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Ledda RE, Marrocchio C, Sverzellati N. Progress in the radiologic diagnosis of idiopathic pulmonary fibrosis. Curr Opin Pulm Med 2024; 30:500-507. [PMID: 38888028 DOI: 10.1097/mcp.0000000000001086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
PURPOSE OF REVIEW To discuss the most recent applications of radiological imaging, from conventional to quantitative, in the setting of idiopathic pulmonary fibrosis (IPF) diagnosis. RECENT FINDINGS In this article, current concepts on radiological diagnosis of IPF, from high-resolution computed tomography (CT) to other imaging modalities, are reviewed. In a separate section, advances in quantitative CT and development of novel imaging biomarkers, as well as current limitations and future research trends, are described. SUMMARY Radiological imaging in IPF, particularly quantitative CT, is an evolving field which holds promise in the future to allow for an increasingly accurate disease assessment and prognostication of IPF patients. However, further standardization and validation studies of alternative imaging applications and quantitative biomarkers are needed.
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Affiliation(s)
- Roberta Eufrasia Ledda
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy
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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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Grenier PA, Brun AL, Mellot F. [The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]. Rev Mal Respir 2024; 41:110-126. [PMID: 38129269 DOI: 10.1016/j.rmr.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.
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Affiliation(s)
- P A Grenier
- Délégation à la recherche clinique et l'innovation, hôpital Foch, Suresnes, France.
| | - A L Brun
- Service de radiologie, hôpital Foch, Suresnes, France
| | - F Mellot
- Service de radiologie, hôpital Foch, Suresnes, France
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Yang X, Yu P, Sun H, Deng M, Liu A, Li C, Meng W, Xu W, Xie B, Geng J, Ren Y, Zhang R, Liu M, Dai H. Assessment of lung deformation in patients with idiopathic pulmonary fibrosis with elastic registration technique on pulmonary three-dimensional ultrashort echo time MRI. Insights Imaging 2024; 15:17. [PMID: 38253739 PMCID: PMC10803694 DOI: 10.1186/s13244-023-01555-x] [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: 07/04/2023] [Accepted: 10/28/2023] [Indexed: 01/24/2024] Open
Abstract
OBJECTIVE To assess lung deformation in patients with idiopathic pulmonary fibrosis (IPF) using with elastic registration algorithm applied to three-dimensional ultrashort echo time (3D-UTE) MRI and analyze relationship of lung deformation with the severity of IPF. METHODS Seventy-six patients with IPF (mean age: 62 ± 6 years) and 62 age- and gender-matched healthy controls (mean age: 58 ± 4 years) were prospectively enrolled. End-inspiration and end-expiration images acquired with a single breath-hold 3D-UTE sequence were registered using elastic registration algorithm. Jacobian determinants were calculated from deformation fields and represented on color maps. Jac-mean (absolute value of the log means of Jacobian determinants) and the Dice similarity coefficient (Dice) were compared between different groups. RESULTS Compared with healthy controls, the Jac-mean of IPF patients significantly decreased (0.21 ± 0.08 vs. 0.27 ± 0. 07, p < 0.001). Furthermore, the Jac-mean and Dice correlated with the metrics of pulmonary function tests and the composite physiological index. The lung deformation in IPF patients with dyspnea Medical Research Council (MRC) ≥ 3 (Jac-mean: 0.16 ± 0.03; Dice: 0.06 ± 0.02) was significantly lower than MRC1 (Jac-mean: 0. 25 ± 0.03, p < 0.001; Dice: 0.10 ± 0.01, p < 0.001) and MRC 2 (Jac-mean: 0.22 ± 0.11, p = 0.001; Dice: 0.08 ± 0.03, p = 0.006). Meanwhile, Jac-mean and Dice correlated with health-related quality of life, 6 min-walk distance, and the extent of pulmonary fibrosis. Jac-mean correlated with pulmonary vascular-related indexes on high-resolution CT. CONCLUSION The decreased lung deformation in IPF patients correlated with the clinical severity of IPF patients. Elastic registration of inspiratory-to-expiratory 3D UTE MRI may be a new morphological and functional marker for non-radiation and noninvasive evaluation of IPF. CRITICAL RELEVANCE STATEMENT This prospective study demonstrated that lung deformation decreased in idiopathic pulmonary fibrosis (IPF) patients and correlated with the severity of IPF. Elastic registration of inspiratory-to-expiratory three-dimensional ultrashort echo time (3D UTE) MRI may be a new morphological and functional marker for non-radiation and noninvasive evaluation of IPF. KEY POINTS • Elastic registration of inspiratory-to-expiratory three-dimensional ultrashort echo time (3D UTE) MRI could evaluate lung deformation. • Lung deformation significantly decreased in idiopathic pulmonary fibrosis (IPF) patients, compared with the healthy controls. • Reduced lung deformation of IPF patients correlated with worsened pulmonary function and the composite physiological index (CPI).
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Affiliation(s)
- Xiaoyan Yang
- Department of Pulmonary and Critical Care Medicine, General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, 2 Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China
| | - Pengxin Yu
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, 100025, China
| | - Haishuang Sun
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, 2 Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China
| | - Mei Deng
- Department of Radiology, China-Japan Friendship Hospital, 2 Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China
| | - Anqi Liu
- Department of Radiology, China-Japan Friendship Hospital, 2 Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China
| | - Chen Li
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, 2 Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China
| | - Wenyan Meng
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, 2 Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China
| | - Wenxiu Xu
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, 2 Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China
| | - Bingbing Xie
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, 2 Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China
| | - Jing Geng
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, 2 Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China
| | - Yanhong Ren
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, 2 Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China
| | - Rongguo Zhang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, 100025, China
| | - Min Liu
- Department of Radiology, China-Japan Friendship Hospital, 2 Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China.
| | - Huaping Dai
- Department of Pulmonary and Critical Care Medicine, General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China.
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, 2 Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China.
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Petelytska L, Bonomi F, Cannistrà C, Fiorentini E, Peretti S, Torracchi S, Bernardini P, Coccia C, De Luca R, Economou A, Levani J, Matucci-Cerinic M, Distler O, Bruni C. Heterogeneity of determining disease severity, clinical course and outcomes in systemic sclerosis-associated interstitial lung disease: a systematic literature review. RMD Open 2023; 9:e003426. [PMID: 37940340 PMCID: PMC10632935 DOI: 10.1136/rmdopen-2023-003426] [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: 06/23/2023] [Accepted: 10/02/2023] [Indexed: 11/10/2023] Open
Abstract
Objective The course of systemic sclerosis-associated interstitial lung disease (SSc-ILD) is highly variable and different from continuously progressive idiopathic pulmonary fibrosis (IPF). Most proposed definitions of progressive pulmonary fibrosis or SSc-ILD severity are based on the research data from patients with IPF and are not validated for patients with SSc-ILD. Our study aimed to gather the current evidence for severity, progression and outcomes of SSc-ILD.Methods A systematic literature review to search for definitions of severity, progression and outcomes recorded for SSc-ILD was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines in Medline, Embase, Web of Science and Cochrane Library up to 1 August 2023.Results A total of 9054 papers were reviewed and 342 were finally included. The most frequent tools used for the definition of SSc-ILD progression and severity were combined changes of carbon monoxide diffusing capacity (DLCO) and forced vital capacity (FVC), isolated FVC or DLCO changes, high-resolution CT (HRCT) extension and composite algorithms including pulmonary function test, clinical signs and HRCT data. Mortality was the most frequently reported long-term event, both from all causes or ILD related.Conclusions The studies presenting definitions of SSc-ILD 'progression', 'severity' and 'outcome' show a large heterogeneity. These results emphasise the need for developing a standardised, consensus definition of severe SSc-ILD, to link a disease specific definition of progression as a surrogate outcome for clinical trials and clinical practice.PROSPERO registration number CRD42022379254.Cite Now.
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Affiliation(s)
- Liubov Petelytska
- Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department Internal Medicine #3, Bogomolets National Medical University, Kiiv, Ukraine
| | - Francesco Bonomi
- Department of Experimental and Clinical Medicine, Division of Rheumatology, University of Florence - Careggi University Hospital, Florence, Italy
| | - Carlo Cannistrà
- Department of Experimental and Clinical Medicine, Division of Rheumatology, University of Florence - Careggi University Hospital, Florence, Italy
| | - Elisa Fiorentini
- Department of Experimental and Clinical Medicine, Division of Rheumatology, University of Florence - Careggi University Hospital, Florence, Italy
| | - Silvia Peretti
- Department of Experimental and Clinical Medicine, Division of Rheumatology, University of Florence - Careggi University Hospital, Florence, Italy
| | - Sara Torracchi
- Department of Experimental and Clinical Medicine, Division of Rheumatology, University of Florence - Careggi University Hospital, Florence, Italy
| | - Pamela Bernardini
- Department of Experimental and Clinical Medicine, Division of Rheumatology, University of Florence - Careggi University Hospital, Florence, Italy
| | - Carmela Coccia
- Department of Experimental and Clinical Medicine, Division of Rheumatology, University of Florence - Careggi University Hospital, Florence, Italy
| | - Riccardo De Luca
- Department of Experimental and Clinical Medicine, Division of Rheumatology, University of Florence - Careggi University Hospital, Florence, Italy
| | - Alessio Economou
- Department of Experimental and Clinical Medicine, Division of Rheumatology, University of Florence - Careggi University Hospital, Florence, Italy
| | - Juela Levani
- Department of Experimental and Clinical Medicine, Division of Rheumatology, University of Florence - Careggi University Hospital, Florence, Italy
| | - Marco Matucci-Cerinic
- Department of Experimental and Clinical Medicine, Division of Rheumatology, University of Florence - Careggi University Hospital, Florence, Italy
- Unit of Immunology, Rheumatology, Allergy and Rare Diseases, San Raffaele Hospital, Milan, Italy
| | - Oliver Distler
- Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Cosimo Bruni
- Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Experimental and Clinical Medicine, Division of Rheumatology, University of Florence - Careggi University Hospital, Florence, Italy
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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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Suman G, Koo CW. Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases. J Thorac Imaging 2023; 38:S7-S18. [PMID: 37015833 DOI: 10.1097/rti.0000000000000705] [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: 04/06/2023]
Abstract
Interstitial lung disease (ILD) is a heterogeneous group of disorders with complex and varied imaging manifestations and prognosis. High-resolution computed tomography (HRCT) is the current standard-of-care imaging tool for ILD assessment. However, visual evaluation of HRCT is limited by interobserver variation and poor sensitivity for subtle changes. Such challenges have led to tremendous recent research interest in objective and reproducible methods to examine ILDs. Computer-aided CT analysis to include texture analysis and machine learning methods have recently been shown to be viable supplements to traditional visual assessment through improved characterization and quantification of ILDs. These quantitative tools have not only been shown to correlate well with pulmonary function tests and patient outcomes but are also useful in disease diagnosis, surveillance and management. In this review, we provide an overview of recent computer-aided tools in diagnosis, prognosis, and longitudinal evaluation of fibrotic ILDs, while outlining some of the pitfalls and challenges that have precluded further advancement of these tools as well as potential solutions and further endeavors.
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Affiliation(s)
- Garima Suman
- Division of Thoracic Imaging, Mayo Clinic, Rochester, MN
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Iwasawa T, Matsushita S, Hirayama M, Baba T, Ogura T. Quantitative Analysis for Lung Disease on Thin-Section CT. Diagnostics (Basel) 2023; 13:2988. [PMID: 37761355 PMCID: PMC10528918 DOI: 10.3390/diagnostics13182988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/30/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Thin-section computed tomography (CT) is widely employed not only for assessing morphology but also for evaluating respiratory function. Three-dimensional images obtained from thin-section CT provide precise measurements of lung, airway, and vessel volumes. These volumetric indices are correlated with traditional pulmonary function tests (PFT). CT also generates lung histograms. The volume ratio of areas with low and high attenuation correlates with PFT results. These quantitative image analyses have been utilized to investigate the early stages and disease progression of diffuse lung diseases, leading to the development of novel concepts such as pre-chronic obstructive pulmonary disease (pre-COPD) and interstitial lung abnormalities. Quantitative analysis proved particularly valuable during the COVID-19 pandemic when clinical evaluations were limited. In this review, we introduce CT analysis methods and explore their clinical applications in the context of various lung diseases. We also highlight technological advances, including images with matrices of 1024 × 1024 and slice thicknesses of 0.25 mm, which enhance the accuracy of these analyses.
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Affiliation(s)
- Tae Iwasawa
- Department of Radiology, Kanagawa Cardiovascular & Respiratory Center, 6-16-1 Tomioka-higashi, Kanazawa-ku, Yokohama 236-0051, Japan; (S.M.); (M.H.)
| | - Shoichiro Matsushita
- Department of Radiology, Kanagawa Cardiovascular & Respiratory Center, 6-16-1 Tomioka-higashi, Kanazawa-ku, Yokohama 236-0051, Japan; (S.M.); (M.H.)
| | - Mariko Hirayama
- Department of Radiology, Kanagawa Cardiovascular & Respiratory Center, 6-16-1 Tomioka-higashi, Kanazawa-ku, Yokohama 236-0051, Japan; (S.M.); (M.H.)
| | - Tomohisa Baba
- Department of Respiratory Medicine, Kanagawa Cardiovascular & Respiratory Center, 6-16-1 Tomioka-higashi, Kanazawa-ku, Yokohama 236-0051, Japan; (T.B.); (T.O.)
| | - Takashi Ogura
- Department of Respiratory Medicine, Kanagawa Cardiovascular & Respiratory Center, 6-16-1 Tomioka-higashi, Kanazawa-ku, Yokohama 236-0051, Japan; (T.B.); (T.O.)
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Yang X, Yu P, Xu W, Sun H, Duan J, Han Y, Zhu L, Xie B, Geng J, Luo S, Wang S, Ren Y, Zhang R, Liu M, Dai H, Wang C. Elastic Registration Algorithm Based on Three-dimensional Pulmonary MRI in Quantitative Assessment of Severity of Idiopathic Pulmonary Fibrosis. J Thorac Imaging 2023; 38:00005382-990000000-00090. [PMID: 37732685 PMCID: PMC10597429 DOI: 10.1097/rti.0000000000000735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
PURPOSE To quantitatively analyze lung elasticity in idiopathic pulmonary fibrosis (IPF) using elastic registration based on 3-dimensional pulmonary magnetic resonance imaging (3D-PMRI) and to assess its' correlations with the severity of IPF patients. MATERIAL AND METHODS Thirty male patients with IPF (mean age: 62±6 y) and 30 age-matched male healthy controls (mean age: 62±6 y) were prospectively enrolled. 3D-PMRI was acquired with a 3-dimensional ultrashort echo time sequence in end-inspiration and end-expiration. MR images were registered from end-inspiration to end-expiration with the elastic registration algorithm. Jacobian determinants were calculated from deformation fields on color maps. The log means of the Jacobian determinants (Jac-mean) and Dice similarity coefficient were used to describe lung elasticity between 2 groups. Then, the correlation of lung elasticity with dyspnea Medical Research Council (MRC) score, exercise tolerance, health-related quality of life, lung function, and the extent of pulmonary fibrosis on chest computed tomography were analyzed. RESULTS The Jac-mean of IPF patients (-0.19, [IQR: -0.22, -0.15]) decreased (absolute value), compared with healthy controls (-0.28, [IQR: -0.31, -0.24], P<0.001). The lung elasticity in IPF patients with dyspnea MRC≥3 (Jac-mean: -0.15; Dice: 0.06) was significantly lower than MRC 1 (Jac-mean: -0.22, P=0.001; Dice: 0.10, P=0.001) and MRC 2 (Jac-mean: -0.21, P=0.007; Dice: 0.09, P<0.001). In addition, the Jac-mean negatively correlated with forced vital capacity % (r=-0.487, P<0.001), forced expiratory volume 1% (r=-0.413, P=0.004), TLC% (r=-0.488, P<0.001), diffusing capacity of the lungs for carbon monoxide % predicted (r=-0.555, P<0.001), 6-minute walk distance (r=-0.441, P=0.030) and positively correlated with respiratory symptoms (r=0.430, P=0.042). Meanwhile, the Dice similarity coefficient positively correlated with forced vital capacity % (r=0.577, P=0.004), forced expiratory volume 1% (r=0.526, P=0.012), diffusing capacity of the lungs for carbon monoxide % predicted (r=0.435, P=0.048), 6-minute walk distance (r=0.473, P=0.016), final peripheral oxygen saturation (r=0.534, P=0.004), the extent of fibrosis on chest computed tomography (r=-0.421, P=0.021) and negatively correlated with activity (r=-0.431, P=0.048). CONCLUSION Lung elasticity decreased in IPF patients and correlated with dyspnea, exercise tolerance, health-related quality of life, lung function, and the extent of pulmonary fibrosis. The lung elasticity based on elastic registration of 3D-PMRI may be a new nonradiation imaging biomarker for quantitative evaluation of the severity of IPF.
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Affiliation(s)
- Xiaoyan Yang
- Capital Medical University
- National Center for Respiratory Medicine
- National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital
| | - Pengxin Yu
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd
| | - Wenqing Xu
- Department of Radiology, China-Japan Friendship Hospital
| | - Haishuang Sun
- National Center for Respiratory Medicine
- National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital
| | - Jianghui Duan
- Department of Radiology, China-Japan Friendship Hospital
| | - Yueyin Han
- National Center for Respiratory Medicine
- National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital
- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lili Zhu
- National Center for Respiratory Medicine
- National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital
| | - Bingbing Xie
- National Center for Respiratory Medicine
- National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital
| | - Jing Geng
- National Center for Respiratory Medicine
- National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital
| | - Sa Luo
- National Center for Respiratory Medicine
- National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital
| | - Shiyao Wang
- National Center for Respiratory Medicine
- National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital
| | - Yanhong Ren
- National Center for Respiratory Medicine
- National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital
| | - Rongguo Zhang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd
| | - Min Liu
- Department of Radiology, China-Japan Friendship Hospital
| | - Huaping Dai
- Capital Medical University
- National Center for Respiratory Medicine
- National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital
| | - Chen Wang
- Capital Medical University
- National Center for Respiratory Medicine
- National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital
- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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10
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Chassagnon G, Revel MP. Artificial intelligence in thoracic imaging: the transition from research to practice. Eur Radiol 2023; 33:6318-6319. [PMID: 37186215 DOI: 10.1007/s00330-023-09635-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 03/28/2023] [Accepted: 03/30/2023] [Indexed: 05/17/2023]
Affiliation(s)
- Guillaume Chassagnon
- Radiology Department, Hôpital Cochin, Paris, France.
- Université de Paris, Paris, France.
| | - Marie-Pierre Revel
- Radiology Department, Hôpital Cochin, Paris, France
- Université de Paris, Paris, France
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11
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Alamgeer M, Alruwais N, Alshahrani HM, Mohamed A, Assiri M. Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification. Cancers (Basel) 2023; 15:3982. [PMID: 37568800 PMCID: PMC10417684 DOI: 10.3390/cancers15153982] [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: 05/25/2023] [Revised: 07/27/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023] Open
Abstract
Lung cancer is the main cause of cancer deaths all over the world. An important reason for these deaths was late analysis and worse prediction. With the accelerated improvement of deep learning (DL) approaches, DL can be effectively and widely executed for several real-world applications in healthcare systems, like medical image interpretation and disease analysis. Medical imaging devices can be vital in primary-stage lung tumor analysis and the observation of lung tumors from the treatment. Many medical imaging modalities like computed tomography (CT), chest X-ray (CXR), molecular imaging, magnetic resonance imaging (MRI), and positron emission tomography (PET) systems are widely analyzed for lung cancer detection. This article presents a new dung beetle optimization modified deep feature fusion model for lung cancer detection and classification (DBOMDFF-LCC) technique. The presented DBOMDFF-LCC technique mainly depends upon the feature fusion and hyperparameter tuning process. To accomplish this, the DBOMDFF-LCC technique uses a feature fusion process comprising three DL models, namely residual network (ResNet), densely connected network (DenseNet), and Inception-ResNet-v2. Furthermore, the DBO approach was employed for the optimum hyperparameter selection of three DL approaches. For lung cancer detection purposes, the DBOMDFF-LCC system utilizes a long short-term memory (LSTM) approach. The simulation result analysis of the DBOMDFF-LCC technique of the medical dataset is investigated using different evaluation metrics. The extensive comparative results highlighted the betterment of the DBOMDFF-LCC technique of lung cancer classification.
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Affiliation(s)
- Mohammad Alamgeer
- Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Abha 61421, Saudi Arabia
| | - Nuha Alruwais
- Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, P.O. Box 22459, Riyadh 11495, Saudi Arabia;
| | - Haya Mesfer Alshahrani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Abdullah Mohamed
- Research Centre, Future University in Egypt, New Cairo 11845, Egypt;
| | - Mohammed Assiri
- Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Aflaj 16273, Saudi Arabia;
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Rea G, Sverzellati N, Bocchino M, Lieto R, Milanese G, D'Alto M, Bocchini G, Maniscalco M, Valente T, Sica G. Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis "Expanding Horizons in Radiology". Diagnostics (Basel) 2023; 13:2333. [PMID: 37510077 PMCID: PMC10378251 DOI: 10.3390/diagnostics13142333] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 07/07/2023] [Accepted: 07/08/2023] [Indexed: 07/30/2023] Open
Abstract
Diffuse lung disorders (DLDs) and interstitial lung diseases (ILDs) are pathological conditions affecting the lung parenchyma and interstitial network. There are approximately 200 different entities within this category. Radiologists play an increasingly important role in diagnosing and monitoring ILDs, as they can provide non-invasive, rapid, and repeatable assessments using high-resolution computed tomography (HRCT). HRCT offers a detailed view of the lung parenchyma, resembling a low-magnification anatomical preparation from a histological perspective. The intrinsic contrast provided by air in HRCT enables the identification of even the subtlest morphological changes in the lung tissue. By interpreting the findings observed on HRCT, radiologists can make a differential diagnosis and provide a pattern diagnosis in collaboration with the clinical and functional data. The use of quantitative software and artificial intelligence (AI) further enhances the analysis of ILDs, providing an objective and comprehensive evaluation. The integration of "meta-data" such as demographics, laboratory, genomic, metabolomic, and proteomic data through AI could lead to a more comprehensive clinical and instrumental profiling beyond the human eye's capabilities.
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Affiliation(s)
- Gaetano Rea
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Nicola Sverzellati
- Section of Radiology, Unit of Surgical Science, Department of Medicine and Surgery (DiMeC), University of Parma, 43121 Parma, Italy
| | - Marialuisa Bocchino
- Department of Clinical Medicine and Surgery, Section of Respiratory Diseases, University Federico II, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Roberta Lieto
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Gianluca Milanese
- Section of Radiology, Unit of Surgical Science, Department of Medicine and Surgery (DiMeC), University of Parma, 43121 Parma, Italy
| | - Michele D'Alto
- Department of Cardiology, University "L. Vanvitelli"-Monaldi Hospital, 80131 Naples, Italy
| | - Giorgio Bocchini
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Mauro Maniscalco
- Department of Pneumology Clinical and Scientific Institutes Maugeri IRCSS, 82037 Telese, Italy
| | - Tullio Valente
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Giacomo Sica
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
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13
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Sun H, Yang X, Sun X, Meng X, Kang H, Zhang R, Zhang H, Liu M, Dai H, Wang C. Lung shrinking assessment on HRCT with elastic registration technique for monitoring idiopathic pulmonary fibrosis. Eur Radiol 2023; 33:2279-2288. [PMID: 36424500 PMCID: PMC10017651 DOI: 10.1007/s00330-022-09248-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 10/16/2022] [Accepted: 10/17/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVES Evaluation and follow-up of idiopathic pulmonary fibrosis (IPF) mainly rely on high-resolution computed tomography (HRCT) and pulmonary function tests (PFTs). The elastic registration technique can quantitatively assess lung shrinkage. We aimed to investigate the correlation between lung shrinkage and morphological and functional deterioration in IPF. METHODS Patients with IPF who underwent at least two HRCT scans and PFTs were retrospectively included. Elastic registration was performed on the baseline and follow-up HRCTs to obtain deformation maps of the whole lung. Jacobian determinants were calculated from the deformation fields and after logarithm transformation, log_jac values were represented on color maps to describe morphological deterioration, and to assess the correlation between log_jac values and PFTs. RESULTS A total of 69 patients with IPF (male 66) were included. Jacobian maps demonstrated constriction of the lung parenchyma marked at the lung base in patients who were deteriorated on visual and PFT assessment. The log_jac values were significantly reduced in the deteriorated patients compared to the stable patients. Mean log_jac values showed positive correlation with baseline percentage of predicted vital capacity (VC%) (r = 0.394, p < 0.05) and percentage of predicted forced vital capacity (FVC%) (r = 0.395, p < 0.05). Additionally, the mean log_jac values were positively correlated with pulmonary vascular volume (r = 0.438, p < 0.01) and the number of pulmonary vascular branches (r = 0.326, p < 0.01). CONCLUSIONS Elastic registration between baseline and follow-up HRCT was helpful to quantitatively assess the morphological deterioration of lung shrinkage in IPF, and the quantitative indicator log_jac values were significantly correlated with PFTs. KEY POINTS • The elastic registration on HRCT was helpful to quantitatively assess the deterioration of IPF. • Jacobian logarithm was significantly reduced in deteriorated patients and mean log_jac values were correlated with PFTs. • The mean log_jac values were related to the changes of pulmonary vascular volume and the number of vascular branches.
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Affiliation(s)
- Haishuang Sun
- Department of Respiratory Medicine, The First Hospital of Jilin University, Changchun, 130021, China.,Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, National Center for Respiratory Medicine, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, National Clinical Research Center for Respiratory Diseases, Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China.,Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Xiaoyan Yang
- Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, National Center for Respiratory Medicine, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, National Clinical Research Center for Respiratory Diseases, Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China
| | - Xuebiao Sun
- Department of Radiology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Xiapei Meng
- Department of Radiology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Han Kang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, 100025, China
| | - Rongguo Zhang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, 100025, China
| | - Haoyue Zhang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, 100025, China.,Department of Radiology, University of California, Los Angeles, Los Angeles, 90095, USA
| | - Min Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, 100029, China.
| | - Huaping Dai
- Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, National Center for Respiratory Medicine, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, National Clinical Research Center for Respiratory Diseases, Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China. .,Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
| | - Chen Wang
- Department of Respiratory Medicine, The First Hospital of Jilin University, Changchun, 130021, China. .,Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, National Center for Respiratory Medicine, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, National Clinical Research Center for Respiratory Diseases, Yinghua Dong Street, Hepingli, Chao Yang District, Beijing, 100029, China. .,Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
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14
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Milam ME, Koo CW. The current status and future of FDA-approved artificial intelligence tools in chest radiology in the United States. Clin Radiol 2023; 78:115-122. [PMID: 36180271 DOI: 10.1016/j.crad.2022.08.135] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/19/2022] [Indexed: 01/18/2023]
Abstract
Artificial intelligence (AI) is becoming more widespread within radiology. Capabilities that AI algorithms currently provide include detection, segmentation, classification, and quantification of pathological findings. Artificial intelligence software have created challenges for the traditional United States Food and Drug Administration (FDA) approval process for medical devices given their abilities to evolve over time with incremental data input. Currently, there are 190 FDA-approved radiology AI-based software devices, 42 of which pertain specifically to thoracic radiology. The majority of these algorithms are approved for the detection and/or analysis of pulmonary nodules, for monitoring placement of endotracheal tubes and indwelling catheters, for detection of emergent findings, and for assessment of pulmonary parenchyma; however, as technology evolves, there are many other potential applications that can be explored. For example, evaluation of non-idiopathic pulmonary fibrosis interstitial lung diseases, synthesis of imaging, clinical and/or laboratory data to yield comprehensive diagnoses, and survival or prognosis prediction of certain pathologies. With increasing physician and developer engagement, transparency and frequent communication between developers and regulatory agencies, such as the FDA, AI medical devices will be able to provide a critical supplement to patient management and ultimately enhance physicians' ability to improve patient care.
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Affiliation(s)
- M E Milam
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - C W Koo
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
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15
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Artificial intelligence in lung cancer: current applications and perspectives. Jpn J Radiol 2023; 41:235-244. [PMID: 36350524 PMCID: PMC9643917 DOI: 10.1007/s11604-022-01359-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/30/2022] [Indexed: 11/10/2022]
Abstract
Artificial intelligence (AI) has been a very active research topic over the last years and thoracic imaging has particularly benefited from the development of AI and in particular deep learning. We have now entered a phase of adopting AI into clinical practice. The objective of this article was to review the current applications and perspectives of AI in thoracic oncology. For pulmonary nodule detection, computer-aided detection (CADe) tools have been commercially available since the early 2000s. The more recent rise of deep learning and the availability of large annotated lung nodule datasets have allowed the development of new CADe tools with fewer false-positive results per examination. Classical machine learning and deep-learning methods were also used for pulmonary nodule segmentation allowing nodule volumetry and pulmonary nodule characterization. For pulmonary nodule characterization, radiomics and deep-learning approaches were used. Data from the National Lung Cancer Screening Trial (NLST) allowed the development of several computer-aided diagnostic (CADx) tools for diagnosing lung cancer on chest computed tomography. Finally, AI has been used as a means to perform virtual biopsies and to predict response to treatment or survival. Thus, many detection, characterization and stratification tools have been proposed, some of which are commercially available.
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16
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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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17
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Chan J, Auffermann WF. Artificial Intelligence in the Imaging of Diffuse Lung Disease. Radiol Clin North Am 2022; 60:1033-1040. [DOI: 10.1016/j.rcl.2022.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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18
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Ninagawa K, Kato M, Kikuchi Y, Sugimori H, Kono M, Fujieda Y, Tsujino I, Atsumi T. Predicting the response to pulmonary vasodilator therapy in systemic sclerosis with pulmonary hypertension by using quantitative chest CT. Mod Rheumatol 2022:6687422. [PMID: 36053564 DOI: 10.1093/mr/roac102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/20/2022] [Accepted: 09/01/2022] [Indexed: 11/14/2022]
Abstract
OBJECTIVE Systemic sclerosis (SSc) is associated with pulmonary vascular disease (PVD) and interstitial lung disease (ILD), making it difficult to differentiate pulmonary arterial hypertension and pulmonary hypertension (PH) due to lung diseases and/or hypoxia and to decide treatments. We aimed to predict the response to pulmonary vasodilators in patients with SSc and PH. METHODS 84 SSc patients were included with 47 having PH. Chest CT was evaluated using a software to calculate abnormal lung volume (ALV). To define the response to vasodilators, Δ mean pulmonary artery pressure (mPAP)/basal mPAP was used (cut-off value: 10%). The predictive value was evaluated by using receiver operating characteristic curve. RESULTS The mean (±SD) value of ALV was 26.8 (±32.2) %. A weak correlation was observed between ALV and forced vital capacity (FVC) (R = -0.46). The predictive value of ALV (area under curve; AUC = 0.74) was superior to that of FVC (AUC = 0.62) for the response to vasodilators. No hemodynamic parameters differed between patients with high and low ALV, whereas survival was worse in high ALV. CONCLUSION Quantitative chest CT well predicted the response to vasodilators in patients with SSc and PH. Our results suggest its utility in differentiating the dominance of PVD or ILD.
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Affiliation(s)
- Keita Ninagawa
- Department of Rheumatology, Endocrinology and Nephrology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Masaru Kato
- Department of Rheumatology, Endocrinology and Nephrology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Yasuka Kikuchi
- Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Hiroyuki Sugimori
- Department of Biomedical Science and Engineering, Faculty of Health Sciences, Hokkaido University, Sapporo, Japan
| | - Michihito Kono
- Department of Rheumatology, Endocrinology and Nephrology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Yuichiro Fujieda
- Department of Rheumatology, Endocrinology and Nephrology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Ichizo Tsujino
- First Department of Medicine, Hokkaido University Hospital, Sapporo, Japan
| | - Tatsuya Atsumi
- Department of Rheumatology, Endocrinology and Nephrology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
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Hoang-Thi TN, Chassagnon G, Tran HD, Le-Dong NN, Dinh-Xuan AT, Revel MP. How Artificial Intelligence in Imaging Can Better Serve Patients with Bronchial and Parenchymal Lung Diseases? J Pers Med 2022; 12:jpm12091429. [PMID: 36143214 PMCID: PMC9505778 DOI: 10.3390/jpm12091429] [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: 08/12/2022] [Revised: 08/25/2022] [Accepted: 08/26/2022] [Indexed: 11/16/2022] Open
Abstract
With the rapid development of computing today, artificial intelligence has become an essential part of everyday life, with medicine and lung health being no exception. Big data-based scientific research does not mean simply gathering a large amount of data and letting the machines do the work by themselves. Instead, scientists need to identify problems whose solution will have a positive impact on patients’ care. In this review, we will discuss the role of artificial intelligence from both physiological and anatomical standpoints, starting with automatic quantitative assessment of anatomical structures using lung imaging and considering disease detection and prognosis estimation based on machine learning. The evaluation of current strengths and limitations will allow us to have a broader view for future developments.
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Affiliation(s)
- Trieu-Nghi Hoang-Thi
- Department of Diagnostic Imaging, Vinmec Healthcare System, Ho Chi Minh City 70000, Vietnam
| | - Guillaume Chassagnon
- AP-HP. Centre, Cochin Hospital, Department of Radiology, Université de Paris, 75005 Paris, France
| | - Hai-Dang Tran
- Department of Diagnostic Imaging, Vinmec Healthcare System, Ho Chi Minh City 70000, Vietnam
| | - Nhat-Nam Le-Dong
- AP-HP. Centre, Cochin Hospital, Department of Respiratory Physiology, Université de Paris, 75005 Paris, France
| | - Anh Tuan Dinh-Xuan
- AP-HP. Centre, Cochin Hospital, Department of Respiratory Physiology, Université de Paris, 75005 Paris, France
| | - Marie-Pierre Revel
- AP-HP. Centre, Cochin Hospital, Department of Radiology, Université de Paris, 75005 Paris, France
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Fourcade C, Ferrer L, Moreau N, Santini G, Brennan A, Rousseau C, Lacombe M, Fleury V, Colombié M, Jézéquel P, Rubeaux M, Mateus D. Deformable image registration with deep network priors: a study on longitudinal PET images. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7e17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 07/04/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. This paper proposes a novel approach for the longitudinal registration of PET imaging acquired for the monitoring of patients with metastatic breast cancer. Unlike with other image analysis tasks, the use of deep learning (DL) has not significantly improved the performance of image registration. With this work, we propose a new registration approach to bridge the performance gap between conventional and DL-based methods: medical image registration method regularized by architecture (MIRRBA). Approach.
MIRRBA is a subject-specific deformable registration method which relies on a deep pyramidal architecture to parametrize the deformation field. Diverging from the usual deep-learning paradigms, MIRRBA does not require a learning database, but only a pair of images to be registered that is used to optimize the network's parameters. We applied MIRRBA on a private dataset of 110 whole-body PET images of patients with metastatic breast cancer. We used different architecture configurations to produce the deformation field and studied the results obtained. We also compared our method to several standard registration approaches: two conventional iterative registration methods (ANTs and Elastix) and two supervised DL-based models (LapIRN and Voxelmorph). Registration accuracy was evaluated using the Dice score, the target registration error, the average Hausdorff distance and the detection rate, while the realism of the registration obtained was evaluated using Jacobian's determinant. The ability of the different methods to shrink disappearing lesions was also computed with the disappearing rate. Main results. MIRRBA significantly improved all metrics when compared to DL-based approaches. The organ and lesion Dice scores of Voxelmorph improved by 6% and 52% respectively, while the ones of LapIRN increased by 5% and 65%. Regarding conventional approaches, MIRRBA presented comparable results showing the feasibility of our method. Significance. In this paper, we also demonstrate the regularizing power of deep architectures and present new elements to understand the role of the architecture in DL methods used for registration.
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Si-Mohamed SA, Nasser M, Colevray M, Nempont O, Lartaud PJ, Vlachomitrou A, Broussaud T, Ahmad K, Traclet J, Cottin V, Boussel L. Automatic quantitative computed tomography measurement of longitudinal lung volume loss in interstitial lung diseases. Eur Radiol 2022; 32:4292-4303. [PMID: 35029730 PMCID: PMC9123030 DOI: 10.1007/s00330-021-08482-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 11/09/2021] [Accepted: 11/24/2021] [Indexed: 12/01/2022]
Abstract
OBJECTIVES To compare the lung CT volume (CTvol) and pulmonary function tests in an interstitial lung disease (ILD) population. Then to evaluate the CTvol loss between idiopathic pulmonary fibrosis (IPF) and non-IPF and explore a prognostic value of annual CTvol loss in IPF. METHODS We conducted in an expert center a retrospective study between 2005 and 2018 on consecutive patients with ILD. CTvol was measured automatically using commercial software based on a deep learning algorithm. In the first group, Spearman correlation coefficients (r) between forced vital capacity (FVC), total lung capacity (TLC), and CTvol were calculated. In a second group, annual CTvol loss was calculated using linear regression analysis and compared with the Mann-Whitney test. In a last group of IPF patients, annual CTvol loss was calculated between baseline and 1-year CTs for investigating with the Youden index a prognostic value of major adverse event at 3 years. Univariate and log-rank tests were calculated. RESULTS In total, 560 patients (4610 CTs) were analyzed. For 1171 CTs, CTvol was correlated with FVC (r: 0.86) and TLC (r: 0.84) (p < 0.0001). In 408 patients (3332 CT), median annual CTvol loss was 155.7 mL in IPF versus 50.7 mL in non-IPF (p < 0.0001) over 5.03 years. In 73 IPF patients, a relative annual CTvol loss of 7.9% was associated with major adverse events (log-rank, p < 0.0001) in univariate analysis (p < 0.001). CONCLUSIONS Automated lung CT volume may be an alternative or a complementary biomarker to pulmonary function tests for the assessment of lung volume loss in ILD. KEY POINTS • There is a good correlation between lung CT volume and forced vital capacity, as well as for with total lung capacity measurements (r of 0.86 and 0.84 respectively, p < 0.0001). • Median annual CT volume loss is significantly higher in patients with idiopathic pulmonary fibrosis than in patients with other fibrotic interstitial lung diseases (155.7 versus 50.7 mL, p < 0.0001). • In idiopathic pulmonary fibrosis, a relative annual CT volume loss higher than 9.4% is associated with a significantly reduced mean survival time at 2.0 years versus 2.8 years (log-rank, p < 0.0001).
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Affiliation(s)
- Salim A. Si-Mohamed
- Radiology Department, Department of Cardiovascular and Thoracic Radiology, CHU Cardiologique Louis Pradel, Louis Pradel Hospital, 59 Boulevard Pinel, 69500 Bron, France ,University of Lyon, University Claude-Bernard Lyon 1, UJM-Saint-Étienne, CNRS, Inserm, CREATIS UMR 5220, U1206, 69621 Lyon, France
| | - Mouhamad Nasser
- National Reference Center for Rare Pulmonary Diseases, Louis Pradel Hospital, Hospices Civils de Lyon, UMR 754, INRAE, Claude Bernard University Lyon 1, Lyon, France
| | - Marion Colevray
- Radiology Department, Hôpital de La Croix-Rousse, 103 Grande rue de la Croix Rousse, 69004 Lyon, France
| | | | - Pierre-Jean Lartaud
- University of Lyon, University Claude-Bernard Lyon 1, UJM-Saint-Étienne, CNRS, Inserm, CREATIS UMR 5220, U1206, 69621 Lyon, France
| | | | - Thomas Broussaud
- University of Lyon, University Claude-Bernard Lyon 1, UJM-Saint-Étienne, CNRS, Inserm, CREATIS UMR 5220, U1206, 69621 Lyon, France
| | - Kais Ahmad
- National Reference Center for Rare Pulmonary Diseases, Louis Pradel Hospital, Hospices Civils de Lyon, UMR 754, INRAE, Claude Bernard University Lyon 1, Lyon, France
| | - Julie Traclet
- National Reference Center for Rare Pulmonary Diseases, Louis Pradel Hospital, Hospices Civils de Lyon, UMR 754, INRAE, Claude Bernard University Lyon 1, Lyon, France
| | - Vincent Cottin
- National Reference Center for Rare Pulmonary Diseases, Louis Pradel Hospital, Hospices Civils de Lyon, UMR 754, INRAE, Claude Bernard University Lyon 1, Lyon, France
| | - Loic Boussel
- University of Lyon, University Claude-Bernard Lyon 1, UJM-Saint-Étienne, CNRS, Inserm, CREATIS UMR 5220, U1206, 69621 Lyon, France ,Radiology Department, Hôpital de La Croix-Rousse, 103 Grande rue de la Croix Rousse, 69004 Lyon, France
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Abstract
PURPOSE OF REVIEW To discuss rationale and methods for determining progressive lung fibrosis on thoracic computed tomography (CT) and describe limitations and challenges. RECENT FINDINGS Identifying patients with progressive lung fibrosis is important to determine optimal treatment. Serial high-resolution computed tomography is a method of determining disease progression. A number of studies are reviewed in this article, that have explored various parameters (both visual and automated) that signify progressive fibrosis on CT. SUMMARY To reliably use serial CT as a marker of disease progression in fibrotic lung disease, clinicians and radiologists need to be aware of the optimal methods for identifying changes in disease extent, and understand their limitations.
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Bergier H, Duron L, Sordet C, Kawka L, Schlencker A, Chasset F, Arnaud L. Digital health, big data and smart technologies for the care of patients with systemic autoimmune diseases: Where do we stand? Autoimmun Rev 2021; 20:102864. [PMID: 34118454 DOI: 10.1016/j.autrev.2021.102864] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 04/03/2021] [Indexed: 12/22/2022]
Abstract
The past decade has seen tremendous development in digital health, including in innovative new technologies such as Electronic Health Records, telemedicine, virtual visits, wearable technology and sophisticated analytical tools such as artificial intelligence (AI) and machine learning for the deep-integration of big data. In the field of rare connective tissue diseases (rCTDs), these opportunities include increased access to scarce and remote expertise, improved patient monitoring, increased participation and therapeutic adherence, better patient outcomes and patient empowerment. In this review, we discuss opportunities and key-barriers to improve application of digital health technologies in the field of autoimmune diseases. We also describe what could be the fully digital pathway of rCTD patients. Smart technologies can be used to provide real-world evidence about the natural history of rCTDs, to determine real-life drug utilization, advanced efficacy and safety data for rare diseases and highlight significant unmet needs. Yet, digitalization remains one of the most challenging issues faced by rCTD patients, their physicians and healthcare systems. Digital health technologies offer enormous potential to improve autoimmune rCTD care but this potential has so far been largely unrealized due to those significant obstacles. The need for robust assessments of the efficacy, affordability and scalability of AI in the context of digital health is crucial to improve the care of patients with rare autoimmune diseases.
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Affiliation(s)
- Hugo Bergier
- Service de rhumatologie, Centre National de Référence des Maladies Auto-immunes Systémiques Rares Est Sud-Ouest (RESO), Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Loïc Duron
- Department of neuroradiology, A. Rothshield Foundation Hospital, Paris, France
| | - Christelle Sordet
- Service de rhumatologie, Centre National de Référence des Maladies Auto-immunes Systémiques Rares Est Sud-Ouest (RESO), Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Lou Kawka
- Service de rhumatologie, Centre National de Référence des Maladies Auto-immunes Systémiques Rares Est Sud-Ouest (RESO), Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Aurélien Schlencker
- Service de rhumatologie, Centre National de Référence des Maladies Auto-immunes Systémiques Rares Est Sud-Ouest (RESO), Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - François Chasset
- Sorbonne Université, Faculté de médecine, Service de dermatologie et Allergologie, Hôpital Tenon, Paris, France
| | - Laurent Arnaud
- Department of neuroradiology, A. Rothshield Foundation Hospital, Paris, France.
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24
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Verschakelen JA. Lung Shrinkage: An Additional CT Marker in the Follow-up of Fibrotic Interstitial Lung Disease. Radiology 2020; 298:199-200. [PMID: 33084508 DOI: 10.1148/radiol.2020203767] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Johny A Verschakelen
- From the Department of Radiology, University Hospitals Leuven, Herestraat 49, B-3000 Leuven, Belgium
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