT-ventilation process and develops methods that can facilitate the integration of this novel imaging in busy clinics.
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Affiliation(s)
- Yingxuan Chen
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | | | | | - Taindra Neupane
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | | | - Edward Castillo
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, Georgia
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania.
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Xue P, Fu Y, Zhang J, Ma L, Ren M, Zhang Z, Dong E. Effective lung ventilation estimation based on 4D CT image registration and supervoxels. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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Yaremko BP, Capaldi DP, Sheikh K, Palma DA, Warner A, Dar AR, Yu E, Rodrigues GB, Louie AV, Landis M, Sanatani M, Vincent MD, Younus J, Kuruvilla S, Chen JZ, Erickson A, Gaede S, Parraga G, Hoover DA. Functional Lung Avoidance for Individualized Radiotherapy (FLAIR): Results of a Double-Blind, Randomized Controlled Trial. Int J Radiat Oncol Biol Phys 2022; 113:1072-1084. [DOI: 10.1016/j.ijrobp.2022.04.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/29/2022] [Accepted: 04/30/2022] [Indexed: 10/18/2022]
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Vinogradskiy Y, Castillo R, Castillo E, Schubert L, Jones BL, Faught A, Gaspar LE, Kwak J, Bowles DW, Waxweiler T, Dougherty JM, Gao D, Stevens C, Miften M, Kavanagh B, Grills I, Rusthoven CG, Guerrero T. Results of a Multi-Institutional Phase 2 Clinical Trial for 4DCT-Ventilation Functional Avoidance Thoracic Radiation Therapy. Int J Radiat Oncol Biol Phys 2022; 112:986-995. [PMID: 34767934 PMCID: PMC8863640 DOI: 10.1016/j.ijrobp.2021.10.147] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 10/07/2021] [Accepted: 10/22/2021] [Indexed: 12/25/2022]
Abstract
PURPOSE Radiation pneumonitis remains a major limitation in the radiation therapy treatment of patients with lung cancer. Functional avoidance radiation therapy uses functional imaging to reduce pulmonary toxic effects by designing radiation therapy plans that reduce doses to functional regions of the lung. Lung functional imaging has been developed that uses 4-dimensional computed tomography (4DCT) imaging to calculate 4DCT-based lung ventilation (4DCT-ventilation). A phase 2 multicenter study was initiated to evaluate 4DCT-ventilation functional avoidance radiation therapy. The study hypothesis was that functional avoidance radiation therapy could reduce the rate of grade ≥2 radiation pneumonitis to 12% compared with a 25% historical rate, with the trial being positive if ≤16.4% of patients experienced grade ≥2 pneumonitis. METHODS AND MATERIALS Lung cancer patients receiving curative-intent radiation therapy (prescription doses of 45-75 Gy) and chemotherapy were accrued. Patient 4DCT scans were used to generate 4DCT-ventilation images. The 4DCT-ventilation images were used to generate functional avoidance plans that reduced doses to functional portions of the lung while delivering the prescribed tumor dose. Pneumonitis was evaluated by a clinician at 3, 6, and 12 months after radiation therapy. RESULTS Sixty-seven evaluable patients were accrued between April 2015 and December 2019. The median prescription dose was 60 Gy (range, 45-66 Gy) delivered in 30 fractions (range, 15-33 fractions). The average reduction in the functional volume of lung receiving ≥20 Gy with functional avoidance was 3.5% (range, 0%-12.8%). The median follow-up was 312 days. The rate of grade ≥2 radiation pneumonitis was 10 of 67 patients (14.9%; 95% upper CI, 24.0%), meeting the phase 2 criteria. CONCLUSIONS 4DCT-ventilation offers an imaging modality that is convenient and provides functional imaging without an extra procedure necessary. This first report of a multicenter study of 4DCT-ventilation functional avoidance radiation therapy provided data showing that the trial met phase 2 criteria and that evaluation in a phase 3 study is warranted.
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Affiliation(s)
- Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado; Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania.
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, Georgia
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Leah Schubert
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Bernard L Jones
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Austin Faught
- Department of Radiation Oncology, St Jude Children's Research Hospital, Memphis, Tennessee
| | - Laurie E Gaspar
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Jennifer Kwak
- Department of Radiology, University of Colorado School of Medicine, Aurora, Colorado
| | - Daniel W Bowles
- Division of Medical Oncology, University of Colorado School of Medicine, Aurora, Colorado; Rocky Mountain Regional VA Medical Center, Aurora, Colorado
| | - Timothy Waxweiler
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | | | - Dexiang Gao
- Departments of Pediatrics and Biostatistics and Informatics, University of Colorado School of Medicine, Aurora, Colorado
| | - Craig Stevens
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Brian Kavanagh
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Inga Grills
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Chad G Rusthoven
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
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Nair GB, Al-Katib S, Podolsky R, Quinn T, Stevens C, Castillo E. Dynamic lung compliance imaging from 4DCT-derived volume change estimation. Phys Med Biol 2021; 66. [PMID: 34560677 DOI: 10.1088/1361-6560/ac29ce] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 09/24/2021] [Indexed: 11/12/2022]
Abstract
Background. Lung compliance (LC) is the ability of the lung to expand with changes in pressure and is one of the earliest physiological measurements to be altered in patients with parenchymal lung disease. Therefore, compliance monitoring could potentially identify patients at risk for disease progression. However, in clinical practice, compliance measurements are prohibitively invasive for use as a routine monitoring tool.Purpose. We propose a novel method for computing dynamic lung compliance imaging (LCI) from non-contrast computed tomography (CT) scans. LCI applies image processing methods to free-breathing 4DCT images, acquired under two different continuous positive airway pressures (CPAP) applied using a full-face mask, in order to compute the lung volume change induced by the pressure change. LCI provides a quantitative volumetric map of lung stiffness.Methods. We compared mean LCI values computed for 10 patients with idiopathic pulmonary fibrosis (IPF) and 7 non-IPF patients who were screened for lung nodules. 4DCTs were acquired for each patient at 5 cm and 10 cm H20 CPAP, as the patients were free breathing at functional residual capacity. LCI was computed from the two 4DCTs. Mean LCI intensities, which represent relative voxel volume change induced by the change in CPAP pressure, were computed.Results.The mean LCI values for patients with IPF ranged between [0.0309, 0.1165], whereas the values ranged between [0.0704, 0.2185] for the lung nodule cohort. Two-sided Wilcoxon rank sum test indicated that the difference in medians is statistically significant (pvalue = 0.009) and that LCI -measured compliance is overall lower in the IPF patient cohort.Conclusion. There is considerable difference in LC scores between patients with IPF compared to controls. Future longitudinal studies should look for LC alterations in areas of lung prior to radiographic detection of fibrosis to further characterize LCI's potential utility as an image marker for disease progression.
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Affiliation(s)
- Girish B Nair
- Division of Pulmonary and Critical Care, Beaumont Health, OUWB School of Medicine, United States of America
| | - Sayf Al-Katib
- Department of Radiology and Molecular Imaging, Beaumont Health, OUWB School of Medicine, United States of America
| | - Robert Podolsky
- Division of Informatics & Biostatistics, Beaumont Research Institute, Beaumont Health, United States of America
| | - Thomas Quinn
- Department of Radiation Oncology, Beaumont Health, OUWB School of Medicine, United States of America
| | - Craig Stevens
- Department of Radiation Oncology, Beaumont Health, OUWB School of Medicine, United States of America
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health, OUWB School of Medicine, United States of America.,Department of Biomedical Engineering, The University of Texas at Austin, United States of America
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Castillo E, Nair G, Turner-Lawrence D, Myziuk N, Emerson S, Al-Katib S, Westergaard S, Castillo R, Vinogradskiy Y, Quinn T, Guerrero T, Stevens C. Quantifying pulmonary perfusion from noncontrast computed tomography. Med Phys 2021; 48:1804-1814. [PMID: 33608933 PMCID: PMC8252085 DOI: 10.1002/mp.14792] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 01/14/2021] [Accepted: 02/08/2021] [Indexed: 11/29/2022] Open
Abstract
Purpose Computed tomography (CT)‐derived ventilation methods compute respiratory induced volume changes as a surrogate for pulmonary ventilation. Currently, there are no known methods to derive perfusion information from noncontrast CT. We introduce a novel CT‐Perfusion (CT‐P) method for computing the magnitude mass changes apparent on dynamic noncontrast CT as a surrogate for pulmonary perfusion. Methods CT‐Perfusion is based on a mass conservation model which describes the unknown mass change as a linear combination of spatially corresponding inhale and exhale HU estimated voxel densities. CT‐P requires a deformable image registration (DIR) between the inhale/exhale lung CT pair, a preprocessing lung volume segmentation, and an estimate for the Jacobian of the DIR transformation. Given this information, the CT‐P image, which provides the magnitude mass change for each voxel within the lung volume, is formulated as the solution to a constrained linear least squares problem defined by a series of subregional mean magnitude mass change measurements. Similar to previous robust CT‐ventilation methods, the amount of uncertainty in a subregional sample mean measurement is related to measurement resolution and can be characterized with respect to a tolerance parameter τ. Spatial Spearman correlation between single photon emission CT perfusion (SPECT‐P) and the proposed CT‐P method was assessed in two patient cohorts via a parameter sweep of τ. The first cohort was comprised of 15 patients diagnosed with pulmonary embolism (PE) who had SPECT‐P and 4DCT imaging acquired within 24 h of PE diagnosis. The second cohort was comprised of 15 nonsmall cell lung cancer patients who had SPECT‐P and 4DCT images acquired prior to radiotherapy. For each test case, CT‐P images were computed for 30 different uncertainty parameter values, uniformly sampled from the range [0.01, 0.125], and the Spearman correlation between the SPECT‐P and the resulting CT‐P images were computed. Results The median correlations between CT‐P and SPECT‐P taken over all 30 test cases ranged between 0.49 and 0.57 across the parameter sweep. For the optimal tolerance τ = 0.0385, the CT‐P and SPECT‐P correlations across all 30 test cases ranged between 0.02 and 0.82. A one‐sample sign test was applied separately to the PE and lung cancer cohorts. A low Spearmen correlation of 15% was set as the null median value and two‐sided alternative was tested. The PE patients showed a median correlation of 0.57 (IQR = 0.305). One‐sample sign test was statistically significant with 96.5 % confidence interval: 0.20–0.63, P < 0.00001. Lung cancer patients had a median correlation of 0.57(IQR = 0.230). Again, a one‐sample sign test for median was statistically significant with 96.5 percent confidence interval: 0.45–0.71, P < 0.00001. Conclusion CT‐Perfusion is the first mechanistic model designed to quantify magnitude blood mass changes on noncontrast dynamic CT as a surrogate for pulmonary perfusion. While the reported correlations with SPECT‐P are promising, further investigation is required to determine the optimal CT acquisition protocol and numerical method implementation for CT‐P imaging.
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Affiliation(s)
- Edward Castillo
- Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, USA.,Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA
| | - Girish Nair
- Department of Internal Medicine, Beaumont Health, Royal Oak, MI, USA
| | | | - Nicholas Myziuk
- Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, USA
| | - Scott Emerson
- Department of Diagnostic Radiology, Beaumont Health, Royal Oak, MI, USA
| | - Sayf Al-Katib
- Department of Diagnostic Radiology, Beaumont Health, Royal Oak, MI, USA
| | - Sarah Westergaard
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | | | - Thomas Quinn
- Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, USA
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, USA
| | - Craig Stevens
- Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, USA
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A prospective study to validate pulmonary blood mass changes on non-contrast 4DCT in pulmonary embolism patients. Clin Imaging 2021; 78:179-183. [PMID: 33839544 DOI: 10.1016/j.clinimag.2021.02.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 02/08/2021] [Accepted: 02/11/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE Limited diagnostic options exist for patients with suspected pulmonary embolism (PE) who cannot undergo CT-angiogram (CTA). CT-ventilation methods recover respiratory motion-induced lung volume changes as a surrogate for ventilation. We recently demonstrated that pulmonary blood mass change, induced by tidal respiratory motion, is a potential surrogate for pulmonary perfusion. In this study, we examine blood mass and volume change in patients with PE and parenchymal lung abnormalities (PLA). METHODS A cross-sectional analysis was conducted on a prospective, cohort-study with 129 consecutive PE suspected patients. Patients received 4DCT within 48 h of CTA and were classified as having PLA and/or PE. Global volume change (VC) and percent global pulmonary blood mass change (PBM) were calculated for each patient. Associations with disease type were evaluated using quantile regression. RESULTS 68 of 129 patients were PE positive on CTA. Median change in PBM for PE-positive patients (0.056; 95% CI: 0.045, 0.068; IQR: 0.051) was smaller than that of PE-negative patients (0.077; 95% CI: 0.064, 0.089; IQR: 0.056), with an estimated difference of 0.021 (95% CI: 0.003, 0.038; p = 0.0190). PLA was detected in 57 (44.2%) patients. Median VC for PLA-positive patients (1.26; 95% CI: 1.22, 1.30; IQR: 0.15) showed no significant difference from PLA-negative VC (1.25; 95% CI: 1.21, 1.28; IQR: 0.15). CONCLUSIONS We demonstrate that pulmonary blood mass change is significantly lower in PE-positive patients compared to PE-negative patients, indicating that PBM derived from dynamic non-contrast CT is a potentially useful surrogate for pulmonary perfusion.
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Castillo E, Castillo R, Vinogradskiy Y, Nair G, Grills I, Guerrero T, Stevens C. Technical Note: On the spatial correlation between robust CT-ventilation methods and SPECT ventilation. Med Phys 2020; 47:5731-5738. [PMID: 33007118 PMCID: PMC7727923 DOI: 10.1002/mp.14511] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 08/03/2020] [Accepted: 09/21/2020] [Indexed: 11/18/2022] Open
Abstract
Purpose The computed tomography (CT)‐derived ventilation imaging methodology employs deformable image registration (DIR) to recover respiratory motion‐induced volume changes from an inhale/exhale CT image pair, as a surrogate for ventilation. The Integrated Jacobian Formulation (IJF) and Mass Conserving Volume Change (MCVC) numerical methods for volume change estimation represent two classes of ventilation methods, namely transformation based and intensity (Hounsfield Unit) based, respectively. Both the IJF and MCVC methods utilize subregional volume change measurements that satisfy a specified uncertainty tolerance. In previous publications, the ventilation images resulting from this numerical strategy demonstrated robustness to DIR variations. However, the reduced measurement uncertainty comes at the expense of measurement resolution. The purpose of this study was to examine the spatial correlation between robust CT‐ventilation images and single photon emission CT‐ventilation (SPECT‐V). Methods Previously described implementations of IJF and MCVC require the solution of a large scale, constrained linear least squares problem defined by a series of robust subregional volume change measurements. We introduce a simpler parameterized implementation that reduces the number of unknowns while increasing the number of data points in the resulting least squares problem. A parameter sweep of the measurement uncertainty tolerance, τ, was conducted using the 4DCT and SPECT‐V images acquired for 15 non‐small cell lung cancer patients prior to radiotherapy. For each test case, MCVC and IJF CT‐ventilation images were created for 30 different uncertainty parameter values, uniformly sampled from the range 0.01,0.25. Voxel‐wise Spearman correlation between the SPECT‐V and the resulting CT‐ventilation images was computed. Results The median correlations between MCVC and SPECT‐V ranged from 0.20 to 0.48 across the parameter sweep, while the median correlations for IJF and SPECT‐V ranged between 0.79 and 0.82. For the optimal IJF tolerance τ=0.07, the IJF and SPECT‐V correlations across all 15 test cases ranged between 0.12 and 0.90. For the optimal MCVC tolerance τ=0.03, the MCVC and SPECT‐V correlations across all 15 test cases ranged between −0.06 and 0.84. Conclusion The reported correlations indicate that robust methods generate ventilation images that are spatially consistent with SPECT‐V, with the transformation‐based IJF method yielding higher correlations than those previously reported in the literature. For both methods, overall correlations were found to marginally vary for τ∈[0.03,0.15], indicating that the clinical utility of both methods is robust to both uncertainty tolerance and DIR solution.
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Affiliation(s)
- Edward Castillo
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA.,Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | | | - Girish Nair
- Department of Internal Medicine, Beaumont Health Systems, Royal Oak, MI, USA
| | - Inga Grills
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA
| | - Craig Stevens
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA
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Song L, Leppig JA, Hubner RH, Lassen-Schmidt BC, Neumann K, Theilig DC, Feldhaus FW, Fahlenkamp UL, Hamm B, Song W, Jin Z, Doellinger F. Quantitative CT Analysis in Patients with Pulmonary Emphysema: Do Calculated Differences Between Full Inspiration and Expiration Correlate with Lung Function? Int J Chron Obstruct Pulmon Dis 2020; 15:1877-1886. [PMID: 32801683 PMCID: PMC7413697 DOI: 10.2147/copd.s253602] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 07/02/2020] [Indexed: 11/23/2022] Open
Abstract
Purpose The aim of this retrospective study was to evaluate correlations between parameters of quantitative computed tomography (QCT) analysis, especially the 15th percentile of lung attenuation (P15), and parameters of clinical tests in a large group of patients with pulmonary emphysema. Patients and Methods One hundred and seventy-two patients with pulmonary emphysema and chronic obstructive pulmonary disease (COPD) global initiative for chronic obstructive lung disease (GOLD) stage 3 or 4 were assessed by nonenhanced thin-section CT scans in full inspiratory and expiratory breath-hold, pulmonary function test (PFT), a 6-minute walk test (6MWT), and quality of life questionnaires (SGRQ and CAT). QCT parameters included total lung volume (TLV), total emphysema score (TES), and P15, all measured at inspiration (IN) and expiration (EX). Differences between inspiration and expiration were calculated for TLV (TLVDiff), TES (TESDiff), and P15 (P15Diff). Spearman correlation analysis was performed. Results CT-measured lung volume in inspiration (TLVIN) correlated strongly with spirometry-measured total lung capacity (TLC) (r=0.81, p<0.001) and moderately to strongly with residual volume (RV), forced vital capacity (FVC), and forced expiratory volume in 1 second (FEV1)/FVC (r=0.60, 0.56, and −0.49, each p<0.001). Lung volume in expiration (TLVEX) correlated moderately to strongly with TLC, RV and FEV1/FVC ratio (r=0.75, 0.66, and −0.43, each p<0.001). TES and P15 showed stronger correlations with the carbon monoxide transfer coefficient (KCO%) (r= −0.42, 0.44, both p<0.001), when measured during expiration. P15Diff correlated moderately with KCO% and carbon monoxide diffusing capacity (DLCO%) (r= 0.41, 0.40, both p<0.001). The 6MWT and most QCT parameters showed significant differences between COPD GOLD 3 and 4 groups. Conclusion Our results suggest that QCT can help predict the severity of lung function decrease in patients with pulmonary emphysema and COPD GOLD 3 or 4. Some QCT parameters, including P15EX and P15Diff, correlated moderately to strongly with parameters of pulmonary function tests.
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Affiliation(s)
- Lan Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Jonas A Leppig
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Ralf H Hubner
- Department of Internal Medicine/Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | | | - Konrad Neumann
- Institute of Biometrics and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Dorothea C Theilig
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Felix W Feldhaus
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Ute L Fahlenkamp
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Bernd Hamm
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Wei Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Felix Doellinger
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
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