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Nan Y, Xing X, Wang S, Tang Z, Felder FN, Zhang S, Ledda RE, Ding X, Yu R, Liu W, Shi F, Sun T, Cao Z, Zhang M, Gu Y, Zhang H, Gao J, Wang P, Tang W, Yu P, Kang H, Chen J, Lu X, Zhang B, Mamalakis M, Prinzi F, Carlini G, Cuneo L, Banerjee A, Xing Z, Zhu L, Mesbah Z, Jain D, Mayet T, Yuan H, Lyu Q, Qayyum A, Mazher M, Wells A, Walsh SL, Yang G. Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge. Med Image Anal 2024; 97:103253. [PMID: 38968907 DOI: 10.1016/j.media.2024.103253] [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: 12/20/2023] [Revised: 04/16/2024] [Accepted: 06/22/2024] [Indexed: 07/07/2024]
Abstract
Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.
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Affiliation(s)
- Yang Nan
- Bioengineering Department and Imperial-X, Imperial College London, London, UK; Royal Brompton Hospital, London, UK.
| | - Xiaodan Xing
- Bioengineering Department and Imperial-X, Imperial College London, London, UK.
| | - Shiyi Wang
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Zeyu Tang
- Bioengineering Department and Imperial-X, Imperial College London, London, UK
| | - Federico N Felder
- Royal Brompton Hospital, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Sheng Zhang
- National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Xiaoliu Ding
- Shanghai MicroPort MedBot (Group) Co., Ltd., China
| | - Ruiqi Yu
- Shanghai MicroPort MedBot (Group) Co., Ltd., China
| | - Weiping Liu
- Shanghai MicroPort MedBot (Group) Co., Ltd., China
| | - Feng Shi
- Shanghai United Imaging Intelligence Co., Ltd., China
| | - Tianyang Sun
- Shanghai United Imaging Intelligence Co., Ltd., China
| | - Zehong Cao
- Shanghai United Imaging Intelligence Co., Ltd., China
| | - Minghui Zhang
- Institute of Medical Robotics, Shanghai Jiao Tong University, China
| | - Yun Gu
- Institute of Medical Robotics, Shanghai Jiao Tong University, China
| | - Hanxiao Zhang
- Institute of Medical Robotics, Shanghai Jiao Tong University, China
| | - Jian Gao
- Department Computational Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Pingyu Wang
- Cambridge International Exam Centre in Shanghai Experimental School, China
| | - Wen Tang
- InferVision Medical Technology Co., Ltd., China
| | - Pengxin Yu
- InferVision Medical Technology Co., Ltd., China
| | - Han Kang
- InferVision Medical Technology Co., Ltd., China
| | - Junqiang Chen
- Shanghai MediWorks Precision Instruments Co., Ltd, China
| | - Xing Lu
- Sanmed Biotech Ltd., Zhuhai, China
| | | | | | - Francesco Prinzi
- Department of Biomedicine, University of Palermo, Palermo, Italy
| | - Gianluca Carlini
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Lisa Cuneo
- Istituto Italiano di Tecnologia, Nanoscopy, Genova, Italy
| | - Abhirup Banerjee
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
| | - Zhaohu Xing
- Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
| | - Lei Zhu
- Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
| | - Zacharia Mesbah
- INSA Rouen Normandie, Univ Rouen Normandie, Université Le Havre Normandie, France; Nuclear Medicine Department, Henri Becquerel Cancer Center, Rouen, France
| | - Dhruv Jain
- INSA Rouen Normandie, Univ Rouen Normandie, Université Le Havre Normandie, France
| | - Tsiry Mayet
- INSA Rouen Normandie, Univ Rouen Normandie, Université Le Havre Normandie, France
| | - Hongyu Yuan
- Department of Radiology, Wake Forest University School of Medicine, USA
| | - Qing Lyu
- Department of Radiology, Wake Forest University School of Medicine, USA
| | - Abdul Qayyum
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Moona Mazher
- Department of Computer Science, University College London, United Kingdom
| | - Athol Wells
- Royal Brompton Hospital, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Simon Lf Walsh
- Royal Brompton Hospital, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Guang Yang
- Bioengineering Department and Imperial-X, Imperial College London, London, UK; Royal Brompton Hospital, London, UK; National Heart and Lung Institute, Imperial College London, London, UK; School of Biomedical Engineering & Imaging Sciences, King's College London, UK.
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Larici AR, Biederer J, Cicchetti G, Franquet Casas T, Screaton N, Remy-Jardin M, Parkar A, Prosch H, Schaefer-Prokop C, Frauenfelder T, Ghaye B, Sverzellati N. ESR Essentials: imaging in fibrotic lung diseases-practice recommendations by the European Society of Thoracic Imaging. Eur Radiol 2024:10.1007/s00330-024-11054-2. [PMID: 39242399 DOI: 10.1007/s00330-024-11054-2] [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: 05/25/2024] [Revised: 07/29/2024] [Accepted: 08/03/2024] [Indexed: 09/09/2024]
Abstract
Fibrotic lung diseases (FLDs) represent a subgroup of interstitial lung diseases (ILDs), which can progress over time and carry a poor prognosis. Imaging has increased diagnostic discrimination in the evaluation of FLDs. International guidelines have stated the role of radiologists in the diagnosis and management of FLDs, in the context of the interdisciplinary discussion. Chest computed tomography (CT) with high-resolution technique is recommended to correctly recognise signs, patterns, and distribution of individual FLDs. Radiologists may be the first to recognise the presence of previously unknown interstitial lung abnormalities (ILAs) in various settings. A systematic approach to CT images may lead to a non-invasive diagnosis of FLDs. Careful comparison of serial CT exams is crucial in determining either disease progression or supervening complications. This 'Essentials' aims to provide radiologists a concise and practical approach to FLDs, focusing on CT technical requirements, pattern recognition, and assessment of disease progression and complications. Hot topics such as ILAs and progressive pulmonary fibrosis (PPF) are also discussed. KEY POINTS: Chest CT with high-resolution technique is the recommended imaging modality to diagnose pulmonary fibrosis. CT pattern recognition is central for an accurate diagnosis of fibrotic lung diseases (FLDs) by interdisciplinary discussion. Radiologists are to evaluate disease behaviour by accurately comparing serial CT scans.
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Affiliation(s)
- Anna Rita Larici
- Department of Radiological and Hematological Sciences, Catholic University of the Sacred Heart, Rome, Italy.
- Department of Diagnostic Imaging and Oncological Radiotherapy, Advanced Radiology Center, 'A. Gemelli' University Polyclinic Foundation IRCCS, Rome, Italy.
| | - Juergen Biederer
- Diagnostic and Interventional Radiology, University Hospital, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
- University of Latvia, Faculty of Medicine, Riga, Latvia
- Christian-Albrechts-Universität zu Kiel, Faculty of Medicine, Kiel, Germany
| | - Giuseppe Cicchetti
- Department of Diagnostic Imaging and Oncological Radiotherapy, Advanced Radiology Center, 'A. Gemelli' University Polyclinic Foundation IRCCS, Rome, Italy
| | | | - Nick Screaton
- Department of Radiology, Royal Papworth Hospital NHSFT, Cambridge, United Kingdom
| | - Martine Remy-Jardin
- IMALLIANCE-Haut-de-France, Valenciennes, France
- Department of Thoracic Imaging, University of Lille, Lille, France
| | - Anagha Parkar
- Radiology Department, Haraldsplass Deaconess Hospital, Bergen, Norway
- Department of Clinical Medicine, Faculty of Medicine and Dentistry, University of Bergen, Bergen, Norway
| | - Helmut Prosch
- Department of Radiology, Medical University of Vienna, Vienna, Austria
| | - Cornelia Schaefer-Prokop
- Radiology, Meander Medical Centre Amersfoort, Amersfoort, Netherlands
- Department of Radiology, Nuclear Medicine and Anatomy, RadboudUMC, Nijmegen, Netherlands
| | - Thomas Frauenfelder
- Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland
| | - Benoit Ghaye
- Department of Radiology, Cliniques Universitaires St-Luc, Catholic University of Louvain, Brussels, Belgium
| | - Nicola Sverzellati
- Scienze Radiologiche, Department of Medicine and Surgery, University of Parma, Parma, Italy
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de la Orden Kett Morais SR, Felder FN, Walsh SLF. From pixels to prognosis: unlocking the potential of deep learning in fibrotic lung disease imaging analysis. Br J Radiol 2024; 97:1517-1525. [PMID: 38781513 PMCID: PMC11332672 DOI: 10.1093/bjr/tqae108] [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: 03/22/2024] [Accepted: 05/21/2024] [Indexed: 05/25/2024] Open
Abstract
The licensing of antifibrotic therapy for fibrotic lung diseases, including idiopathic pulmonary fibrosis (IPF), has created an urgent need for reliable biomarkers to predict disease progression and treatment response. Some patients experience stable disease trajectories, while others deteriorate rapidly, making treatment decisions challenging. High-resolution chest CT has become crucial for diagnosis, but visual assessments by radiologists suffer from low reproducibility and high interobserver variability. To address these issues, computer-based image analysis, called quantitative CT, has emerged. However, many quantitative CT methods rely on human input for training, therefore potentially incorporating human error into computer training. Rapid advances in artificial intelligence, specifically deep learning, aim to overcome this limitation by enabling autonomous quantitative analysis. While promising, deep learning also presents challenges including the need to minimize algorithm biases, ensuring explainability, and addressing accessibility and ethical concerns. This review explores the development and application of deep learning in improving the imaging process for fibrotic lung disease.
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Affiliation(s)
| | - Federico N Felder
- National Heart and Lung Institute, Imperial College, London, SW3 6LY, United Kingdom
| | - Simon L F Walsh
- National Heart and Lung Institute, Imperial College, London, SW3 6LY, United Kingdom
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Zhang J, Peng P. Quantitative parameters of HRCT target scan to predict the risk of lung adenocarcinoma based on the detection of lung ground-glass nodules. Clin Transl Oncol 2024:10.1007/s12094-024-03676-1. [PMID: 39180703 DOI: 10.1007/s12094-024-03676-1] [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: 07/23/2024] [Accepted: 08/13/2024] [Indexed: 08/26/2024]
Abstract
BACKGROUND To explore the value of high-resolution computed tomography (HRCT) in the differential diagnosis of benign and malignant ground-glass nodules (GGNs), and to provide a theoretical basis for the clinical application of HRCT. METHODS A total of 208 patients with GGN who had been clinically confirmed by surgical pathology and clinical confirmation were collected, and HRCT target scanning technology was used to scan and collect general information of patients, and observe the distribution of GGN, GGN size, GGN cross-sectional area, diameter, transverse diameter, solid composition, relationship with bronchi, and relationship with blood vessels and other indicators. Multivariate regression analysis and risk factor prediction are performed. RESULTS The differences were statistically significant in multivariate regression analysis, such as nodule location, maximum diameter, maximum cross-sectional area, GGN status, nodule boundary and relationship with blood vessels (P < 0.05). The results of ROC curve showed that the AUC value of nodule site and nodule boundary was greater than 0.5, and the nodule boundary AUC value was 0.676, which was more sensitive to predict whether GGN deteriorated to lung adenocarcinoma (LUAD). CONCLUSION Nodule site and nodule boundary are effective risk predictors for LUAD in patients with GGN, and nodule boundary is the most valuable independent predictor.
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Affiliation(s)
- Jingfang Zhang
- Diagnostic Radiology Department of the 988, Hospital of the Joint Support Force of the People's Liberation Army, 602 Zhengshang Road, Zhongyuan District, Zhengzhou City, 450000, Henan Province, China.
| | - Peili Peng
- Diagnostic Radiology Department of the 988, Hospital of the Joint Support Force of the People's Liberation Army, 602 Zhengshang Road, Zhongyuan District, Zhengzhou City, 450000, Henan Province, China.
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Choe J, Hwang HJ, Lee SM, Yoon J, Kim N, Seo JB. CT Quantification of Interstitial Lung Abnormality and Interstitial Lung Disease: From Technical Challenges to Future Directions. Invest Radiol 2024:00004424-990000000-00233. [PMID: 39008898 DOI: 10.1097/rli.0000000000001103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
ABSTRACT Interstitial lung disease (ILD) encompasses a variety of lung disorders with varying degrees of inflammation or fibrosis, requiring a combination of clinical, imaging, and pathologic data for evaluation. Imaging is essential for the noninvasive diagnosis of the disease, as well as for assessing disease severity, monitoring its progression, and evaluating treatment response. However, traditional visual assessments of ILD with computed tomography (CT) suffer from reader variability. Automated quantitative CT offers a more objective approach by using computer-based analysis to consistently evaluate and measure ILD. Advancements in technology have significantly improved the accuracy and reliability of these measurements. Recently, interstitial lung abnormalities (ILAs), which represent potential preclinical ILD incidentally found on CT scans and are characterized by abnormalities in over 5% of any lung zone, have gained attention and clinical importance. The challenge lies in the accurate and consistent identification of ILA, given that its definition relies on a subjective threshold, making quantitative tools crucial for precise ILA evaluation. This review highlights the state of CT quantification of ILD and ILA, addressing clinical and research disparities while emphasizing how machine learning or deep learning in quantitative imaging can improve diagnosis and management by providing more accurate assessments, and finally, suggests the future directions of quantitative CT in this area.
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Affiliation(s)
- Jooae Choe
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.C., H.J.H., S.M.L., J.Y., N.K., J.B.S.); and Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.Y. and N.K.)
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Lee T, Ahn SY, Kim J, Park JS, Kwon BS, Choi SM, Goo JM, Park CM, Nam JG. Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs. Eur Radiol 2024; 34:4206-4217. [PMID: 38112764 DOI: 10.1007/s00330-023-10501-w] [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: 10/06/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 12/21/2023]
Abstract
OBJECTIVES To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs. METHODS To develop a deep learning-based prognostic model using chest radiographs (DLPM), the patients diagnosed with IPF during 2011-2021 were retrospectively collected and were divided into training (n = 1007), validation (n = 117), and internal test (n = 187) datasets. Up to 10 consecutive radiographs were included for each patient. For external testing, three cohorts from independent institutions were collected (n = 152, 141, and 207). The discrimination performance of DLPM was evaluated using areas under the time-dependent receiver operating characteristic curves (TD-AUCs) for 3-year survival and compared with that of forced vital capacity (FVC). Multivariable Cox regression was performed to investigate whether the DLPM was an independent prognostic factor from FVC. We devised a modified gender-age-physiology (GAP) index (GAP-CR), by replacing DLCO with DLPM. RESULTS DLPM showed similar-to-higher performance at predicting 3-year survival than FVC in three external test cohorts (TD-AUC: 0.83 [95% CI: 0.76-0.90] vs. 0.68 [0.59-0.77], p < 0.001; 0.76 [0.68-0.85] vs. 0.70 [0.60-0.80], p = 0.21; 0.79 [0.72-0.86] vs. 0.76 [0.69-0.83], p = 0.41). DLPM worked as an independent prognostic factor from FVC in all three cohorts (ps < 0.001). The GAP-CR index showed a higher 3-year TD-AUC than the original GAP index in two of the three external test cohorts (TD-AUC: 0.85 [0.80-0.91] vs. 0.79 [0.72-0.86], p = 0.02; 0.72 [0.64-0.80] vs. 0.69 [0.61-0.78], p = 0.56; 0.76 [0.69-0.83] vs. 0.68 [0.60-0.76], p = 0.01). CONCLUSIONS A deep learning model successfully predicted survival in patients with IPF from chest radiographs, comparable to and independent of FVC. CLINICAL RELEVANCE STATEMENT Deep learning-based prognostication from chest radiographs offers comparable-to-higher prognostic performance than forced vital capacity. KEY POINTS • A deep learning-based prognostic model for idiopathic pulmonary fibrosis was developed using 6063 radiographs. • The prognostic performance of the model was comparable-to-higher than forced vital capacity, and was independent from FVC in all three external test cohorts. • A modified gender-age-physiology index replacing diffusing capacity for carbon monoxide with the deep learning model showed higher performance than the original index in two external test cohorts.
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Affiliation(s)
- Taehee Lee
- Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101, Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Su Yeon Ahn
- Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, 05030, Republic of Korea
| | - Jihang Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, 13620, Republic of Korea
| | - Jong Sun Park
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam, 13620, Republic of Korea
| | - Byoung Soo Kwon
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam, 13620, Republic of Korea
| | - Sun Mi Choi
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital and College of Medicine, Seoul, 03080, Republic of Korea
| | - Jin Mo Goo
- Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101, Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, 03080, Republic of Korea
| | - Chang Min Park
- Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101, Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea.
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, 03080, Republic of Korea.
- Institute of Medical and Biological Engineering, Seoul National University Medical Research Center, Seoul, 03080, Republic of Korea.
| | - Ju Gang Nam
- Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101, Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea.
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Ahn Y, Kim HC, Lee JK, Noh HN, Choe J, Seo JB, Lee SM. Usefulness of CT Quantification-Based Assessment in Defining Progressive Pulmonary Fibrosis. Acad Radiol 2024:S1076-6332(24)00286-1. [PMID: 38876844 DOI: 10.1016/j.acra.2024.05.005] [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: 01/02/2024] [Revised: 04/28/2024] [Accepted: 05/05/2024] [Indexed: 06/16/2024]
Abstract
RATIONALE AND OBJECTIVES To establish a quantitative CT threshold for radiological disease progression of progressive pulmonary fibrosis (PPF) and evaluate its feasibility in patients with connective tissue disease-related interstitial lung disease (CTD-ILD). MATERIALS AND METHODS Between April 2007 and October 2022, patients diagnosed with CTD-ILD retrospectively evaluated. CT quantification was conducted using a commercial software by summing the percentages of ground-glass opacity, consolidation, reticular opacity, and honeycombing. The quantitative threshold for radiological progression was determined based on the highest discrimination on overall survival (OS). Two thoracic radiologists independently evaluated visual radiological progression, and the senior radiologist's assessment was used as the final result. Cox regression was used to assess prognosis of PPF based on the visual assessment and quantitative threshold. RESULTS 97 patients were included and followed up for a median of 30.3 months (range, 4.7-198.1 months). For defining radiological disease progression, the optimal quantitative CT threshold was 4%. Using this threshold, 12 patients were diagnosed with PPF, while 14 patients were diagnosed with PPF based on the visual assessment, with an agreement rate of 97.9% (95/97). Worsening respiratory symptoms (hazard ratio [HR], 12.73; P < .001), PPF based on the visual assessment (HR, 8.86; P = .002) and based on the quantitative threshold (HR, 6.72; P = .009) were independent risk factors for poor OS. CONCLUSION The quantitative CT threshold for radiological disease progression (4%) was feasible in defining PPF in terms of its agreement with PPF grouping and prognostic performance when compared to visual assessment.
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Affiliation(s)
- Yura Ahn
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (Y.A., H.N.N., J.C., J.B.S., S.M.L.)
| | - Ho Cheol Kim
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea (H.C.K.)
| | - Ju Kwang Lee
- Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea (J.K.L.)
| | - Han Na Noh
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (Y.A., H.N.N., J.C., J.B.S., S.M.L.)
| | - Jooae Choe
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (Y.A., H.N.N., J.C., J.B.S., S.M.L.)
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (Y.A., H.N.N., J.C., J.B.S., S.M.L.)
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (Y.A., H.N.N., J.C., J.B.S., S.M.L.).
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Mismetti V, Si-Mohamed S, Cottin V. Interstitial Lung Disease Associated with Systemic Sclerosis. Semin Respir Crit Care Med 2024; 45:342-364. [PMID: 38714203 DOI: 10.1055/s-0044-1786698] [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: 05/09/2024]
Abstract
Systemic sclerosis (SSc) is a rare autoimmune disease characterized by a tripod combining vasculopathy, fibrosis, and immune-mediated inflammatory processes. The prevalence of interstitial lung disease (ILD) in SSc varies according to the methods used to detect it, ranging from 25 to 95%. The fibrotic and vascular pulmonary manifestations of SSc, particularly ILD, are the main causes of morbidity and mortality, contributing to 35% of deaths. Although early trials were conducted with cyclophosphamide, more recent randomized controlled trials have been performed to assess the efficacy and tolerability of several medications, mostly mycophenolate, rituximab, tocilizumab, and nintedanib. Although many uncertainties remain, expert consensus is emerging to optimize the therapeutic management and to provide clinicians with evidence-based clinical practice guidelines for patients with SSc-ILD. This article provides an overview, in the light of the latest advances, of the available evidence for the diagnosis and management of SSc-ILD.
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Affiliation(s)
- Valentine Mismetti
- Department of Respiratory Medicine, National Coordinating Reference Centre for Rare Pulmonary Diseases, ERN-LUNG, Louis Pradel Hospital, Hospices Civils de Lyon, Lyon, France
| | - Salim Si-Mohamed
- INSA-Lyon, University of Lyon, University Claude-Bernard Lyon 1, Lyon, France
- Radiology Department, Hospices Civils de Lyon, Lyon, France
| | - Vincent Cottin
- Department of Respiratory Medicine, National Coordinating Reference Centre for Rare Pulmonary Diseases, ERN-LUNG, Louis Pradel Hospital, Hospices Civils de Lyon, Lyon, France
- UMR 754, INRAE, Claude Bernard University Lyon 1, Lyon, France
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Guerra X, Rennotte S, Fetita C, Boubaya M, Debray MP, Israël-Biet D, Bernaudin JF, Valeyre D, Cadranel J, Naccache JM, Nunes H, Brillet PY. U-net convolutional neural network applied to progressive fibrotic interstitial lung disease: Is progression at CT scan associated with a clinical outcome? Respir Med Res 2024; 85:101058. [PMID: 38141579 DOI: 10.1016/j.resmer.2023.101058] [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: 05/31/2023] [Revised: 09/18/2023] [Accepted: 10/17/2023] [Indexed: 12/25/2023]
Abstract
BACKGROUND Computational advances in artificial intelligence have led to the recent emergence of U-Net convolutional neural networks (CNNs) applied to medical imaging. Our objectives were to assess the progression of fibrotic interstitial lung disease (ILD) using routine CT scans processed by a U-Net CNN developed by our research team, and to identify a progression threshold indicative of poor prognosis. METHODS CT scans and clinical history of 32 patients with idiopathic fibrotic ILDs were retrospectively reviewed. Successive CT scans were processed by the U-Net CNN and ILD quantification was obtained. Correlation between ILD and FVC changes was assessed. ROC curve was used to define a threshold of ILD progression rate (PR) to predict poor prognostic (mortality or lung transplantation). The PR threshold was used to compare the cohort survival with Kaplan Mayer curves and log-rank test. RESULTS The follow-up was 3.8 ± 1.5 years encompassing 105 CT scans, with 3.3 ± 1.1 CT scans per patient. A significant correlation between ILD and FVC changes was obtained (p = 0.004, ρ = -0.30 [95% CI: -0.16 to -0.45]). Sixteen patients (50%) experienced unfavorable outcome including 13 deaths and 3 lung transplantations. ROC curve analysis showed an aera under curve of 0.83 (p < 0.001), with an optimal cut-off PR value of 4%/year. Patients exhibiting a PR ≥ 4%/year during the first two years had a poorer prognosis (p = 0.001). CONCLUSIONS Applying a U-Net CNN to routine CT scan allowed identifying patients with a rapid progression and unfavorable outcome.
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Affiliation(s)
- Xavier Guerra
- Department of Radiology, Avicenne Hospital, Assistance Publique - Hôpitaux de Paris, Bobigny, France.
| | - Simon Rennotte
- Samovar Laboratory, Télécom SudParis, Institut Polytechnique de Paris, Evry, France
| | - Catalin Fetita
- Samovar Laboratory, Télécom SudParis, Institut Polytechnique de Paris, Evry, France
| | - Marouane Boubaya
- Clinical Research Unit, Avicenne Hospital, Assistance Publique - Hôpitaux de Paris, Sorbonne Paris-Nord, Bobigny, France
| | - Marie-Pierre Debray
- Department of Radiology, Bichat-Claude Bernard Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Dominique Israël-Biet
- Department of Pulmonology, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France; Université Paris - Cité, Paris, France
| | - Jean-François Bernaudin
- INSERM UMR 1272 Hypoxie & Poumon SMBH, Université Sorbonne Paris - Nord, Bobigny, France; Medicine Sorbonne Université, Paris, France
| | - Dominique Valeyre
- INSERM UMR 1272 Hypoxie & Poumon SMBH, Université Sorbonne Paris - Nord, Bobigny, France; Department of Pulmonology, Avicenne Hospital, Assistance Publique - Hôpitaux de Paris, Bobigny, France
| | - Jacques Cadranel
- Medicine Sorbonne Université, Paris, France; Department of Pulmonology, Tenon Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Jean-Marc Naccache
- Department of Pulmonology, Groupe Hospitalier Paris Saint Joseph, Paris, France
| | - Hilario Nunes
- INSERM UMR 1272 Hypoxie & Poumon SMBH, Université Sorbonne Paris - Nord, Bobigny, France; Department of Pulmonology, Avicenne Hospital, Assistance Publique - Hôpitaux de Paris, Bobigny, France
| | - Pierre-Yves Brillet
- Department of Radiology, Avicenne Hospital, Assistance Publique - Hôpitaux de Paris, Bobigny, France; INSERM UMR 1272 Hypoxie & Poumon SMBH, Université Sorbonne Paris - Nord, Bobigny, France
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10
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Humphries SM, Thieke D, Baraghoshi D, Strand MJ, Swigris JJ, Chae KJ, Hwang HJ, Oh AS, Flaherty KR, Adegunsoye A, Jablonski R, Lee CT, Husain AN, Chung JH, Strek ME, Lynch DA. Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes. Am J Respir Crit Care Med 2024; 209:1121-1131. [PMID: 38207093 DOI: 10.1164/rccm.202307-1191oc] [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: 07/12/2023] [Accepted: 01/04/2024] [Indexed: 01/13/2024] Open
Abstract
Rationale: Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. Objectives: Apply multiple instance learning (MIL) to develop an explainable deep learning algorithm for prediction of UIP from CT and validate its performance in independent cohorts. Methods: We trained an MIL algorithm using a pooled dataset (n = 2,143) and tested it in three independent populations: data from a prior publication (n = 127), a single-institution clinical cohort (n = 239), and a national registry of patients with pulmonary fibrosis (n = 979). We tested UIP classification performance using receiver operating characteristic analysis, with histologic UIP as ground truth. Cox proportional hazards and linear mixed-effects models were used to examine associations between MIL predictions and survival or longitudinal FVC. Measurements and Main Results: In two cohorts with biopsy data, MIL improved accuracy for histologic UIP (area under the curve, 0.77 [n = 127] and 0.79 [n = 239]) compared with visual assessment (area under the curve, 0.65 and 0.71). In cohorts with survival data, MIL-UIP classifications were significant for mortality (n = 239, mortality to April 2021: unadjusted hazard ratio, 3.1; 95% confidence interval [CI], 1.96-4.91; P < 0.001; and n = 979, mortality to July 2022: unadjusted hazard ratio, 3.64; 95% CI, 2.66-4.97; P < 0.001). Individuals classified as UIP positive by the algorithm had a significantly greater annual decline in FVC than those classified as UIP negative (-88 ml/yr vs. -45 ml/yr; n = 979; P < 0.01), adjusting for extent of lung fibrosis. Conclusions: Computerized assessment using MIL identifies clinically significant features of UIP on CT. Such a method could improve confidence in radiologic assessment of patients with interstitial lung disease, potentially enabling earlier and more precise diagnosis.
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Affiliation(s)
| | | | | | | | - Jeffrey J Swigris
- Division of Pulmonary and Critical Care Medicine, National Jewish Health, Denver, Colorado
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea
| | - Hye Jeon Hwang
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Andrea S Oh
- Department of Radiology, University of California Los Angeles, Los Angeles, California
| | - Kevin R Flaherty
- Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, Michigan
| | | | - Renea Jablonski
- Section of Pulmonary and Critical Care, Department of Medicine
| | - Cathryn T Lee
- Section of Pulmonary and Critical Care, Department of Medicine
| | - Aliya N Husain
- Department of Pathology, The University of Chicago, Chicago, Illinois
| | | | - Mary E Strek
- Section of Pulmonary and Critical Care, Department of Medicine
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11
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Kanne JP, Walker CM, Brixey AG, Brown KK, Chelala L, Kazerooni EA, Walsh SLF, Lynch DA. Progressive Pulmonary Fibrosis and Interstitial Lung Abnormalities: AJR Expert Panel Narrative Review. AJR Am J Roentgenol 2024. [PMID: 38656115 DOI: 10.2214/ajr.24.31125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Progressive pulmonary fibrosis (PPF) and interstitial lung abnormalities (ILA) are relatively new concepts in interstitial lung disease (ILD) imaging and clinical management. Recognition of signs of PPF, as well as identification and classification of ILA, are important tasks during chest high-resolution CT interpretation, to optimize management of patients with ILD and those at risk of developing ILD. However, following professional society guidance, the role of imaging surveillance remains unclear in stable patients with ILD, asymptomatic patients with ILA who are at risk of progression, and asymptomatic patients at risk of developing ILD without imaging abnormalities. In this AJR Expert Panel Narrative Review, we summarize the current knowledge regarding PPF and ILA and describe the range of clinical practice with respect to imaging patients with ILD, those with ILA, and those at risk of developing ILD. In addition, we offer suggestions to help guide surveillance imaging in areas with an absence of published guidelines, where such decisions are currently driven primarily by local pulmonologists' preference.
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Affiliation(s)
- Jeffrey P Kanne
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Christopher M Walker
- Department of Radiology, The University of Kansas Medical Center, Kansas City, KS
| | - Anupama G Brixey
- Department of Radiology, Portland VA Healthcare System, Oregon Health & Science University, Portland, OR
| | - Kevin K Brown
- Department of Medicine, National Jewish Health, Denver, CO
| | - Lydia Chelala
- Department of Radiology, University of Chicago Medicine, Chicago, IL
| | - Ella A Kazerooni
- Departments of Radiology & Internal Medicine, University of Michigan Medical School / Michigan Medicine, Ann Arbor, MI
| | - Simon L F Walsh
- Department of Radiology, Imperial College, London, United Kingdom
| | - David A Lynch
- Department of Radiology, National Jewish Health, Denver, CO
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12
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Raghu G, Ghazipura M, Fleming TR, Aronson KI, Behr J, Brown KK, Flaherty KR, Kazerooni EA, Maher TM, Richeldi L, Lasky JA, Swigris JJ, Busch R, Garrard L, Ahn DH, Li J, Puthawala K, Rodal G, Seymour S, Weir N, Danoff SK, Ettinger N, Goldin J, Glassberg MK, Kawano-Dourado L, Khalil N, Lancaster L, Lynch DA, Mageto Y, Noth I, Shore JE, Wijsenbeek M, Brown R, Grogan D, Ivey D, Golinska P, Karimi-Shah B, Martinez FJ. Meaningful Endpoints for Idiopathic Pulmonary Fibrosis (IPF) Clinical Trials: Emphasis on 'Feels, Functions, Survives'. Report of a Collaborative Discussion in a Symposium with Direct Engagement from Representatives of Patients, Investigators, the National Institutes of Health, a Patient Advocacy Organization, and a Regulatory Agency. Am J Respir Crit Care Med 2024; 209:647-669. [PMID: 38174955 DOI: 10.1164/rccm.202312-2213so] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 01/02/2024] [Indexed: 01/05/2024] Open
Abstract
Background: Idiopathic pulmonary fibrosis (IPF) carries significant mortality and unpredictable progression, with limited therapeutic options. Designing trials with patient-meaningful endpoints, enhancing the reliability and interpretability of results, and streamlining the regulatory approval process are of critical importance to advancing clinical care in IPF. Methods: A landmark in-person symposium in June 2023 assembled 43 participants from the US and internationally, including patients with IPF, investigators, and regulatory representatives, to discuss the immediate future of IPF clinical trial endpoints. Patient advocates were central to discussions, which evaluated endpoints according to regulatory standards and the FDA's 'feels, functions, survives' criteria. Results: Three themes emerged: 1) consensus on endpoints mirroring the lived experiences of patients with IPF; 2) consideration of replacing forced vital capacity (FVC) as the primary endpoint, potentially by composite endpoints that include 'feels, functions, survives' measures or FVC as components; 3) support for simplified, user-friendly patient-reported outcomes (PROs) as either components of primary composite endpoints or key secondary endpoints, supplemented by functional tests as secondary endpoints and novel biomarkers as supportive measures (FDA Guidance for Industry (Multiple Endpoints in Clinical Trials) available at: https://www.fda.gov/media/162416/download). Conclusions: This report, detailing the proceedings of this pivotal symposium, suggests a potential turning point in designing future IPF clinical trials more attuned to outcomes meaningful to patients, and documents the collective agreement across multidisciplinary stakeholders on the importance of anchoring IPF trial endpoints on real patient experiences-namely, how they feel, function, and survive. There is considerable optimism that clinical care in IPF will progress through trials focused on patient-centric insights, ultimately guiding transformative treatment strategies to enhance patients' quality of life and survival.
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Affiliation(s)
- Ganesh Raghu
- Center for Interstitial Lung Diseases, Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine
- Department of Laboratory Medicine and Pathology, and
| | - Marya Ghazipura
- ZS Associates, Global Health Economics and Outcomes Research, New York, New York
- Division of Epidemiology and
- Division of Biostatistics, Department of Population Health, New York University Langone Health, New York, New York
| | - Thomas R Fleming
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - Kerri I Aronson
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Jürgen Behr
- Department of Medicine V, LMU University Hospital, Ludwig-Maximilians-University Munich, Member of the German Center for Lung Research, Munich, Germany
| | | | - Kevin R Flaherty
- Division of Pulmonary and Critical Care, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Ella A Kazerooni
- Division of Pulmonary and Critical Care, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
- Division of Cardiothoracic Radiology, Department of Radiology, University of Michigan Health System, Detroit, Michigan
| | - Toby M Maher
- Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Luca Richeldi
- Divisione di Medicina Polmonare, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Joseph A Lasky
- Department of Medicine, Tulane University, New Orleans, Louisiana
| | | | - Robert Busch
- Division of Pulmonology, Allergy, and Critical Care, Office of Immunology and Inflammation, and
| | - Lili Garrard
- Division of Biometrics III, Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, and
| | - Dong-Hyun Ahn
- Division of Biometrics III, Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, and
| | - Ji Li
- Division of Clinical Outcome Assessment, Office of Drug Evaluation Sciences, Office of New Drugs, and
| | - Khalid Puthawala
- Division of Pulmonology, Allergy, and Critical Care, Office of Immunology and Inflammation, and
| | - Gabriela Rodal
- Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland
| | - Sally Seymour
- Division of Pulmonology, Allergy, and Critical Care, Office of Immunology and Inflammation, and
| | - Nargues Weir
- Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland
| | - Sonye K Danoff
- Division of Pulmonary and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Neil Ettinger
- Division of Pulmonary Medicine, St. Luke's Hospital, Chesterfield, Missouri
| | - Jonathan Goldin
- Department of Radiology, University of California, Los Angeles, Los Angeles, California
| | - Marilyn K Glassberg
- Department of Medicine, Stritch School of Medicine, Loyola Chicago, Chicago, Illinois
| | - Leticia Kawano-Dourado
- Hcor Research Institute - Hcor Hospital, São Paolo, Brazil
- Pulmonary Division, Heart Institute (InCor), University of São Paulo, São Paulo, Brazil
| | - Nasreen Khalil
- Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Lisa Lancaster
- Division of Pulmonary, Critical Care, and Sleep Medicine, Vanderbilt University, Nashville, Tennessee
| | - David A Lynch
- Department of Radiology, National Jewish Health, Denver, Colorado
| | - Yolanda Mageto
- Division of Pulmonary, Critical Care, and Sleep Medicine, Baylor University, Dallas, Texas
| | - Imre Noth
- Division of Pulmonary and Critical Care Medicine, University of Virginia, Charlottesville, Virginia
| | | | - Marlies Wijsenbeek
- Centre of Interstitial Lung Diseases, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands
| | - Robert Brown
- Patient representative and patient living with IPF, Lovettsville, Virginia
| | - Daniel Grogan
- Patient representative and patient living with IPF, Charlottesville, Virginia; and
| | - Dorothy Ivey
- Patient representative and patient living with IPF, Richmond, Virginia
| | - Patrycja Golinska
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Banu Karimi-Shah
- Division of Pulmonology, Allergy, and Critical Care, Office of Immunology and Inflammation, and
| | - Fernando J Martinez
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, New York
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13
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Huang X, Si W, Ye X, Zhao Y, Gu H, Zhang M, Wu S, Shi Y, Gui X, Xiao Y, Cao M. Novel 3D-based deep learning for classification of acute exacerbation of idiopathic pulmonary fibrosis using high-resolution CT. BMJ Open Respir Res 2024; 11:e002226. [PMID: 38460976 DOI: 10.1136/bmjresp-2023-002226] [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: 12/01/2023] [Accepted: 02/28/2024] [Indexed: 03/11/2024] Open
Abstract
PURPOSE Acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) is the primary cause of death in patients with IPF, characterised by diffuse, bilateral ground-glass opacification on high-resolution CT (HRCT). This study proposes a three-dimensional (3D)-based deep learning algorithm for classifying AE-IPF using HRCT images. MATERIALS AND METHODS A novel 3D-based deep learning algorithm, SlowFast, was developed by applying a database of 306 HRCT scans obtained from two centres. The scans were divided into four separate subsets (training set, n=105; internal validation set, n=26; temporal test set 1, n=79; and geographical test set 2, n=96). The final training data set consisted of 1050 samples with 33 600 images for algorithm training. Algorithm performance was evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve and weighted κ coefficient. RESULTS The accuracy of the algorithm in classifying AE-IPF on the test sets 1 and 2 was 93.9% and 86.5%, respectively. Interobserver agreements between the algorithm and the majority opinion of the radiologists were good (κw=0.90 for test set 1 and κw=0.73 for test set 2, respectively). The ROC accuracy of the algorithm for classifying AE-IPF on the test sets 1 and 2 was 0.96 and 0.92, respectively. The algorithm performance was superior to visual analysis in accurately diagnosing radiological findings. Furthermore, the algorithm's categorisation was a significant predictor of IPF progression. CONCLUSIONS The deep learning algorithm provides high auxiliary diagnostic efficiency in patients with AE-IPF and may serve as a useful clinical aid for diagnosis.
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Affiliation(s)
- Xinmei Huang
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
- Nanjing Institute of Respiratory Diseases, Nanjing, Jiangsu, China
| | - Wufei Si
- Purple Mountain Laboratories, Nanjing, Jiangsu, China
| | - Xu Ye
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yichao Zhao
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Huimin Gu
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Mingrui Zhang
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Shufei Wu
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Yanchen Shi
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Xianhua Gui
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
- Nanjing Institute of Respiratory Diseases, Nanjing, Jiangsu, China
| | - Yonglong Xiao
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
- Nanjing Institute of Respiratory Diseases, Nanjing, Jiangsu, China
| | - Mengshu Cao
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
- Nanjing Institute of Respiratory Diseases, Nanjing, Jiangsu, China
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu, China
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14
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Oh AS, Lynch DA, Swigris JJ, Baraghoshi D, Dyer DS, Hale VA, Koelsch TL, Marrocchio C, Parker KN, Teague SD, Flaherty KR, Humphries SM. Deep Learning-based Fibrosis Extent on Computed Tomography Predicts Outcome of Fibrosing Interstitial Lung Disease Independent of Visually Assessed Computed Tomography Pattern. Ann Am Thorac Soc 2024; 21:218-227. [PMID: 37696027 DOI: 10.1513/annalsats.202301-084oc] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 09/08/2023] [Indexed: 09/13/2023] Open
Abstract
Rationale: Radiologic pattern has been shown to predict survival in patients with fibrosing interstitial lung disease. The additional prognostic value of fibrosis extent by quantitative computed tomography (CT) is unknown. Objectives: We hypothesized that fibrosis extent provides information beyond visually assessed CT pattern that is useful for outcome prediction. Methods: We performed a retrospective analysis of chest CT, demographics, longitudinal pulmonary function, and transplantation-free survival among participants in the Pulmonary Fibrosis Foundation Patient Registry. CT pattern was classified visually according to the 2018 usual interstitial pneumonia criteria. Extent of fibrosis was objectively quantified using data-driven textural analysis. We used Kaplan-Meier plots and Cox proportional hazards and linear mixed-effects models to evaluate the relationships between CT-derived metrics and outcomes. Results: Visual assessment and quantitative analysis were performed on 979 enrollment CT scans. Linear mixed-effect modeling showed that greater baseline fibrosis extent was significantly associated with the annual rate of decline in forced vital capacity. In multivariable models that included CT pattern and fibrosis extent, quantitative fibrosis extent was strongly associated with transplantation-free survival independent of CT pattern (hazard ratio, 1.04; 95% confidence interval, 1.04-1.05; P < 0.001; C statistic = 0.73). Conclusions: The extent of lung fibrosis by quantitative CT is a strong predictor of physiologic progression and survival, independent of visually assessed CT pattern.
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Affiliation(s)
- Andrea S Oh
- Department of Radiology, University of California, Los Angeles, Los Angeles, California
| | | | - Jeffrey J Swigris
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, and
| | - David Baraghoshi
- Department of Biostatistics, National Jewish Health, Denver, Colorado
| | | | | | | | | | | | | | - Kevin R Flaherty
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Michigan, Ann Arbor, Michigan
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15
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Wells AU, Walsh SLF. Quantifying Fibrosis in Fibrotic Lung Disease: A Good Human Plus a Machine Is the Best Combination? Ann Am Thorac Soc 2024; 21:204-205. [PMID: 38299920 PMCID: PMC10848908 DOI: 10.1513/annalsats.202311-954ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024] Open
Affiliation(s)
- Athol U Wells
- Royal Brompton Hospital, London, United Kingdom; and
- Imperial College, London, United Kingdom
| | - Simon L F Walsh
- Royal Brompton Hospital, London, United Kingdom; and
- Imperial College, London, United Kingdom
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16
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DeBoer EM, Weinman JP, Ley-Zaporozhan J, Griese M, Deterding R, Lynch DA, Humphries SM, Jacob J. Imaging of pulmonary fibrosis in children: A review, with proposed diagnostic criteria. Pediatr Pulmonol 2024. [PMID: 38214442 DOI: 10.1002/ppul.26857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 11/29/2023] [Accepted: 01/02/2024] [Indexed: 01/13/2024]
Abstract
Computed tomography (CT) imaging findings of pulmonary fibrosis are well established for adults and have been shown to correlate with prognosis and outcome. Recognition of fibrotic CT findings in children is more limited. With approved treatments for adult pulmonary fibrosis, it has become critical to define CT criteria for fibrosis in children, to identify patients in need of treatment and those eligible for clinical trials. Understanding how pediatric fibrosis compares with idiopathic pulmonary fibrosis and other causes of fibrosis in adults is increasingly important as these patients transition to adult care teams. Here, we review what is known regarding the features of pulmonary fibrosis in children compared with adults. Pulmonary fibrosis in children may be associated with genetic surfactant dysfunction disorders, autoimmune systemic disorders, and complications after radiation, chemotherapy, transplantation, and other exposures. Rather than a basal-predominant usual interstitial pneumonia pattern with honeycombing, pediatric fibrosis is primarily characterized by reticulation, traction bronchiectasis, architectural distortion, or cystic lucencies/abnormalities. Ground-glass opacities are more frequent in children with fibrotic interstitial lung disease than adults, and disease distribution appears more diffuse, without clearly defined axial or craniocaudal predominance. Following discussion and consensus amongst a panel of expert radiologists, pathologists and physicians, distinctive disease features were integrated to develop criteria for the first global Phase III trial in children with pulmonary fibrosis.
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Affiliation(s)
- Emily M DeBoer
- University of Colorado Anschutz Medical Campus, and Children's Hospital Colorado, Aurora, Colorado, USA
| | - Jason P Weinman
- University of Colorado Anschutz Medical Campus, and Children's Hospital Colorado, Aurora, Colorado, USA
| | - Julia Ley-Zaporozhan
- Department of Radiology, Pediatric Radiology, German Center for Lung Research (DZL), University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Matthias Griese
- Hauner Children's Hospital, Ludwig-Maximilian University, German Center for Lung Research (DZL), Munich, Germany
| | - Robin Deterding
- University of Colorado Anschutz Medical Campus, and Children's Hospital Colorado, Aurora, Colorado, USA
| | | | | | - Joseph Jacob
- University College London, UCL Respiratory, London, UK
- Satsuma Lab, Centre for Medical Image Computing, University College London, London, UK
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17
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Fernández Pérez ER, Leach SM, Vestal B. Rationale and design of the prognostic transcriptomic signature in fibrotic hypersensitivity pneumonitis (PREDICT) study. ERJ Open Res 2024; 10:00625-2023. [PMID: 38264150 PMCID: PMC10805267 DOI: 10.1183/23120541.00625-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 10/17/2023] [Indexed: 01/25/2024] Open
Abstract
Hypersensitivity pneumonitis is an immunologically mediated form of lung disease, resulting from inhalational exposure to a large variety of antigens. A subgroup of patients with fibrotic hypersensitivity pneumonitis (FHP) develop symptomatic, functional and radiographic disease progression. Mortality occurs primarily from respiratory failure as a result of progressive and self-sustaining lung injury that often occurs despite immunosuppression and removal of the inciting antigen. The development and validation of a prognostic transcriptomic signature for FHP (PREDICT-HP) is an observational multicentre cohort study designed to explore a transcriptomic signature from peripheral blood mononuclear cells in patients with FHP that is predictive of disease progression. This article describes the design and rationale of the PREDICT-HP study. This study will enrol ∼135 patients with FHP at approximately seven academic medical sites. Participants with a confirmed diagnosis of FHP are followed over 24 months and undergo physical examinations, self-administered questionnaires, chest computed tomography, pulmonary function tests, a 6-min walk test and blood testing for transcriptomic analyses. At each 6-month follow-up visit the study will assess the participants' clinical course and clinical events including hospitalisations and respiratory exacerbations. The PREDICT study has the potential to enhance our ability to predict disease progression and fundamentally advance our understanding of the pathobiology of FHP disease progression.
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Affiliation(s)
- Evans R. Fernández Pérez
- Division of Pulmonary, Critical Care and Sleep Medicine, National Jewish Health, Denver, CO, USA
| | - Sonia M. Leach
- Center for Genes, Environment and Health, National Jewish Health, Denver, CO, USA
| | - Brian Vestal
- Center for Genes, Environment and Health, National Jewish Health, Denver, CO, USA
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18
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Kim JS, Montesi SB, Adegunsoye A, Humphries SM, Salisbury ML, Hariri LP, Kropski JA, Richeldi L, Wells AU, Walsh S, Jenkins RG, Rosas I, Noth I, Hunninghake GM, Martinez FJ, Podolanczuk AJ. Approach to Clinical Trials for the Prevention of Pulmonary Fibrosis. Ann Am Thorac Soc 2023; 20:1683-1693. [PMID: 37703509 PMCID: PMC10704236 DOI: 10.1513/annalsats.202303-188ps] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 09/13/2023] [Indexed: 09/15/2023] Open
Affiliation(s)
- John S. Kim
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Virginia, Charlottesville, Virginia
- Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | | | - Ayodeji Adegunsoye
- Department of Medicine, The University of Chicago Medicine, Chicago, Illinois
| | | | - Margaret L. Salisbury
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Lida P. Hariri
- Division of Pulmonary and Critical Care Medicine, and
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jonathan A. Kropski
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Luca Richeldi
- Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Athol U. Wells
- Department of Radiology, and
- Interstitial Lung Disease Service, Royal Brompton Hospital, London, United Kingdom
| | - Simon Walsh
- National Heart and Lung Institute, Imperial College, London, United Kingdom
| | - R. Gisli Jenkins
- National Heart and Lung Institute, Imperial College, London, United Kingdom
| | - Ivan Rosas
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Imre Noth
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Virginia, Charlottesville, Virginia
| | - Gary M. Hunninghake
- Pulmonary and Critical Care Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts; and
| | - Fernando J. Martinez
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Anna J. Podolanczuk
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, New York
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19
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Handa T. The potential role of artificial intelligence in the clinical practice of interstitial lung disease. Respir Investig 2023; 61:702-710. [PMID: 37708636 DOI: 10.1016/j.resinv.2023.08.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/26/2023] [Accepted: 08/09/2023] [Indexed: 09/16/2023]
Abstract
Artificial intelligence (AI) is being widely applied in the field of medicine, in areas such as drug discovery, diagnostic support, and assistance with medical practice. Among these, medical imaging is an area where AI is expected to make a significant contribution. In Japan, as of November 2022, 23 AI medical devices have received regulatory approval; all these devices are related to image analysis. In interstitial lung diseases, technologies have been developed that use AI to analyze high-resolution computed tomography and pathological images, and gene expression patterns in tissue taken from transbronchial lung biopsies to assist in the diagnosis of idiopathic pulmonary fibrosis. Some of these technologies are already being used in clinical practice in the United States. AI is expected to reduce the burden on physicians, improve reproducibility, and advance personalized medicine. Obtaining sufficient data for diseases with a small number of patients is difficult. Additionally, certain issues must be addressed in order for AI to be applied in healthcare. These issues include taking responsibility for the AI results output, updating software after the launch of technology, and adapting to new imaging technologies. Establishing research infrastructures such as large-scale databases and common platforms is important for the development of AI technology: their use requires an understanding of the characteristics and limitations of the systems. CLINICAL TRIAL REGISTRATION: Not applicable.
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Affiliation(s)
- Tomohiro Handa
- Department of Advanced Medicine for Respiratory Failure and Graduate School of Medicine, Kyoto University, Kyoto, Japan; Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
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20
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Fernández Pérez ER, Crooks JL, Lynch DA, Humphries SM, Koelsch TL, Swigris JJ, Solomon JJ, Mohning MP, Groshong SD, Fier K. Pirfenidone in fibrotic hypersensitivity pneumonitis: a double-blind, randomised clinical trial of efficacy and safety. Thorax 2023; 78:1097-1104. [PMID: 37028940 DOI: 10.1136/thorax-2022-219795] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 03/18/2023] [Indexed: 04/09/2023]
Abstract
BACKGROUND Fibrotic hypersensitivity pneumonitis (FHP) is an irreversible lung disease with high morbidity and mortality. We sought to evaluate the safety and effect of pirfenidone on disease progression in such patients. METHODS We conducted a single-centre, randomised, double-blinded, placebo-controlled trial in adults with FHP and disease progression. Patients were assigned in a 2:1 ratio to receive either oral pirfenidone (2403 mg/day) or placebo for 52 weeks. The primary end point was the mean absolute change in the per cent predicted forced vital capacity (FVC%). Secondary end points included progression-free survival (PFS, time to a relative decline ≥10% in FVC and/or diffusing capacity of the lung for carbon monoxide (DLCO), acute respiratory exacerbation, a decrease of ≥50 m in the 6 min walk distance, increase or introduction of immunosuppressive drugs or death), change in FVC slope and mean DLCO%, hospitalisations, radiological progression of lung fibrosis and safety. RESULTS After randomising 40 patients, enrolment was interrupted by the COVID-19 pandemic. There was no significant between-group difference in FVC% at week 52 (mean difference -0.76%, 95% CI -6.34 to 4.82). Pirfenidone resulted in a lower rate of decline in the adjusted FVC% at week 26 and improved PFS (HR 0.26, 95% CI 0.12 to 0.60). Results for other secondary end points showed no significant difference between groups. No deaths occurred in the pirfenidone group and one death (respiratory) occurred in the placebo group. There were no treatment-emergent serious adverse events. CONCLUSIONS The trial was underpowered to detect a difference in the primary end point. Pirfenidone was found to be safe and improved PFS in patients with FHP. TRIAL REGISTRATION MUMBER NCT02958917.
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Affiliation(s)
| | - James L Crooks
- Biostatistics and Bioinformatics, National Jewish Health, Denver, Colorado, USA
| | - David A Lynch
- Radiology, National Jewish Health, Denver, Colorado, USA
| | | | | | - Jeffrey J Swigris
- Pulmonary, Critical Care and Sleep Medicine, National Jewish Health, Denver, Colorado, USA
| | - Joshua J Solomon
- Pulmonary, Critical Care and Sleep Medicine, National Jewish Health, Denver, Colorado, USA
| | - Michael P Mohning
- Pulmonary, Critical Care and Sleep Medicine, National Jewish Health, Denver, Colorado, USA
| | | | - Kaitlin Fier
- Clinical and Translational Research Unit, National Jewish Health, Denver, Colorado, USA
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21
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Ash SY, Choi B, Oh A, Lynch DA, Humphries SM. Deep Learning Assessment of Progression of Emphysema and Fibrotic Interstitial Lung Abnormality. Am J Respir Crit Care Med 2023; 208:666-675. [PMID: 37364281 PMCID: PMC10515569 DOI: 10.1164/rccm.202211-2098oc] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 06/26/2023] [Indexed: 06/28/2023] Open
Abstract
Rationale: Although studies have evaluated emphysema and fibrotic interstitial lung abnormality individually, less is known about their combined progression. Objectives: To define clinically meaningful progression of fibrotic interstitial lung abnormality in smokers without interstitial lung disease and evaluate the effects of fibrosis and emphysema progression on mortality. Methods: Emphysema and pulmonary fibrosis were assessed on the basis of baseline and 5-year follow-up computed tomography scans of 4,450 smokers in the COPDGene Study using deep learning algorithms. Emphysema was classified as absent, trace, mild, moderate, confluent, or advanced destructive. Fibrosis was expressed as a percentage of lung volume. Emphysema progression was defined as an increase by at least one grade. A hybrid distribution and anchor-based method was used to determine the minimal clinically important difference in fibrosis. The relationship between progression and mortality was evaluated using multivariable shared frailty models using an age timescale. Measurements and Main Results: The minimal clinically important difference for fibrosis was 0.58%. On the basis of this threshold, 2,822 (63%) had progression of neither emphysema nor fibrosis, 841 (19%) had emphysema progression alone, 512 (12%) had fibrosis progression alone, and 275 (6.2%) had progression of both. Compared with nonprogressors, hazard ratios for mortality were 1.42 (95% confidence interval, 1.11-1.82) in emphysema progressors, 1.49 (1.14-1.94) in fibrosis progressors, and 2.18 (1.58-3.02) in those with progression of both emphysema and fibrosis. Conclusions: In smokers without known interstitial lung disease, small changes in fibrosis may be clinically significant, and combined progression of emphysema and fibrosis is associated with increased mortality.
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Affiliation(s)
- Samuel Y. Ash
- Department of Critical Care, South Shore Hospital, South Weymouth, Massachusetts
- Applied Chest Imaging Laboratory and
| | - Bina Choi
- Applied Chest Imaging Laboratory and
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Andrea Oh
- Department of Radiology, University of California, Los Angeles Health, Los Angeles, California; and
| | - David A. Lynch
- Department of Radiology, National Jewish Health, Denver, Colorado
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22
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DeBoer EM, Morgan WJ, Quiros-Alcala L, Rosenfeld M, Stout JW, Davis SD, Gaffin JM. Defining and Promoting Pediatric Pulmonary Health: Assessing Lung Function and Structure. Pediatrics 2023; 152:e2023062292E. [PMID: 37656029 PMCID: PMC10484309 DOI: 10.1542/peds.2023-062292e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/16/2023] [Indexed: 09/02/2023] Open
Abstract
Lifelong respiratory health is rooted in the structural and functional development of the respiratory system in early life. Exposures and interventions antenatally through childhood can influence lung development into young adulthood, the life stage with the highest achievable lung function. Because early respiratory health sets the stage for adult lung function trajectories and risk of developing chronic obstructive pulmonary disease, understanding how to promote lung health in children will have far reaching personal and population benefits. To achieve this, it is critical to have accurate and precise measures of structural and functional lung development that track throughout life stages. From this foundation, evaluation of environmental, genetic, metabolic, and immune mechanisms involved in healthy lung development can be investigated. These goals require the involvement of general pediatricians, pediatric subspecialists, patients, and researchers to design and implement studies that are broadly generalizable and applicable to otherwise healthy and chronic disease populations. This National Institutes of Health workshop report details the key gaps and opportunities regarding lung function and structure.
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Affiliation(s)
- Emily M. DeBoer
- University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Wayne J. Morgan
- Department of Pediatrics, University of Arizona, Tucson, Arizona
| | - Lesliam Quiros-Alcala
- Johns Hopkins University, Bloomberg School of Public Health and Whiting School of Engineering, Environmental Health and Engineering, Baltimore, Maryland
| | - Margaret Rosenfeld
- Department of Pediatrics, University of Washington School of Medicine, Seattle, Washington
| | - James W. Stout
- Department of Pediatrics, University of Washington School of Medicine, Seattle, Washington
| | - Stephanie D. Davis
- Department of Pediatrics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina
| | - Jonathan M. Gaffin
- Division of Pulmonary Medicine, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
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23
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Vaselli M, Kalverda-Mooij K, Thunnissen E, Tanck MWT, Mets OM, van den Berk IAH, Annema JT, Bonta PI, de Boer JF. In vivo polarisation sensitive optical coherence tomography for fibrosis assessment in interstitial lung disease: a prospective, exploratory, observational study. BMJ Open Respir Res 2023; 10:e001628. [PMID: 37553184 PMCID: PMC10414088 DOI: 10.1136/bmjresp-2023-001628] [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: 01/17/2023] [Accepted: 07/20/2023] [Indexed: 08/10/2023] Open
Abstract
INTRODUCTION Endobronchial polarisation sensitive optical coherence tomography (EB-PS-OCT) is a bronchoscopic imaging technique exceeding resolution of high-resolution CT (HRCT) by 50-fold. It detects collagen birefringence, enabling identification and quantification of fibrosis. STUDY AIM To assess pulmonary fibrosis in interstitial lung diseases (ILD) patients with in vivo EB-PS-OCT using histology as reference standard. PRIMARY OBJECTIVE Visualisation and quantification of pulmonary fibrosis by EB-PS-OCT. SECONDARY OBJECTIVES Comparison of EB-PS-OCT and HRCT detected fibrosis with histology, identification of ILD histological features in EB-PS-OCT images and comparison of ex vivo PS-OCT results with histology. METHODS Observational prospective exploratory study. Patients with ILD scheduled for transbronchial cryobiopsy or surgical lung biopsy underwent in vivo EB-PS-OCT imaging prior to tissue acquisition. Asthma patients were included as non-fibrotic controls. Per imaged lung segment, fibrosis was automatically quantified assessing the birefringent area in EB-PS-OCT images. Fibrotic extent in corresponding HRCT areas and biopsies were compared with EB-PS-OCT detected fibrosis. Microscopic ILD features were identified on EB-PS-OCT images and matched with biopsies from the same segment. RESULTS 19 patients were included (16 ILD; 3 asthma). In 49 in vivo imaged airway segments the parenchymal birefringent area was successfully quantified and ranged from 2.54% (no to minimal fibrosis) to 21.01% (extensive fibrosis). Increased EB-PS-OCT detected birefringent area corresponded to increased histologically confirmed fibrosis, with better predictive value than HRCT. Microscopic ILD features were identified on both in vivo and ex vivo PS-OCT images. CONCLUSIONS EB-PS-OCT enables pulmonary fibrosis quantification, thereby has potential to serve as an add-on bronchoscopic imaging technique to diagnose and detect (early) fibrosis in ILD.
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Affiliation(s)
- Margherita Vaselli
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - Erik Thunnissen
- Department of Pathology, Amsterdam University Medical Centra, Amsterdam, The Netherlands
| | - Michael W T Tanck
- Department of Epidemiology and Data Science, University of Amsterdam, Amsterdam, The Netherlands
| | - Onno M Mets
- Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, The Netherlands
| | - Inge A H van den Berk
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location AMC, Amsterdam, The Netherlands
| | - Jouke T Annema
- Respiratory Medicine, Amsterdam UMC - Locatie AMC, Amsterdam, The Netherlands
| | - Peter I Bonta
- Amsterdam UMC - Locatie AMC, Amsterdam, The Netherlands
| | - Johannes F de Boer
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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24
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Felder FN, Walsh SL. Exploring computer-based imaging analysis in interstitial lung disease: opportunities and challenges. ERJ Open Res 2023; 9:00145-2023. [PMID: 37404849 PMCID: PMC10316044 DOI: 10.1183/23120541.00145-2023] [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: 03/08/2023] [Accepted: 05/03/2023] [Indexed: 07/06/2023] Open
Abstract
The advent of quantitative computed tomography (QCT) and artificial intelligence (AI) using high-resolution computed tomography data has revolutionised the way interstitial diseases are studied. These quantitative methods provide more accurate and precise results compared to prior semiquantitative methods, which were limited by human error such as interobserver disagreement or low reproducibility. The integration of QCT and AI and the development of digital biomarkers has facilitated not only diagnosis but also prognostication and prediction of disease behaviour, not just in idiopathic pulmonary fibrosis in which they were initially studied, but also in other fibrotic lung diseases. These tools provide reproducible, objective prognostic information which may facilitate clinical decision-making. However, despite the benefits of QCT and AI, there are still obstacles that need to be addressed. Important issues include optimal data management, data sharing and maintenance of data privacy. In addition, the development of explainable AI will be essential to develop trust within the medical community and facilitate implementation in routine clinical practice.
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Affiliation(s)
| | - Simon L.F. Walsh
- National Heart and Lung Institute, Imperial College London, London, UK
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25
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Wilson TM, Solomon JJ, Humphries SM, Swigris JJ, Ahmed F, Wang H, Darrah E, Demoruelle MK. Serum antibodies to peptidylarginine deiminase-4 in rheumatoid arthritis associated-interstitial lung disease are associated with decreased lung fibrosis and improved survival. Am J Med Sci 2023; 365:480-487. [PMID: 36918112 PMCID: PMC10247162 DOI: 10.1016/j.amjms.2023.03.003] [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: 10/24/2022] [Revised: 12/29/2022] [Accepted: 03/07/2023] [Indexed: 03/14/2023]
Abstract
OBJECTIVE Interstitial lung disease (ILD) is a leading cause of mortality in rheumatoid arthritis (RA), particularly in those with the usual interstitial pneumonia subtype (RA-UIP). Serum antibodies to peptidylarginine deiminase type 4 (anti-PAD4), particularly a subset that cross-react with PAD3 (PAD3/4XR), have been associated with imaging evidence of ILD. We aimed to determine the specificity of anti-PAD4 antibodies in RA-ILD and to examine associations with markers of ILD severity. METHODS 48 RA-ILD and 31 idiopathic pulmonary fibrosis (IPF) patients were identified from the National Jewish Health Biobank. RA-ILD subtype was defined by imaging pattern on high-resolution chest computed tomography (CT), and serum was tested for anti-PAD4 and anti-PAD3/4XR antibodies. Antibody prevalence, measures of ILD severity (% predicted forced vital capacity, FVC; % predicted diffusion capacity carbon monoxide, DLCO; quantitative CT fibrosis) and mortality were compared between groups. RESULTS Anti-PAD4 antibodies were present in 9/48 (19%) subjects with RA-ILD and no subjects with IPF. Within RA-ILD, anti-PAD4 antibodies were found almost exclusively in RA-UIP (89%). Within RA-UIP subjects, % predicted FVC was higher in anti-PAD4+ subjects, and this finding was most strongly associated with anti-PAD3/4XR antibodies. In addition, quantitative CT fibrosis score was lower in anti-PAD4+ RA-UIP subjects, including those with mono-reactive anti-PAD4 antibodies and anti-PAD3/4XR antibodies. Anti-PAD4+ RA-UIP subjects also exhibited decreased mortality. CONCLUSIONS We demonstrate the presence of serum anti-PAD4 antibodies in a subset of patients with RA-UIP that were notably associated with better lung function, less fibrosis and decreased mortality.
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Affiliation(s)
- Timothy M Wilson
- Division of Rheumatology, Department of Medicine, Thomas Jefferson University, Philadelphia, PA, USA.
| | - Joshua J Solomon
- Division of Pulmonary Critical Care and Sleep Medicine, Department of Medicine, National Jewish Health, Denver, CO, USA
| | - Stephen M Humphries
- Division of Pulmonary Critical Care and Sleep Medicine, Department of Medicine, National Jewish Health, Denver, CO, USA
| | - Jeffrey J Swigris
- Division of Pulmonary Critical Care and Sleep Medicine, Department of Medicine, National Jewish Health, Denver, CO, USA
| | - Faduma Ahmed
- Division of Pulmonary Critical Care and Sleep Medicine, Department of Medicine, National Jewish Health, Denver, CO, USA
| | - Hong Wang
- Division of Rheumatology, Department of Medicine, The Johns Hopkins University, Baltimore, MD, USA
| | - Erika Darrah
- Division of Rheumatology, Department of Medicine, The Johns Hopkins University, Baltimore, MD, USA
| | - M Kristen Demoruelle
- Division of Rheumatology, Department of Medicine, University of Colorado Denver, Aurora, CO, USA
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26
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McGroder CF, Hansen S, Hinckley Stukovsky K, Zhang D, Nath PH, Salvatore MM, Sonavane SK, Terry N, Stowell JT, D'Souza BM, Leb JS, Dumeer S, Aziz MU, Batra K, Hoffman EA, Bernstein EJ, Kim JS, Podolanczuk AJ, Rotter JI, Manichaikul AW, Rich SS, Lederer DJ, Barr RG, McClelland RL, Garcia CK. Incidence of Interstitial Lung Abnormalities: The MESA Lung Study. Eur Respir J 2023; 61:2201950. [PMID: 37202153 PMCID: PMC10773573 DOI: 10.1183/13993003.01950-2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 01/18/2023] [Indexed: 05/20/2023]
Abstract
The incidence of newly developed interstitial lung abnormalities (ILA) and fibrotic ILA have not been previously reported.Trained thoracic radiologists evaluated 13 944 cardiac CT scans for the presence of ILA in 6197 Multi-Ethnic Study of Atherosclerosis longitudinal cohort study participants >45 years of age from 2000 to 2012. 5% of the scans were re-read by the same or a different observer in a blinded fashion. After exclusion of participants with ILA at baseline, incidence rates and incidence rate ratios for ILA and fibrotic ILA were calculated.The intra-reader agreement of ILA was 92.0% (Gwet AC1=0.912, ICC=0.982) and the inter-reader agreement of ILA was 83.5% (Gwet AC1=0.814; ICC=0.969). Incidence of ILA and fibrotic ILA was estimated to be 13.1 cases/1000 person-years and 3.5/1000 person-years, respectively. In multivariable analyses, age (HR 1.06 (1.05, 1.08), p <0.001; HR 1.08 (1.06, 1.11), p <0.001), high attenuation area (HAA) at baseline (HR 1.05 (1.03, 1.07), p <0.001; HR 1.06 (1.02, 1.10), p=0.002), and the MUC5B promoter SNP (HR 1.73 (1.17, 2.56) p=0.01; HR 4.96 (2.68, 9.15), p <0.001) were associated with incident ILA and fibrotic ILA, respectively. Ever smoking (HR 2.31 (1.34, 3.96), p= 0.002) and an IPF polygenic risk score (HR 2.09 (1.61-2.71), p<0.001) were associated only with incident fibrotic ILA.Incident ILA and fibrotic ILA were estimated by review of cardiac imaging studies. These findings may lead to wider application of a screening tool for atherosclerosis to identify preclinical lung disease.
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Affiliation(s)
- Claire F McGroder
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
| | - Spencer Hansen
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | | | - David Zhang
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
| | - P Hrudaya Nath
- Department of Radiology, University of Alabama, Birmingham, AL, USA
| | - Mary M Salvatore
- Department of Radiology, Columbia University Medical Center, New York, NY, USA
| | | | - Nina Terry
- Department of Radiology, University of Alabama, Birmingham, AL, USA
| | - Justin T Stowell
- Department of Radiology, Mayo Clinic at Jacksonville, Jacksonville, FL, USA
| | - Belinda M D'Souza
- Department of Radiology, Columbia University Medical Center, New York, NY, USA
| | - Jay S Leb
- Department of Radiology, Columbia University Medical Center, New York, NY, USA
| | - Shifali Dumeer
- Department of Radiology, Columbia University Medical Center, New York, NY, USA
| | - Muhammad U Aziz
- Department of Radiology, University of Alabama, Birmingham, AL, USA
| | - Kiran Batra
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Eric A Hoffman
- Department of Radiology, University of Iowa, Iowa City, IA, USA
| | - Elana J Bernstein
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
| | - John S Kim
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
- Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Anna J Podolanczuk
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
- Department of Medicine, Weill Medical College of Cornell University, New York, NY, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ani W Manichaikul
- Department of Public Health Sciences, University of Virginia, Charlotte, VA, USA
- Center for Public Health Genomics, University of Virginia, Charlotte, VA, USA
| | - Stephen S Rich
- Department of Public Health Sciences, University of Virginia, Charlotte, VA, USA
- Center for Public Health Genomics, University of Virginia, Charlotte, VA, USA
| | - David J Lederer
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
- Regeneron Pharmaceuticals, Inc., Tarrytown, NY, USA
| | - R Graham Barr
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
- Department of Epidemiology, Columbia University Medical Center, New York, NY, USA
| | | | - Christine Kim Garcia
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA
- Center for Precision Medicine and Genomics, Columbia University Medical Center, New York, NY, USA
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27
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Becker L, Lu CE, Montes-Mojarro IA, Layland SL, Khalil S, Nsair A, Duffy GP, Fend F, Marzi J, Schenke-Layland K. Raman microspectroscopy identifies fibrotic tissues in collagen-related disorders via deconvoluted collagen type I spectra. Acta Biomater 2023; 162:278-291. [PMID: 36931422 DOI: 10.1016/j.actbio.2023.03.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 02/28/2023] [Accepted: 03/09/2023] [Indexed: 03/18/2023]
Abstract
Fibrosis is a consequence of the pathological remodeling of extracellular matrix (ECM) structures in the connective tissue of an organ. It is often caused by chronic inflammation, which over time, progressively leads to an excess deposition of collagen type I (COL I) that replaces healthy tissue structures, in many cases leaving a stiff scar. Increasing fibrosis can lead to organ failure and death; therefore, developing methods that potentially allow real-time monitoring of early onset or progression of fibrosis are highly valuable. In this study, the ECM structures of diseased and healthy human tissue from multiple organs were investigated for the presence of fibrosis using routine histology and marker-independent Raman microspectroscopy and Raman imaging. Spectral deconvolution of COL I Raman spectra allowed the discrimination of fibrotic and non-fibrotic COL I fibers. Statistically significant differences were identified in the amide I region of the spectral subpeak at 1608 cm-1, which was deemed to be representative for structural changes in COL I fibers in all examined fibrotic tissues. Raman spectroscopy-based methods in combination with this newly discovered spectroscopic biomarker potentially offer a diagnostic approach to non-invasively track and monitor the progression of fibrosis. STATEMENT OF SIGNIFICANCE: Current diagnosis of fibrosis still relies on histopathological examination with invasive biopsy procedures. Although, several non-invasive imaging techniques such as positron emission tomography, single-photon emission computed tomography and second harmonic generation are gradually employed in preclinical or clinical studies, these techniques are limited in spatial resolution and the morphological interpretation highly relies on individual experience and knowledge. In this study, we propose a non-destructive technique, Raman microspectroscopy, to discriminate fibrotic changes of collagen type I based on a molecular biomarker. The changes of the secondary structure of collagen type I can be identified by spectral deconvolution, which potentially can provide an automatic diagnosis for fibrotic tissues in the clinical applicaion.
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Affiliation(s)
- Lucas Becker
- Institute of Biomedical Engineering, Department for Medical Technologies and Regenerative Medicine, Silcherstr. 7/1, Eberhard Karls University, 72076 Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", Eberhard Karls University, Tübingen, Germany
| | - Chuan-En Lu
- Institute of Biomedical Engineering, Department for Medical Technologies and Regenerative Medicine, Silcherstr. 7/1, Eberhard Karls University, 72076 Tübingen, Germany
| | | | - Shannon L Layland
- Institute of Biomedical Engineering, Department for Medical Technologies and Regenerative Medicine, Silcherstr. 7/1, Eberhard Karls University, 72076 Tübingen, Germany
| | - Suzan Khalil
- Department of Medicine/Cardiology, Cardiovascular Research Laboratories, David Geffen School of Medicine at UCLA, 675 Charles E. Young Drive South, MRL 3645 Los Angeles, CA, USA
| | - Ali Nsair
- Department of Medicine/Cardiology, Cardiovascular Research Laboratories, David Geffen School of Medicine at UCLA, 675 Charles E. Young Drive South, MRL 3645 Los Angeles, CA, USA
| | - Garry P Duffy
- Anatomy & Regenerative Medicine Institute, School of Medicine, College of Medicine, Nursing and Health Sciences, National University of Ireland Galway, H91 TK33, Galway, Ireland
| | - Falko Fend
- Institute of Pathology and Neuropathology, University Hospital Tübingen, Tübingen, Germany
| | - Julia Marzi
- Institute of Biomedical Engineering, Department for Medical Technologies and Regenerative Medicine, Silcherstr. 7/1, Eberhard Karls University, 72076 Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", Eberhard Karls University, Tübingen, Germany; NMI Natural and Medical Sciences Institute at the University of Tübingen, Markwiesenstr. 55, 72770 Reutlingen, Germany
| | - Katja Schenke-Layland
- Institute of Biomedical Engineering, Department for Medical Technologies and Regenerative Medicine, Silcherstr. 7/1, Eberhard Karls University, 72076 Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", Eberhard Karls University, Tübingen, Germany; NMI Natural and Medical Sciences Institute at the University of Tübingen, Markwiesenstr. 55, 72770 Reutlingen, Germany.
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28
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Nam JG, Choi Y, Lee SM, Yoon SH, Goo JM, Kim H. Prognostic value of deep learning-based fibrosis quantification on chest CT in idiopathic pulmonary fibrosis. Eur Radiol 2023; 33:3144-3155. [PMID: 36928568 DOI: 10.1007/s00330-023-09534-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 01/16/2023] [Accepted: 02/03/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVE To investigate the prognostic value of deep learning (DL)-driven CT fibrosis quantification in idiopathic pulmonary fibrosis (IPF). METHODS Patients diagnosed with IPF who underwent nonenhanced chest CT and spirometry between 2005 and 2009 were retrospectively collected. Proportions of normal (CT-Norm%) and fibrotic lung volume (CT-Fib%) were calculated on CT using the DL software. The correlations of CT-Norm% and CT-Fib% with forced vital capacity (FVC) and diffusion capacity of carbon monoxide (DLCO) were evaluated. The multivariable-adjusted hazard ratios (HRs) of CT-Norm% and CT-Fib% for overall survival were calculated with clinical and physiologic variables as covariates using Cox regression. The feasibility of substituting CT-Norm% for DLCO in the GAP index was investigated using time-dependent areas under the receiver operating characteristic curve (TD-AUCs) at 3 years. RESULTS In total, 161 patients (median age [IQR], 68 [62-73] years; 104 men) were evaluated. CT-Norm% and CT-Fib% showed significant correlations with FVC (Pearson's r, 0.40 for CT-Norm% and - 0.37 for CT-Fib%; both p < 0.001) and DLCO (0.52 for CT-Norm% and - 0.46 for CT-Fib%; both p < 0.001). On multivariable Cox regression, both CT-Norm% and CT-Fib% were independent prognostic factors when adjusted to age, sex, smoking status, comorbid chronic diseases, FVC, and DLCO (HRs, 0.98 [95% CI 0.97-0.99; p < 0.001] for CT-Norm% at 3 years and 1.03 [1.01-1.05; p = 0.01] for CT-Fib%). Substituting CT-Norm% for DLCO showed comparable discrimination to the original GAP index (TD-AUC, 0.82 [0.78-0.85] vs. 0.82 [0.79-0.86]; p = 0.75). CONCLUSION CT-Norm% and CT-Fib% calculated using chest CT-based deep learning software were independent prognostic factors for overall survival in IPF. KEY POINTS • Normal and fibrotic lung volume proportions were automatically calculated using commercial deep learning software from chest CT taken from 161 patients diagnosed with idiopathic pulmonary fibrosis. • CT-quantified volumetric parameters from commercial deep learning software were correlated with forced vital capacity (Pearson's r, 0.40 for normal and - 0.37 for fibrotic lung volume proportions) and diffusion capacity of carbon monoxide (Pearson's r, 0.52 and - 0.46, respectively). • Normal and fibrotic lung volume proportions (hazard ratios, 0.98 and 1.04; both p < 0.001) independently predicted overall survival when adjusted for clinical and physiologic variables.
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Affiliation(s)
- Ju Gang Nam
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea
| | - Yunhee Choi
- Medical Research Collaborating Center, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea
| | - Sang-Min Lee
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea.,Cancer Research Institute, Seoul National University, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea
| | - Hyungjin Kim
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea.
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Steele MP, Peljto AL, Mathai SK, Humphries S, Bang TJ, Oh A, Teague S, Cicchetti G, Sigakis C, Kropski JA, Loyd JE, Blackwell TS, Brown KK, Schwarz MI, Warren RA, Powers J, Walts AD, Markin C, Fingerlin TE, Yang IV, Lynch DA, Lee JS, Schwartz DA. Incidence and Progression of Fibrotic Lung Disease in an At-Risk Cohort. Am J Respir Crit Care Med 2023; 207:587-593. [PMID: 36094461 PMCID: PMC10870916 DOI: 10.1164/rccm.202206-1075oc] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 09/12/2022] [Indexed: 11/16/2022] Open
Abstract
Rationale: Relatives of patients with familial interstitial pneumonia (FIP) are at increased risk for pulmonary fibrosis and develop preclinical pulmonary fibrosis (PrePF). Objectives: We defined the incidence and progression of new-onset PrePF and its relationship to survival among first-degree relatives of families with FIP. Methods: This is a cohort study of family members with FIP who were initially screened with a health questionnaire and chest high-resolution computed tomography (HRCT) scan, and approximately 4 years later, the evaluation was repeated. A total of 493 asymptomatic first-degree relatives of patients with FIP were evaluated at baseline, and 296 (60%) of the original subjects participated in the subsequent evaluation. Measurements and Main Results: The median interval between HRCTs was 3.9 years (interquartile range, 3.5-4.4 yr). A total of 252 subjects who agreed to repeat evaluation were originally determined not to have PrePF at baseline; 16 developed PrePF. A conservative estimate of the annual incidence of PrePF is 1,023 per 100,000 person-years (95% confidence interval, 511-1,831 per 100,000 person-years). Of 44 subjects with PrePF at baseline, 38.4% subjects had worsening dyspnea compared with 15.4% of those without PrePF (P = 0.002). Usual interstitial pneumonia by HRCT (P < 0.0002) and baseline quantitative fibrosis score (P < 0.001) are also associated with worsening dyspnea. PrePF at the initial screen is associated with decreased survival (P < 0.001). Conclusions: The incidence of PrePF in this at-risk population is at least 100-fold higher than that reported for sporadic idiopathic pulmonary fibrosis (IPF). Although PrePF and IPF represent distinct entities, our study demonstrates that PrePF, like IPF, is progressive and associated with decreased survival.
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Affiliation(s)
| | | | - Susan K. Mathai
- Center for Advanced Heart and Lung Disease, Baylor University Medical Center at Dallas, Dallas, Texas
| | | | | | | | | | - Giuseppe Cicchetti
- Department of Diagnostic Imaging, Oncological Radiotherapy, and Hematology, Fondazione Policlinico University Gemelli, Rome, Italy
| | - Christopher Sigakis
- Department of Regional Radiology, Cleveland Clinic Imaging Institute, Cleveland, Ohio; and
| | | | - James E. Loyd
- Department of Medicine, Vanderbilt University, Nashville, Tennessee
| | | | | | | | | | | | | | - Cheryl Markin
- Department of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Tasha E. Fingerlin
- Center for Genes, Environment, and Health, National Jewish Health, Denver, Colorado
| | | | | | | | - David A. Schwartz
- Department of Medicine
- Department of Microbiology and Immunology, University of Colorado School of Medicine, Aurora, Colorado
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30
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Hsia CCW, Bates JHT, Driehuys B, Fain SB, Goldin JG, Hoffman EA, Hogg JC, Levin DL, Lynch DA, Ochs M, Parraga G, Prisk GK, Smith BM, Tawhai M, Vidal Melo MF, Woods JC, Hopkins SR. Quantitative Imaging Metrics for the Assessment of Pulmonary Pathophysiology: An Official American Thoracic Society and Fleischner Society Joint Workshop Report. Ann Am Thorac Soc 2023; 20:161-195. [PMID: 36723475 PMCID: PMC9989862 DOI: 10.1513/annalsats.202211-915st] [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] [Indexed: 02/02/2023] Open
Abstract
Multiple thoracic imaging modalities have been developed to link structure to function in the diagnosis and monitoring of lung disease. Volumetric computed tomography (CT) renders three-dimensional maps of lung structures and may be combined with positron emission tomography (PET) to obtain dynamic physiological data. Magnetic resonance imaging (MRI) using ultrashort-echo time (UTE) sequences has improved signal detection from lung parenchyma; contrast agents are used to deduce airway function, ventilation-perfusion-diffusion, and mechanics. Proton MRI can measure regional ventilation-perfusion ratio. Quantitative imaging (QI)-derived endpoints have been developed to identify structure-function phenotypes, including air-blood-tissue volume partition, bronchovascular remodeling, emphysema, fibrosis, and textural patterns indicating architectural alteration. Coregistered landmarks on paired images obtained at different lung volumes are used to infer airway caliber, air trapping, gas and blood transport, compliance, and deformation. This document summarizes fundamental "good practice" stereological principles in QI study design and analysis; evaluates technical capabilities and limitations of common imaging modalities; and assesses major QI endpoints regarding underlying assumptions and limitations, ability to detect and stratify heterogeneous, overlapping pathophysiology, and monitor disease progression and therapeutic response, correlated with and complementary to, functional indices. The goal is to promote unbiased quantification and interpretation of in vivo imaging data, compare metrics obtained using different QI modalities to ensure accurate and reproducible metric derivation, and avoid misrepresentation of inferred physiological processes. The role of imaging-based computational modeling in advancing these goals is emphasized. Fundamental principles outlined herein are critical for all forms of QI irrespective of acquisition modality or disease entity.
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31
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Nick JA, Malcolm KC, Hisert KB, Wheeler EA, Rysavy NM, Poch K, Caceres S, Lovell VK, Armantrout E, Saavedra MT, Calhoun K, Chatterjee D, Aboellail I, De P, Martiniano SL, Jia F, Davidson RM. Culture independent markers of nontuberculous mycobacterial (NTM) lung infection and disease in the cystic fibrosis airway. Tuberculosis (Edinb) 2023; 138:102276. [PMID: 36417800 PMCID: PMC10965158 DOI: 10.1016/j.tube.2022.102276] [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: 10/15/2022] [Revised: 11/12/2022] [Accepted: 11/15/2022] [Indexed: 11/18/2022]
Abstract
Nontuberculous mycobacteria (NTM) are opportunistic pathogens that affect a relatively small but significant portion of the people with cystic fibrosis (CF), and may cause increased morbidity and mortality in this population. Cultures from the airway are the only test currently in clinical use for detecting NTM. Culture techniques used in clinical laboratories are insensitive and poorly suited for population screening or to follow progression of disease or treatment response. The lack of sensitive and quantitative markers of NTM in the airway impedes patient care and clinical trial design, and has limited our understanding of patterns of acquisition, latency and pathogenesis of disease. Culture-independent markers of NTM infection have the potential to overcome many of the limitations of standard NTM cultures, especially the very slow growth, inability to quantitate bacterial burden, and low sensitivity due to required decontamination procedures. A range of markers have been identified in sputum, saliva, breath, blood, urine, as well as radiographic studies. Proposed markers to detect presence of NTM or transition to NTM disease include bacterial cell wall products and DNA, as well as markers of host immune response such as immunoglobulins and the gene expression of circulating leukocytes. In all cases the sensitivity of culture-independent markers is greater than standard cultures; however, most do not discriminate between various NTM species. Thus, each marker may be best suited for a specific clinical application, or combined with other markers and traditional cultures to improve diagnosis and monitoring of treatment response.
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Affiliation(s)
- Jerry A Nick
- Department of Medicine, National Jewish Health, Denver, CO, 80206, USA; Department of Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
| | - Kenneth C Malcolm
- Department of Medicine, National Jewish Health, Denver, CO, 80206, USA
| | - Katherine B Hisert
- Department of Medicine, National Jewish Health, Denver, CO, 80206, USA; Department of Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Emily A Wheeler
- Department of Medicine, National Jewish Health, Denver, CO, 80206, USA
| | - Noel M Rysavy
- Department of Medicine, National Jewish Health, Denver, CO, 80206, USA
| | - Katie Poch
- Department of Medicine, National Jewish Health, Denver, CO, 80206, USA
| | - Silvia Caceres
- Department of Medicine, National Jewish Health, Denver, CO, 80206, USA
| | - Valerie K Lovell
- Department of Medicine, National Jewish Health, Denver, CO, 80206, USA
| | - Emily Armantrout
- Department of Medicine, National Jewish Health, Denver, CO, 80206, USA
| | - Milene T Saavedra
- Department of Medicine, National Jewish Health, Denver, CO, 80206, USA; Department of Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Kara Calhoun
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Delphi Chatterjee
- Mycobacteria Research Laboratories, Department of Microbiology, Immunology and Pathology, Colorado State University, 1682 Campus Delivery, Fort Collins, CO, 80523, USA
| | - Ibrahim Aboellail
- Mycobacteria Research Laboratories, Department of Microbiology, Immunology and Pathology, Colorado State University, 1682 Campus Delivery, Fort Collins, CO, 80523, USA
| | - Prithwiraj De
- Mycobacteria Research Laboratories, Department of Microbiology, Immunology and Pathology, Colorado State University, 1682 Campus Delivery, Fort Collins, CO, 80523, USA
| | - Stacey L Martiniano
- Department of Pediatrics, Children's Hospital Colorado, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Fan Jia
- Center for Genes, Environment, and Health, National Jewish Health, Denver, CO, 80206, USA
| | - Rebecca M Davidson
- Center for Genes, Environment, and Health, National Jewish Health, Denver, CO, 80206, USA
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Kim JS, Kim J, Yin X, Hiura GT, Anderson MR, Hoffman EA, Raghu G, Noth I, Manichaikul A, Rich SS, Smith BM, Podolanczuk AJ, Garcia CK, Barr RG, Prince MR, Oelsner EC. Associations of hiatus hernia with CT-based interstitial lung changes: the MESA Lung Study. Eur Respir J 2023; 61:2103173. [PMID: 35777776 PMCID: PMC10203882 DOI: 10.1183/13993003.03173-2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/02/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND Hiatus hernia (HH) is prevalent in adults with pulmonary fibrosis. We hypothesised that HH would be associated with markers of lung inflammation and fibrosis among community-dwelling adults and stronger among MUC5B (rs35705950) risk allele carriers. METHODS In the Multi-Ethnic Study of Atherosclerosis, HH was assessed from cardiac and full-lung computed tomography (CT) scans performed at Exam 1 (2000-2002, n=3342) and Exam 5 (2010-2012, n=3091), respectively. Percentage of high attenuation areas (HAAs; percentage of voxels with attenuation between -600 and -250 HU) was measured from cardiac and lung scans. Interstitial lung abnormalities (ILAs) were examined from Exam 5 scans (n=2380). Regression models were used to examine the associations of HH with HAAs, ILAs and serum matrix metalloproteinase-7 (MMP-7), and adjusted for age, sex, race/ethnicity, educational attainment, smoking, height, weight and scanner parameters for HAA analysis. RESULTS HH detected from Exam 5 scans was associated with a mean percentage difference in HAAs of 2.23% (95% CI 0.57-3.93%) and an increase of 0.48% (95% CI 0.07-0.89%) per year, particularly in MUC5B risk allele carriers (p-value for interaction=0.02). HH was associated with ILAs among those <80 years of age (OR for ILAs 1.78, 95% CI 1.14-2.80) and higher serum MMP-7 level among smokers (p-value for smoking interaction=0.04). CONCLUSIONS HH was associated with more HAAs over time, particularly among MUC5B risk allele carriers, and ILAs in younger adults, and may be a risk factor in the early stages of interstitial lung disease.
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Affiliation(s)
- John S Kim
- Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Jinhye Kim
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
- Department of Radiology, Westchester Medical Center, Valhalla, NY, USA
| | - Xiaorui Yin
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | - Grant T Hiura
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | | | - Eric A Hoffman
- Department of Radiology, Carver School of Medicine, University of Iowa, Iowa City, IA, USA
| | - Ganesh Raghu
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Imre Noth
- Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Ani Manichaikul
- Center for Public Health Genomics and Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Stephen S Rich
- Center for Public Health Genomics and Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Benjamin M Smith
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Medicine, McGill University, Montreal, QC, Canada
| | - Anna J Podolanczuk
- Division of Pulmonary and Critical Care, Weill Cornell Medical College, New York, NY, USA
| | - Christine Kim Garcia
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - R Graham Barr
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Martin R Prince
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Elizabeth C Oelsner
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
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Barnes H, Humphries SM, George PM, Assayag D, Glaspole I, Mackintosh JA, Corte TJ, Glassberg M, Johannson KA, Calandriello L, Felder F, Wells A, Walsh S. Machine learning in radiology: the new frontier in interstitial lung diseases. Lancet Digit Health 2023; 5:e41-e50. [PMID: 36517410 DOI: 10.1016/s2589-7500(22)00230-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 10/03/2022] [Accepted: 11/14/2022] [Indexed: 12/15/2022]
Abstract
Challenges for the effective management of interstitial lung diseases (ILDs) include difficulties with the early detection of disease, accurate prognostication with baseline data, and accurate and precise response to therapy. The purpose of this Review is to describe the clinical and research gaps in the diagnosis and prognosis of ILD, and how machine learning can be applied to image biomarker research to close these gaps. Machine-learning algorithms can identify ILD in at-risk populations, predict the extent of lung fibrosis, correlate radiological abnormalities with lung function decline, and be used as endpoints in treatment trials, exemplifying how this technology can be used in care for people with ILD. Advances in image processing and analysis provide further opportunities to use machine learning that incorporates deep-learning-based image analysis and radiomics. Collaboration and consistency are required to develop optimal algorithms, and candidate radiological biomarkers should be validated against appropriate predictors of disease outcomes.
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Affiliation(s)
- Hayley Barnes
- Department of Respiratory Medicine, Alfred Health, Melbourne, VIC, Australia; Central Clinical School, Monash University, Melbourne, VIC, Australia; Centre for Occupational and Environmental Health, Monash University, Melbourne, VIC, Australia.
| | | | - Peter M George
- Interstitial Lung Disease Unit, Royal Brompton and Harefield Hospitals, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Deborah Assayag
- Department of Medicine, McGill University, Montreal, QC, Canada
| | - Ian Glaspole
- Department of Respiratory Medicine, Alfred Health, Melbourne, VIC, Australia; Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - John A Mackintosh
- Department of Thoracic Medicine, The Prince Charles Hospital, Brisbane, QLD, Australia
| | - Tamera J Corte
- Department of Respiratory Medicine, Royal Prince Alfred Hospital, Sydney, NSW, Australia; Central Clinical School, University of Sydney, Sydney, NSW, Australia
| | - Marilyn Glassberg
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Arizona College of Medicine Phoenix, Phoenix, AR, USA
| | | | - Lucio Calandriello
- Department of Diagnostic Imaging, Oncological Radiotherapy and Haematology, Fondazione Policlinico Universitario A Gemelli, IRCCS, Rome, Italy
| | - Federico Felder
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Athol Wells
- Interstitial Lung Disease Unit, Royal Brompton and Harefield Hospitals, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Simon Walsh
- National Heart and Lung Institute, Imperial College London, London, UK
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Shingai K, Matsuda T, Kondoh Y, Kimura T, Kataoka K, Yokoyama T, Yamano Y, Ogawa T, Watanabe F, Hirasawa J, Reid WD, Kozu R. Physical activity in idiopathic pulmonary fibrosis: Longitudinal change and minimal clinically important difference. Chron Respir Dis 2023; 20:14799731231221818. [PMID: 38108832 DOI: 10.1177/14799731231221818] [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] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Reference values of physical activity to interpret longitudinal changes are not available in patients with idiopathic pulmonary fibrosis (IPF). This study aimed to define the minimal clinical important difference (MCID) of longitudinal changes in physical activity in patients with IPF. METHODS Using accelerometry, physical activity (steps per day) was measured and compared at baseline and 6-months follow-up in patients with IPF. We calculated MCID of daily step count using multiple anchor-based and distribution-based methods. Forced vital capacity and 6-minute walk distance were applied as anchors in anchor-based methods. Effect size and standard error of measurement were used to calculate MCID in distribution-based methods. RESULTS One-hundred and five patients were enrolled in the study (mean age: 68.5 ± 7.5 years). Step count significantly decreased from baseline to 6-months follow-up (-461 ± 2402, p = .031). MCID calculated by anchor-based and distribution-based methods ranged from 570-1358 steps. CONCLUSION Daily step count significantly declined over 6-months in patients with IPF. MCID calculated by multiple anchor-based and distribution-based methods was 570 to 1358 steps/day. These findings contribute to interpretation of the longitudinal changes of physical activity that will assist its use as a clinical and research outcome in patients with IPF.
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Affiliation(s)
- Kazuya Shingai
- Department of Physical Therapy Science, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Toshiaki Matsuda
- Department of Respiratory Medicine and Allergy, Tosei General Hospital, Seto, Japan
| | - Yasuhiro Kondoh
- Department of Respiratory Medicine and Allergy, Tosei General Hospital, Seto, Japan
| | - Tomoki Kimura
- Department of Respiratory Medicine and Allergy, Tosei General Hospital, Seto, Japan
| | - Kensuke Kataoka
- Department of Respiratory Medicine and Allergy, Tosei General Hospital, Seto, Japan
| | - Toshiki Yokoyama
- Department of Respiratory Medicine and Allergy, Tosei General Hospital, Seto, Japan
| | - Yasuhiko Yamano
- Department of Respiratory Medicine and Allergy, Tosei General Hospital, Seto, Japan
| | - Tomoya Ogawa
- Department of Rehabilitation, Tosei General Hospital, Seto, Japan
| | - Fumiko Watanabe
- Department of Rehabilitation, Tosei General Hospital, Seto, Japan
| | - Jun Hirasawa
- Department of Rehabilitation, Tosei General Hospital, Seto, Japan
| | - W Darlene Reid
- Department of Physical Therapy, University of Toronto, Toronto, Canada
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada
- KITE, Toronto Rehabilitation Institute, University Health Network, Toronto, Canada
| | - Ryo Kozu
- Department of Physical Therapy Science, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
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Humphries SM, Mackintosh JA, Jo HE, Walsh SLF, Silva M, Calandriello L, Chapman S, Ellis S, Glaspole I, Goh N, Grainge C, Hopkins PMA, Keir GJ, Moodley Y, Reynolds PN, Walters EH, Baraghoshi D, Wells AU, Lynch DA, Corte TJ. Quantitative computed tomography predicts outcomes in idiopathic pulmonary fibrosis. Respirology 2022; 27:1045-1053. [PMID: 35875881 PMCID: PMC9796832 DOI: 10.1111/resp.14333] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 07/03/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Prediction of disease course in patients with progressive pulmonary fibrosis remains challenging. The purpose of this study was to assess the prognostic value of lung fibrosis extent quantified at computed tomography (CT) using data-driven texture analysis (DTA) in a large cohort of well-characterized patients with idiopathic pulmonary fibrosis (IPF) enrolled in a national registry. METHODS This retrospective analysis included participants in the Australian IPF Registry with available CT between 2007 and 2016. CT scans were analysed using the DTA method to quantify the extent of lung fibrosis. Demographics, longitudinal pulmonary function and quantitative CT metrics were compared using descriptive statistics. Linear mixed models, and Cox analyses adjusted for age, gender, BMI, smoking history and treatment with anti-fibrotics were performed to assess the relationships between baseline DTA, pulmonary function metrics and outcomes. RESULTS CT scans of 393 participants were analysed, 221 of which had available pulmonary function testing obtained within 90 days of CT. Linear mixed-effect modelling showed that baseline DTA score was significantly associated with annual rate of decline in forced vital capacity and diffusing capacity of carbon monoxide. In multivariable Cox proportional hazard models, greater extent of lung fibrosis was associated with poorer transplant-free survival (hazard ratio [HR] 1.20, p < 0.0001) and progression-free survival (HR 1.14, p < 0.0001). CONCLUSION In a multi-centre observational registry of patients with IPF, the extent of fibrotic abnormality on baseline CT quantified using DTA is associated with outcomes independent of pulmonary function.
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Affiliation(s)
| | - John A. Mackintosh
- Department of Thoracic MedicineThe Prince Charles HospitalBrisbaneQueenslandAustralia,NHMRC Centre of Research Excellence in Pulmonary FibrosisCamperdownNew South WalesAustralia
| | - Helen E. Jo
- NHMRC Centre of Research Excellence in Pulmonary FibrosisCamperdownNew South WalesAustralia,Department of Respiratory MedicineRoyal Prince Alfred HospitalSydneyNew South WalesAustralia
| | - Simon L. F. Walsh
- Department of RadiologyKing's College Hospital Foundation TrustLondonUK
| | - Mario Silva
- Section of "Scienze Radiologiche", Department of Medicine and Surgery (DiMeC)University of ParmaParmaItaly,Department of RadiologyUniversity of Massachusetts Medical School, UMass Memorial Health CareWorcesterMassachusettsUSA
| | - Lucio Calandriello
- Dipartimento di Diagnostica per immagini, Radioterapia, Oncologia ed EmatologiaFondazione Policlinico Universitario A. Gemelli, IRCCSRomeItaly
| | - Sally Chapman
- Respiratory ConsultantsAdelaideSouth AustraliaAustralia
| | - Samantha Ellis
- Department of RadiologyAlfred HealthMelbourneVictoriaAustralia
| | - Ian Glaspole
- NHMRC Centre of Research Excellence in Pulmonary FibrosisCamperdownNew South WalesAustralia,Department of Allergy and Respiratory MedicineAlfred HospitalMelbourneVictoriaAustralia
| | - Nicole Goh
- Respiratory and Sleep MedicineAustin HospitalMelbourneVictoriaAustralia
| | - Christopher Grainge
- Department of Respiratory MedicineJohn Hunter HospitalNewcastleNew South WalesAustralia
| | - Peter M. A. Hopkins
- Department of Thoracic MedicineThe Prince Charles HospitalBrisbaneQueenslandAustralia,Faculty of MedicineThe University of QueenslandBrisbaneQueenslandAustralia
| | - Gregory J. Keir
- Department of Respiratory MedicinePrincess Alexandra HospitalBrisbaneQueenslandAustralia
| | - Yuben Moodley
- School of Medicine & PharmacologyUniversity of Western AustraliaPerthWestern AustraliaAustralia
| | - Paul N. Reynolds
- Department of Thoracic MedicineRoyal Adelaide HospitalAdelaideSouth AustraliaAustralia
| | - E. Haydn Walters
- Department of MedicineUniversity of TasmaniaHobartTasmaniaAustralia
| | - David Baraghoshi
- Division of BiostatisticsNational Jewish HealthDenverColoradoUSA
| | - Athol U. Wells
- Royal Brompton and Harefield NHS Foundation TrustLondonUK,National Heart and Lung InstituteImperial College LondonLondonUK
| | - David A. Lynch
- Department of RadiologyNational Jewish HealthDenverColoradoUSA
| | - Tamera J. Corte
- NHMRC Centre of Research Excellence in Pulmonary FibrosisCamperdownNew South WalesAustralia,Department of Respiratory MedicineRoyal Prince Alfred HospitalSydneyNew South WalesAustralia
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Egashira R, Raghu G. Quantitative computed tomography of the chest for fibrotic lung diseases: Prime time for its use in routine clinical practice? Respirology 2022; 27:1008-1011. [PMID: 35999171 DOI: 10.1111/resp.14351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 08/15/2022] [Indexed: 12/13/2022]
Affiliation(s)
- Ryoko Egashira
- Department of Radiology, Faculty of Medicine, Graduate School of Medical Sciences, Saga University, Saga, Japan
| | - Ganesh Raghu
- Division of Pulmonary, Sleep & Critical Care Medicine and Center for Interstitial Lung Disease, University of Washington, Washington, USA
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Walsh SL, Mackintosh JA, Calandriello L, Silva M, Sverzellati N, Larici AR, Humphries SM, Lynch DA, Jo HE, Glaspole I, Grainge C, Goh N, Hopkins PMA, Moodley Y, Reynolds PN, Zappala C, Keir G, Cooper WA, Mahar AM, Ellis S, Wells AU, Corte TJ. Deep Learning-based Outcome Prediction in Progressive Fibrotic Lung Disease Using High-resolution Computed Tomography. Am J Respir Crit Care Med 2022; 206:883-891. [PMID: 35696341 DOI: 10.1164/rccm.202112-2684oc] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
RATIONALE Reliable outcome prediction in patients with fibrotic lung disease using baseline high-resolution computed tomography (HRCT) data remains challenging. OBJECTIVES To evaluate the prognostic accuracy of a deep learning algorithm (SOFIA), trained and validated in the identification of UIP-like features on HRCT (UIP probability), in a large cohort of well characterised patients with progressive fibrotic lung disease, drawn from a national registry. METHODS SOFIA and radiologist-UIP probabilities were converted to PIOPED-based UIP probability categories (UIP not included in the differential: 0-4%, low probability of UIP: 5-29%, intermediate probability of UIP: 30-69%, high probability of UIP: 70-94%, and pathognomonic for UIP:95-100%) and their prognostic utility assessed using Cox proportional hazards modelling. MEASUREMENTS AND MAIN RESULTS On multivariable analysis adjusting for age, gender, guideline based radiologic diagnosis and disease severity (using total ILD extent on HRCT, %predicted FVC, DLco or the CPI), only SOFIA-UIP probability PIOPED categories predicted survival. SOFIA-PIOPED UIP probability categories remained prognostically significant in patients considered indeterminate (n=83) by expert radiologist consensus (HR1.73, p<0.0001, 95%CI 1.40-2.14). In patients undergoing surgical lung biopsy (SLB) (n=86), after adjusting for guideline-based histologic pattern and total ILD extent on HRCT, only SOFIA-PIOPED probabilities were predictive of mortality (HR1.75, p<0.0001, 95%CI 1.37-2.25). CONCLUSIONS Deep learning-based UIP probability on HRCT provides enhanced outcome prediction in patients with progressive fibrotic lung disease when compared to expert radiologist evaluation or guideline-based histologic pattern. In principle this tool may be useful in multidisciplinary characterisation of fibrotic lung disease. The utility of this technology as a decision support system when ILD expertise is unavailable requires further investigation.
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Affiliation(s)
- Simon Lf Walsh
- Imperial College London, 4615, National Heart and Lung Institute, London, United Kingdom of Great Britain and Northern Ireland;
| | | | - Lucio Calandriello
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 18654, Dipartimento di Diagnostica per immagini, Roma, Italy
| | - Mario Silva
- Universita degli Studi di Parma, 9370, Section of Radiology, Department of Medicine and Surgery, Parma, Italy
| | - Nicola Sverzellati
- Department of Surgical Sciences, Ospedale Maggiore di Parma, Parma, Italy
| | - Anna Rita Larici
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 18654, Dipartimento di Diagnostica per immagini, Roma, Italy
| | | | - David A Lynch
- National Jewish Health, Radiology, Denver, Colorado, United States
| | - Helen E Jo
- Centre of Research Excellence in Pulmonary Fibrosis, Camperdown , New South Wales, Australia.,The University of Sydney, 4334, Sydney, New South Wales, Australia.,Royal Prince Alfred Hospital, 2205, Department of Respiratory and Sleep Medicine, Camperdown, New South Wales, Australia
| | - Ian Glaspole
- The Alfred Hospital, Melbourne, Australia.,Monash University, Melbourne, Australia
| | - Christopher Grainge
- John Hunter Hospital, 37024, New Lambton Heights, New South Wales, Australia
| | - Nicole Goh
- Austin Health, Department of Respiratory and Sleep Medicine, Heidelberg, Victoria, Australia.,Alfred Health, 5392, Allergy, Immunology & Respiratory Medicine Department, Melbourne, Victoria, Australia
| | - Peter M A Hopkins
- The Prince Charles Hospital, 67567, Chermside, Queensland, Australia
| | - Yuben Moodley
- The University of Western Australia, Respiratory Medicine, Perth, Western Australia, Australia
| | - Paul N Reynolds
- Royal Adelaide Hospital, Thoracic Medicine, Adelaide, South Australia, Australia
| | | | - Gregory Keir
- Princess Alexandra Hospital, Brisbane, Queensland, Australia
| | - Wendy A Cooper
- Royal Prince Alfred Hospital, 2205, Tissue Pathology and Diagnostic Oncology, NSW Health Pathology, Camperdown, New South Wales, Australia
| | - Annabelle M Mahar
- Royal Prince Alfred Hospital, 2205, Tissue Pathology and Diagnostic Oncology, NSW Health Pathology, Camperdown, New South Wales, Australia
| | - Samantha Ellis
- Alfred Health, 5392, Department of Radiology, Melbourne, Victoria, Australia
| | - Athol U Wells
- Royal Brompton Hospital, Interstitial Lung Disease Unit, London, United Kingdom of Great Britain and Northern Ireland
| | - Tamera J Corte
- Royal Prince Alfred Hospital, 2205, Department of Respiratory and Sleep Medicine, Camperdown, New South Wales, Australia
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Raghu G, Remy-Jardin M, Richeldi L, Thomson CC, Inoue Y, Johkoh T, Kreuter M, Lynch DA, Maher TM, Martinez FJ, Molina-Molina M, Myers JL, Nicholson AG, Ryerson CJ, Strek ME, Troy LK, Wijsenbeek M, Mammen MJ, Hossain T, Bissell BD, Herman DD, Hon SM, Kheir F, Khor YH, Macrea M, Antoniou KM, Bouros D, Buendia-Roldan I, Caro F, Crestani B, Ho L, Morisset J, Olson AL, Podolanczuk A, Poletti V, Selman M, Ewing T, Jones S, Knight SL, Ghazipura M, Wilson KC. Idiopathic Pulmonary Fibrosis (an Update) and Progressive Pulmonary Fibrosis in Adults: An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline. Am J Respir Crit Care Med 2022; 205:e18-e47. [PMID: 35486072 PMCID: PMC9851481 DOI: 10.1164/rccm.202202-0399st] [Citation(s) in RCA: 878] [Impact Index Per Article: 439.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Background: This American Thoracic Society, European Respiratory Society, Japanese Respiratory Society, and Asociación Latinoamericana de Tórax guideline updates prior idiopathic pulmonary fibrosis (IPF) guidelines and addresses the progression of pulmonary fibrosis in patients with interstitial lung diseases (ILDs) other than IPF. Methods: A committee was composed of multidisciplinary experts in ILD, methodologists, and patient representatives. 1) Update of IPF: Radiological and histopathological criteria for IPF were updated by consensus. Questions about transbronchial lung cryobiopsy, genomic classifier testing, antacid medication, and antireflux surgery were informed by systematic reviews and answered with evidence-based recommendations using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. 2) Progressive pulmonary fibrosis (PPF): PPF was defined, and then radiological and physiological criteria for PPF were determined by consensus. Questions about pirfenidone and nintedanib were informed by systematic reviews and answered with evidence-based recommendations using the GRADE approach. Results:1) Update of IPF: A conditional recommendation was made to regard transbronchial lung cryobiopsy as an acceptable alternative to surgical lung biopsy in centers with appropriate expertise. No recommendation was made for or against genomic classifier testing. Conditional recommendations were made against antacid medication and antireflux surgery for the treatment of IPF. 2) PPF: PPF was defined as at least two of three criteria (worsening symptoms, radiological progression, and physiological progression) occurring within the past year with no alternative explanation in a patient with an ILD other than IPF. A conditional recommendation was made for nintedanib, and additional research into pirfenidone was recommended. Conclusions: The conditional recommendations in this guideline are intended to provide the basis for rational, informed decisions by clinicians.
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Radiologic evaluation of compensatory lung growth using computed tomography by comparison with histological data from a large animal model. Sci Rep 2022; 12:2520. [PMID: 35169160 PMCID: PMC8847356 DOI: 10.1038/s41598-022-06398-y] [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: 05/06/2021] [Accepted: 01/11/2022] [Indexed: 11/08/2022] Open
Abstract
Non-invasive analysis using computed tomography (CT) data may be a promising candidate to evaluate neo-alveolarization in adult lungs following lung resection. This study evaluates and compares the validity of CT analysis with histologic morphometry for compensatory lung growth in a large animal model. We calculated the radiologic tissue volume and the radiologic lung weight from CT data taken at 1, 3, and 6 months post-surgery on 15 male beagle dogs that had a right thoractotomy, bilobectomy, or pneumonectomy (n = 5 in each group). Results were analyzed using one-way ANOVA and were subsequently compared to histologic findings of tissue samples at 6 months post-surgery using Pearson's correlation. An increase in radiologic tissue volume and radiologic lung weight was identified, which was positively correlated with histologic lung parenchymal amounts (correlation coefficient = 0.955 and 0.934, respectively, p < 0.001). Histology of lung specimens at 6 months post-surgery revealed an increase in the tissue amount in both Bilobectomy and Peumonectomy groups, which was consistent with compensatory lung growth. Radiologic tissue volume and radiologic lung weight reflected compensatory lung growth following lung resection. Radiologic assessment using CT data can be a promising clinical modality to evaluate postoperative lung growth.
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40
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Popmihajlov Z, Sutherland DJ, Horan GS, Ghosh A, Lynch DA, Noble PW, Richeldi L, Reiss TF, Greenberg S. CC-90001, a c-Jun N-terminal kinase (JNK) inhibitor, in patients with pulmonary fibrosis: design of a phase 2, randomised, placebo-controlled trial. BMJ Open Respir Res 2022; 9:9/1/e001060. [PMID: 35058236 PMCID: PMC8783810 DOI: 10.1136/bmjresp-2021-001060] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 12/18/2021] [Indexed: 11/12/2022] Open
Abstract
Introduction Idiopathic pulmonary fibrosis (IPF) is a progressive and often fatal interstitial lung disease (ILD); other ILDs have a progressive, fibrotic phenotype (PF-ILD). Antifibrotic agents can slow but not stop disease progression in patients with IPF or PF-ILD. c-Jun N-terminal kinases (JNKs) are stress-activated protein kinases implicated in the underlying mechanisms of fibrosis, including epithelial cell death, inflammation and polarisation of profibrotic macrophages, fibroblast activation and collagen production. CC-90001, an orally administered (PO), one time per day, JNK inhibitor, is being evaluated in IPF and PF-ILD. Methods and analysis This is a phase 2, randomised, double-blind, placebo-controlled study evaluating efficacy and safety of CC-90001 in patients with IPF (main study) and patients with PF-ILD (substudy). Both include an 8-week screening period, a 24-week treatment period, up to an 80-week active-treatment extension and a 4-week post-treatment follow-up. Patients with IPF (n=165) will be randomised 1:1:1 to receive 200 mg or 400 mg CC-90001 or placebo administered PO one time per day; up to 25 patients/arm will be permitted concomitant pirfenidone use. Forty-five patients in the PF-ILD substudy will be randomised 2:1 to receive 400 mg CC-90001 or placebo. The primary endpoint is change in per cent predicted forced vital capacity from baseline to Week 24 in patients with IPF. Ethics and dissemination This study will be conducted in accordance with Good Clinical Practice guidelines, Declaration of Helsinki principles and local ethical and legal requirements. Results will be reported in a peer-reviewed publication. Trial registration number NCT03142191.
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Affiliation(s)
| | | | | | | | - David A Lynch
- Department of Radiology, National Jewish Health, Denver, Colorado, USA
| | - Paul W Noble
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Luca Richeldi
- Università Cattolica del Sacro Cuore, Fondazione Policlinico A. Gemelli IRCSS, Rome, Italy
| | | | - Steven Greenberg
- Bristol Myers Squibb, Princeton, New Jersey, USA
- Division of Pulmonary, Allergy, and Critical Care Medicine, Columbia University, New York, New York, USA
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Abstract
The acute course of COVID-19 is variable and ranges from asymptomatic infection to fulminant respiratory failure. Patients recovering from COVID-19 can have persistent symptoms and CT abnormalities of variable severity. At 3 months after acute infection, a subset of patients will have CT abnormalities that include ground-glass opacity (GGO) and subpleural bands with concomitant pulmonary function abnormalities. At 6 months after acute infection, some patients have persistent CT changes to include the resolution of GGOs seen in the early recovery phase and the persistence or development of changes suggestive of fibrosis, such as reticulation with or without parenchymal distortion. The etiology of lung disease after COVID-19 may be a sequela of prolonged mechanical ventilation, COVID-19-induced acute respiratory distress syndrome (ARDS), or direct injury from the virus. Predictors of lung disease after COVID-19 include need for intensive care unit admission, mechanical ventilation, higher inflammatory markers, longer hospital stay, and a diagnosis of ARDS. Treatments of lung disease after COVID-19 are being investigated, including the potential of antifibrotic agents for prevention of lung fibrosis after COVID-19. Future research is needed to determine the long-term persistence of lung disease after COVID-19, its impact on patients, and methods to either prevent or treat it. © RSNA, 2021.
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Affiliation(s)
| | - Brooke Heyman
- Division of Pulmonary, Sleep and Critical Care Medicine, Department
of Medicine, NYU Langone Health, NYU Grossman School of Medicine, New York,
NY
| | - Jane P. Ko
- Department of Radiology, NYU Langone Health, NYU Grossman School of
Medicine, New York, NY
| | - Rany Condos
- Division of Pulmonary, Sleep and Critical Care Medicine, Department
of Medicine, NYU Langone Health, NYU Grossman School of Medicine, New York,
NY
| | - David A. Lynch
- Department of Radiology, National Jewish Health, Denver, CO,
USA
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42
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Hobbs SB, Chung JH, Walker CM, Bang TJ, Carter BW, Christensen JD, Danoff SK, Kandathil A, Madan R, Moore WH, Shah SD, Kanne JP. ACR Appropriateness Criteria® Diffuse Lung Disease. J Am Coll Radiol 2021; 18:S320-S329. [PMID: 34794591 DOI: 10.1016/j.jacr.2021.08.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 08/26/2021] [Indexed: 11/28/2022]
Abstract
Diffuse lung disease, frequently referred to as interstitial lung disease, encompasses numerous disorders affecting the lung parenchyma. The potential etiologies of diffuse lung disease are broad with several hundred established clinical syndromes and pathologies currently identified. Imaging plays a critical role in diagnosis and follow-up of many of these diseases, although multidisciplinary discussion is the current standard for diagnosis of several DLDs. This document aims to establish guidelines for evaluation of diffuse lung diseases for 1) initial imaging of suspected diffuse lung disease, 2) initial imaging of suspected acute exacerbation or acute deterioration in cases of confirmed diffuse lung disease, and 3) clinically indicated routine follow-up of confirmed diffuse lung disease without acute deterioration. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
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Affiliation(s)
- Stephen B Hobbs
- Vice-Chair, Informatics and Integrated Clinical Operations and Division Chief, Cardiovascular and Thoracic Radiology, University of Kentucky, Lexington, Kentucky.
| | - Jonathan H Chung
- Panel Chair; and Vice-Chair of Quality, and Section Chief, Chest Imaging, Department of Radiology, University of Chicago, Chicago, Illinois
| | | | - Tami J Bang
- Co-Director, Cardiothoracic Imaging Fellowship Committee, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, Colorado; Co-Chair, membership committee, NASCI; and Membership committee, ad-hoc online content committee, STR
| | - Brett W Carter
- The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jared D Christensen
- Vice-Chair, Department of Radiology, Duke University Medical Center, Durham, North Carolina; and Chair, ACR Lungs-RADS
| | - Sonye K Danoff
- Johns Hopkins Medicine, Baltimore, Maryland; Board of Directors, American Thoracic Society; Senior Medical Advisor, Pulmonary Fibrosis Foundation; and Medical Advisory Board Member, The Myositis Association
| | | | - Rachna Madan
- Associate Fellowship Director, Division of Thoracic Imaging, Brigham & Women's Hospital, Boston, Massachusetts
| | - William H Moore
- Associate Chair, Clinical Informatics and Chief, Thoracic Imaging, New York University Langone Medical Center, New York, New York
| | - Sachin D Shah
- Associate Chief and Medical Information Officer, University of Chicago, Chicago, Illinois; and Primary care physician
| | - Jeffrey P Kanne
- Specialty Chair, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
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43
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Quantifying CLAD: A Promising Imaging-Based Diagnostic Innovation. Transplantation 2021; 106:1111-1112. [PMID: 34534194 DOI: 10.1097/tp.0000000000003951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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44
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DeBoer EM, Liptzin DR, Humphries SM, Lynch DA, Robison K, Galambos C, Dishop MK, Deterding RR, Weinman JP. Ground glass and fibrotic change in children with surfactant protein C dysfunction mutations. Pediatr Pulmonol 2021; 56:2223-2231. [PMID: 33666361 DOI: 10.1002/ppul.25356] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 02/08/2021] [Accepted: 02/21/2021] [Indexed: 11/08/2022]
Abstract
INTRODUCTION Therapeutics exist to treat fibrotic lung disease in adults, but these have not been investigated in children. Defining biomarkers for pediatric fibrotic lung disease in children is crucial for clinical trials. Children with surfactant protein C (SFTPC) dysfunction mutations develop fibrotic lung disease over time. We evaluated chest computed tomography (CT) changes over time in children with SFTPC dysfunction mutations. METHODS We performed an institutional review board-approved retrospective review of children with SFTPC dysfunction mutations. We collected demographic and clinical information. Chest CT scans were evaluated using visual and computerized scores. Chest CT scores and pulmonary function tests were reviewed. RESULTS Eleven children were included. All children presented in infancy and four children suffered from respiratory failure requiring mechanical ventilation. Those who performed pulmonary function tests had stable forced vital capacities over time by percent predicted, but increased forced vital capacity in liters. CT findings evolved over time in most patients with earlier CT scans demonstrating ground glass opacities and later CT scans with more fibrotic features. In a pilot analysis, data-driven textural analysis software identified fibrotic features in children with SFTPC dysfunction that increased over time and correlated with visual CT scores. DISCUSSION We describe 11 children with SFTPC dysfunction mutations. Increases in forced vital capacity over time suggest that these children experience lung growth and that therapeutic intervention may maximize lung growth. Ground glass opacities are the primary early imaging findings while fibrotic features dominate later. CT findings suggest the development of and increases in fibrotic features that may serve as potential biomarkers for antifibrotic therapeutic trials.
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Affiliation(s)
- Emily M DeBoer
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.,Breathing Institute, Children's Hospital Colorado, Aurora, Colorado, USA
| | - Deborah R Liptzin
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.,Breathing Institute, Children's Hospital Colorado, Aurora, Colorado, USA
| | | | - David A Lynch
- Department of Radiology, National Jewish Health, Denver, Colorado, USA
| | - Kyle Robison
- Breathing Institute, Children's Hospital Colorado, Aurora, Colorado, USA
| | - Csaba Galambos
- Breathing Institute, Children's Hospital Colorado, Aurora, Colorado, USA.,Department of Pathology and Laboratory Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Megan K Dishop
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.,Breathing Institute, Children's Hospital Colorado, Aurora, Colorado, USA.,Department of Pathology, University of Arizona, Phoenix, Arizona, USA
| | - Robin R Deterding
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.,Breathing Institute, Children's Hospital Colorado, Aurora, Colorado, USA
| | - Jason P Weinman
- Breathing Institute, Children's Hospital Colorado, Aurora, Colorado, USA.,Department of Radiology, University of Colorado Anschutz Medical Campus and Children's Hospital Colorado, Aurora, Colorado, USA
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Ryerson CJ, Corte TJ, Myers JL, Walsh SLF, Guler SA. A contemporary practical approach to the multidisciplinary management of unclassifiable interstitial lung disease. Eur Respir J 2021; 58:13993003.00276-2021. [PMID: 34140296 PMCID: PMC8674517 DOI: 10.1183/13993003.00276-2021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 05/04/2021] [Indexed: 11/05/2022]
Abstract
Fibrotic interstitial lung diseases (ILDs) frequently have nonspecific and overlapping clinical and radiological features, resulting in approximately 10-20% of patients with ILD lacking a clear diagnosis and thus being labelled with unclassifiable ILD. The objective of this review is to describe how patients with unclassifiable ILD should be evaluated and what impact specific clinical, radiological, and histopathological features may have on management decisions, focusing on patients with a predominantly fibrotic phenotype. We highlight recent data that have suggested an increasing role for antifibrotic medications in a variety of fibrotic ILDs, but justify the ongoing importance of making an accurate ILD diagnosis given the benefit of immunomodulatory therapies in many patient populations. We provide a practical approach to support management decisions that can be used by clinicians and tested by clinical researchers, and further identify the need for additional research to support a rational and standardised approach to the management of patients with unclassifiable ILD.
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Affiliation(s)
- Christopher J Ryerson
- Department of Medicine, University of British Columbia and Centre for Heart Lung Innovation, St. Paul"s Hospital, Vancouver, Canada
| | - Tamera J Corte
- Department of Respiratory Medicine, Royal Prince Alfred Hospital, Sydney; University of Sydney; Centre of Research Excellence for Pulmonary Fibrosis, Sydney, Australia
| | - Jeffrey L Myers
- Department of Pathology, Michigan Medicine, Ann Arbor, Michigan, United States
| | - Simon L F Walsh
- National Heart and Lung Institute, Imperial College, London, United Kingdom
| | - Sabina A Guler
- Department of Pulmonary Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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46
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Redente EF, Black BP, Backos DS, Bahadur AN, Humphries SM, Lynch DA, Tuder RM, Zemans RL, Riches DWH. Persistent, Progressive Pulmonary Fibrosis and Epithelial Remodeling in Mice. Am J Respir Cell Mol Biol 2021; 64:669-676. [PMID: 33406369 PMCID: PMC8456888 DOI: 10.1165/rcmb.2020-0542ma] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 12/22/2020] [Indexed: 12/15/2022] Open
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic interstitial lung disease with underlying mechanisms that have been primarily investigated in mice after intratracheal instillation of a single dose of bleomycin. However, the model has significant limitations, including transient fibrosis that spontaneously resolves and its failure to fully recapitulate the epithelial remodeling in the lungs of patients with IPF. Thus, there remains an unmet need for a preclinical model with features that more closely resemble the human disease. Repetitive intratracheal instillation of bleomycin has previously been shown to recapitulate some of these features, but the instillation procedure is complex, and the long-term consequences on epithelial remodeling and fibrosis persistence and progression remain poorly understood. Here, we developed a simplified repetitive bleomycin instillation strategy consisting of three bi-weekly instillations that leads to persistent and progressive pulmonary fibrosis. Lung histology demonstrates increased collagen deposition, fibroblast accumulation, loss of type I and type II alveolar epithelial cells within fibrotic areas, bronchiolization of the lung parenchyma with CCSP+ cells, remodeling of the distal lung into cysts reminiscent of simple honeycombing, and accumulation of hyperplastic transitional KRT8+ epithelial cells. Micro-computed tomographic imaging demonstrated significant traction bronchiectasis and subpleural fibrosis. Thus, the simplified repetitive bleomycin instillation strategy leads to progressive fibrosis and recapitulates the histological and radiographic characteristics of IPF. Compared with the single bleomycin instillation model, we suggest that the simplified repetitive instillation model may be better suited to address mechanistic questions about IPF pathogenesis and preclinical studies of antifibrotic drug candidates.
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Affiliation(s)
- Elizabeth F. Redente
- Program in Cell Biology, Department of Pediatrics, and
- Department of Research, Veterans Affairs Eastern Colorado Health Care System, Denver, Colorado
- Division of Pulmonary Sciences and Critical Care Medicine, Department of Medicine
| | - Bart P. Black
- Program in Cell Biology, Department of Pediatrics, and
| | | | - Ali N. Bahadur
- Bruker BioSpin Corporation, Billerica, Massachusetts; and
| | | | - David A. Lynch
- Department of Radiology, National Jewish Health, Denver, Colorado
- Division of Pulmonary Sciences and Critical Care Medicine, Department of Medicine
| | - Rubin M. Tuder
- Division of Pulmonary Sciences and Critical Care Medicine, Department of Medicine
| | - Rachel L. Zemans
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, and
- Program in Cellular and Molecular Biology, University of Michigan, Ann Arbor, Michigan
| | - David W. H. Riches
- Program in Cell Biology, Department of Pediatrics, and
- Department of Research, Veterans Affairs Eastern Colorado Health Care System, Denver, Colorado
- Division of Pulmonary Sciences and Critical Care Medicine, Department of Medicine
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado
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47
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Calandriello L, Walsh SL. The evolution of computer-based analysis of high-resolution CT of the chest in patients with IPF. Br J Radiol 2021; 95:20200944. [PMID: 33881923 DOI: 10.1259/bjr.20200944] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
In patients with idiopathic pulmonary fibrosis (IPF), there is an urgent need of biomarkers which can predict disease behaviour or response to treatment. Most published studies report results based on continuous data which can be difficult to apply to individual patients in clinical practice. Having antifibrotic therapies makes it even more important that we can accurately diagnose and prognosticate in IPF patients. Advances in computer technology over the past decade have provided computer-based methods for objectively quantifying fibrotic lung disease on high-resolution CT of the chest with greater strength than visual CT analysis scores. These computer-based methods and, more recently, the arrival of deep learning-based image analysis might provide a response to these unsolved problems. The purpose of this commentary is to provide insights into the problems associated with visual interpretation of HRCT, describe of the current technologies used to provide quantification of disease on HRCT and prognostication in IPF patients, discuss challenges to the implementation of this technology and future directions.
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Affiliation(s)
- Lucio Calandriello
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Simon Lf Walsh
- National Heart and Lung Institute, Imperial College, London, UK
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48
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Jankharia BG, Angirish BA. Computer-Aided quantitative analysis in interstitial lung diseases - A pictorial review using CALIPER. Lung India 2021; 38:161-167. [PMID: 33687011 PMCID: PMC8098894 DOI: 10.4103/lungindia.lungindia_244_20] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Computer-based quantitative computed tomography analysis has a growing role in the clinical evaluation, prognosis, and longitudinal management of diffuse parenchymal diseases. It provides improved characterization and quantification of disease. The pulmonary vessel-related structure score is a purely computer-based parameter that cannot be evaluated by the human eye and allows us to prognosticate outcomes in patients with fibrosing interstitial lung disease.
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Affiliation(s)
- Bhavin G Jankharia
- Department of Radiology, Picture This by Jankharia, Mumbai, Maharashtra, India
| | - Bhoomi A Angirish
- Department of Radiology, Picture This by Jankharia, Mumbai, Maharashtra, India
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Chassagnon G, Vakalopoulou M, Régent A, Sahasrabudhe M, Marini R, Hoang-Thi TN, Dinh-Xuan AT, Dunogué B, Mouthon L, Paragios N, Revel MP. Elastic Registration-driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT. Radiology 2020; 298:189-198. [PMID: 33078999 DOI: 10.1148/radiol.2020200319] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Background Longitudinal follow-up of interstitial lung diseases (ILDs) at CT mainly relies on the evaluation of the extent of ILD, without accounting for lung shrinkage. Purpose To develop a deep learning-based method to depict worsening of ILD based on lung shrinkage detection from elastic registration of chest CT scans in patients with systemic sclerosis (SSc). Materials and Methods Patients with SSc evaluated between January 2009 and October 2017 who had undergone at least two unenhanced supine CT scans of the chest and pulmonary function tests (PFTs) performed within 3 months were retrospectively included. Morphologic changes on CT scans were visually assessed by two observers and categorized as showing improvement, stability, or worsening of ILD. Elastic registration between baseline and follow-up CT images was performed to obtain deformation maps of the whole lung. Jacobian determinants calculated from the deformation maps were given as input to a deep learning-based classifier to depict morphologic and functional worsening. For this purpose, the set was randomly split into training, validation, and test sets. Correlations between mean Jacobian values and changes in PFT measurements were evaluated with the Spearman correlation. Results A total of 212 patients (median age, 53 years; interquartile range, 45-62 years; 177 women) were included as follows: 138 for the training set (65%), 34 for the validation set (16%), and 40 for the test set (21%). Jacobian maps demonstrated lung parenchyma shrinkage of the posterior lung bases in patients found to have worsened ILD at visual assessment. The classifier detected morphologic and functional worsening with an accuracy of 80% (32 of 40 patients; 95% confidence interval [CI]: 64%, 91%) and 83% (33 of 40 patients; 95% CI: 67%, 93%), respectively. Jacobian values correlated with changes in forced vital capacity (R = -0.38; 95% CI: -0.25, -0.49; P < .001) and diffusing capacity for carbon monoxide (R = -0.42; 95% CI: -0.27, -0.54; P < .001). Conclusion Elastic registration of CT scans combined with a deep learning classifier aided in the diagnosis of morphologic and functional worsening of interstitial lung disease in patients with systemic sclerosis. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Verschakelen in this issue.
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Affiliation(s)
- Guillaume Chassagnon
- From the Department of Radiology (G.C., T.N.H.T., M.P.R.), Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France (A.R., B.D., L.M.), and Department of Physiology (A.T.D.X.), Hôpital Cochin, AP-HP Centre, Université de Paris, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France; Center for Visual Computing, École CentraleSupélec, Png-sur-Yvette, France (G.C., M.V., M.S., N.P.); and TheraPanacea, Paris, France (R.M., N.P.)
| | - Maria Vakalopoulou
- From the Department of Radiology (G.C., T.N.H.T., M.P.R.), Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France (A.R., B.D., L.M.), and Department of Physiology (A.T.D.X.), Hôpital Cochin, AP-HP Centre, Université de Paris, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France; Center for Visual Computing, École CentraleSupélec, Png-sur-Yvette, France (G.C., M.V., M.S., N.P.); and TheraPanacea, Paris, France (R.M., N.P.)
| | - Alexis Régent
- From the Department of Radiology (G.C., T.N.H.T., M.P.R.), Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France (A.R., B.D., L.M.), and Department of Physiology (A.T.D.X.), Hôpital Cochin, AP-HP Centre, Université de Paris, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France; Center for Visual Computing, École CentraleSupélec, Png-sur-Yvette, France (G.C., M.V., M.S., N.P.); and TheraPanacea, Paris, France (R.M., N.P.)
| | - Mihir Sahasrabudhe
- From the Department of Radiology (G.C., T.N.H.T., M.P.R.), Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France (A.R., B.D., L.M.), and Department of Physiology (A.T.D.X.), Hôpital Cochin, AP-HP Centre, Université de Paris, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France; Center for Visual Computing, École CentraleSupélec, Png-sur-Yvette, France (G.C., M.V., M.S., N.P.); and TheraPanacea, Paris, France (R.M., N.P.)
| | - Rafael Marini
- From the Department of Radiology (G.C., T.N.H.T., M.P.R.), Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France (A.R., B.D., L.M.), and Department of Physiology (A.T.D.X.), Hôpital Cochin, AP-HP Centre, Université de Paris, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France; Center for Visual Computing, École CentraleSupélec, Png-sur-Yvette, France (G.C., M.V., M.S., N.P.); and TheraPanacea, Paris, France (R.M., N.P.)
| | - Trieu-Nghi Hoang-Thi
- From the Department of Radiology (G.C., T.N.H.T., M.P.R.), Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France (A.R., B.D., L.M.), and Department of Physiology (A.T.D.X.), Hôpital Cochin, AP-HP Centre, Université de Paris, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France; Center for Visual Computing, École CentraleSupélec, Png-sur-Yvette, France (G.C., M.V., M.S., N.P.); and TheraPanacea, Paris, France (R.M., N.P.)
| | - Anh-Tuan Dinh-Xuan
- From the Department of Radiology (G.C., T.N.H.T., M.P.R.), Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France (A.R., B.D., L.M.), and Department of Physiology (A.T.D.X.), Hôpital Cochin, AP-HP Centre, Université de Paris, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France; Center for Visual Computing, École CentraleSupélec, Png-sur-Yvette, France (G.C., M.V., M.S., N.P.); and TheraPanacea, Paris, France (R.M., N.P.)
| | - Bertrand Dunogué
- From the Department of Radiology (G.C., T.N.H.T., M.P.R.), Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France (A.R., B.D., L.M.), and Department of Physiology (A.T.D.X.), Hôpital Cochin, AP-HP Centre, Université de Paris, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France; Center for Visual Computing, École CentraleSupélec, Png-sur-Yvette, France (G.C., M.V., M.S., N.P.); and TheraPanacea, Paris, France (R.M., N.P.)
| | - Luc Mouthon
- From the Department of Radiology (G.C., T.N.H.T., M.P.R.), Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France (A.R., B.D., L.M.), and Department of Physiology (A.T.D.X.), Hôpital Cochin, AP-HP Centre, Université de Paris, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France; Center for Visual Computing, École CentraleSupélec, Png-sur-Yvette, France (G.C., M.V., M.S., N.P.); and TheraPanacea, Paris, France (R.M., N.P.)
| | - Nikos Paragios
- From the Department of Radiology (G.C., T.N.H.T., M.P.R.), Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France (A.R., B.D., L.M.), and Department of Physiology (A.T.D.X.), Hôpital Cochin, AP-HP Centre, Université de Paris, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France; Center for Visual Computing, École CentraleSupélec, Png-sur-Yvette, France (G.C., M.V., M.S., N.P.); and TheraPanacea, Paris, France (R.M., N.P.)
| | - Marie-Pierre Revel
- From the Department of Radiology (G.C., T.N.H.T., M.P.R.), Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France (A.R., B.D., L.M.), and Department of Physiology (A.T.D.X.), Hôpital Cochin, AP-HP Centre, Université de Paris, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France; Center for Visual Computing, École CentraleSupélec, Png-sur-Yvette, France (G.C., M.V., M.S., N.P.); and TheraPanacea, Paris, France (R.M., N.P.)
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Tielemans B, Dekoster K, Verleden SE, Sawall S, Leszczyński B, Laperre K, Vanstapel A, Verschakelen J, Kachelriess M, Verbeken E, Swoger J, Vande Velde G. From Mouse to Man and Back: Closing the Correlation Gap between Imaging and Histopathology for Lung Diseases. Diagnostics (Basel) 2020; 10:E636. [PMID: 32859103 PMCID: PMC7554749 DOI: 10.3390/diagnostics10090636] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 08/21/2020] [Accepted: 08/24/2020] [Indexed: 02/07/2023] Open
Abstract
Lung diseases such as fibrosis, asthma, cystic fibrosis, infection and cancer are life-threatening conditions that slowly deteriorate quality of life and for which our diagnostic power is high, but our knowledge on etiology and/or effective treatment options still contains important gaps. In the context of day-to-day practice, clinical and preclinical studies, clinicians and basic researchers team up and continuously strive to increase insights into lung disease progression, diagnostic and treatment options. To unravel disease processes and to test novel therapeutic approaches, investigators typically rely on end-stage procedures such as serum analysis, cyto-/chemokine profiles and selective tissue histology from animal models. These techniques are useful but provide only a snapshot of disease processes that are essentially dynamic in time and space. Technology allowing evaluation of live animals repeatedly is indispensable to gain a better insight into the dynamics of lung disease progression and treatment effects. Computed tomography (CT) is a clinical diagnostic imaging technique that can have enormous benefits in a research context too. Yet, the implementation of imaging techniques in laboratories lags behind. In this review we want to showcase the integrated approaches and novel developments in imaging, lung functional testing and pathological techniques that are used to assess, diagnose, quantify and treat lung disease and that may be employed in research on patients and animals. Imaging approaches result in often novel anatomical and functional biomarkers, resulting in many advantages, such as better insight in disease progression and a reduction in the numbers of animals necessary. We here showcase integrated assessment of lung disease with imaging and histopathological technologies, applied to the example of lung fibrosis. Better integration of clinical and preclinical imaging technologies with pathology will ultimately result in improved clinical translation of (therapy) study results.
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Affiliation(s)
- Birger Tielemans
- Department of Imaging and Pathology, KU Leuven, University of Leuven, 3000 Leuven, Belgium; (B.T.); (K.D.); (J.V.); (E.V.)
| | - Kaat Dekoster
- Department of Imaging and Pathology, KU Leuven, University of Leuven, 3000 Leuven, Belgium; (B.T.); (K.D.); (J.V.); (E.V.)
| | - Stijn E. Verleden
- Department of CHROMETA, BREATHE lab, KU Leuven, 3000 Leuven, Belgium; (S.E.V.); (A.V.)
| | - Stefan Sawall
- German Cancer Research Center (DKFZ), X-Ray Imaging and CT, Heidelberg University, 69117 Heidelberg, Germany; (S.S.); (M.K.)
| | - Bartosz Leszczyński
- Department of Medical Physics, M. Smoluchowski Institute of Physics, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, 31-007 Kraków, Poland;
| | | | - Arno Vanstapel
- Department of CHROMETA, BREATHE lab, KU Leuven, 3000 Leuven, Belgium; (S.E.V.); (A.V.)
| | - Johny Verschakelen
- Department of Imaging and Pathology, KU Leuven, University of Leuven, 3000 Leuven, Belgium; (B.T.); (K.D.); (J.V.); (E.V.)
| | - Marc Kachelriess
- German Cancer Research Center (DKFZ), X-Ray Imaging and CT, Heidelberg University, 69117 Heidelberg, Germany; (S.S.); (M.K.)
| | - Erik Verbeken
- Department of Imaging and Pathology, KU Leuven, University of Leuven, 3000 Leuven, Belgium; (B.T.); (K.D.); (J.V.); (E.V.)
| | - Jim Swoger
- European Molecular Biology Laboratory (EMBL) Barcelona, 08003 Barcelona, Spain;
| | - Greetje Vande Velde
- Department of Imaging and Pathology, KU Leuven, University of Leuven, 3000 Leuven, Belgium; (B.T.); (K.D.); (J.V.); (E.V.)
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