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Friedlander S, Pogatchnik B, Furuya Y, Allen T. Pulmonary transplant complications: a radiologic review. J Cardiothorac Surg 2024; 19:270. [PMID: 38702686 PMCID: PMC11067284 DOI: 10.1186/s13019-024-02731-w] [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: 12/19/2023] [Accepted: 03/29/2024] [Indexed: 05/06/2024] Open
Abstract
Lung transplantation has become the definitive treatment for end stage respiratory disease. Numbers and survival rates have increased over the past decade, with transplant recipients living longer and with greater comorbidities, resulting in greater complexity of care. Common and uncommon complications that occur in the immediate, early, intermediate, and late periods can have significant impact on the course of the transplant. Fortunately, advancements in surgery, medical care, and imaging as well as other diagnostics work to prevent, identify, and manage complications that would otherwise have a negative impact on survivability. This review will focus on contextualizing complications both categorically and chronologically, with highlights of specific imaging and clinical features in order to inform both radiologists and clinicians involved in post-transplant care.
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Affiliation(s)
- Samuel Friedlander
- Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA.
| | - Brian Pogatchnik
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Yuka Furuya
- Medical Director of Lung Transplant, CareDX, Inc, Brisbane, CA, 94005, USA
| | - Tadashi Allen
- Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
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Chan SS, Coblentz A, Bhatia A, Kaste SC, Mhlanga J, Parisi MT, Thacker P, Voss SD, Weidman EK, Siegel MJ. Imaging of pediatric hematopoietic stem cell transplant recipients: A COG Diagnostic Imaging Committee/SPR Oncology Committee White Paper. Pediatr Blood Cancer 2023; 70 Suppl 4:e30013. [PMID: 36546505 PMCID: PMC10644273 DOI: 10.1002/pbc.30013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/28/2022] [Accepted: 09/05/2022] [Indexed: 12/24/2022]
Abstract
Imaging in hematopoietic stem cell transplantation patients is not targeted at evaluating the transplant per se. Rather, imaging is largely confined to evaluating peri-procedural and post-procedural complications. Alternatively, imaging may be performed to establish a baseline study for comparison should the patient develop certain post-procedural complications. This article looks to describe the various imaging modalities available with recommendations for which imaging study should be performed in specific complications. We also provide select imaging protocols for different indications and modalities for the purpose of establishing a set minimal standard for imaging in these complex patients.
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Affiliation(s)
- Sherwin S Chan
- Department of Radiology, Children’s Mercy Kansas City, Kansas City, MO; Department of Radiology, University of Missouri at Kansas City School of Medicine, Kansas City, MO
| | - Ailish Coblentz
- Department of Diagnostic Imaging, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Aashim Bhatia
- Department of Radiology, Division of Neuroradiology Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Sue C. Kaste
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital, Memphis, TN
| | - Joyce Mhlanga
- Department of Radiology, Edward Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Marguerite T. Parisi
- Departments of Radiology and Pediatrics, University of Washington School of Medicine and Seattle Children’s Hospital, Seattle, WA
| | | | - Stephan D. Voss
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA. 02115
| | - Elizabeth K. Weidman
- Department of Radiology, Weill Cornell Medicine – New York Presbyterian Hospital, New York, NY
| | - Marilyn J Siegel
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital, Memphis, TN
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Hasenstab KA, Tabalon J, Yuan N, Retson T, Hsiao A. CNN-based Deformable Registration Facilitates Fast and Accurate Air Trapping Measurements at Inspiratory and Expiratory CT. Radiol Artif Intell 2022; 4:e210211. [PMID: 35146437 PMCID: PMC8823452 DOI: 10.1148/ryai.2021210211] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/05/2021] [Accepted: 10/22/2021] [Indexed: 06/14/2023]
Abstract
PURPOSE To develop a convolutional neural network (CNN)-based deformable lung registration algorithm to reduce computation time and assess its potential for lobar air trapping quantification. MATERIALS AND METHODS In this retrospective study, a CNN algorithm was developed to perform deformable registration of lung CT (LungReg) using data on 9118 patients from the COPDGene Study (data collected between 2007 and 2012). Loss function constraints included cross-correlation, displacement field regularization, lobar segmentation overlap, and the Jacobian determinant. LungReg was compared with a standard diffeomorphic registration (SyN) for lobar Dice overlap, percentage voxels with nonpositive Jacobian determinants, and inference runtime using paired t tests. Landmark colocalization error (LCE) across 10 patients was compared using a random effects model. Agreement between LungReg and SyN air trapping measurements was assessed using intraclass correlation coefficient. The ability of LungReg versus SyN emphysema and air trapping measurements to predict Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages was compared using area under the receiver operating characteristic curves. RESULTS Average performance of LungReg versus SyN showed lobar Dice overlap score of 0.91-0.97 versus 0.89-0.95, respectively (P < .001); percentage voxels with nonpositive Jacobian determinant of 0.04 versus 0.10, respectively (P < .001); inference run time of 0.99 second (graphics processing unit) and 2.27 seconds (central processing unit) versus 418.46 seconds (central processing unit) (P < .001); and LCE of 7.21 mm versus 6.93 mm (P < .001). LungReg and SyN whole-lung and lobar air trapping measurements achieved excellent agreement (intraclass correlation coefficients > 0.98). LungReg versus SyN area under the receiver operating characteristic curves for predicting GOLD stage were not statistically different (range, 0.88-0.95 vs 0.88-0.95, respectively; P = .31-.95). CONCLUSION CNN-based deformable lung registration is accurate and fully automated, with runtime feasible for clinical lobar air trapping quantification, and has potential to improve diagnosis of small airway diseases.Keywords: Air Trapping, Convolutional Neural Network, Deformable Registration, Small Airway Disease, CT, Lung, Semisupervised Learning, Unsupervised Learning Supplemental material is available for this article. © RSNA, 2021 An earlier incorrect version of this article appeared online. This article was corrected on December 22, 2021.
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Affiliation(s)
- Kyle A. Hasenstab
- From the Department of Radiology, University of California San Diego,
9500 Gilman Dr, San Diego, CA 92093 (K.A.H., N.Y., T.R., A.H.); and Department
of Mathematics and Statistics, San Diego State University, San Diego, Calif
(K.A.H., J.T.)
| | - Joseph Tabalon
- From the Department of Radiology, University of California San Diego,
9500 Gilman Dr, San Diego, CA 92093 (K.A.H., N.Y., T.R., A.H.); and Department
of Mathematics and Statistics, San Diego State University, San Diego, Calif
(K.A.H., J.T.)
| | - Nancy Yuan
- From the Department of Radiology, University of California San Diego,
9500 Gilman Dr, San Diego, CA 92093 (K.A.H., N.Y., T.R., A.H.); and Department
of Mathematics and Statistics, San Diego State University, San Diego, Calif
(K.A.H., J.T.)
| | - Tara Retson
- From the Department of Radiology, University of California San Diego,
9500 Gilman Dr, San Diego, CA 92093 (K.A.H., N.Y., T.R., A.H.); and Department
of Mathematics and Statistics, San Diego State University, San Diego, Calif
(K.A.H., J.T.)
| | - Albert Hsiao
- From the Department of Radiology, University of California San Diego,
9500 Gilman Dr, San Diego, CA 92093 (K.A.H., N.Y., T.R., A.H.); and Department
of Mathematics and Statistics, San Diego State University, San Diego, Calif
(K.A.H., J.T.)
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