학술논문
CT-based Machine Learning for Donor Lung Screening Prior to Transplantation.
Document Type
Author
Ram S; Department of Radiology, University of Michigan, Ann Arbor, MI, United States.; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States.; Verleden SE; Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium.; Department of Imaging & Pathology, KU Leuven, Leuven, Belgium.; Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.; Kumar M; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States.; Bell AJ; Department of Radiology, University of Michigan, Ann Arbor, MI, United States.; Pal R; Department of Radiology, University of Michigan, Ann Arbor, MI, United States.; Ordies S; Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium.; Vanstapel A; Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium.; Department of Imaging & Pathology, KU Leuven, Leuven, Belgium.; Dubbeldam A; Department of Imaging & Pathology, KU Leuven, Leuven, Belgium.; Vos R; Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium.; Galban S; Department of Radiology, University of Michigan, Ann Arbor, MI, United States.; Ceulemans LJ; Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium.; Frick AE; Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium.; Van Raemdonck DE; Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium.; Verschakelen J; Department of Imaging & Pathology, KU Leuven, Leuven, Belgium.; Vanaudenaerde BM; Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium.; Verleden GM; Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium.; Lama VN; Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, United States.; Neyrinck AP; Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium.; Galban CJ; Department of Radiology, University of Michigan, Ann Arbor, MI, United States.; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States.
Source
Country of Publication: United States NLM ID: 101767986 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: medRxiv Subsets: PubMed not MEDLINE
Subject
Language
English
Abstract
Background: Assessment and selection of donor lungs remains largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo CT images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs prior to transplantation.
Methods: Clinical measures and ex-situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner prior to transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning , which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures.
Results: Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) prior to CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the ICU and were at 19 times higher risk of developing CLAD within 2 years post-transplant.
Conclusions: We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of post-transplant complications.
Methods: Clinical measures and ex-situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner prior to transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning , which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures.
Results: Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) prior to CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the ICU and were at 19 times higher risk of developing CLAD within 2 years post-transplant.
Conclusions: We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of post-transplant complications.