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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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Midroni J, Salunkhe R, Liu Z, Chow R, Boldt G, Palma D, Hoover D, Vinogradskiy Y, Raman S. Incorporation of Functional Lung Imaging Into Radiation Therapy Planning in Patients With Lung Cancer: A Systematic Review and Meta-Analysis. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00481-4. [PMID: 38631538 DOI: 10.1016/j.ijrobp.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/27/2024] [Accepted: 04/02/2024] [Indexed: 04/19/2024]
Abstract
Our purpose was to provide an understanding of current functional lung imaging (FLI) techniques and their potential to improve dosimetry and outcomes for patients with lung cancer receiving radiation therapy (RT). Excerpta Medica dataBASE (EMBASE), PubMed, and Cochrane Library were searched from 1990 until April 2023. Articles were included if they reported on FLI in one of: techniques, incorporation into RT planning for lung cancer, or quantification of RT-related outcomes for patients with lung cancer. Studies involving all RT modalities, including stereotactic body RT and particle therapy, were included. Meta-analyses were conducted to investigate differences in dose-function parameters between anatomic and functional RT planning techniques, as well as to investigate correlations of dose-function parameters with grade 2+ radiation pneumonitis (RP). One hundred seventy-eight studies were included in the narrative synthesis. We report on FLI modalities, dose-response quantification, functional lung (FL) definitions, FL avoidance techniques, and correlations between FL irradiation and toxicity. Meta-analysis results show that FL avoidance planning gives statistically significant absolute reductions of 3.22% to the fraction of well-ventilated lung receiving 20 Gy or more, 3.52% to the fraction of well-perfused lung receiving 20 Gy or more, 1.3 Gy to the mean dose to the well-ventilated lung, and 2.41 Gy to the mean dose to the well-perfused lung. Increases in the threshold value for defining FL are associated with decreases in functional parameters. For intensity modulated RT and volumetric modulated arc therapy, avoidance planning results in a 13% rate of grade 2+ RP, which is reduced compared with results from conventional planning cohorts. A trend of increased predictive ability for grade 2+ RP was seen in models using FL information but was not statistically significant. FLI shows promise as a method to spare FL during thoracic RT, but interventional trials related to FL avoidance planning are sparse. Such trials are critical to understanding the effect of FL avoidance planning on toxicity reduction and patient outcomes.
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Affiliation(s)
- Julie Midroni
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada
| | - Rohan Salunkhe
- Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Zhihui Liu
- Biostatistics, Princess Margaret Cancer Center, Toronto, Canada
| | - Ronald Chow
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - Gabriel Boldt
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - David Palma
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada; Ontario Institute for Cancer Research, Toronto, Canada
| | - Douglas Hoover
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, United States of America; Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, United States of America
| | - Srinivas Raman
- Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada.
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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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5
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Yang D, Huang Y, Li B, Cai J, Ren G. Dynamic Chest Radiograph Simulation Technique with Deep Convolutional Neural Networks: A Proof-of-Concept Study. Cancers (Basel) 2023; 15:5768. [PMID: 38136313 PMCID: PMC10741831 DOI: 10.3390/cancers15245768] [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: 11/09/2023] [Revised: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023] Open
Abstract
In this study, we present an innovative approach that harnesses deep neural networks to simulate respiratory lung motion and extract local functional information from single-phase chest X-rays, thus providing valuable auxiliary data for early diagnosis of lung cancer. A novel radiograph motion simulation (RMS) network was developed by combining a U-Net and a long short-term memory (LSTM) network for image generation and sequential prediction. By utilizing a spatial transformer network to deform input images, our proposed network ensures accurate image generation. We conducted both qualitative and quantitative assessments to evaluate the effectiveness and accuracy of our proposed network. The simulated respiratory motion closely aligns with pulmonary biomechanics and reveals enhanced details of pulmonary diseases. The proposed network demonstrates precise prediction of respiratory motion in the test cases, achieving remarkable average Dice scores exceeding 0.96 across all phases. The maximum variation in lung length prediction was observed during the end-exhale phase, with average deviation of 4.76 mm (±6.64) for the left lung and 4.77 mm (±7.00) for the right lung. This research validates the feasibility of generating patient-specific respiratory motion profiles from single-phase chest radiographs.
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Affiliation(s)
- Dongrong Yang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong; (D.Y.); (Y.H.); (B.L.)
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27708, USA
| | - Yuhua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong; (D.Y.); (Y.H.); (B.L.)
| | - Bing Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong; (D.Y.); (Y.H.); (B.L.)
- The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong; (D.Y.); (Y.H.); (B.L.)
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong; (D.Y.); (Y.H.); (B.L.)
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518000, China
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6
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Capaldi DPI, Konyer NB, Kjarsgaard M, Dvorkin-Gheva A, Dandurand RJ, Nair P, Svenningsen S. Specific Ventilation in Severe Asthma Evaluated with Noncontrast Tidal Breathing 1H MRI. Radiol Cardiothorac Imaging 2023; 5:e230054. [PMID: 38166343 PMCID: PMC11163249 DOI: 10.1148/ryct.230054] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 08/21/2023] [Accepted: 11/01/2023] [Indexed: 01/04/2024]
Abstract
Purpose To determine if proton (1H) MRI-derived specific ventilation is responsive to bronchodilator (BD) therapy and associated with clinical biomarkers of type 2 airway inflammation and airways dysfunction in severe asthma. Materials and Methods In this prospective study, 27 participants with severe asthma (mean age, 52 years ± 9 [SD]; 17 female, 10 male) and seven healthy controls (mean age, 47 years ± 16; five female, two male), recruited between 2018 and 2021, underwent same-day spirometry, respiratory oscillometry, and tidal breathing 1H MRI. Participants with severe asthma underwent all assessments before and after BD therapy, and type 2 airway inflammatory biomarkers were determined (blood eosinophil count, sputum eosinophil percentage, sputum eosinophil-free granules, and fraction of exhaled nitric oxide) to generate a cumulative type 2 biomarker score. Specific ventilation was derived from tidal breathing 1H MRI and its response to BD therapy, and relationships with biomarkers of type 2 airway inflammation and airway dysfunction were evaluated. Results Mean MRI specific ventilation improved with BD inhalation (from 0.07 ± 0.04 to 0.11 ± 0.04, P < .001). Post-BD MRI specific ventilation (P = .046) and post-BD change in MRI specific ventilation (P = .006) were greater in participants with asthma with type 2 low biomarkers compared with participants with type 2 high biomarkers of airway inflammation. Post-BD change in MRI specific ventilation was correlated with change in forced expiratory volume in 1 second (r = 0.40, P = .04), resistance at 5 Hz (r = -0.50, P = .01), resistance at 19 Hz (r = -0.42, P = .01), reactance area (r = -0.54, P < .01), and reactance at 5 Hz (r = 0.48, P = .01). Conclusion Specific ventilation evaluated with tidal breathing 1H MRI was responsive to BD therapy and was associated with clinical biomarkers of airways disease in participants with severe asthma. Keywords: MRI, Severe Asthma, Ventilation, Type 2 Inflammation Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Moore and Chandarana in this issue.
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Affiliation(s)
- Dante P. I. Capaldi
- From the Department of Radiation Oncology, Division of Physics,
University of California San Francisco, San Francisco, Calif (D.P.I.C.);
Division of Respirology, Department of Medicine (A.D.G., P.N., S.S.), Imaging
Research Centre (N.B.K., S.S.), and Firestone Institute for Respiratory Health
(M.K., P.N., S.S.), St Joseph's Healthcare Hamilton, McMaster University,
50 Charlton Ave E, Hamilton, ON, Canada L8N 4A6; and Lakeshore General Hospital,
Montreal Chest Institute, Meakins-Christie Laboratories, and Oscillometry Unit
of the Centre for Innovative Medicine, McGill University Health Centre and
Research Institute, and McGill University, Montreal, Canada (R.J.D.)
| | - Norman B. Konyer
- From the Department of Radiation Oncology, Division of Physics,
University of California San Francisco, San Francisco, Calif (D.P.I.C.);
Division of Respirology, Department of Medicine (A.D.G., P.N., S.S.), Imaging
Research Centre (N.B.K., S.S.), and Firestone Institute for Respiratory Health
(M.K., P.N., S.S.), St Joseph's Healthcare Hamilton, McMaster University,
50 Charlton Ave E, Hamilton, ON, Canada L8N 4A6; and Lakeshore General Hospital,
Montreal Chest Institute, Meakins-Christie Laboratories, and Oscillometry Unit
of the Centre for Innovative Medicine, McGill University Health Centre and
Research Institute, and McGill University, Montreal, Canada (R.J.D.)
| | - Melanie Kjarsgaard
- From the Department of Radiation Oncology, Division of Physics,
University of California San Francisco, San Francisco, Calif (D.P.I.C.);
Division of Respirology, Department of Medicine (A.D.G., P.N., S.S.), Imaging
Research Centre (N.B.K., S.S.), and Firestone Institute for Respiratory Health
(M.K., P.N., S.S.), St Joseph's Healthcare Hamilton, McMaster University,
50 Charlton Ave E, Hamilton, ON, Canada L8N 4A6; and Lakeshore General Hospital,
Montreal Chest Institute, Meakins-Christie Laboratories, and Oscillometry Unit
of the Centre for Innovative Medicine, McGill University Health Centre and
Research Institute, and McGill University, Montreal, Canada (R.J.D.)
| | - Anna Dvorkin-Gheva
- From the Department of Radiation Oncology, Division of Physics,
University of California San Francisco, San Francisco, Calif (D.P.I.C.);
Division of Respirology, Department of Medicine (A.D.G., P.N., S.S.), Imaging
Research Centre (N.B.K., S.S.), and Firestone Institute for Respiratory Health
(M.K., P.N., S.S.), St Joseph's Healthcare Hamilton, McMaster University,
50 Charlton Ave E, Hamilton, ON, Canada L8N 4A6; and Lakeshore General Hospital,
Montreal Chest Institute, Meakins-Christie Laboratories, and Oscillometry Unit
of the Centre for Innovative Medicine, McGill University Health Centre and
Research Institute, and McGill University, Montreal, Canada (R.J.D.)
| | - Ronald J. Dandurand
- From the Department of Radiation Oncology, Division of Physics,
University of California San Francisco, San Francisco, Calif (D.P.I.C.);
Division of Respirology, Department of Medicine (A.D.G., P.N., S.S.), Imaging
Research Centre (N.B.K., S.S.), and Firestone Institute for Respiratory Health
(M.K., P.N., S.S.), St Joseph's Healthcare Hamilton, McMaster University,
50 Charlton Ave E, Hamilton, ON, Canada L8N 4A6; and Lakeshore General Hospital,
Montreal Chest Institute, Meakins-Christie Laboratories, and Oscillometry Unit
of the Centre for Innovative Medicine, McGill University Health Centre and
Research Institute, and McGill University, Montreal, Canada (R.J.D.)
| | - Parameswaran Nair
- From the Department of Radiation Oncology, Division of Physics,
University of California San Francisco, San Francisco, Calif (D.P.I.C.);
Division of Respirology, Department of Medicine (A.D.G., P.N., S.S.), Imaging
Research Centre (N.B.K., S.S.), and Firestone Institute for Respiratory Health
(M.K., P.N., S.S.), St Joseph's Healthcare Hamilton, McMaster University,
50 Charlton Ave E, Hamilton, ON, Canada L8N 4A6; and Lakeshore General Hospital,
Montreal Chest Institute, Meakins-Christie Laboratories, and Oscillometry Unit
of the Centre for Innovative Medicine, McGill University Health Centre and
Research Institute, and McGill University, Montreal, Canada (R.J.D.)
| | - Sarah Svenningsen
- From the Department of Radiation Oncology, Division of Physics,
University of California San Francisco, San Francisco, Calif (D.P.I.C.);
Division of Respirology, Department of Medicine (A.D.G., P.N., S.S.), Imaging
Research Centre (N.B.K., S.S.), and Firestone Institute for Respiratory Health
(M.K., P.N., S.S.), St Joseph's Healthcare Hamilton, McMaster University,
50 Charlton Ave E, Hamilton, ON, Canada L8N 4A6; and Lakeshore General Hospital,
Montreal Chest Institute, Meakins-Christie Laboratories, and Oscillometry Unit
of the Centre for Innovative Medicine, McGill University Health Centre and
Research Institute, and McGill University, Montreal, Canada (R.J.D.)
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7
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Ghassemi N, Castillo R, Castillo E, Jones BL, Miften M, Kavanagh B, Werner-Wasik M, Miller R, Barta JA, Grills I, Leiby BE, Guerrero T, Rusthoven CG, Vinogradskiy Y. Evaluation of variables predicting PFT changes for lung cancer patients treated on a prospective 4DCT-ventilation functional avoidance clinical trial. Radiother Oncol 2023; 187:109821. [PMID: 37516361 PMCID: PMC10529225 DOI: 10.1016/j.radonc.2023.109821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/09/2023] [Accepted: 07/18/2023] [Indexed: 07/31/2023]
Abstract
PURPOSE Functional avoidance radiotherapy uses functional imaging to reduce pulmonary toxicity by designing radiotherapy plans that reduce doses to functional regions of the lung. A phase-II, multi-center, prospective study of 4DCT-ventilation functional avoidance was completed. Pre and post-treatment pulmonary function tests (PFTs) were acquired and assessed pulmonary function change. This study aims to evaluate which clinical, dose and dose-function factors predict PFT changes for patients treated with 4DCT-ventilation functional avoidance radiotherapy. MATERIALS AND METHODS 56 patients with locally advanced lung cancer receiving radiotherapy were accrued. PFTs were obtained at baseline and three months following radiotherapy and included forced expiratory volume in 1-second (FEV1), forced vital capacity (FVC), and FEV1/FVC. The ability of patient, clinical, dose (lung and heart), and dose-function metrics (metrics that combine dose and 4DCT-ventilation-based function) to predict PFT changes were evaluated using univariate and multivariate linear regression. RESULTS Univariate analysis showed that only dose-function metrics and the presence of chronic obstructive pulmonary disease (COPD) were significant (p<0.05) in predicting FEV1 decline. Multivariate analysis identified a combination of clinical (immunotherapy status, presence of thoracic comorbidities, smoking status, and age), along with lung dose, heart dose, and dose-function metrics in predicting FEV1 and FEV1/FVC changes. CONCLUSION The current work evaluated factors predicting PFT changes for patients treated in a prospective functional avoidance radiotherapy study. The data revealed that lung dose- function metrics could predict PFT changes, validating the significance of reducing the dose to the functional lung to mitigate the decline in pulmonary function and providing guidance for future clinical trials.
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Affiliation(s)
- Nader Ghassemi
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA.
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | | | - Bernard L Jones
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Brian Kavanagh
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Maria Werner-Wasik
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Ryan Miller
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Julie A Barta
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Inga Grills
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Benjamin E Leiby
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA.
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Chad G Rusthoven
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA.
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8
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Lauria M, Stiehl B, Santhanam A, O’Connell D, Naumann L, McNitt-Gray M, Raldow A, Goldin J, Barjaktarevic I, Low DA. An analysis of the regional heterogeneity in tissue elasticity in lung cancer patients with COPD. Front Med (Lausanne) 2023; 10:1151867. [PMID: 37840998 PMCID: PMC10575648 DOI: 10.3389/fmed.2023.1151867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 09/08/2023] [Indexed: 10/17/2023] Open
Abstract
Purpose Recent advancements in obtaining image-based biomarkers from CT images have enabled lung function characterization, which could aid in lung interventional planning. However, the regional heterogeneity in these biomarkers has not been well documented, yet it is critical to several procedures for lung cancer and COPD. The purpose of this paper is to analyze the interlobar and intralobar heterogeneity of tissue elasticity and study their relationship with COPD severity. Methods We retrospectively analyzed a set of 23 lung cancer patients for this study, 14 of whom had COPD. For each patient, we employed a 5DCT scanning protocol to obtain end-exhalation and end-inhalation images and semi-automatically segmented the lobes. We calculated tissue elasticity using a biomechanical property estimation model. To obtain a measure of lobar elasticity, we calculated the mean of the voxel-wise elasticity values within each lobe. To analyze interlobar heterogeneity, we defined an index that represented the properties of the least elastic lobe as compared to the rest of the lobes, termed the Elasticity Heterogeneity Index (EHI). An index of 0 indicated total homogeneity, and higher indices indicated higher heterogeneity. Additionally, we measured intralobar heterogeneity by calculating the coefficient of variation of elasticity within each lobe. Results The mean EHI was 0.223 ± 0.183. The mean coefficient of variation of the elasticity distributions was 51.1% ± 16.6%. For mild COPD patients, the interlobar heterogeneity was low compared to the other categories. For moderate-to-severe COPD patients, the interlobar and intralobar heterogeneities were highest, showing significant differences from the other groups. Conclusion We observed a high level of lung tissue heterogeneity to occur between and within the lobes in all COPD severity cases, especially in moderate-to-severe cases. Heterogeneity results demonstrate the value of a regional, function-guided approach like elasticity for procedures such as surgical decision making and treatment planning.
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Affiliation(s)
- Michael Lauria
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Bradley Stiehl
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Anand Santhanam
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Dylan O’Connell
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Louise Naumann
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Michael McNitt-Gray
- Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Ann Raldow
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jonathan Goldin
- Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Igor Barjaktarevic
- Division of Pulmonary and Critical Care Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Daniel A. Low
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
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9
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Shiinoki T, Fujimoto K, Kawazoe Y, Yuasa Y, Kajima M, Manabe Y, Hirano T, Matsunaga K, Tanaka H. Assessing four-dimensional CT stress maps derived from patient-specific biomechanical models of the lung with pulmonary function test data in lung cancer patients. Br J Radiol 2023; 96:20221149. [PMID: 37393529 PMCID: PMC10461275 DOI: 10.1259/bjr.20221149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 05/23/2023] [Accepted: 06/12/2023] [Indexed: 07/03/2023] Open
Abstract
OBJECTIVE This study aims to retrospectively compare the stress map of the lung with pulmonary function test (PFT) results in lung cancer patients and to evaluate the potential of the stress map as an imaging biomarker for chronic obstructive pulmonary disease (COPD). METHODS 25 lung cancer patients with pre-treatment four-dimensional CT (4DCT) and PFT data were retrospectively analysed. PFT metrics were used to diagnose obstructive lung disease. For each patient, forced expiratory volume in 1 s (FEV1 % predicted) and the ratio of FEV1 and forced vital capacity (FEV1/FVC) were recorded. 4DCT and biomechanical model-deformable image registration (BM-DIR) were used to obtain the lung stress map. The relationship between the mean of the total lung stress and PFT data was evaluated, and the COPD classification grade was also evaluated. RESULTS The mean values of the total lung stress and FEV1 % predicted showed a significant strong correlation [R = 0.833, (p < 0.001)]. The mean values and FEV1/FVC showed a significant strong correlation [R = 0.805, (p < 0.001)]. For the total lung stress, the area under the curve and the optimal cut-off value were 0.94 and 510.8 Pa for the classification of normal or abnormal lung function, respectively. CONCLUSION This study has demonstrated the potential of lung stress maps based on BM-DIR to accurately assess lung function by comparing them with PFT data. ADVANCES IN KNOWLEDGE The derivation of stress map directly from 4DCT is novel method. The BM-DIR-based lung stress map can provide an accurate assessment of lung function.
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Affiliation(s)
- Takehiro Shiinoki
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Koya Fujimoto
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Yusuke Kawazoe
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Yuki Yuasa
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Miki Kajima
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Yuki Manabe
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Tsunahiko Hirano
- Department of Respiratory Medicine and Infectious Disease, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Kazuto Matsunaga
- Department of Respiratory Medicine and Infectious Disease, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Hidekazu Tanaka
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
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10
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Huang YH, Teng X, Zhang J, Chen Z, Ma Z, Ren G, Kong FMS, Ge H, Cai J. Respiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization. J Thorac Imaging 2023; 38:286-296. [PMID: 37265243 DOI: 10.1097/rti.0000000000000717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
PURPOSE The inherent characteristics of lung tissue independent of breathing maneuvers may provide fundamental information for function assessment. This paper attempted to correlate textural signatures from computed tomography (CT) with pulmonary function measurements. MATERIALS AND METHODS Twenty-one lung cancer patients with thoracic 4-dimensional CT, DTPA-single-photon emission CT ventilation ( VNM ) scans, and available spirometry measurements (forced expiratory volume in 1 s, FEV 1 ; forced vital capacity, FVC; and FEV 1 /FVC) were collected. In subregional feature discovery, function-correlated candidates were identified from 79 radiomic features based on the statistical strength to differentiate defected/nondefected lung regions. Feature maps (FMs) of selected candidates were generated on 4-dimensional CT phases for a voxel-wise feature distribution study. Quantitative metrics were applied for validations, including the Spearman correlation coefficient (SCC) and the Dice similarity coefficient for FM- VNM spatial agreement assessments, intraclass correlation coefficient for FM interphase robustness evaluations, and FM-spirometry comparisons. RESULTS At the subregion level, 8 function-correlated features were identified (effect size>0.330). The FMs of candidates yielded moderate-to-strong voxel-wise correlations with the reference VNM . The FMs of gray level dependence matrix dependence nonuniformity showed the highest robust (intraclass correlation coefficient=0.96 and P <0.0001) spatial correlation, with median SCCs ranging from 0.54 to 0.59 throughout the 10 breathing phases. Its phase-averaged FM achieved a median SCC of 0.60, a median Dice similarity coefficient of 0.60 (0.65) for high (low) functional lung volumes, and a correlation of 0.565 (0.646) between the spatially averaged feature values and FEV 1 (FEV 1 /FVC). CONCLUSIONS The results provide further insight into the underlying association of specific pulmonary textures with both local ( VNM ) and global (FEV 1 /FVC, FEV 1 ) functions. Further validations of the FM generalizability and the standardization of implementation protocols are warranted before clinically relevant investigations.
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Affiliation(s)
- Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Zhi Chen
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Zongrui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Feng-Ming Spring Kong
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR
- Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen
| | - Hong Ge
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
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11
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Astley JR, Biancardi AM, Marshall H, Hughes PJC, Collier GJ, Hatton MQ, Wild JM, Tahir BA. A hybrid model- and deep learning-based framework for functional lung image synthesis from multi-inflation CT and hyperpolarized gas MRI. Med Phys 2023; 50:5657-5670. [PMID: 36932692 PMCID: PMC10946819 DOI: 10.1002/mp.16369] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 02/25/2023] [Accepted: 03/04/2023] [Indexed: 03/19/2023] Open
Abstract
BACKGROUND Hyperpolarized gas MRI is a functional lung imaging modality capable of visualizing regional lung ventilation with exceptional detail within a single breath. However, this modality requires specialized equipment and exogenous contrast, which limits widespread clinical adoption. CT ventilation imaging employs various metrics to model regional ventilation from non-contrast CT scans acquired at multiple inflation levels and has demonstrated moderate spatial correlation with hyperpolarized gas MRI. Recently, deep learning (DL)-based methods, utilizing convolutional neural networks (CNNs), have been leveraged for image synthesis applications. Hybrid approaches integrating computational modeling and data-driven methods have been utilized in cases where datasets are limited with the added benefit of maintaining physiological plausibility. PURPOSE To develop and evaluate a multi-channel DL-based method that combines modeling and data-driven approaches to synthesize hyperpolarized gas MRI lung ventilation scans from multi-inflation, non-contrast CT and quantitatively compare these synthetic ventilation scans to conventional CT ventilation modeling. METHODS In this study, we propose a hybrid DL configuration that integrates model- and data-driven methods to synthesize hyperpolarized gas MRI lung ventilation scans from a combination of non-contrast, multi-inflation CT and CT ventilation modeling. We used a diverse dataset comprising paired inspiratory and expiratory CT and helium-3 hyperpolarized gas MRI for 47 participants with a range of pulmonary pathologies. We performed six-fold cross-validation on the dataset and evaluated the spatial correlation between the synthetic ventilation and real hyperpolarized gas MRI scans; the proposed hybrid framework was compared to conventional CT ventilation modeling and other non-hybrid DL configurations. Synthetic ventilation scans were evaluated using voxel-wise evaluation metrics such as Spearman's correlation and mean square error (MSE), in addition to clinical biomarkers of lung function such as the ventilated lung percentage (VLP). Furthermore, regional localization of ventilated and defect lung regions was assessed via the Dice similarity coefficient (DSC). RESULTS We showed that the proposed hybrid framework is capable of accurately replicating ventilation defects seen in the real hyperpolarized gas MRI scans, achieving a voxel-wise Spearman's correlation of 0.57 ± 0.17 and an MSE of 0.017 ± 0.01. The hybrid framework significantly outperformed CT ventilation modeling alone and all other DL configurations using Spearman's correlation. The proposed framework was capable of generating clinically relevant metrics such as the VLP without manual intervention, resulting in a Bland-Altman bias of 3.04%, significantly outperforming CT ventilation modeling. Relative to CT ventilation modeling, the hybrid framework yielded significantly more accurate delineations of ventilated and defect lung regions, achieving a DSC of 0.95 and 0.48 for ventilated and defect regions, respectively. CONCLUSION The ability to generate realistic synthetic ventilation scans from CT has implications for several clinical applications, including functional lung avoidance radiotherapy and treatment response mapping. CT is an integral part of almost every clinical lung imaging workflow and hence is readily available for most patients; therefore, synthetic ventilation from non-contrast CT can provide patients with wider access to ventilation imaging worldwide.
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Affiliation(s)
- Joshua R Astley
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Alberto M Biancardi
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Helen Marshall
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Paul J C Hughes
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Guilhem J Collier
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Matthew Q Hatton
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK
| | - Jim M Wild
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
- Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Bilal A Tahir
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
- Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, UK
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12
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Gerard SE, Chaudhary MFA, Herrmann J, Christensen GE, Estépar RSJ, Reinhardt JM, Hoffman EA. Direct estimation of regional lung volume change from paired and single CT images using residual regression neural network. Med Phys 2023; 50:5698-5714. [PMID: 36929883 PMCID: PMC10743098 DOI: 10.1002/mp.16365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 02/11/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND Chest computed tomography (CT) enables characterization of pulmonary diseases by producing high-resolution and high-contrast images of the intricate lung structures. Deformable image registration is used to align chest CT scans at different lung volumes, yielding estimates of local tissue expansion and contraction. PURPOSE We investigated the utility of deep generative models for directly predicting local tissue volume change from lung CT images, bypassing computationally expensive iterative image registration and providing a method that can be utilized in scenarios where either one or two CT scans are available. METHODS A residual regression convolutional neural network, called Reg3DNet+, is proposed for directly regressing high-resolution images of local tissue volume change (i.e., Jacobian) from CT images. Image registration was performed between lung volumes at total lung capacity (TLC) and functional residual capacity (FRC) using a tissue mass- and structure-preserving registration algorithm. The Jacobian image was calculated from the registration-derived displacement field and used as the ground truth for local tissue volume change. Four separate Reg3DNet+ models were trained to predict Jacobian images using a multifactorial study design to compare the effects of network input (i.e., single image vs. paired images) and output space (i.e., FRC vs. TLC). The models were trained and evaluated on image datasets from the COPDGene study. Models were evaluated against the registration-derived Jacobian images using local, regional, and global evaluation metrics. RESULTS Statistical analysis revealed that both factors - network input and output space - were significant determinants for change in evaluation metrics. Paired-input models performed better than single-input models, and model performance was better in the output space of FRC rather than TLC. Mean structural similarity index for paired-input models was 0.959 and 0.956 for FRC and TLC output spaces, respectively, and for single-input models was 0.951 and 0.937. Global evaluation metrics demonstrated correlation between registration-derived Jacobian mean and predicted Jacobian mean: coefficient of determination (r2 ) for paired-input models was 0.974 and 0.938 for FRC and TLC output spaces, respectively, and for single-input models was 0.598 and 0.346. After correcting for effort, registration-derived lobar volume change was strongly correlated with the predicted lobar volume change: for paired-input models r2 was 0.899 for both FRC and TLC output spaces, and for single-input models r2 was 0.803 and 0.862, respectively. CONCLUSIONS Convolutional neural networks can be used to directly predict local tissue mechanics, eliminating the need for computationally expensive image registration. Networks that use paired CT images acquired at TLC and FRC allow for more accurate prediction of local tissue expansion compared to networks that use a single image. Networks that only require a single input image still show promising results, particularly after correcting for effort, and allow for local tissue expansion estimation in cases where multiple CT scans are not available. For single-input networks, the FRC image is more predictive of local tissue volume change compared to the TLC image.
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Affiliation(s)
- Sarah E. Gerard
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
| | | | - Jacob Herrmann
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Gary E. Christensen
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, USA
| | | | - Joseph M. Reinhardt
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
| | - Eric A. Hoffman
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
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13
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Li T, Wang J, Yang Y, Glide-Hurst CK, Wen N, Cai J. Multi-parametric MRI for radiotherapy simulation. Med Phys 2023; 50:5273-5293. [PMID: 36710376 PMCID: PMC10382603 DOI: 10.1002/mp.16256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 09/10/2022] [Accepted: 12/06/2022] [Indexed: 01/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.
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Affiliation(s)
- Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jihong Wang
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Yingli Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ning Wen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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14
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Astley JR, Biancardi AM, Marshall H, Smith LJ, Hughes PJC, Collier GJ, Saunders LC, Norquay G, Tofan MM, Hatton MQ, Hughes R, Wild JM, Tahir BA. PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation. Sci Rep 2023; 13:11273. [PMID: 37438406 DOI: 10.1038/s41598-023-38105-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 07/03/2023] [Indexed: 07/14/2023] Open
Abstract
Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; however, these techniques require specialized equipment and exogenous contrast, limiting clinical adoption. Physiologically-informed techniques to map proton (1H)-MRI ventilation have been proposed. These approaches have demonstrated moderate correlation with hyperpolarized gas MRI. Recently, deep learning (DL) has been used for image synthesis applications, including functional lung image synthesis. Here, we propose a 3D multi-channel convolutional neural network that employs physiologically-informed ventilation mapping and multi-inflation structural 1H-MRI to synthesize 3D ventilation surrogates (PhysVENeT). The dataset comprised paired inspiratory and expiratory 1H-MRI scans and corresponding hyperpolarized gas MRI scans from 170 participants with various pulmonary pathologies. We performed fivefold cross-validation on 150 of these participants and used 20 participants with a previously unseen pathology (post COVID-19) for external validation. Synthetic ventilation surrogates were evaluated using voxel-wise correlation and structural similarity metrics; the proposed PhysVENeT framework significantly outperformed conventional 1H-MRI ventilation mapping and other DL approaches which did not utilize structural imaging and ventilation mapping. PhysVENeT can accurately reflect ventilation defects and exhibits minimal overfitting on external validation data compared to DL approaches that do not integrate physiologically-informed mapping.
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Affiliation(s)
- Joshua R Astley
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Alberto M Biancardi
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Helen Marshall
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Laurie J Smith
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Paul J C Hughes
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Guilhem J Collier
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Laura C Saunders
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Graham Norquay
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Malina-Maria Tofan
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK
| | - Matthew Q Hatton
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK
| | - Rod Hughes
- Early Development Respiratory Medicine, AstraZeneca, Cambridge, UK
| | - Jim M Wild
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
- Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Bilal A Tahir
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK.
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK.
- Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK.
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15
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Mid-treatment adaptive planning during thoracic radiation using 68 Ventilation-Perfusion Positron emission tomography. Clin Transl Radiat Oncol 2023; 40:100599. [PMID: 36879654 PMCID: PMC9984948 DOI: 10.1016/j.ctro.2023.100599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 02/11/2023] [Accepted: 02/12/2023] [Indexed: 02/17/2023] Open
Abstract
Four-Dimensional Gallium 68 Ventilation-Perfusion Positron Emission Tomography (68Ga-4D-V/Q PET/CT) allows for dynamic imaging of lung function. To date there has been no assessment of the feasibility of adapting radiation therapy plans to changes in lung function imaged at mid-treatment function using 68Ga-4D-V/Q PET/CT. This study assessed the potential reductions of dose to the functional lung when radiation therapy plans were adapted to avoid functional lung at the mid-treatment timepoint using volumetric arc radiotherapy (VMAT). Methods A prospective clinical trial (U1111-1138-4421) was performed in patients undergoing conventionally fractionated radiation therapy for non-small cell lung cancer (NSCLC). A 68Ga-4D-V/Q PET/CT was acquired at baseline and in the 4th week of treatment. Functional lung target volumes using the ventilated and perfused lung were created. Baseline functional volumes were compared to the week 4 V/Q functional volumes to describe the change in function over time. For each patient, 3 VMAT plans were created and optimised to spare ventilated, perfused or anatomical lung. All key dosimetry metrics were then compared including dose to target volumes, dose to organs at risk and dose to the anatomical and functional sub-units of lung. Results 25 patients had both baseline and 4 week mid treatment 68Ga-4D-V/Q PET/CT imaging. This resulted in a total of 75 adapted VMAT plans. The HPLung volume decreased in 16/25 patients with a mean of the change in volume (cc) -28 ± 515 cc [±SD, range -996 cc to 1496 cc]. The HVLung volume increased in 13/25 patients with mean of the change in volume (cc) + 112 ± 590 cc. [±SD, range -1424 cc to 950 cc]. The functional lung sparing technique was found to be feasible with no significant differences in dose to anatomically defined organs at risk. Most patients did derive a benefit with a reduction in functional volume receiving 20 Gy (fV20) and/or functional mean lung dose (fMLD) in either perfusion and/or ventilation. Patients with the most reduction in fV20 and fMLD were those with stage III NSCLC. Conclusion Functional lung volumes change during treatment. Some patients benefit from using 68Ga-4D-V/Q PET/CT in the 4th week of radiation therapy to adapt radiation plans. In these patients, the role of mid-treatment adaptation requires further prospective investigation.
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16
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Yamamoto T, Kabus S, Bal M, Keall PJ, Moran A, Wright C, Benedict SH, Holland D, Mahaffey N, Qi L, Daly ME. Four-Dimensional Computed Tomography Ventilation Image-Guided Lung Functional Avoidance Radiation Therapy: A Single-Arm Prospective Pilot Clinical Trial. Int J Radiat Oncol Biol Phys 2023; 115:1144-1154. [PMID: 36427643 DOI: 10.1016/j.ijrobp.2022.11.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/28/2022] [Accepted: 11/09/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE The primary objective of this prospective pilot trial was to assess the safety and feasibility of lung functional avoidance radiation therapy (RT) with 4-dimensional (4D) computed tomography (CT) ventilation imaging. METHODS AND MATERIALS Patients with primary lung cancer or metastatic disease to the lungs to receive conventionally fractionated RT (CFRT) or stereotactic body RT (SBRT) were eligible. Standard-of-care 4D-CT scans were used to generate ventilation images through image processing/analysis. Each patient required a standard intensity modulated RT plan and ventilation image guided functional avoidance plan. The primary endpoint was the safety of functional avoidance RT, defined as the rate of grade ≥3 adverse events (AEs) that occurred ≤12 months after treatment. Protocol treatment was considered safe if the rates of grade ≥3 pneumonitis and esophagitis were <13% and <21%, respectively for CFRT, and if the rate of any grade ≥3 AEs was <28% for SBRT. Feasibility of functional avoidance RT was assessed by comparison of dose metrics between the 2 plans using the Wilcoxon signed-rank test. RESULTS Between May 2015 and November 2019, 34 patients with non-small cell lung cancer were enrolled, and 33 patients were evaluable (n = 24 for CFRT; n = 9 for SBRT). Median follow-up was 14.7 months. For CFRT, the rates of grade ≥3 pneumonitis and esophagitis were 4.2% (95% confidence interval, 0.1%-21.1%) and 12.5% (2.7%-32.4%). For SBRT, no patients developed grade ≥3 AEs. Compared with the standard plans, the functional avoidance plans significantly (P < .01) reduced the lung dose-function metrics without compromising target coverage or adherence to standard organs at risk constraints. CONCLUSIONS This study, representing one of the first prospective investigations on lung functional avoidance RT, demonstrated that the 4D-CT ventilation image guided functional avoidance RT that significantly reduced dose to ventilated lung regions could be safely administered, adding to the growing body of evidence for its clinical utility.
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Affiliation(s)
- Tokihiro Yamamoto
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California.
| | - Sven Kabus
- Department of Medical Image Processing & Analytics, Philips Research, Hamburg, Germany
| | | | - Paul J Keall
- ACRF Image X Institute, University of Sydney, Sydney, New South Wales, Australia
| | - Angel Moran
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California
| | - Cari Wright
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California
| | - Devin Holland
- Office of Clinical Research, University of California Davis Comprehensive Cancer Center, Sacramento, California
| | - Nichole Mahaffey
- Office of Clinical Research, University of California Davis Comprehensive Cancer Center, Sacramento, California
| | - Lihong Qi
- Department of Public Health Sciences, University of California, Davis, California
| | - Megan E Daly
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California
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Miller R, Castillo R, Castillo E, Jones BL, Miften M, Kavanagh B, Lu B, Werner-Wasik M, Ghassemi N, Lombardo J, Barta J, Grills I, Rusthoven CG, Guerrero T, Vinogradskiy Y. Characterizing Pulmonary Function Test Changes for Patients With Lung Cancer Treated on a 2-Institution, 4-Dimensional Computed Tomography-Ventilation Functional Avoidance Prospective Clinical Trial. Adv Radiat Oncol 2023; 8:101133. [PMID: 36618762 PMCID: PMC9816902 DOI: 10.1016/j.adro.2022.101133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 11/17/2022] [Indexed: 12/05/2022] Open
Abstract
Purpose Four-dimensional computed tomography (4DCT)-ventilation-based functional avoidance uses 4DCT images to generate plans that avoid functional regions of the lung with the goal of reducing pulmonary toxic effects. A phase 2, multicenter, prospective study was completed to evaluate 4DCT-ventilation functional avoidance radiation therapy. The purpose of this study was to report the results for pretreatment to posttreatment pulmonary function test (PFT) changes for patients treated with functional avoidance radiation therapy. Methods and Materials Patients with locally advanced lung cancer receiving chemoradiation were accrued. Functional avoidance plans based on 4DCT-ventilation images were generated. PFTs were obtained at baseline and 3 months after chemoradiation. Differences for PFT metrics are reported, including diffusing capacity for carbon monoxide (DLCO), forced expiratory volume in 1 second (FEV1), and forced vital capacity (FVC). PFT metrics were compared for patients who did and did not experience grade 2 or higher pneumonitis. Results Fifty-six patients enrolled on the study had baseline and posttreatment PFTs evaluable for analysis. The mean change in DLCO, FEV1, and FVC was -11.6% ± 14.2%, -5.6% ± 16.9%, and -9.0% ± 20.1%, respectively. The mean change in DLCO was -15.4% ± 14.4% for patients with grade 2 or higher radiation pneumonitis and -10.8% ± 14.1% for patients with grade <2 radiation pneumonitis (P = .37). The mean change in FEV1 was -14.3% ± 22.1% for patients with grade 2 or higher radiation pneumonitis and -3.9% ± 15.4% for patients with grade <2 radiation pneumonitis (P = .09). Conclusions The current work is the first to quantitatively characterize PFT changes for patients with lung cancer treated on a prospective functional avoidance radiation therapy study. In comparison with patients treated with standard thoracic radiation planning, the data qualitatively show that functional avoidance resulted in less of a decline in DLCO and FEV1. The presented data can help elucidate the potential pulmonary function improvement with functional avoidance radiation therapy.
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Affiliation(s)
- Ryan Miller
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, Georgia
| | - Edward Castillo
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas
| | - Bernard L. Jones
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - 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
| | - Bo Lu
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Maria Werner-Wasik
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Nader Ghassemi
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Joseph Lombardo
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Julie Barta
- Department of Thoracic Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Inga Grills
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Chad G. Rusthoven
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
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18
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Xenon-Enhanced Ventilation Computed Tomography for Functional Lung Avoidance Radiation Therapy in Patients With Lung Cancer. Int J Radiat Oncol Biol Phys 2023; 115:356-365. [PMID: 36029910 DOI: 10.1016/j.ijrobp.2022.07.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 06/22/2022] [Accepted: 07/19/2022] [Indexed: 01/14/2023]
Abstract
PURPOSE This phase 2 trial aimed to determine whether xenon-enhanced ventilation computed tomography (XeCT)-guided functional-lung-avoidance radiation therapy could reduce the radiation pneumonitis (RP) rate in patients with lung cancer undergoing definitive chemoradiation therapy. METHODS AND MATERIALS Functional lung ventilation was measured via pulmonary function testing (PFT) and XeCT. A standard plan (SP) without reference to XeCT and a functional-lung-avoidance plan (fAP) optimized for lowering the radiation dose to the functional lung at the guidance of XeCT were designed. Dosimetric parameters and predicted RP risks modeled by biological evaluation were compared between the 2 plans in a treatment planning system (TPS). All patients received the approved fAP. The primary endpoint was the rate of grade ≥2 RP, and the secondary endpoints were the survival outcomes. The study hypothesis was that fAP could reduce the rate of grade ≥2 RP to 12% compared with a 30% historical rate. RESULTS Thirty-six patients were evaluated. Xenon-enhanced total functional lung volumes positively correlated with PFT ventilation parameters (forced vital capacity, P = .012; forced expiratory volume in 1 second, P = .035), whereas they were not correlated with the diffusion capacity parameter. We observed a 17% rate of grade ≥2 RP (6 of 36 patients), which was significantly different (P = .040) compared with the historical control. Compared with the SP, the fAP significantly spared the total ventilated lung, leading to a reduction in predicted grade ≥2 RP (P = .001) by TPS biological evaluation. The median follow-up was 15.2 months. The 1-year local control (LC), disseminated failure-free survival (DFFS), and overall survival (OS) rates were 88%, 66%, and 91%, respectively. The median LC and OS were not reached, and the median DFFS was 24.0 months (95% confidence interval, 15.7-32.3 months). CONCLUSIONS This report of XeCT-guided functional-lung-avoidance radiation therapy provided evidence showing its feasibility in clinical practice. Its benefit should be assessed in a broader multicenter trial setting.
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Katsuta Y, Kadoya N, Kajikawa T, Mouri S, Kimura T, Takeda K, Yamamoto T, Imano N, Tanaka S, Ito K, Kanai T, Nakajima Y, Jingu K. Radiation pneumonitis prediction model with integrating multiple dose-function features on 4DCT ventilation images. Phys Med 2023; 105:102505. [PMID: 36535238 DOI: 10.1016/j.ejmp.2022.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 11/18/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
PURPOSE Radiation pneumonitis (RP) is dose-limiting toxicity for non-small-cell cancer (NSCLC). This study developed an RP prediction model by integrating dose-function features from computed four-dimensional computed tomography (4DCT) ventilation using the least absolute shrinkage and selection operator (LASSO). METHODS Between 2013 and 2020, 126 NSCLC patients were included in this study who underwent a 4DCT scan to calculate ventilation images. We computed two sets of candidate dose-function features from (1) the percentage volume receiving > 20 Gy or the mean dose on the functioning zones determined with the lower cutoff percentile ventilation value, (2) the functioning zones determined with lower and upper cutoff percentile ventilation value using 4DCT ventilation images. An RP prediction model was developed by LASSO while simultaneously determining the regression coefficient and feature selection through fivefold cross-validation. RESULTS We found 39.3 % of our patients had a ≥ grade 2 RP. The mean area under the curve (AUC) values for the developed models using clinical, dose-volume, and dose-function features with a lower cutoff were 0.791, and the mean AUC values with lower and upper cutoffs were 0.814. The relative regression coefficient (RRC) on dose-function features with upper and lower cutoffs revealed a relative impact of dose to each functioning zone to RP. RRCs were 0.52 for the mean dose on the functioning zone, with top 20 % of all functioning zone was two times greater than that of 0.19 for these with 60 %-80 % and 0.17 with 40 %-60 % (P < 0.01). CONCLUSIONS The introduction of dose-function features computed from functioning zones with lower and upper cutoffs in a machine learning framework can improve RP prediction. The RRC given by LASSO using dose-function features allows for the quantification of the RP impact of dose on each functioning zones and having the potential to support treatment planning on functional image-guided radiotherapy.
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Affiliation(s)
- Yoshiyuki Katsuta
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan.
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Tomohiro Kajikawa
- Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Shina Mouri
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Tomoki Kimura
- Department of Radiation Oncology, Kochi Medical School, Kochi University, Nangoku, Japan
| | - Kazuya Takeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Takaya Yamamoto
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Nobuki Imano
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kengo Ito
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Takayuki Kanai
- Department of Radiation Oncology, Yamagata University, Yamagata, Japan
| | - Yujiro Nakajima
- Department of Radiological Sciences, Komazawa University, Tokyo, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
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20
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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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21
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Chen Z, Huang YH, Kong FM, Ho WY, Ren G, Cai J. A super-voxel-based method for generating surrogate lung ventilation images from CT. Front Physiol 2023; 14:1085158. [PMID: 37179833 PMCID: PMC10171197 DOI: 10.3389/fphys.2023.1085158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 04/06/2023] [Indexed: 05/15/2023] Open
Abstract
Purpose: This study aimed to develop and evaluate CTVISVD , a super-voxel-based method for surrogate computed tomography ventilation imaging (CTVI). Methods and Materials: The study used four-dimensional CT (4DCT) and single-photon emission computed tomography (SPECT) images and corresponding lung masks from 21 patients with lung cancer obtained from the Ventilation And Medical Pulmonary Image Registration Evaluation dataset. The lung volume of the exhale CT for each patient was segmented into hundreds of super-voxels using the Simple Linear Iterative Clustering (SLIC) method. These super-voxel segments were applied to the CT and SPECT images to calculate the mean density values (D mean) and mean ventilation values (Vent mean), respectively. The final CT-derived ventilation images were generated by interpolation from the D mean values to yield CTVISVD. For the performance evaluation, the voxel- and region-wise differences between CTVISVD and SPECT were compared using Spearman's correlation and the Dice similarity coefficient index. Additionally, images were generated using two deformable image registration (DIR)-based methods, CTVIHU and CTVIJac, and compared with the SPECT images. Results: The correlation between the D mean and Vent mean of the super-voxel was 0.59 ± 0.09, representing a moderate-to-high correlation at the super-voxel level. In the voxel-wise evaluation, the CTVISVD method achieved a stronger average correlation (0.62 ± 0.10) with SPECT, which was significantly better than the correlations achieved with the CTVIHU (0.33 ± 0.14, p < 0.05) and CTVIJac (0.23 ± 0.11, p < 0.05) methods. For the region-wise evaluation, the Dice similarity coefficient of the high functional region for CTVISVD (0.63 ± 0.07) was significantly higher than the corresponding values for the CTVIHU (0.43 ± 0.08, p < 0.05) and CTVIJac (0.42 ± 0.05, p < 0.05) methods. Conclusion: The strong correlation between CTVISVD and SPECT demonstrates the potential usefulness of this novel method of ventilation estimation for surrogate ventilation imaging.
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Affiliation(s)
- Zhi Chen
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Feng-Ming Kong
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, China
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China
| | - Wai Yin Ho
- Department of Nuclear Medicine, Queen Mary Hospital, Hong Kong, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- *Correspondence: Ge Ren, ; Jing Cai,
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- *Correspondence: Ge Ren, ; Jing Cai,
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22
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Pan Y, Wang D, Chaudhary MFA, Shao W, Gerard SE, Durumeric OC, Bhatt SP, Barr RG, Hoffman EA, Reinhardt JM, Christensen GE. Robust Measures of Image-Registration-Derived Lung Biomechanics in SPIROMICS. J Imaging 2022; 8:309. [PMID: 36422058 PMCID: PMC9693030 DOI: 10.3390/jimaging8110309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/03/2022] [Accepted: 11/08/2022] [Indexed: 11/18/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is an umbrella term used to define a collection of inflammatory lung diseases that cause airflow obstruction and severe damage to the lung parenchyma. This study investigated the robustness of image-registration-based local biomechanical properties of the lung in individuals with COPD as a function of Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage. Image registration was used to estimate the pointwise correspondences between the inspiration (total lung capacity) and expiration (residual volume) computed tomography (CT) images of the lung for each subject. In total, three biomechanical measures were computed from the correspondence map: the Jacobian determinant; the anisotropic deformation index (ADI); and the slab-rod index (SRI). CT scans from 245 subjects with varying GOLD stages were analyzed from the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS). Results show monotonic increasing or decreasing trends in the three biomechanical measures as a function of GOLD stage for the entire lung and on a lobe-by-lobe basis. Furthermore, these trends held across all five image registration algorithms. The consistency of the five image registration algorithms on a per individual basis is shown using Bland-Altman plots.
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Affiliation(s)
- Yue Pan
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, USA
| | - Di Wang
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, USA
| | - Muhammad F. A. Chaudhary
- The Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA
| | - Wei Shao
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Sarah E. Gerard
- The Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA
| | - Oguz C. Durumeric
- Department of Mathematics, University of Iowa, Iowa City, IA 52242, USA
| | - Surya P. Bhatt
- UAB Lung Imaging Core, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - R. Graham Barr
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Eric A. Hoffman
- The Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA
- Department of Radiology, University of Iowa, Iowa City, IA 52242, USA
| | - Joseph M. Reinhardt
- The Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA
- Department of Radiology, University of Iowa, Iowa City, IA 52242, USA
| | - Gary E. Christensen
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, USA
- Department of Radiology, University of Iowa, Iowa City, IA 52242, USA
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23
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Yang Z, Lafata KJ, Chen X, Bowsher J, Chang Y, Wang C, Yin FF. Quantification of lung function on CT images based on pulmonary radiomic filtering. Med Phys 2022; 49:7278-7286. [PMID: 35770964 DOI: 10.1002/mp.15837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 06/06/2022] [Accepted: 06/08/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE To develop a radiomics filtering technique for characterizing spatial-encoded regional pulmonary ventilation information on lung computed tomography (CT). METHODS The lung volume was segmented on 46 CT images, and a 3D sliding window kernel was implemented across the lung volume to capture the spatial-encoded image information. Fifty-three radiomic features were extracted within the kernel, resulting in a fourth-order tensor object. As such, each voxel coordinate of the original lung was represented as a 53-dimensional feature vector, such that radiomic features could be viewed as feature maps within the lungs. To test the technique as a potential pulmonary ventilation biomarker, the radiomic feature maps were compared to paired functional images (Galligas PET or DTPA-SPECT) based on the Spearman correlation (ρ) analysis. RESULTS The radiomic feature maps GLRLM-based Run-Length Non-Uniformity and GLCOM-based Sum Average are found to be highly correlated with the functional imaging. The achieved ρ (median [range]) for the two features are 0.46 [0.05, 0.67] and 0.45 [0.21, 0.65] across 46 patients and 2 functional imaging modalities, respectively. CONCLUSIONS The results provide evidence that local regions of sparsely encoded heterogeneous lung parenchyma on CT are associated with diminished radiotracer uptake and measured lung ventilation defects on PET/SPECT imaging. These findings demonstrate the potential of radiomics to serve as a complementary tool to the current lung quantification techniques and provide hypothesis-generating data for future studies.
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Affiliation(s)
- Zhenyu Yang
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
| | - Kyle J Lafata
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA
- Department of Radiology, Duke University, Durham, North Carolina, USA
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA
| | - Xinru Chen
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
| | - James Bowsher
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
| | - Yushi Chang
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA
| | - Chunhao Wang
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
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24
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Huang YH, Ren G, Xiao H, Yang D, Kong FMS, Ho WY, Cai J. Volumetric multiphase ventilation imaging based on four-dimensional computed tomography for functional lung avoidance radiotherapy. Med Phys 2022; 49:7237-7246. [PMID: 35841346 DOI: 10.1002/mp.15847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 04/20/2022] [Accepted: 06/23/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Current computed tomography (CT)-based lung ventilation imaging (CTVI) techniques derive a static ventilation image without temporal information. This research aims to develop a four-dimensional CT (4DCT)-based multiphase dynamic ventilation imaging framework capable of recovering the entire ventilation process throughout the breathing cycle for functional lung avoidance radiotherapy (FLART). METHODS A total of 15 free-breathing thoracic 4DCT scans of lung or esophageal cancer patients were collected from the public datasets. The lung region of each phase image was first delineated, and then the mask-free isotropic total variation image registration algorithm was used to derive the deformation vector fields between the end-expiration (EE) phase and other phases. As a surrogate of ventilation, the voxel-wise local expansion ratio of each phase relative to the EE phase was estimated using the parameterized Integrated Jacobian Formulation method in the EE phase coordinate. Lastly, the dynamic ventilation images were generated by warping these phase-specific local expansion distributions with a same geometry into their respective breathing phases. Quantitative analysis, including interphase Spearman correlation coefficients, voxel-wise, and regional-wise expansion/contraction tracking, were performed to indirectly validate the proposed method. RESULTS The proposed method maintains the physiological meaning of ventilation on each phase and enables to recover the dynamic lung ventilation process. The mean interphase Spearman correlations ranged between 0.23 ± 0.20 and 0.93 ± 0.04 and decreased near the EE phase. Only 26.2% (2.59E + 6 out of 9.89E + 6) of lung voxels exhibited the same expansion/contraction pattern as the global lung. Qualitative and quantitative evaluations of the interphase ventilation distribution difference show that ventilation spatiotemporal heterogeneities generally exist during respiration. CONCLUSIONS In contrast to conventional CTVI metrics, our method enables to extract additional phase-resolved respiration-correlated information and reflects the generally existed ventilation spatiotemporal heterogeneities. Subsequent studies with quantitative phase-by-phase cross-modality evaluations will further explore its potential to deepen our understanding of lung function and respiration mechanics and also to facilitate more accurate implementation of FLART.
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Affiliation(s)
- Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
| | - Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
| | - Dongrong Yang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
| | - Feng-Ming Spring Kong
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, Hong Kong SAR
| | - Wai Yin Ho
- Department of Nuclear Medicine, Queen Mary Hospital, Hong Kong, Hong Kong SAR
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
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25
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Ren G, Li B, Lam SK, Xiao H, Huang YH, Cheung ALY, Lu Y, Mao R, Ge H, Kong FM(S, Ho WY, Cai J. A Transfer Learning Framework for Deep Learning-Based CT-to-Perfusion Mapping on Lung Cancer Patients. Front Oncol 2022; 12:883516. [PMID: 35847874 PMCID: PMC9283770 DOI: 10.3389/fonc.2022.883516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 06/02/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose Deep learning model has shown the feasibility of providing spatial lung perfusion information based on CT images. However, the performance of this method on lung cancer patients is yet to be investigated. This study aims to develop a transfer learning framework to evaluate the deep learning based CT-to-perfusion mapping method specifically on lung cancer patients. Methods SPECT/CT perfusion scans of 33 lung cancer patients and 137 non-cancer patients were retrospectively collected from two hospitals. To adapt the deep learning model on lung cancer patients, a transfer learning framework was developed to utilize the features learned from the non-cancer patients. These images were processed to extract features from three-dimensional CT images and synthesize the corresponding CT-based perfusion images. A pre-trained model was first developed using a dataset of patients with lung diseases other than lung cancer, and subsequently fine-tuned specifically on lung cancer patients under three-fold cross-validation. A multi-level evaluation was performed between the CT-based perfusion images and ground-truth SPECT perfusion images in aspects of voxel-wise correlation using Spearman’s correlation coefficient (R), function-wise similarity using Dice Similarity Coefficient (DSC), and lobe-wise agreement using mean perfusion value for each lobe of the lungs. Results The fine-tuned model yielded a high voxel-wise correlation (0.8142 ± 0.0669) and outperformed the pre-trained model by approximately 8%. Evaluation of function-wise similarity indicated an average DSC value of 0.8112 ± 0.0484 (range: 0.6460-0.8984) for high-functional lungs and 0.8137 ± 0.0414 (range: 0.6743-0.8902) for low-functional lungs. Among the 33 lung cancer patients, high DSC values of greater than 0.7 were achieved for high functional volumes in 32 patients and low functional volumes in all patients. The correlations of the mean perfusion value on the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe were 0.7314, 0.7134, 0.5108, 0.4765, and 0.7618, respectively. Conclusion For lung cancer patients, the CT-based perfusion images synthesized by the transfer learning framework indicated a strong voxel-wise correlation and function-wise similarity with the SPECT perfusion images. This suggests the great potential of the deep learning method in providing regional-based functional information for functional lung avoidance radiation therapy.
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Affiliation(s)
- Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Bing Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Sai-kit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Andy Lai-yin Cheung
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
| | - Yufei Lu
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Ronghu Mao
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Hong Ge
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Feng-Ming (Spring) Kong
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Wai-yin Ho
- Department of Nuclear Medicine, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- *Correspondence: Jing Cai,
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Vicente EM, Modiri A, Kipritidis J, Yu KC, Sun K, Cammin J, Gopal A, Xu J, Mossahebi S, Hagan A, Yan Y, Owen DR, Mohindra P, Matuszak MM, Timmerman RD, Sawant A. Combining Serial and Parallel Functionality in Functional Lung Avoidance Radiation Therapy. Int J Radiat Oncol Biol Phys 2022; 113:456-468. [PMID: 35279324 DOI: 10.1016/j.ijrobp.2022.01.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 01/10/2022] [Accepted: 01/26/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE Functional lung avoidance (FLA) radiation therapy (RT) aims to minimize post-RT pulmonary toxicity by preferentially avoiding dose to high-functioning lung (HFL) regions. A common limitation is that FLA approaches do not consider the conducting architecture for gas exchange. We previously proposed the functionally weighted airway sparing (FWAS) method to spare airways connected to HFL regions, showing that it is possible to substantially reduce risk of radiation-induced airway injury. Here, we compare the performance of FLA and FWAS and propose a novel method combining both approaches. METHODS We used breath-hold computed tomography (BHCT) and simulation 4-dimensional computed tomography (4DCT) from 12 lung stereotactic ablative radiation therapy patients. Four planning strategies were examined: (1) Conventional: no sparing other than clinical dose-volume constraints; (2) FLA: using a 4DCT-based ventilation map to delineate the HFL, plans were optimized to reduce mean dose and V13.50 in HFL; (3) FWAS: we autosegemented 11 to 13 generations of individual airways from each patient's BHCT and assigned priorities based on the relative contribution of each airway to total ventilation. We used these priorities in the optimization along with airway dose constraints, estimated as a function of airway diameter and 5% probability of collapse; and (4) FLA + FWAS: we combined information from the 2 strategies. We prioritized clinical dose constraints for organs at risk and planning target volume in all plans. We performed the evaluation in terms of ventilation preservation accounting for radiation-induced damage to both lung parenchyma and airways. RESULTS We observed average ventilation preservation for FLA, FWAS, and FLA + FWAS as 3%, 8.5%, and 14.5% higher, respectively, than for Conventional plans for patients with ventilation preservation in Conventional plans <90%. Generalized estimated equations showed that all improvements were statistically significant (P ≤ .036). We observed no clinically relevant improvements in outcomes of the sparing techniques in patients with ventilation preservation in Conventional plans ≥90%. CONCLUSIONS These initial results suggest that it is crucial to consider the parallel and the serial nature of the lung to improve post-radiation therapy lung function and, consequently, quality of life for patients.
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Affiliation(s)
| | - Arezoo Modiri
- University of Maryland School of Medicine, Baltimore, Maryland
| | | | | | - Kai Sun
- University of Maryland School of Medicine, Baltimore, Maryland
| | - Jochen Cammin
- University of Maryland School of Medicine, Baltimore, Maryland
| | - Arun Gopal
- University of Maryland School of Medicine, Baltimore, Maryland
| | - Jingzhu Xu
- University of Maryland School of Medicine, Baltimore, Maryland
| | - Sina Mossahebi
- University of Maryland School of Medicine, Baltimore, Maryland
| | - Aaron Hagan
- University of Maryland School of Medicine, Baltimore, Maryland
| | - Yulong Yan
- UT Southwestern Medical Center, Dallas, Texas
| | | | | | | | | | - Amit Sawant
- University of Maryland School of Medicine, Baltimore, Maryland
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Grover J, Byrne HL, Sun Y, Kipritidis J, Keall P. Investigating the use of machine learning to generate ventilation images from CT scans. Med Phys 2022; 49:5258-5267. [PMID: 35502763 PMCID: PMC9545612 DOI: 10.1002/mp.15688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/15/2022] [Accepted: 04/25/2022] [Indexed: 11/16/2022] Open
Abstract
Background Radiotherapy treatment planning incorporating ventilation imaging can reduce the incidence of radiation‐induced lung injury. The gold‐standard of ventilation imaging, using nuclear medicine, has limitations with respect to availability and cost. Purpose An alternative type of ventilation imaging to nuclear medicine uses 4DCT (or breath‐hold CT [BHCT] pair) with deformable image registration (DIR) and a ventilation metric to produce a CT ventilation image (CTVI). The purpose of this study is to investigate the application of machine learning as an alternative to DIR‐based methods when producing CTVIs. Methods A patient dataset of 15 inhale and exhale BHCTs and Galligas PET ventilation images were used to train and test a 2D U‐Net style convolutional neural network. The neural network established relationships between axial input BHCT image pairs and axial labeled Galligas PET images and was evaluated using eightfold cross‐validation. Once trained, the neural network could produce a CTVI from an input BHCT image pair. The CTVIs produced by the neural network were qualitatively assessed visually and quantitatively compared to a Galligas PET ventilation image using a Spearman correlation and Dice similarity coefficient (DSC). The DSC measured the spatial overlap between three segmented equal lung volumes by ventilation (high, medium, and low functioning lung [LFL]). Results The mean Spearman correlation between the CTVIs and the Galligas PET ventilation images was 0.58 ± 0.14. The mean DSC over high, medium, and LFL between the CTVIs and Galligas PET ventilation images was 0.55 ± 0.06. Visually, a systematic overprediction of ventilation within the lung was observed in the CTVIs with respect to the Galligas PET ventilation images, with jagged regions of ventilation in the sagittal and coronal planes. Conclusions A convolutional neural network was developed that could produce a CTVI from a BHCT image pair, which was then compared with a Galligas PET ventilation image. The performance of this machine learning method was comparable to previous benchmark studies investigating a DIR‐based CTVI, warranting future development, and investigation of applying machine learning to a CTVI.
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Affiliation(s)
- James Grover
- ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, Australia.,School of Physics, The University of Sydney, Australia
| | - Hilary L Byrne
- ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, Australia
| | - Yu Sun
- School of Physics, The University of Sydney, Australia
| | - John Kipritidis
- ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, Australia.,Northern Sydney Cancer Centre, Royal North Shore Hospital, Australia
| | - Paul Keall
- ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, Australia
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28
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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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Abstract
Artificial intelligence (AI) is transforming the way we perform advanced imaging. From high-resolution image reconstruction to predicting functional response from clinically acquired data, AI is promising to revolutionize clinical evaluation of lung performance, pushing the boundary in pulmonary functional imaging for patients suffering from respiratory conditions. In this review, we overview the current developments and expound on some of the encouraging new frontiers. We focus on the recent advances in machine learning and deep learning that enable reconstructing images, quantitating, and predicting functional responses of the lung. Finally, we shed light on the potential opportunities and challenges ahead in adopting AI for functional lung imaging in clinical settings.
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Affiliation(s)
- Raúl San José Estépar
- Applied Chest Imaging Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
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30
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Astley JR, Wild JM, Tahir BA. Deep learning in structural and functional lung image analysis. Br J Radiol 2022; 95:20201107. [PMID: 33877878 PMCID: PMC9153705 DOI: 10.1259/bjr.20201107] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
The recent resurgence of deep learning (DL) has dramatically influenced the medical imaging field. Medical image analysis applications have been at the forefront of DL research efforts applied to multiple diseases and organs, including those of the lungs. The aims of this review are twofold: (i) to briefly overview DL theory as it relates to lung image analysis; (ii) to systematically review the DL research literature relating to the lung image analysis applications of segmentation, reconstruction, registration and synthesis. The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. 479 studies were initially identified from the literature search with 82 studies meeting the eligibility criteria. Segmentation was the most common lung image analysis DL application (65.9% of papers reviewed). DL has shown impressive results when applied to segmentation of the whole lung and other pulmonary structures. DL has also shown great potential for applications in image registration, reconstruction and synthesis. However, the majority of published studies have been limited to structural lung imaging with only 12.9% of reviewed studies employing functional lung imaging modalities, thus highlighting significant opportunities for further research in this field. Although the field of DL in lung image analysis is rapidly expanding, concerns over inconsistent validation and evaluation strategies, intersite generalisability, transparency of methodological detail and interpretability need to be addressed before widespread adoption in clinical lung imaging workflow.
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Affiliation(s)
| | - Jim M Wild
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, United Kingdom
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31
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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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32
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Wang C, Li H, Xiao S, Li Z, Zhao X, Xie J, Ye C, Xia L, Lou X, Zhou X. Abnormal dynamic ventilation function of COVID-19 survivors detected by pulmonary free-breathing proton MRI. Eur Radiol 2022; 32:5297-5307. [PMID: 35184219 PMCID: PMC8858033 DOI: 10.1007/s00330-022-08605-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/13/2021] [Accepted: 01/22/2022] [Indexed: 01/03/2023]
Abstract
Objectives To visualize and quantitatively assess regional lung function of survivors of COVID-19 who were hospitalized using pulmonary free-breathing 1H MRI. Methods A total of 12 healthy volunteers and 27 COVID-19 survivors (62.4 ± 8.1 days between infection and image acquisition) were recruited in this prospective study and performed chest 1H MRI acquisitions with free tidal breathing. Then, conventional Fourier decomposition ventilation (FD-V) and global fractional ventilation (FVGlobal) were analyzed. Besides, a modified PREFUL (mPREFUL) method was developed to adapt to COVID-19 survivors and generate dynamic ventilation maps and parameters. All the ventilation maps and parameters were analyzed using Student’s t-test. Pearson’s correlation and a Bland-Altman plot between FVGlobal and mPREFUL were analyzed. Results There was no significant difference between COVID-19 and healthy groups regarding a static FD-V map (0.47 ± 0.12 vs 0.42 ± 0.08; p = .233). However, mPREFUL demonstrated lots of regional high ventilation areas (high ventilation percentage (HVP): 23.7% ± 10.6%) existed in survivors. This regional heterogeneity (i.e., HVP) in survivors was significantly higher than in healthy volunteers (p = .003). The survivors breathed deeper (flow-volume loop: 5375 ± 3978 vs 1688 ± 789; p = .005), and breathed more air in respiratory cycle (total amount: 62.6 ± 19.3 vs 37.3 ± 9.9; p < .001). Besides, mPREFUL showed both good Pearson’s correlation (r = 0.74; p < .001) and Bland-Altman consistency (mean bias = −0.01) with FVGlobal. Conclusions Dynamic ventilation imaging using pulmonary free-breathing 1H MRI found regional abnormity of dynamic ventilation function in COVID-19 survivors. Key Points • Pulmonary free-breathing1H MRI was used to visualize and quantitatively assess regional lung ventilation function of COVID-19 survivors. • Dynamic ventilation maps generated from1H MRI were more sensitive to distinguish the COVID-19 and healthy groups (total air amount: 62.6 ± 19.3 vs 37.3 ± 9.9; p < .001), compared with static ventilation maps (FD-V value: 0.47 ± 0.12 vs 0.42 ± 0.08; p = .233). • COVID-19 survivors had larger regional heterogeneity (high ventilation percentage: 23.7% ± 10.6% vs 13.1% ± 7.9%; p = .003), and breathed deeper (flow-volume loop: 5375 ± 3978 vs 1688 ± 789; p = .005) than healthy volunteers. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08605-w.
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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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Low DA, O'Connell D, Lauria M, Stiehl B, Naumann L, Lee P, Hegde J, Barjaktarevic I, Goldin J, Santhanam A. Ventilation measurements using fast-helical free-breathing computed tomography. Med Phys 2021; 48:6094-6105. [PMID: 34410014 DOI: 10.1002/mp.15173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 07/28/2021] [Accepted: 08/01/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE To examine the use of multiple fast-helical free breathing computed tomography (FHFBCT) scans for ventilation measurement. METHODS Ten patients were scanned 25 times in alternating directions using a FHFBCT protocol. Simultaneously, an abdominal pneumatic bellows was used as a real-time breathing surrogate. Regions-of-interest (ROIs) were selected from the upper right lungs of each patient for analysis. The ROIs were first registered using a published registration technique (pTV). A subsequent follow-up registration employed an objective function with two terms, a ventilation-adjusted Hounsfield Unit difference and a conservation-of-mass term labeled ΔΓ that denoted the difference between the deformation Jacobian and the tissue density ratio. The ventilations were calculated voxel-by-voxel as the slope of a first-order fit of the Jacobian as a function of the breathing amplitude. RESULTS The ventilations of the 10 patients showed different patterns and magnitudes. The average ventilation calculated from the deformation vector fields (DVFs) of the pTV and secondary registration was nearly identical, but the standard deviation of the voxel-to-voxel differences was approximately 0.1. The mean of the 90th percentile values of ΔΓ was reduced from 0.153 to 0.079 between the pTV and secondary registration, implying first that the secondary registration improved the conservation-of-mass criterion by almost 50% and that on average the correspondence between the Jacobian and density ratios as demonstrated by ΔΓ was less than 0.1. This improvement occurred in spite of the average of the 90th percentile changes in the DVF magnitudes being only 0.58 mm. CONCLUSIONS This work introduces the use of multiple free-breathing CT scans for free-breathing ventilation measurements. The approach has some benefits over the traditional use of 4-dimensional CT (4DCT) or breath-hold scans. The benefit over 4DCT is that FHFBCT does not have sorting artifacts. The benefits over breath-hold scans include the relatively small motion induced by quiet respiration versus deep-inspiration breath hold and the potential for characterizing dynamic breathing processes that disappear during breath hold.
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Affiliation(s)
- Daniel A Low
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, USA
| | - Dylan O'Connell
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, USA
| | - Michael Lauria
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, USA
| | - Bradley Stiehl
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, USA
| | - Louise Naumann
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, USA
| | - Percy Lee
- Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA
| | - John Hegde
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, USA
| | - Igor Barjaktarevic
- Department of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Jonathan Goldin
- Department of Radiology, University of California, Los Angeles, Los Angeles, California, USA
| | - Anand Santhanam
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, USA
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Hyperpolarized 129Xe Magnetic Resonance Imaging for Functional Avoidance Treatment Planning in Thoracic Radiation Therapy: A Comparison of Ventilation- and Gas Exchange-Guided Treatment Plans. Int J Radiat Oncol Biol Phys 2021; 111:1044-1057. [PMID: 34265395 DOI: 10.1016/j.ijrobp.2021.07.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 05/19/2021] [Accepted: 07/02/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE To present a methodology to use pulmonary gas exchange maps to guide functional avoidance treatment planning in radiation therapy (RT) and evaluate its efficacy compared with ventilation-guided treatment planning. METHODS AND MATERIALS Before receiving conventional RT for non-small cell lung cancer, 11 patients underwent hyperpolarized 129Xe gas exchange magnetic resonance imaging to map the distribution of xenon in its gas phase (ventilation) and transiently bound to red blood cells in the alveolar capillaries (gas exchange). Both ventilation and gas exchange maps were independently used to guide development of new functional avoidance treatment plans for every patient, while adhering to institutional dose-volume constraints for normal tissues and target coverage. Furthermore, dose-volume histogram (DVH)-based reoptimizations of the clinical plan, with reductions in mean lung dose (MLD) equal to the functional avoidance plans, were created to serve as the control group. To evaluate each plan (regardless of type), gas exchange maps, representing end-to-end lung function, were used to calculate gas exchange-weighted MLD (fMLD), gas exchange-weighted volume receiving ≥20 Gy (fV20), and mean dose in the highest gas exchanging 33% and 50% volumes of lung (MLD-f33% and MLD-f50%). Using each clinically approved plan as a baseline, the reductions in functional metrics were compared for ventilation-optimization, gas exchange optimization, and DVH-based reoptimization. Statistical significance was determined using the Freidman test, with subsequent subdivision when indicated by P values less than .10 and post hoc testing with Wilcoxon signed rank tests to determine significant differences (P < .05). Toxicity modeling was performed using an established function-based model to estimate clinical significance of the results. RESULTS Compared with DVH-based reoptimization of the clinically approved plans, gas exchange-guided functional avoidance planning more effectively reduced the gas exchange-weighted metrics fMLD (average ± SD, -78 ± 79 cGy for gas exchange, compared with -45 ± 34 cGy for DVH-based; P = .03), MLD-f33% (-135 ± 136 cGy, compared with -52 ± 47 cGy; P = .004), and MLD-f50% (-96 ± 95 cGy, compared with -47 ± 40 cGy; P = .01). Comparing the 2 functional planning types, gas exchange-guided planning more effectively reduced MLD-f33% compared with ventilation-guided planning (-64 ± 95; P = .009). For some patients, gas exchange-guided functional avoidance plans demonstrated clinically significant reductions in model-predicted toxicity, more so than the accompanying ventilation-guided plans and DVH-based reoptimizations. CONCLUSION Gas exchange-guided planning effectively reduced dose to high gas exchanging regions of lung while maintaining clinically acceptable plan quality. In many patients, ventilation-guided planning incidentally reduced dose to higher gas exchange regions, to a lesser extent. This methodology enables future prospective trials to examine patient outcomes.
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Forghani F, Patton T, Kwak J, Thomas D, Diot Q, Rusthoven C, Castillo R, Castillo E, Grills I, Guerrero T, Miften M, Vinogradskiy Y. Characterizing spatial differences between SPECT-ventilation and SPECT-perfusion in patients with lung cancer undergoing radiotherapy. Radiother Oncol 2021; 160:120-124. [PMID: 33964328 PMCID: PMC8489737 DOI: 10.1016/j.radonc.2021.04.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/24/2021] [Accepted: 04/28/2021] [Indexed: 12/25/2022]
Abstract
This study investigates agreement between ventilation and perfusion for lung cancer patients undergoing radiotherapy. Ventilation-perfusion scans of nineteen patients with stage III lung cancer from a prospective protocol were compared using voxel-wise Spearman correlation-coefficients. The presented results show in about 25% of patients, ventilation and perfusion exhibit lower agreement.
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Affiliation(s)
- Farnoush Forghani
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Taylor Patton
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States, United States(1); Department of Radiation Oncology, Mayo Clinic, Rochester, MN, United States(2)
| | - Jennifer Kwak
- Department of Radiology, University of Colorado School of Medicine, Aurora, CO, United States
| | - David Thomas
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Quentin Diot
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Chad Rusthoven
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, United States
| | - Inga Grills
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, United States
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, United States
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States, United States(1); Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, United States(2)
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Radiation-induced Hounsfield unit change correlates with dynamic CT perfusion better than 4DCT-based ventilation measures in a novel-swine model. Sci Rep 2021; 11:13156. [PMID: 34162987 PMCID: PMC8222280 DOI: 10.1038/s41598-021-92609-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 06/04/2021] [Indexed: 12/14/2022] Open
Abstract
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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Shao W, Pan Y, Durumeric OC, Reinhardt JM, Bayouth JE, Rusu M, Christensen GE. Geodesic density regression for correcting 4DCT pulmonary respiratory motion artifacts. Med Image Anal 2021; 72:102140. [PMID: 34214957 DOI: 10.1016/j.media.2021.102140] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 06/12/2021] [Accepted: 06/17/2021] [Indexed: 11/25/2022]
Abstract
Pulmonary respiratory motion artifacts are common in four-dimensional computed tomography (4DCT) of lungs and are caused by missing, duplicated, and misaligned image data. This paper presents a geodesic density regression (GDR) algorithm to correct motion artifacts in 4DCT by correcting artifacts in one breathing phase with artifact-free data from corresponding regions of other breathing phases. The GDR algorithm estimates an artifact-free lung template image and a smooth, dense, 4D (space plus time) vector field that deforms the template image to each breathing phase to produce an artifact-free 4DCT scan. Correspondences are estimated by accounting for the local tissue density change associated with air entering and leaving the lungs, and using binary artifact masks to exclude regions with artifacts from image regression. The artifact-free lung template image is generated by mapping the artifact-free regions of each phase volume to a common reference coordinate system using the estimated correspondences and then averaging. This procedure generates a fixed view of the lung with an improved signal-to-noise ratio. The GDR algorithm was evaluated and compared to a state-of-the-art geodesic intensity regression (GIR) algorithm using simulated CT time-series and 4DCT scans with clinically observed motion artifacts. The simulation shows that the GDR algorithm has achieved significantly more accurate Jacobian images and sharper template images, and is less sensitive to data dropout than the GIR algorithm. We also demonstrate that the GDR algorithm is more effective than the GIR algorithm for removing clinically observed motion artifacts in treatment planning 4DCT scans. Our code is freely available at https://github.com/Wei-Shao-Reg/GDR.
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Affiliation(s)
- Wei Shao
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242 USA; Department of Radiology, Stanford University, Stanford, CA 94305 USA.
| | - Yue Pan
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242 USA
| | - Oguz C Durumeric
- Department of Mathematics, University of Iowa, Iowa City, IA 52242 USA
| | - Joseph M Reinhardt
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242 USA
| | - John E Bayouth
- Department of Human Oncology, University of Wisconsin - Madison, Madison, WI 53792 USA
| | - Mirabela Rusu
- Department of Radiology, Stanford University, Stanford, CA 94305 USA.
| | - Gary E Christensen
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242 USA; Department of Radiation Oncology, University of Iowa, Iowa City, IA 52242 USA.
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Wang C, Huang L, Xiao S, Li Z, Ye C, Xia L, Zhou X. Early prediction of lung lesion progression in COVID-19 patients with extended CT ventilation imaging. Eur J Nucl Med Mol Imaging 2021; 48:4339-4349. [PMID: 34137946 PMCID: PMC8210511 DOI: 10.1007/s00259-021-05435-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 05/25/2021] [Indexed: 12/25/2022]
Abstract
PURPOSE In the prediction of COVID-19 disease progression, a clear illustration and early determination of an area that will be affected by pneumonia remain great challenges. In this study, we aimed to predict and visualize the progression of lung lesions in COVID-19 patients in the early stage of illness by using chest CT. METHODS COVID-19 patients who underwent three chest CT scans in the progressive phase were retrospectively enrolled. An extended CT ventilation imaging (CTVI) method was proposed in this work that was adapted to use two chest CT scans acquired on different days, and then lung ventilation maps were generated. The prediction maps were obtained according to the fractional ventilation values, which were related to pulmonary regional function and tissue property changes. The third CT scan was used to validate whether the prediction maps could be used to distinguish healthy regions and potential lesions. RESULTS A total of 30 patients (mean age ± SD, 43 ± 10 years, 19 females, and 2-12 days between the second and third CT scans) were included in this study. The predicted lesion locations and sizes were almost the same as the true ones visualized in third CT scan. Quantitatively, the predicted lesion volumes and true lesion volumes showed both a good Pearson correlation (R2 = 0.80; P < 0.001) and good consistency in the Bland-Altman plot (mean bias = 0.04 cm3). Regarding the enlargements of the existing lesions, prediction results also exhibited a good Pearson correlation (R2 = 0.76; P < 0.001) with true lesion enlargements. CONCLUSION The present findings demonstrated that the extended CTVI method could accurately predict and visualize the progression of lung lesions in COVID-19 patients in the early stage of illness, which is helpful for physicians to predetermine the severity of COVID-19 pneumonia and make effective treatment plans in advance.
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Affiliation(s)
- Cheng Wang
- School of Physics, Huazhong University of Science and Technology, Wuhan, 430074, China.,State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, 430071, China
| | - Lu Huang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Sa Xiao
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, 430071, China
| | - Zimeng Li
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, 430071, China
| | - Chaohui Ye
- School of Physics, Huazhong University of Science and Technology, Wuhan, 430074, China.,State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, 430071, China
| | - Liming Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Xin Zhou
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, 430071, China.
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Chang Y, Jiang Z, Segars WP, Zhang Z, Lafata K, Cai J, Yin FF, Ren L. A generative adversarial network (GAN)-based technique for synthesizing realistic respiratory motion in the extended cardiac-torso (XCAT) phantoms. Phys Med Biol 2021; 66. [PMID: 34061044 DOI: 10.1088/1361-6560/ac01b4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 05/14/2021] [Indexed: 11/12/2022]
Abstract
Objective. Synthesize realistic and controllable respiratory motions in the extended cardiac-torso (XCAT) phantoms by developing a generative adversarial network (GAN)-based deep learning technique.Methods. A motion generation model was developed using bicycle-GAN with a novel 4D generator. Input with the end-of-inhale (EOI) phase images and a Gaussian perturbation, the model generates inter-phase deformable-vector-fields (DVFs), which were composed and applied to the input to generate 4D images. The model was trained and validated using 71 4D-CT images from lung cancer patients and then applied to the XCAT EOI images to generate 4D-XCAT with realistic respiratory motions. A separate respiratory motion amplitude control model was built using decision tree regression to predict the input perturbation needed for a specific motion amplitude, and this model was developed using 300 4D-XCAT generated from 6 XCAT phantom sizes with 50 different perturbations for each size. In both patient and phantom studies, Dice coefficients for lungs and lung volume variation during respiration were compared between the simulated images and reference images. The generated DVF was evaluated by deformation energy. DVFs and ventilation maps of the simulated 4D-CT were compared with the reference 4D-CTs using cross correlation and Spearman's correlation. Comparison of DVFs and ventilation maps among the original 4D-XCAT, the generated 4D-XCAT, and reference patient 4D-CTs were made to show the improvement of motion realism by the model. The amplitude control error was calculated.Results. Comparing the simulated and reference 4D-CTs, the maximum deviation of lung volume during respiration was 5.8%, and the Dice coefficient reached at least 0.95 for lungs. The generated DVFs presented comparable deformation energy levels. The cross correlation of DVFs achieved 0.89 ± 0.10/0.86 ± 0.12/0.95 ± 0.04 along thex/y/zdirection in the testing group. The cross correlation of ventilation maps derived achieved 0.80 ± 0.05/0.67 ± 0.09/0.68 ± 0.13, and the Spearman's correlation achieved 0.70 ± 0.05/0, 60 ± 0.09/0.53 ± 0.01, respectively, in the training/validation/testing groups. The generated 4D-XCAT phantoms presented similar deformation energy as patient data while maintained the lung volumes of the original XCAT phantom (Dice = 0.95, maximum lung volume variation = 4%). The motion amplitude control models controlled the motion amplitude control error to be less than 0.5 mm.Conclusions. The results demonstrated the feasibility of synthesizing realistic controllable respiratory motion in the XCAT phantom using the proposed method. This crucial development enhances the value of XCAT phantoms for various 4D imaging and therapy studies.
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Affiliation(s)
- Yushi Chang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC,United States of America.,Medical Physics Graduate Program, Duke University Durham, NC, United States of America
| | - Zhuoran Jiang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC,United States of America
| | - William Paul Segars
- Medical Physics Graduate Program, Duke University Durham, NC, United States of America.,Department of Radiology, Duke University Medical Center, Durham, NC, United States of America.,Carl E. Ravin Advanced Imaging Laboratories, Duke University, Durham, NC, United States of America
| | - Zeyu Zhang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC,United States of America.,Medical Physics Graduate Program, Duke University Durham, NC, United States of America
| | - Kyle Lafata
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC,United States of America
| | - Jing Cai
- Hong Kong Polytechnic University, Hong Kong, HK, CN, Hong Kong, People's Republic of China
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC,United States of America.,Medical Physics Graduate Program, Duke University Durham, NC, United States of America
| | - Lei Ren
- School of Medicine, University of Maryland, Baltimore, MD, 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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Nyeng TB, Møller DS, Farr K, Kramer S, Khalil AA, Grau C, Hoffmann L. A comparison of two methods for segmentation of functional volumes in radiotherapy planning of lung cancer patients. Acta Oncol 2021; 60:353-360. [PMID: 33522851 DOI: 10.1080/0284186x.2021.1877811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
BACKGROUND In radiotherapy (RT) of lung cancer, dose to functional lung (FL) volumes segmented with two different methods (perfusion SPECT (Q-SPECT) and 4D-CT (4D) ventilation (V)) have been shown to correlate with the incidence of radiation pneumonitis (RP). This study aims to compare the FL volumes identified by both methods. MATERIAL AND METHODS Thirty lung cancer patients had a 4D and Q-SPECT prior to treatment. Seventeen of these patients also had a ventilation SPECT (V-SPECT). FL sub-volumes were segmented automatically, using cut-off values. The volumes were compared in terms of overlap fraction (OF) relative to the minimal volume, and intersection fraction (IF) of the FL volume relative to the total lung volume (VLung). RESULTS Cut-off values suggested in literature for Q-SPECT and 4D-V resulted in volumes differing in size by a median 18% [6%;31%], and a median OF and IF of 0.48 [0.23;0.70] and 0.09 [0.02;0.25], respectively. Segmenting volumes of comparable size of about 1/3 of VLung (FL-m(1/3), m = method) resulted in a median OF and IF of 0.43 [0.23;0.58] and 0.12 [0.06;0.19], respectively. Twenty-five patients (83%) had a reasonable overlap between FL-Q(1/3) and FL-4D-V(1/3) volumes, with OF values above 0.33. IF increased significantly (p = .036) compared to using fixed cut-off values. Similarly, volumes of comparable size of about 1/3 VLung were produced for V-SPECT, and FL-Q(1/3), FL-V(1/3), and FL-4D-V(1/3) were compared. The overlaps and intersections of FL-V(1/3) with FL-Q(1/3) volumes were significantly (p<.001) larger than the corresponding overlaps and intersections of FL-Q(1/3) with FL-4D(1/3) and FL-V(1/3) with FL-4D(1/3). CONCLUSION The Q-SPECT and 4D-V methods do not segment entirely the same FL volumes. A reasonable overlap of the volumes along with the findings of other studies that both correlate to RP incidence, suggests that a combination of both volumes, e.g. using the IF, may be useful in RT treatment planning.
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Affiliation(s)
- T. B. Nyeng
- Department of Oncology, Section for Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - D. S. Møller
- Department of Oncology, Section for Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - K. Farr
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - S. Kramer
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Aarhus, Denmark
| | - A. A. Khalil
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - C. Grau
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - L. Hoffmann
- Department of Oncology, Section for Medical Physics, Aarhus University Hospital, Aarhus, Denmark
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Cazoulat G, Balter JM, Matuszak MM, Jolly S, Owen D, Brock KK. Mapping lung ventilation through stress maps derived from biomechanical models of the lung. Med Phys 2020; 48:715-723. [PMID: 33617034 DOI: 10.1002/mp.14643] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 09/16/2020] [Accepted: 11/25/2020] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Most existing computed tomography (CT)-ventilation imaging techniques are based on deformable image registration (DIR) of different respiratory phases of a four-dimensonal CT (4DCT) scan of the lung, followed by the quantification of local breathing-induced changes in Hounsfield Units (HU) or volume. To date, only moderate correlations have been reported between these CT-ventilation metrics and standard ventilation imaging modalities for adaptive lung radiation therapy. This study evaluates the use of stress maps derived from biomechanical model-based DIR as an alternative CT-ventilation metric. MATERIALS AND METHODS Six patients treated for lung cancer with conventional radiation therapy were retrospectively analyzed. For each patient, a 4DCT scan and Tc-99m SPECT-V image acquired for treatment planning were collected. Biomechanical model-based DIR was applied between the inhale and exhale phase of the 4DCT scans and stress maps were calculated. The voxel-wise correlation between the reference SPECT-V image and map of the maximum principal stress was measured with a Spearman correlation coefficient. The overlap between high (above the 75th percentile) and low (below the 25th percentile) functioning volumes extracted from the reference SPECT-V and the stress maps was measured with Dice similarity coefficients (DSC). The results were compared to those obtained when using two classical CT-ventilation metrics: the change in HU and Jacobian determinant. RESULTS The mean Spearman correlation coefficients were: 0.37 ± 18 and 0.39 ± 13 and 0.59 ± 0.13 considering the changes in HU, Jacobian and maximum principal stress, respectively. The corresponding mean DSC coefficients were 0.38 ± 0.09, 0.37 ± 0.07 and 0.52 ± 0.07 for the high ventilation function volumes and 0.48 ± 0.13, 0.44 ± 0.09 and 0.52 ± 0.07 for the low ventilation function volumes. CONCLUSION For presenting a significantly stronger and more consistent correlation with standard SPECT-V images than previously proposed CT-ventilation metrics, stress maps derived with the proposed method appear to be a promising tool for incorporation into functional lung avoidance strategies.
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Affiliation(s)
- Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - James M Balter
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Martha M Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Dawn Owen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Capaldi DPI, Guo F, Xing L, Parraga G. Pulmonary Ventilation Maps Generated with Free-breathing Proton MRI and a Deep Convolutional Neural Network. Radiology 2020; 298:427-438. [PMID: 33289613 DOI: 10.1148/radiol.2020202861] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Background Hyperpolarized noble gas MRI helps measure lung ventilation, but clinical translation remains limited. Free-breathing proton MRI may help quantify lung function using existing MRI systems without contrast material and may assist in providing information about ventilation not visible to the eye or easily extracted with segmentation methods. Purpose To explore the use of deep convolutional neural networks (DCNNs) to generate synthetic MRI ventilation scans from free-breathing MRI (deep learning [DL] ventilation MRI)-derived specific ventilation maps as a surrogate of noble gas MRI and to validate this approach across a wide range of lung diseases. Materials and Methods In this secondary analysis of prospective trials, 114 paired noble gas MRI and two-dimensional free-breathing MRI scans were obtained in healthy volunteers with no history of chronic or acute respiratory disease and in study participants with a range of different obstructive lung diseases, including asthma, bronchiectasis, chronic obstructive pulmonary disease, and non-small-cell lung cancer between September 2013 and April 2018 (ClinicalTrials.gov identifiers: NCT03169673, NCT02351141, NCT02263794, NCT02282202, NCT02279329, and NCT02002052). A U-Net-based DCNN model was trained to map free-breathing proton MRI to hyperpolarized helium 3 (3He) MRI ventilation and validated using a sixfold validation. During training, the DCNN ventilation maps were compared with noble gas MRI scans using the Pearson correlation coefficient (r) and mean absolute error. DCNN ventilation images were segmented for ventilation and ventilation defects and were compared with noble gas MRI scans using the Dice similarity coefficient (DSC). Relationships were evaluated with the Spearman correlation coefficient (rS). Results One hundred fourteen study participants (mean age, 56 years ± 15 [standard deviation]; 66 women) were evaluated. As compared with 3He MRI, DCNN model ventilation maps had a mean r value of 0.87 ± 0.08. The mean DSC for DL ventilation MRI and 3He MRI ventilation was 0.91 ± 0.07. The ventilation defect percentage for DL ventilation MRI was highly correlated with 3He MRI ventilation defect percentage (rS = 0.83, P < .001, mean bias = -2.0% ± 5). Both DL ventilation MRI (rS = -0.51, P < .001) and 3He MRI (rS = -0.61, P < .001) ventilation defect percentage were correlated with the forced expiratory volume in 1 second. The DCNN model required approximately 2 hours for training and approximately 1 second to generate a ventilation map. Conclusion In participants with diverse pulmonary pathologic findings, deep convolutional neural networks generated ventilation maps from free-breathing proton MRI trained with a hyperpolarized noble-gas MRI ventilation map data set. The maps showed correlation with noble gas MRI ventilation and pulmonary function measurements. © RSNA, 2020 See also the editorial by Vogel-Claussen in this issue.
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Affiliation(s)
- Dante P I Capaldi
- From the Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, Calif (D.P.I.C., L.X.); Sunnybrook Research Institute, Department of Medical Biophysics, University of Toronto, Toronto, Canada (F.G.); and Robarts Research Institute, Department of Medical Biophysics, The University of Western Ontario, 1151 Richmond St N, London, ON, Canada N6A 5B7 (G.P.)
| | - Fumin Guo
- From the Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, Calif (D.P.I.C., L.X.); Sunnybrook Research Institute, Department of Medical Biophysics, University of Toronto, Toronto, Canada (F.G.); and Robarts Research Institute, Department of Medical Biophysics, The University of Western Ontario, 1151 Richmond St N, London, ON, Canada N6A 5B7 (G.P.)
| | - Lei Xing
- From the Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, Calif (D.P.I.C., L.X.); Sunnybrook Research Institute, Department of Medical Biophysics, University of Toronto, Toronto, Canada (F.G.); and Robarts Research Institute, Department of Medical Biophysics, The University of Western Ontario, 1151 Richmond St N, London, ON, Canada N6A 5B7 (G.P.)
| | - Grace Parraga
- From the Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, Calif (D.P.I.C., L.X.); Sunnybrook Research Institute, Department of Medical Biophysics, University of Toronto, Toronto, Canada (F.G.); and Robarts Research Institute, Department of Medical Biophysics, The University of Western Ontario, 1151 Richmond St N, London, ON, Canada N6A 5B7 (G.P.)
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46
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Zachiu C, Denis de Senneville B, Willigenburg T, Voort van Zyp JRN, de Boer JCJ, Raaymakers BW, Ries M. Anatomically-adaptive multi-modal image registration for image-guided external-beam radiotherapy. ACTA ACUST UNITED AC 2020; 65:215028. [DOI: 10.1088/1361-6560/abad7d] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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47
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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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48
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Bjarnason TA, Rees R, Kainz J, Le LH, Stewart EE, Preston B, Elbakri I, Fife IAJ, Lee T, Gagnon IMB, Arsenault C, Therrien P, Kendall E, Tonkopi E, Cottreau M, Aldrich JE. An international survey on the clinical use of rigid and deformable image registration in radiotherapy. J Appl Clin Med Phys 2020; 21:10-24. [PMID: 32915492 PMCID: PMC7075391 DOI: 10.1002/acm2.12957] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 01/13/2020] [Accepted: 05/14/2020] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVES Rigid image registration (RIR) and deformable image registration (DIR) are widely used in radiotherapy. This project aims to capture current international approaches to image registration. METHODS A survey was designed to identify variations in use, resources, implementation, and decision-making criteria for clinical image registration. This was distributed to radiotherapy centers internationally in 2018. RESULTS There were 57 responses internationally, from the Americas (46%), Australia/New Zealand (32%), Europe (12%), and Asia (10%). Rigid image registration and DIR were used clinically for computed tomography (CT)-CT registration (96% and 51%, respectively), followed by CT-PET (81% and 47%), CT-CBCT (84% and 19%), CT-MR (93% and 19%), MR-MR (49% and 5%), and CT-US (9% and 0%). Respondent centers performed DIR using dedicated software (75%) and treatment planning systems (29%), with 84% having some form of DIR software. Centers have clinically implemented DIR for atlas-based segmentation (47%), multi-modality treatment planning (65%), and dose deformation (63%). The clinical use of DIR for multi-modality treatment planning and accounting for retreatments was considered to have the highest benefit-to-risk ratio (69% and 67%, respectively). CONCLUSIONS This survey data provides useful insights on where, when, and how image registration has been implemented in radiotherapy centers around the world. DIR is mainly in clinical use for CT-CT (51%) and CT-PET (47%) for the head and neck (43-57% over all use cases) region. The highest benefit-risk ratio for clinical use of DIR was for multi-modality treatment planning and accounting for retreatments, which also had higher clinical use than for adaptive radiotherapy and atlas-based segmentation.
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Affiliation(s)
- Thorarin A. Bjarnason
- Medical ImagingInterior Health AuthorityKelownaBCCanada
- RadiologyUniversity of British ColumbiaVancouverBCCanada
- PhysicsUniversity of British Columbia OkanaganKelownaBCCanada
| | - Robert Rees
- Occupational Health & SafetyYukon Workers' Compensation Health and Safety BoardWhitehorseYKCanada
| | - Judy Kainz
- Workers' Safety and Compensation Commission for Northwest Territories and NunavutYellowknifeNTCanada
| | - Lawrence H. Le
- Diagnostic ImagingAlberta Health ServicesCalgaryABCanada
- Radiology and Diagnostic ImagingUniversity of AlbertaEdmontonABCanada
| | | | - Brent Preston
- Radiation Safety UnitGovernment of SaskatchewanSaskatoonSKCanada
| | - Idris Elbakri
- Cancer Care ManitobaWinnipegMBCanada
- Physics and AstronomyUniversity of ManitobaWinnipegMBCanada
- RadiologyUniversity of ManitobaWinnipegMBCanada
| | - Ingvar A. J. Fife
- Cancer Care ManitobaWinnipegMBCanada
- Physics and AstronomyUniversity of ManitobaWinnipegMBCanada
- RadiologyUniversity of ManitobaWinnipegMBCanada
| | - Ting‐Yim Lee
- St Joseph’s Health Care LondonLondonONCanada
- Lawson Research InstituteLondonONCanada
- Medical ImagingMedical Biophysics, OncologyRobarts Research InstituteUniversity of Western OntarioLondonONCanada
| | | | - Clément Arsenault
- Hôpital Dr Georges–L. DumontCentre d'Oncologie Dr Léon–RichardMonctonNBCanada
| | | | | | - Elena Tonkopi
- Nova Scotia Health AuthorityHalifaxNSCanada
- Diagnostic RadiologyDalhousie UniversityHalifaxNSCanada
- Radiation OncologyDalhousie UniversityHalifaxNSCanada
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Vicente E, Modiri A, Kipritidis J, Hagan A, Yu K, Wibowo H, Yan Y, Owen DR, Matuszak MM, Mohindra P, Timmerman R, Sawant A. Functionally weighted airway sparing (FWAS): a functional avoidance method for preserving post-treatment ventilation in lung radiotherapy. Phys Med Biol 2020; 65:165010. [PMID: 32575096 DOI: 10.1088/1361-6560/ab9f5d] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Recent changes to the guidelines for screening and early diagnosis of lung cancer have increased the interest in preserving post-radiotherapy lung function. Current investigational approaches are based on spatially mapping functional regions and generating regional avoidance plans that preferentially spare highly ventilated/perfused lung. A potentially critical, yet overlooked, aspect of functional avoidance is radiation injury to peripheral airways, which serve as gas conduits to and from functional lung regions. Dose redistribution based solely on regional function may cause irreparable damage to the 'supply chain'. To address this deficiency, we propose the functionally weighted airway sparing (FWAS) method. FWAS (i) maps the bronchial pathways to each functional sub-lobar lung volume; (ii) assigns a weighting factor to each airway based on the relative contribution of the sub-volume to overall lung function; and (iii) creates a treatment plan that aims to preserve these functional pathways. To evaluate it, we used four cases from a retrospective cohort of SAbR patients treated for lung cancer. Each patient's airways were auto-segmented from a diagnostic-quality breath-hold CT using a research virtual bronchoscopy software. A ventilation map was generated from the planning 4DCT to map regional lung function. For each terminal airway, as resolved by the segmentation software, the total ventilation within the sub-lobar volume supported by that airway was estimated and used as a function-based weighting factor. Upstream airways were weighted based on the cumulative volumetric ventilation supported by corresponding downstream airways. Using a previously developed model for airway radiosensitivity, dose constraints were determined for each airway corresponding to a <5% probability of airway collapse. Airway dose constraints, ventilation scores, and clinical dose constraints were input to a swarm optimization-based inverse planning engine to create a 3D conformal SAbR plan (CRT). The FWAS plans were compared to the patients' prescribed CRT clinical plans and the inverse-optimized clinical plans. Depending on the size and location of the tumour, the FWAS plan showed superior preservation of ventilation due to airflow preservation through open pathways (i.e. cumulative ventilation score from the sub-lobar volumes of open pathways). Improvements ranged between 3% and 23%, when comparing to the prescribed clinical plans, and between 3% and 35%, when comparing to the inverse-optimized clinical plans. The three plans satisfied clinical requirements for PTV coverage and OAR dose constraints. These initial results suggest that by sparing pathways to high-functioning lung subregions it is possible to reduce post-SAbR loss of respiratory function.
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Affiliation(s)
- E Vicente
- University of Maryland School of Medicine, Baltimore, MD, United States of America
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50
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Ieko Y, Kadoya N, Kanai T, Nakajima Y, Arai K, Kato T, Ito K, Miyasaka Y, Takeda K, Iwai T, Nemoto K, Jingu K. The impact of 4DCT-ventilation imaging-guided proton therapy on stereotactic body radiotherapy for lung cancer. Radiol Phys Technol 2020; 13:230-237. [PMID: 32537735 DOI: 10.1007/s12194-020-00572-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 06/05/2020] [Accepted: 06/08/2020] [Indexed: 01/01/2023]
Abstract
Functional lung avoidance during radiotherapy can help reduce pulmonary toxicity. This study assessed the potential impact of four-dimensional computed tomography (4DCT)-ventilation imaging-guided proton radiotherapy (PT) on stereotactic body radiotherapy (SBRT) by comparing it with three-dimensional conformal radiotherapy (3D-CRT) and volumetric modulated arc therapy (VMAT), which employ photon beams. Thirteen lung cancer patients who received SBRT with 3D-CRT were included in the study. 4DCT ventilation was calculated using the patients' 4DCT data, deformable image registration, and a density-change-based algorithm. Three functional treatment plans sparing the functional lung regions were developed for each patient using 3D-CRT, VMAT, and PT. The prescribed doses and dose constraints were based on the Radiation Therapy Oncology Group 0618 protocol. We evaluated the region of interest (ROI) and functional map-based dose-function metrics for 4DCT ventilation and the irradiated dose. Using 3D-CRT, VMAT, and PT, the percentages of the functional lung regions that received ≥ 5 Gy (fV5) were 26.0%, 21.9%, and 10.7%, respectively; the fV10 were 14.4%, 11.4%, and 9.0%, respectively; and fV20 were 6.5%, 6.4%, and 6.6%, respectively, and the functional mean lung doses (fMLD) were 5.6 Gy, 5.2 Gy, and 3.8 Gy, respectively. These results indicated that PT resulted in a significant reduction in fMLD, fV5, and fV10, but not fV20. The use of PT reduced the radiation to highly functional lung regions compared with those for 3D-CRT and VMAT while meeting all dose constraints.
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Affiliation(s)
- Yoshiro Ieko
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.,Department of Heavy Particle Medical Science, Yamagata University Graduate School of Medical Science, Yamagata, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
| | - Takayuki Kanai
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.,Department of Radiation Oncology, Faculty of Medicine, Yamagata University, Yamagata, Japan
| | - Yujiro Nakajima
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.,Department of Radiation Oncology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan
| | - Kazuhiro Arai
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.,Department of Radiation Physics and Technology, Southern Tohoku Proton Therapy Center, Koriyama, Japan
| | - Takahiro Kato
- Department of Radiation Physics and Technology, Southern Tohoku Proton Therapy Center, Koriyama, Japan.,Preparing Section for New Facility of Medical Science, Fukushima Medical University, Fukushima, Japan
| | - Kengo Ito
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Yuya Miyasaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.,Department of Heavy Particle Medical Science, Yamagata University Graduate School of Medical Science, Yamagata, Japan
| | - Ken Takeda
- Department of Radiological Technology, Graduate School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Takeo Iwai
- Department of Heavy Particle Medical Science, Yamagata University Graduate School of Medical Science, Yamagata, Japan
| | - Kenji Nemoto
- Department of Heavy Particle Medical Science, Yamagata University Graduate School of Medical Science, Yamagata, Japan.,Department of Radiation Oncology, Faculty of Medicine, Yamagata University, Yamagata, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
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