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Thillai M, Oldham JM, Ruggiero A, Kanavati F, McLellan T, Saini G, Johnson SR, Ble FX, Azim A, Ostridge K, Platt A, Belvisi M, Maher TM, Molyneaux PL. Deep Learning-based Segmentation of Computed Tomography Scans Predicts Disease Progression and Mortality in Idiopathic Pulmonary Fibrosis. Am J Respir Crit Care Med 2024; 210:465-472. [PMID: 38452227 DOI: 10.1164/rccm.202311-2185oc] [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: 11/29/2023] [Accepted: 03/07/2024] [Indexed: 03/09/2024] Open
Abstract
Rationale: Despite evidence demonstrating a prognostic role for computed tomography (CT) scans in idiopathic pulmonary fibrosis (IPF), image-based biomarkers are not routinely used in clinical practice or trials. Objectives: To develop automated imaging biomarkers using deep learning-based segmentation of CT scans. Methods: We developed segmentation processes for four anatomical biomarkers, which were applied to a unique cohort of treatment-naive patients with IPF enrolled in the PROFILE (Prospective Observation of Fibrosis in the Lung Clinical Endpoints) study and tested against a further United Kingdom cohort. The relationships among CT biomarkers, lung function, disease progression, and mortality were assessed. Measurements and Main Results: Data from 446 PROFILE patients were analyzed. Median follow-up duration was 39.1 months (interquartile range, 18.1-66.4 mo), with a cumulative incidence of death of 277 (62.1%) over 5 years. Segmentation was successful on 97.8% of all scans, across multiple imaging vendors, at slice thicknesses of 0.5-5 mm. Of four segmentations, lung volume showed the strongest correlation with FVC (r = 0.82; P < 0.001). Lung, vascular, and fibrosis volumes were consistently associated across cohorts with differential 5-year survival, which persisted after adjustment for baseline gender, age, and physiology score. Lower lung volume (hazard ratio [HR], 0.98 [95% confidence interval (CI), 0.96-0.99]; P = 0.001), increased vascular volume (HR, 1.30 [95% CI, 1.12-1.51]; P = 0.001), and increased fibrosis volume (HR, 1.17 [95% CI, 1.12-1.22]; P < 0.001) were associated with reduced 2-year progression-free survival in the pooled PROFILE cohort. Longitudinally, decreasing lung volume (HR, 3.41 [95% CI, 1.36-8.54]; P = 0.009) and increasing fibrosis volume (HR, 2.23 [95% CI, 1.22-4.08]; P = 0.009) were associated with differential survival. Conclusions: Automated models can rapidly segment IPF CT scans, providing prognostic near and long-term information, which could be used in routine clinical practice or as key trial endpoints.
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Affiliation(s)
- Muhunthan Thillai
- Royal Papworth Hospital, Cambridge, United Kingdom
- Qureight Ltd., Cambridge, United Kingdom
| | - Justin M Oldham
- Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, Michigan
| | - Alessandro Ruggiero
- Royal Papworth Hospital, Cambridge, United Kingdom
- Qureight Ltd., Cambridge, United Kingdom
| | | | - Tom McLellan
- Royal Papworth Hospital, Cambridge, United Kingdom
| | - Gauri Saini
- Translational Medical Sciences, National Institute for Health and Care Research Biomedical Research Centre and Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom
| | - Simon R Johnson
- Translational Medical Sciences, National Institute for Health and Care Research Biomedical Research Centre and Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom
| | - Francois-Xavier Ble
- Translational Science and Experimental Medicine, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Adnan Azim
- Translational Science and Experimental Medicine, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Kristoffer Ostridge
- Translational Science and Experimental Medicine
- Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - Adam Platt
- Translational Science and Experimental Medicine
| | - Maria Belvisi
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Toby M Maher
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Keck School of Medicine, University of Southern California, Los Angeles, California; and
| | - Philip L Molyneaux
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Royal Brompton and Harefield Hospital, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
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2
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Sotoudeh-Paima S, Segars WP, Ghosh D, Luo S, Samei E, Abadi E. A systematic assessment and optimization of photon-counting CT for lung density quantifications. Med Phys 2024; 51:2893-2904. [PMID: 38368605 PMCID: PMC11055522 DOI: 10.1002/mp.16987] [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: 07/21/2023] [Revised: 01/31/2024] [Accepted: 02/02/2024] [Indexed: 02/20/2024] Open
Abstract
BACKGROUND Photon-counting computed tomography (PCCT) has recently emerged into clinical use; however, its optimum imaging protocols and added benefits remains unknown in terms of providing more accurate lung density quantification compared to energy-integrating computed tomography (EICT) scanners. PURPOSE To systematically assess the performance of a clinical PCCT scanner for lung density quantifications and compare it against EICT. METHODS This cross-sectional study involved a retrospective analysis of subjects scanned (August-December 2021) using a clinical PCCT system. The influence of altering reconstruction parameters was studied (reconstruction kernel, pixel size, slice thickness). A virtual CT dataset of anthropomorphic virtual subjects was acquired to demonstrate the correspondence of findings to clinical dataset, and to perform systematic imaging experiments, not possible using human subjects. The virtual subjects were imaged using a validated, scanner-specific CT simulator of a PCCT and two EICT (defined as EICT A and B) scanners. The images were evaluated using mean absolute error (MAE) of lung and emphysema density against their corresponding ground truth. RESULTS Clinical and virtual PCCT datasets showed similar trends, with sharper kernels and smaller voxel sizes increasing percentage of low-attenuation areas below -950 HU (LAA-950) by up to 15.7 ± 6.9% and 11.8 ± 5.5%, respectively. Under the conditions studied, higher doses, thinner slices, smaller pixel sizes, iterative reconstructions, and quantitative kernels with medium sharpness resulted in lower lung MAE values. While using these settings for PCCT, changes in the dose level (13 to 1.3 mGy), slice thickness (0.4 to 1.5 mm), pixel size (0.49 to 0.98 mm), reconstruction technique (70 keV-VMI to wFBP), and kernel (Qr48 to Qr60) increased lung MAE by 15.3 ± 2.0, 1.4 ± 0.6, 2.2 ± 0.3, 4.2 ± 0.8, and 9.1 ± 1.6 HU, respectively. At the optimum settings identified per scanner, PCCT images exhibited lower lung and emphysema MAE than those of EICT scanners (by 2.6 ± 1.0 and 9.6 ± 3.4 HU, compared to EICT A, and by 4.8 ± 0.8 and 7.4 ± 2.3 HU, compared to EICT B). The accuracy of lung density measurements was correlated with subjects' mean lung density (p < 0.05), measured by PCCT at optimum setting under the conditions studied. CONCLUSION Photon-counting CT demonstrated superior performance in density quantifications, with its influences of imaging parameters in line with energy-integrating CT scanners. The technology offers improvement in lung quantifications, thus demonstrating potential toward more objective assessment of respiratory conditions.
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Affiliation(s)
- Saman Sotoudeh-Paima
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, USA
- Department of Electrical & Computer Engineering, Duke University, Durham, USA
| | - W. Paul Segars
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, USA
- Medical Physics Graduate Program, Duke University, Durham, USA
- Department of Biomedical Engineering, Duke University, Durham, USA
| | - Dhrubajyoti Ghosh
- Department of Biostatistics and Bioinformatics, Duke University, Durham, USA
| | - Sheng Luo
- Department of Biostatistics and Bioinformatics, Duke University, Durham, USA
| | - Ehsan Samei
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, USA
- Department of Electrical & Computer Engineering, Duke University, Durham, USA
- Medical Physics Graduate Program, Duke University, Durham, USA
- Department of Biomedical Engineering, Duke University, Durham, USA
- Department of Physics, Duke University, Durham, USA
| | - Ehsan Abadi
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, USA
- Department of Electrical & Computer Engineering, Duke University, Durham, USA
- Medical Physics Graduate Program, Duke University, Durham, USA
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3
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Hsiao CC, Peng CH, Wu FZ, Cheng DC. Impact of Voxel Normalization on a Machine Learning-Based Method: A Study on Pulmonary Nodule Malignancy Diagnosis Using Low-Dose Computed Tomography (LDCT). Diagnostics (Basel) 2023; 13:3690. [PMID: 38132274 PMCID: PMC10742752 DOI: 10.3390/diagnostics13243690] [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: 10/20/2023] [Revised: 12/04/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023] Open
Abstract
Lung cancer (LC) stands as the foremost cause of cancer-related fatality rates worldwide. Early diagnosis significantly enhances patient survival rate. Nowadays, low-dose computed tomography (LDCT) is widely employed on the chest as a tool for large-scale lung cancer screening. Nonetheless, a large amount of chest radiographs creates an onerous burden for radiologists. Some computer-aided diagnostic (CAD) tools can provide insight to the use of medical images for diagnosis and can augment diagnostic speed. However, due to the variation in the parameter settings across different patients, substantial discrepancies in image voxels persist. We found that different voxel sizes can create a compromise between model generalization and diagnostic efficacy. This study investigates the performance disparities of diagnostic models trained on original images and LDCT images reconstructed to different voxel sizes while making isotropic. We examined the ability of our method to differentiate between benign and malignant nodules. Using 11 features, a support vector machine (SVM) was trained on LDCT images using an isotropic voxel with a side length of 1.5 mm for 225 patients in-house. The result yields a favorable model performance with an accuracy of 0.9596 and an area under the receiver operating characteristic curve (ROC/AUC) of 0.9855. In addition, to furnish CAD tools for clinical application, future research including LDCT images from multi-centers is encouraged.
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Affiliation(s)
- Chia-Chi Hsiao
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan;
| | - Chen-Hao Peng
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 40400, Taiwan;
| | - Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan;
| | - Da-Chuan Cheng
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 40400, Taiwan;
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4
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Katz SI, Straus CM, Roshkovan L, Blyth KG, Frauenfelder T, Gill RR, Lalezari F, Erasmus J, Nowak AK, Gerbaudo VH, Francis RJ, Armato SG. Considerations for Imaging of Malignant Pleural Mesothelioma: A Consensus Statement from the International Mesothelioma Interest Group. J Thorac Oncol 2023; 18:278-298. [PMID: 36549385 DOI: 10.1016/j.jtho.2022.11.018] [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: 08/02/2022] [Revised: 11/13/2022] [Accepted: 11/17/2022] [Indexed: 12/24/2022]
Abstract
Malignant pleural mesothelioma (MPM) is an aggressive primary malignancy of the pleura that presents unique radiologic challenges with regard to accurate and reproducible assessment of disease extent at staging and follow-up imaging. By optimizing and harmonizing technical approaches to imaging MPM, the best quality imaging can be achieved for individual patient care, clinical trials, and imaging research. This consensus statement represents agreement on harmonized, standard practices for routine multimodality imaging of MPM, including radiography, computed tomography, 18F-2-deoxy-D-glucose positron emission tomography, and magnetic resonance imaging, by an international panel of experts in the field of pleural imaging assembled by the International Mesothelioma Interest Group. In addition, modality-specific technical considerations and future directions are discussed. A bulleted summary of all technical recommendations is provided.
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Affiliation(s)
- Sharyn I Katz
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
| | - Christopher M Straus
- Department of Radiology, University of Chicago Pritzker School of Medicine, Chicago, Illinois
| | - Leonid Roshkovan
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Kevin G Blyth
- Institute of Cancer Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Thomas Frauenfelder
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Ritu R Gill
- Department of Radiology, Beth Israel Lahey Health, Harvard Medical School, Boston, Massachusetts
| | - Ferry Lalezari
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jeremy Erasmus
- Department of Radiology, MD Anderson Cancer Center, Houston, Texas
| | - Anna K Nowak
- Medical School, University of Western Australia, Perth, Australia
| | - Victor H Gerbaudo
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Roslyn J Francis
- Medical School, University of Western Australia, Perth, Australia; Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, Australia
| | - Samuel G Armato
- Department of Radiology, University of Chicago Pritzker School of Medicine, Chicago, Illinois
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5
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Ge G, Zhang J. Feature selection methods and predictive models in CT lung cancer radiomics. J Appl Clin Med Phys 2023; 24:e13869. [PMID: 36527376 PMCID: PMC9860004 DOI: 10.1002/acm2.13869] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/31/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.
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Affiliation(s)
- Gary Ge
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| | - Jie Zhang
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
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6
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Choe J, Lee SM, Hwang HJ, Lee SM, Yun J, Kim N, Seo JB. Artificial Intelligence in Lung Imaging. Semin Respir Crit Care Med 2022; 43:946-960. [PMID: 36174647 DOI: 10.1055/s-0042-1755571] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging. Some of these have been approved by governments and are now commercially available in the marketplace. In the field of chest radiology, there are various tasks and purposes that are suitable for AI: initial evaluation/triage of certain diseases, detection and diagnosis, quantitative assessment of disease severity and monitoring, and prediction for decision support. While AI is a powerful technology that can be applied to medical imaging and is expected to improve our current clinical practice, some obstacles must be addressed for the successful implementation of AI in workflows. Understanding and becoming familiar with the current status and potential clinical applications of AI in chest imaging, as well as remaining challenges, would be essential for radiologists and clinicians in the era of AI. This review introduces the potential clinical applications of AI in chest imaging and also discusses the challenges for the implementation of AI in daily clinical practice and future directions in chest imaging.
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Affiliation(s)
- Jooae Choe
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Hye Jeon Hwang
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jihye Yun
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.,Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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7
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Choi H, Kim H, Jin KN, Jeong YJ, Chae KJ, Lee KH, Yong HS, Gil B, Lee HJ, Lee KY, Jeon KN, Yi J, Seo S, Ahn C, Lee J, Oh K, Goo JM. A Challenge for Emphysema Quantification Using a Deep Learning Algorithm With Low-dose Chest Computed Tomography. J Thorac Imaging 2022; 37:253-261. [PMID: 35749623 DOI: 10.1097/rti.0000000000000647] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE We aimed to identify clinically relevant deep learning algorithms for emphysema quantification using low-dose chest computed tomography (LDCT) through an invitation-based competition. MATERIALS AND METHODS The Korean Society of Imaging Informatics in Medicine (KSIIM) organized a challenge for emphysema quantification between November 24, 2020 and January 26, 2021. Seven invited research teams participated in this challenge. In total, 558 pairs of computed tomography (CT) scans (468 pairs for the training set, and 90 pairs for the test set) from 9 hospitals were collected retrospectively or prospectively. CT acquisition followed the hospitals' protocols to reflect the real-world clinical setting. Using the training set, each team developed an algorithm that generated converted LDCT by changing the pixel values of LDCT to simulate those of standard-dose CT (SDCT). The agreement between SDCT and LDCT was evaluated using the intraclass correlation coefficient (ICC; 2-way random effects, absolute agreement, and single rater) for the percentage of low-attenuated area below -950 HU (LAA-950 HU), κ value for emphysema categorization (LAA-950 HU, <5%, 5% to 10%, and ≥10%) and cosine similarity of LAA-950 HU. RESULTS The mean LAA-950 HU of the test set was 14.2%±10.5% for SDCT, 25.4%±10.2% for unconverted LDCT, and 12.9%±10.4%, 11.7%±10.8%, and 12.4%±10.5% for converted LDCT (top 3 teams). The agreement between the SDCT and converted LDCT of the first-place team was 0.94 (95% confidence interval: 0.90, 0.97) for ICC, 0.71 (95% confidence interval: 0.58, 0.84) for categorical agreement, and 0.97 (interquartile range: 0.94 to 0.99) for cosine similarity. CONCLUSIONS Emphysema quantification with LDCT was feasible through deep learning-based CT conversion strategies.
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Affiliation(s)
- Hyewon Choi
- Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine
| | - Hyungjin Kim
- Department of Radiology, Seoul National University College of Medicine
| | - Kwang Nam Jin
- Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul
| | - Yeon Joo Jeong
- Department of Radiology and Biomedical Research Institute, Pusan National University Hospital, Busan
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju
| | - Kyung Hee Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do
| | - Hwan Seok Yong
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine
| | - Bomi Gil
- Department of Radiology, College of Medicine, The Catholic University of Korea
| | - Hye-Jeong Lee
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine
| | - Ki Yeol Lee
- Department of Radiology, Korea University College of Medicine
| | - Kyung Nyeo Jeon
- Department of Radiology, Gyeongsang National University, Jinju, Korea
| | | | | | | | | | - Kyuhyup Oh
- Bio Medical Research Center, Korea Testing Laboratory
| | - Jin Mo Goo
- Department of Radiology, Seoul National University College of Medicine
- Cancer Research Institute, Seoul National University, Seoul
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8
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Neuroendocrine cell hyperplasia of infancy: Feasibility of objective evaluation with quantitative CT. Clin Imaging 2022; 89:43-48. [PMID: 35700553 DOI: 10.1016/j.clinimag.2022.06.004] [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: 03/13/2022] [Revised: 05/17/2022] [Accepted: 06/03/2022] [Indexed: 11/22/2022]
Abstract
OBJECTIVE To describe quantitative CT parameters of children with a typical pattern for NEHI and compare them to controls. MATERIALS AND METHODS Eleven patients (7 boys) with NEHI and an available chest CT concordant NEHI were identified. Eleven age-, sex-, height-matched, with CT technique-matching were identified for comparison. An open-source software was used to segment the lung parenchyma into lobes using the fissures. Quantitative parameters such as low attenuation areas, mean lung density, kurtosis, skewness, ventilation heterogeneity, lung mass, and volume were calculated for both controls and cases. RESULTS Analysis of the lung parenchyma showed that patients with NEHI had a lower mean lung density (-615 HU vs -556 HU, p = 0.03) with higher ventilation heterogeneity (0.23 vs 0.19, p = 0.04), lung mass (232 g vs 146 g, p = 0.01) and volume (595 mL vs 339 mL, p = 0.008) compared to controls. Most lobes followed this trend, except the middle lobe that showed only a higher lung mass (32.9 g vs 19.6 g, p = 0.02) and volume (77.4 vs 46.9, p = 0.005) in patients with NEHI compared to controls. CONCLUSION Quantitative CT is a feasible technique in children with a typical pattern for NEHI and is associated with differences in attenuation, ventilation heterogeneity, and lung volume.
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9
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Koo CW, Larson NB, Parris-Skeete CT, Karwoski RA, Kalra S, Bartholmai BJ, Carmona EM. Prospective machine learning CT quantitative evaluation of idiopathic pulmonary fibrosis in patients undergoing anti-fibrotic treatment using low- and ultra-low-dose CT. Clin Radiol 2021; 77:e208-e214. [PMID: 34887070 DOI: 10.1016/j.crad.2021.11.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 11/09/2021] [Indexed: 01/01/2023]
Abstract
AIM To compare the machine learning computed tomography (CT) quantification tool, Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER) to pulmonary function testing (PFT) in assessing idiopathic pulmonary fibrosis (IPF) for patients undergoing treatment and determine the effects of limited (LD) and ultra-low dose (ULD) CT on CALIPER performance. MATERIALS AND METHODS Thirty-eight IPF patients underwent PFT and standard, LD, and ULD CT. CALIPER classified each CT voxel into either vessel-related structures (VRS), normal, reticular (R), honeycomb (HC) or ground-glass (GG) features. CALIPER-derived interstitial lung disease (ILD) extent represented the sum of GG, R and HC values. Repeated-measures correlation coefficient (ρrm) and 95% confidence interval (CI) evaluated CALIPER features correlation with PFT. Lin's concordance correlation coefficient (CCC) assessed concordance of CALIPER parameters across different CT dosages. RESULTS Twenty patients completed 12 months of follow-up. CALIPER ILD correlated significantly with percent predicted (%) forced vital capacity (FVC) and forced expiratory volume in 1 second (%FEV1; p=0.004, ρrm -0.343, 95% CI [-0.547, -0.108] and 0.008, -0.321, [-0.518, -0.07], respectively). VRS significantly correlated with %FVC and %FEV1 (p=0.000, ρrm -0.491, 95% CI [-0.685, -0.251] and -0.478, 0.000, [-0.653, -0.231], respectively). There was near perfect LD and moderate ULD concordance with standard dose CT for both ILD (CCC 0.995, 95% CI 0.988-0.999 and 0.9, 0.795-0.983, respectively) and VRS (CCC 0.989, 95% CI 0.963-0.997 and 0.915, 0.806-0.956, respectively). CONCLUSIONS CALIPER parameters correlate well with PFTs for evaluation of IPF in patients undergoing anti-fibrotic treatment without being influenced by dose variation. CALIPER may serve as a robust, objective adjunct to PFTs in assessing anti-fibrotic treatment related changes.
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Affiliation(s)
- C W Koo
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
| | - N B Larson
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, USA
| | | | - R A Karwoski
- Biomedical Imaging Resources, Research Applications Solutions, Mayo Clinic, Rochester, MN, USA
| | - S Kalra
- Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Mayo Clinic, Rochester, MN, USA
| | - B J Bartholmai
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - E M Carmona
- Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Mayo Clinic, Rochester, MN, USA
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10
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Perkonigg M, Hofmanninger J, Herold CJ, Brink JA, Pianykh O, Prosch H, Langs G. Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging. Nat Commun 2021; 12:5678. [PMID: 34584080 PMCID: PMC8479083 DOI: 10.1038/s41467-021-25858-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 08/19/2021] [Indexed: 11/08/2022] Open
Abstract
Medical imaging is a central part of clinical diagnosis and treatment guidance. Machine learning has increasingly gained relevance because it captures features of disease and treatment response that are relevant for therapeutic decision-making. In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures, the diversity of scanners, and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates, or models become outdated due to these domain shifts. We propose a continual learning approach to deal with such domain shifts occurring at unknown time points. We adapt models to emerging variations in a continuous data stream while counteracting catastrophic forgetting. A dynamic memory enables rehearsal on a subset of diverse training data to mitigate forgetting while enabling models to expand to new domains. The technique balances memory by detecting pseudo-domains, representing different style clusters within the data stream. Evaluation of two different tasks, cardiac segmentation in magnetic resonance imaging and lung nodule detection in computed tomography, demonstrate a consistent advantage of the method.
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Affiliation(s)
- Matthias Perkonigg
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Johannes Hofmanninger
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Christian J Herold
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - James A Brink
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Oleg Pianykh
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Helmut Prosch
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
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Ye K, Chen M, Zhu Q, Lu Y, Yuan H. Effect of adaptive statistical iterative reconstruction-V (ASiR-V) levels on ultra-low-dose CT radiomics quantification in pulmonary nodules. Quant Imaging Med Surg 2021; 11:2344-2353. [PMID: 34079706 DOI: 10.21037/qims-20-932] [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] [Indexed: 12/30/2022]
Abstract
Background The weightings of iterative reconstruction algorithm can affect CT radiomic quantification. But, the effect of ASiR-V levels on the reproducibility of CT radiomic features between ultra-low-dose computed tomography (ULDCT) and low-dose computed tomography (LDCT) is still unknown. The purpose of study is to investigate whether adaptive statistical iterative reconstruction-V (ASiR-V) levels affect radiomic feature quantification using ULDCT and to assess the reproducibility of radiomic features between ULDCT and LDCT. Methods Sixty-three patients with pulmonary nodules underwent LDCT (0.70±0.16 mSv) and ULDCT (0.15±0.02 mSv). LDCT was reconstructed with ASiR-V 50%, and ULDCT with ASiR-V 50%, 70%, and 90%. Radiomics analysis was applied, and 107 features were extracted. The concordance correlation coefficient (CCC) was calculated to describe agreement among ULDCTs and between ULDCT and LDCT for each feature. The proportion of features with CCC >0.9 among ULDCTs and between ULDCT and LDCT, and the mean CCC for all features between ULDCT and LDCT were also compared. Results Sixty-three solid nodules (SNs) and 48 pure ground-glass nodules (pGGNs) were analyzed. There was no difference for the proportion of features in SNs among ULDCTs and between ULDCT and LDCT (P>0.05). The proportion of features in pGGNs were highest for ULDCT70% vs. 90% (78.5%) and ULDCT90% vs. LDCT50% (50.5%). In SNs, the mean CCC for ULDCT90% vs. LDCT50% was 0.67±0.26, not different with that for ULDCT50% vs. LDCT50% (0.68±0.24) and ULDCT70% vs. LDCT50% (0.64±0.21) (P>0.05). In pGGNs, the mean CCC for ULDCT90% vs. LDCT50% was 0.79±0.19, higher than that for ULDCT50% vs. LDCT50% (0.61±0.28) and ULDCT70% vs. LDCT50% (0.76±0.24) (P<0.05). Conclusions ASiR-V levels significantly affected ULDCT radiomic feature quantification in pulmonary nodules, with stronger effects in pGGNs than in SNs. The reproducibility of radiomic features was highest between ULDCT90% and LDCT50%.
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Affiliation(s)
- Kai Ye
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Min Chen
- Department of Radiology, Ghent University Hospital, Corneel Heymanslaan 10,9000, Ghent, Belgium
| | - Qiao Zhu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yuliu Lu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
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Goldin JG. The Emerging Role of Quantification of Imaging for Assessing the Severity and Disease Activity of Emphysema, Airway Disease, and Interstitial Lung Disease. Respiration 2021; 100:277-290. [PMID: 33621969 DOI: 10.1159/000513642] [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: 04/23/2020] [Accepted: 12/02/2020] [Indexed: 11/19/2022] Open
Abstract
There has been an explosion of use for quantitative image analysis in the setting of lung disease due to advances in acquisition protocols and postprocessing technology, including machine and deep learning. Despite the plethora of published papers, it is important to understand which approach has clinical validation and can be used in clinical practice. This paper provides an introduction to quantitative image analysis techniques being used in the investigation of lung disease and focusses on the techniques that have a reasonable clinical validation for being used in clinical trials and patient care.
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Affiliation(s)
- Jonathan Gerald Goldin
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, University of California, Los Angeles, California, USA,
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13
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Strand M, Austin E, Moll M, Pratte KA, Regan EA, Hayden LP, Bhatt SP, Boriek AM, Casaburi R, Silverman EK, Fortis S, Ruczinski I, Koegler H, Rossiter HB, Occhipinti M, Hanania NA, Gebrekristos HT, Lynch DA, Kunisaki KM, Young KA, Sieren JC, Ragland M, Hokanson JE, Lutz SM, Make BJ, Kinney GL, Cho MH, Pistolesi M, DeMeo DL, Sciurba FC, Comellas AP, Diaz AA, Barjaktarevic I, Bowler RP, Kanner RE, Peters SP, Ortega VE, Dransfield MT, Crapo JD. A Risk Prediction Model for Mortality Among Smokers in the COPDGene® Study. CHRONIC OBSTRUCTIVE PULMONARY DISEASES (MIAMI, FLA.) 2020; 7:346-361. [PMID: 32877963 PMCID: PMC7883903 DOI: 10.15326/jcopdf.7.4.2020.0146] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/15/2020] [Indexed: 01/23/2023]
Abstract
BACKGROUND Risk factor identification is a proven strategy in advancing treatments and preventive therapy for many chronic conditions. Quantifying the impact of those risk factors on health outcomes can consolidate and focus efforts on individuals with specific high-risk profiles. Using multiple risk factors and longitudinal outcomes in 2 independent cohorts, we developed and validated a risk score model to predict mortality in current and former cigarette smokers. METHODS We obtained extensive data on current and former smokers from the COPD Genetic Epidemiology (COPDGene®) study at enrollment. Based on physician input and model goodness-of-fit measures, a subset of variables was selected to fit final Weibull survival models separately for men and women. Coefficients and predictors were translated into a point system, allowing for easy computation of mortality risk scores and probabilities. We then used the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) cohort for external validation of our model. RESULTS Of 9867 COPDGene participants with standard baseline data, 17.6% died over 10 years of follow-up, and 9074 of these participants had the full set of baseline predictors (standard plus 6-minute walk distance and computed tomography variables) available for full model fits. The average age of participants in the cohort was 60 for both men and women, and the average predicted 10-year mortality risk was 18% for women and 25% for men. Model time-integrated area under the receiver operating characteristic curve statistics demonstrated good predictive model accuracy (0.797 average), validated in the external cohort (0.756 average). Risk of mortality was impacted most by 6-minute walk distance, forced expiratory volume in 1 second and age, for both men and women. CONCLUSIONS Current and former smokers exhibited a wide range of mortality risk over a 10- year period. Our models can identify higher risk individuals who can be targeted for interventions to reduce risk of mortality, for participants with or without chronic obstructive pulmonary disease (COPD) using current Global initiative for obstructive Lung Disease (GOLD) criteria.
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Affiliation(s)
| | | | - Matthew Moll
- Brigham and Women’s Hospital, Boston, Massachusetts
| | | | | | | | | | | | - Richard Casaburi
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California
| | | | | | - Ingo Ruczinski
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | | | - Harry B. Rossiter
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California
- University of Leeds, Leeds, United Kingdom
| | - Mariaelena Occhipinti
- University of Florence, Florence, Italy
- *Dr. Occhipinti is now at the Imaging Institute, Ente Ospedaliero Cantonale, Lugano, Switzerland
| | | | | | | | - Ken M. Kunisaki
- Minneapolis Veterans Administration Health Care System, Minnesota
| | | | | | | | | | - Sharon M. Lutz
- Harvard Medical School, Harvard University, Boston, Massachusetts
| | | | | | | | | | - Dawn L. DeMeo
- Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Harvard University, Boston, Massachusetts
| | | | | | | | - Igor Barjaktarevic
- David Geffen School of Medicine, University of California-Los Angeles, Los Angeles
| | | | | | - Stephen P. Peters
- Wake Forest School of Medicine, Wake Forest University, Winston-Salem, North Carolina
| | - Victor E. Ortega
- Wake Forest School of Medicine, Wake Forest University, Winston-Salem, North Carolina
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Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison. Eur Radiol 2020; 30:6779-6787. [PMID: 32601950 DOI: 10.1007/s00330-020-07020-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 04/17/2020] [Accepted: 06/08/2020] [Indexed: 10/24/2022]
Abstract
OBJECTIVE This study determined the effect of dose reduction and kernel selection on quantifying emphysema using low-dose computed tomography (LDCT) and evaluated the efficiency of a deep learning-based kernel conversion technique in normalizing kernels for emphysema quantification. METHODS A sample of 131 participants underwent LDCT and standard-dose computed tomography (SDCT) at 1- to 2-year intervals. LDCT images were reconstructed with B31f and B50f kernels, and SDCT images were reconstructed with B30f kernels. A deep learning model was used to convert the LDCT image from a B50f kernel to a B31f kernel. Emphysema indices (EIs), lung attenuation at 15th percentile (perc15), and mean lung density (MLD) were calculated. Comparisons among the different kernel types for both LDCT and SDCT were performed using Friedman's test and Bland-Altman plots. RESULTS All values of LDCT B50f were significantly different compared with the values of LDCT B31f and SDCT B30f (p < 0.05). Although there was a statistical difference, the variation of the values of LDCT B50f significantly decreased after kernel normalization. The 95% limits of agreement between the SDCT and LDCT kernels (B31f and converted B50f) ranged from - 2.9 to 4.3% and from - 3.2 to 4.4%, respectively. However, there were no significant differences in EIs and perc15 between SDCT and LDCT converted B50f in the non-chronic obstructive pulmonary disease (COPD) participants (p > 0.05). CONCLUSION The deep learning-based CT kernel conversion of sharp kernel in LDCT significantly reduced variation in emphysema quantification, and could be used for emphysema quantification. KEY POINTS • Low-dose computed tomography with smooth kernel showed adequate performance in quantifying emphysema compared with standard-dose CT. • Emphysema quantification is affected by kernel selection and the application of a sharp kernel resulted in a significant overestimation of emphysema. • Deep learning-based kernel normalization of sharp kernel significantly reduced variation in emphysema quantification.
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Hwang EJ, Park CM. Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges. Korean J Radiol 2020; 21:511-525. [PMID: 32323497 PMCID: PMC7183830 DOI: 10.3348/kjr.2019.0821] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 01/31/2020] [Indexed: 12/25/2022] Open
Abstract
Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic radiology, are under active investigation with deep learning technology, which has shown promising performance in various tasks, including detection, classification, segmentation, and image synthesis, outperforming conventional methods and suggesting its potential for clinical implementation. However, the implementation of deep learning in daily clinical practice is in its infancy and facing several challenges, such as its limited ability to explain the output results, uncertain benefits regarding patient outcomes, and incomplete integration in daily workflow. In this review article, we will introduce the potential clinical applications of deep learning technology in thoracic radiology and discuss several challenges for its implementation in daily clinical practice.
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Affiliation(s)
- Eui Jin Hwang
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Chang Min Park
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
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Test-retest reproducibility of a deep learning-based automatic detection algorithm for the chest radiograph. Eur Radiol 2020; 30:2346-2355. [PMID: 31900698 DOI: 10.1007/s00330-019-06589-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 10/11/2019] [Accepted: 11/12/2019] [Indexed: 01/17/2023]
Abstract
OBJECTIVES To perform test-retest reproducibility analyses for deep learning-based automatic detection algorithm (DLAD) using two stationary chest radiographs (CRs) with short-term intervals, to analyze influential factors on test-retest variations, and to investigate the robustness of DLAD to simulated post-processing and positional changes. METHODS This retrospective study included patients with pulmonary nodules resected in 2017. Preoperative CRs without interval changes were used. Test-retest reproducibility was analyzed in terms of median differences of abnormality scores, intraclass correlation coefficients (ICC), and 95% limits of agreement (LoA). Factors associated with test-retest variation were investigated using univariable and multivariable analyses. Shifts in classification between the two CRs were analyzed using pre-determined cutoffs. Radiograph post-processing (blurring and sharpening) and positional changes (translations in x- and y-axes, rotation, and shearing) were simulated and agreement of abnormality scores between the original and simulated CRs was investigated. RESULTS Our study analyzed 169 patients (median age, 65 years; 91 men). The median difference of abnormality scores was 1-2% and ICC ranged from 0.83 to 0.90. The 95% LoA was approximately ± 30%. Test-retest variation was negatively associated with solid portion size (β, - 0.50; p = 0.008) and good nodule conspicuity (β, - 0.94; p < 0.001). A small fraction (15/169) showed discordant classifications when the high-specificity cutoff (46%) was applied to the model outputs (p = 0.04). DLAD was robust to the simulated positional change (ICC, 0.984, 0.996), but relatively less robust to post-processing (ICC, 0.872, 0.968). CONCLUSIONS DLAD was robust to the test-retest variation. However, inconspicuous nodules may cause fluctuations of the model output and subsequent misclassifications. KEY POINTS • The deep learning-based automatic detection algorithm was robust to the test-retest variation of the chest radiographs in general. • The test-retest variation was negatively associated with solid portion size and good nodule conspicuity. • High-specificity cutoff (46%) resulted in discordant classifications of 8.9% (15/169; p = 0.04) between the test-retest radiographs.
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Introduction to the Special Issue on Advances in Chest Imaging From the International Workshop for Pulmonary Functional Imaging (IWPFI): Context is Everything. J Thorac Imaging 2019; 34:73-74. [PMID: 30801448 DOI: 10.1097/rti.0000000000000386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Chen A, Karwoski RA, Gierada DS, Bartholmai BJ, Koo CW. Quantitative CT Analysis of Diffuse Lung Disease. Radiographics 2019; 40:28-43. [PMID: 31782933 DOI: 10.1148/rg.2020190099] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Quantitative analysis of thin-section CT of the chest has a growing role in the clinical evaluation and management of diffuse lung diseases. This heterogeneous group includes diseases with markedly different prognoses and treatment options. Quantitative tools can assist in both accurate diagnosis and longitudinal management by improving characterization and quantification of disease and increasing the reproducibility of disease severity assessment. Furthermore, a quantitative index of disease severity may serve as a useful tool or surrogate endpoint in evaluating treatment efficacy. The authors explore the role of quantitative imaging tools in the evaluation and management of diffuse lung diseases. Lung parenchymal features can be classified with threshold, histogram, morphologic, and texture-analysis-based methods. Quantitative CT analysis has been applied in obstructive, infiltrative, and restrictive pulmonary diseases including emphysema, cystic fibrosis, asthma, idiopathic pulmonary fibrosis, hypersensitivity pneumonitis, connective tissue-related interstitial lung disease, and combined pulmonary fibrosis and emphysema. Some challenges limiting the development and practical application of current quantitative analysis tools include the quality of training data, lack of standard criteria to validate the accuracy of the results, and lack of real-world assessments of the impact on outcomes. Artifacts such as patient motion or metallic beam hardening, variation in inspiratory effort, differences in image acquisition and reconstruction techniques, or inaccurate preprocessing steps such as segmentation of anatomic structures may lead to inaccurate classification. Despite these challenges, as new techniques emerge, quantitative analysis is developing into a viable tool to supplement the traditional visual assessment of diffuse lung diseases and to provide decision support regarding diagnosis, prognosis, and longitudinal evaluation of disease. ©RSNA, 2019.
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Affiliation(s)
- Alicia Chen
- From the Department of Radiology (A.C., B.J.B., C.W.K.) and Biomedical Medicine Imaging Resource (R.A.K.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (D.S.G.)
| | - Ronald A Karwoski
- From the Department of Radiology (A.C., B.J.B., C.W.K.) and Biomedical Medicine Imaging Resource (R.A.K.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (D.S.G.)
| | - David S Gierada
- From the Department of Radiology (A.C., B.J.B., C.W.K.) and Biomedical Medicine Imaging Resource (R.A.K.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (D.S.G.)
| | - Brian J Bartholmai
- From the Department of Radiology (A.C., B.J.B., C.W.K.) and Biomedical Medicine Imaging Resource (R.A.K.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (D.S.G.)
| | - Chi Wan Koo
- From the Department of Radiology (A.C., B.J.B., C.W.K.) and Biomedical Medicine Imaging Resource (R.A.K.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (D.S.G.)
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Li X, Thrall JH, Digumarthy SR, Kalra MK, Pandharipande PV, Zhang B, Nitiwarangkul C, Singh R, Khera RD, Li Q. Deep learning-enabled system for rapid pneumothorax screening on chest CT. Eur J Radiol 2019; 120:108692. [DOI: 10.1016/j.ejrad.2019.108692] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 09/13/2019] [Accepted: 09/19/2019] [Indexed: 11/26/2022]
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