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Zhao J, Long Y, Li S, Li X, Zhang Y, Hu J, Han L, Ren L. Use of artificial intelligence algorithms to analyse systemic sclerosis-interstitial lung disease imaging features. Rheumatol Int 2024; 44:2027-2041. [PMID: 39207588 PMCID: PMC11393027 DOI: 10.1007/s00296-024-05681-7] [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: 06/12/2024] [Accepted: 08/04/2024] [Indexed: 09/04/2024]
Abstract
The use of artificial intelligence (AI) in high-resolution computed tomography (HRCT) for diagnosing systemic sclerosis-associated interstitial lung disease (SSc-ILD) is relatively limited. This study aimed to analyse lung HRCT images of patients with systemic sclerosis with interstitial lung disease (SSc-ILD) using artificial intelligence (AI), conduct correlation analysis with clinical manifestations and prognosis, and explore the features and prognosis of SSc-ILD. Overall, 72 lung HRCT images and clinical data of 58 patients with SSC-ILD were collected. ILD lesion type, location, and volume on HRCT images were identified and evaluated using AI. The imaging characteristics of diffuse SSC (dSSc)-ILD and limited SSc-ILD (lSSc-ILD) were statistically analysed. Furthermore, the correlations between lesion type, clinical indicators, and prognosis were investigated. dSSc and lSSc were more prevalent in patients with a disease duration of < 1 and ≥ 5 years, respectively. SSc-ILD mainly comprises non-specific interstitial pneumonia (NSIP), usual interstitial pneumonia (UIP), and unclassifiable idiopathic interstitial pneumonia. HRCT reveals various lesion types in the early stages of the disease, with an increase in the number of lesion types as the disease progresses. Lesions appearing as grid, ground-glass, and nodular shadows were dispersed throughout both lungs, while those appearing as consolidation shadows and honeycomb were distributed across the lungs. Ground-glass opacity lesion type was absent on HRCT images of patients with SSc-ILD and pulmonary hypertension. This study showed that AI can efficiently analyse imaging characteristics of SSc-ILD, demonstrating its potential to learn from complex images with high generalisation ability.
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Affiliation(s)
- Jing Zhao
- Department of Rheumatology, People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture (The First Affiliated Hospital of Jishou University), Intersection of Shiji Avenue and Jianxin Road, Jishou, 416000, Hunan, People's Republic of China
| | - Ying Long
- Department of Rheumatology, Xiangya Hospital of Central South University, Changsha, People's Republic of China
- Provincial Clinical Research Center for Rheumatic and Immunologic Diseases, Xiangya Hospital of Central South University, Changsha, People's Republic of China
| | - Shengtao Li
- Department of Urology, People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture (The First Affiliated Hospital of Jishou University), Jishou, 416000, Hunan, People's Republic of China
| | - Xiaozhen Li
- Department of Rheumatology, People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture (The First Affiliated Hospital of Jishou University), Intersection of Shiji Avenue and Jianxin Road, Jishou, 416000, Hunan, People's Republic of China
| | - Yi Zhang
- Department of Rheumatology, People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture (The First Affiliated Hospital of Jishou University), Intersection of Shiji Avenue and Jianxin Road, Jishou, 416000, Hunan, People's Republic of China
| | - Juan Hu
- Department of Rheumatology, People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture (The First Affiliated Hospital of Jishou University), Intersection of Shiji Avenue and Jianxin Road, Jishou, 416000, Hunan, People's Republic of China
| | - Lin Han
- Department of Imaging, People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture (The First Affiliated Hospital of Jishou University), Jishou, 416000, Hunan, People's Republic of China
| | - Li Ren
- Department of Rheumatology, People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture (The First Affiliated Hospital of Jishou University), Intersection of Shiji Avenue and Jianxin Road, Jishou, 416000, Hunan, People's Republic of China.
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Zagurovskaya M. Sequential Prediction of Interstitial Lung Abnormalities Using Machine Learning. Radiology 2024; 312:e242020. [PMID: 39225602 DOI: 10.1148/radiol.242020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Affiliation(s)
- Marianna Zagurovskaya
- From the Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 University Blvd, Indianapolis, IN 46202
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3
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Pugashetti JV, Khanna D, Kazerooni EA, Oldham J. Clinically Relevant Biomarkers in Connective Tissue Disease-Associated Interstitial Lung Disease. Rheum Dis Clin North Am 2024; 50:439-461. [PMID: 38942579 DOI: 10.1016/j.rdc.2024.03.007] [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: 06/30/2024]
Abstract
Interstitial lung disease (ILD) complicates connective tissue disease (CTD) with variable incidence and is a leading cause of death in these patients. To improve CTD-ILD outcomes, early recognition and management of ILD is critical. Blood-based and radiologic biomarkers that assist in the diagnosis CTD-ILD have long been studied. Recent studies, including -omic investigations, have also begun to identify biomarkers that may help prognosticate such patients. This review provides an overview of clinically relevant biomarkers in patients with CTD-ILD, highlighting recent advances to assist in the diagnosis and prognostication of CTD-ILD.
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Affiliation(s)
- Janelle Vu Pugashetti
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan.
| | - Dinesh Khanna
- Scleroderma Program, Division of Rheumatology, Department of Internal Medicine, University of Michigan
| | - Ella A Kazerooni
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan; Division of Cardiothoracic Radiology, Department of Radiology, University of Michigan
| | - Justin Oldham
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan; Department of Epidemiology, University of Michigan
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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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Chen JX, Shen YC, Peng SL, Chen YW, Fang HY, Lan JL, Shih CT. Pattern classification of interstitial lung diseases from computed tomography images using a ResNet-based network with a split-transform-merge strategy and split attention. Phys Eng Sci Med 2024; 47:755-767. [PMID: 38436886 DOI: 10.1007/s13246-024-01404-1] [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: 09/08/2023] [Accepted: 02/09/2024] [Indexed: 03/05/2024]
Abstract
In patients with interstitial lung disease (ILD), accurate pattern assessment from their computed tomography (CT) images could help track lung abnormalities and evaluate treatment efficacy. Based on excellent image classification performance, convolutional neural networks (CNNs) have been massively investigated for classifying and labeling pathological patterns in the CT images of ILD patients. However, previous studies rarely considered the three-dimensional (3D) structure of the pathological patterns of ILD and used two-dimensional network input. In addition, ResNet-based networks such as SE-ResNet and ResNeXt with high classification performance have not been used for pattern classification of ILD. This study proposed a SE-ResNeXt-SA-18 for classifying pathological patterns of ILD. The SE-ResNeXt-SA-18 integrated the multipath design of the ResNeXt and the feature weighting of the squeeze-and-excitation network with split attention. The classification performance of the SE-ResNeXt-SA-18 was compared with the ResNet-18 and SE-ResNeXt-18. The influence of the input patch size on classification performance was also evaluated. Results show that the classification accuracy was increased with the increase of the patch size. With a 32 × 32 × 16 input, the SE-ResNeXt-SA-18 presented the highest performance with average accuracy, sensitivity, and specificity of 0.991, 0.979, and 0.994. High-weight regions in the class activation maps of the SE-ResNeXt-SA-18 also matched the specific pattern features. In comparison, the performance of the SE-ResNeXt-SA-18 is superior to the previously reported CNNs in classifying the ILD patterns. We concluded that the SE-ResNeXt-SA-18 could help track or monitor the progress of ILD through accuracy pattern classification.
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Affiliation(s)
- Jian-Xun Chen
- Department of Thoracic Surgery, China Medical University Hospital, Taichung, Taiwan
| | - Yu-Cheng Shen
- Department of Thoracic Surgery, China Medical University Hospital, Taichung, Taiwan
| | - Shin-Lei Peng
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan
| | - Yi-Wen Chen
- x-Dimension Center for Medical Research and Translation, China Medical University Hospital, Taichung, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
| | - Hsin-Yuan Fang
- x-Dimension Center for Medical Research and Translation, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
| | - Joung-Liang Lan
- School of Medicine, China Medical University, Taichung, Taiwan
- Rheumatology and Immunology Center, China Medical University Hospital, Taichung, Taiwan
| | - Cheng-Ting Shih
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan.
- x-Dimension Center for Medical Research and Translation, China Medical University Hospital, Taichung, Taiwan.
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Qiu J, Mitra J, Ghose S, Dumas C, Yang J, Sarachan B, Judson MA. A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis. Diagnostics (Basel) 2024; 14:1049. [PMID: 38786347 PMCID: PMC11120014 DOI: 10.3390/diagnostics14101049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings poses challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we develop a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures the localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieves the highest performance with accuracy, sensitivity, specificity, precision, F1-score, and combined AUC of 0.93 ± 0.04, 0.94 ± 0.04, 0.93 ± 0.08, 0.95 ± 0.05, 0.94 ± 0.04, and 0.97, respectively, in a five-fold cross-validation study with pulmonary sarcoidosis (n = 126) and LCa (n = 93) cases. A detailed ablation study showing the impact of CNN + ViT compared to CNN or ViT alone, and CT + radiomics input, compared to CT or radiomics alone, is also presented in this work. Overall, the AI model developed in this work offers promising potential for triaging the pulmonary sarcoidosis patients for timely diagnosis and treatment from chest CT.
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Affiliation(s)
- Jianwei Qiu
- GE HealthCare, Niskayuna, NY 12309, USA; (J.Q.); (S.G.); (B.S.)
| | - Jhimli Mitra
- GE HealthCare, Niskayuna, NY 12309, USA; (J.Q.); (S.G.); (B.S.)
| | - Soumya Ghose
- GE HealthCare, Niskayuna, NY 12309, USA; (J.Q.); (S.G.); (B.S.)
| | - Camille Dumas
- Department of Medical Imaging, Albany Medical College, Albany, NY 12208, USA; (C.D.); (J.Y.)
| | - Jun Yang
- Department of Medical Imaging, Albany Medical College, Albany, NY 12208, USA; (C.D.); (J.Y.)
| | - Brion Sarachan
- GE HealthCare, Niskayuna, NY 12309, USA; (J.Q.); (S.G.); (B.S.)
| | - Marc A. Judson
- Department of Medicine, Albany Medical College, Albany, NY 12208, USA;
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Ahn Y, Lee SM, Nam Y, Lee H, Choe J, Do KH, Seo JB. Deep Learning-Based CT Reconstruction Kernel Conversion in the Quantification of Interstitial Lung Disease: Effect on Reproducibility. Acad Radiol 2024; 31:693-705. [PMID: 37516583 DOI: 10.1016/j.acra.2023.06.008] [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: 04/21/2023] [Revised: 06/11/2023] [Accepted: 06/12/2023] [Indexed: 07/31/2023]
Abstract
RATIONALE AND OBJECTIVES The effect of different computed tomography (CT) reconstruction kernels on the quantification of interstitial lung disease (ILD) has not been clearly demonstrated. The study aimed to investigate the effect of reconstruction kernels on the quantification of ILD on CT and determine whether deep learning-based kernel conversion can reduce the variability of automated quantification results between different CT kernels. MATERIALS AND METHODS Patients with ILD or interstitial lung abnormality who underwent noncontrast high-resolution CT between June 2022 and September 2022 were retrospectively included. Images were reconstructed with three different kernels: B30f, B50f, and B60f. B60f was regarded as the reference standard for quantification, and B30f and B50f images were converted to B60f images using a deep learning-based algorithm. Each disease pattern of ILD and the fibrotic score were quantified using commercial software. The effect of kernel conversion on measurement variability was estimated using intraclass correlation coefficient (ICC) and Bland-Altman method. RESULTS A total of 194 patients were included in the study. Application of different kernels induced differences in the quantified extent of each pattern. Reticular opacity and honeycombing were underestimated on B30f images and overestimated on B50f images. After kernel conversion, measurement variability was reduced (mean difference, from -2.0 to 3.9 to -0.3 to 0.4%, and 95% limits of agreement [LOA], from [-5.0, 12.7] to [-2.7, 2.1]). The fibrotic score for converted B60f from B50f images was almost equivalent to the original B60f (ICC, 1.000; mean difference, 0.0; and 95% LOA [-0.4, 0.4]). CONCLUSION Quantitative CT analysis of ILD was affected by the application of different kernels, but deep learning-based kernel conversion effectively reduced measurement variability, improving the reproducibility of quantification.
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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, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 138-736, Republic of Korea (Y.A., S.M.L., J.C., K.-H.D., J.B.S.)
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 138-736, Republic of Korea (Y.A., S.M.L., J.C., K.-H.D., J.B.S.).
| | - Yujin Nam
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Republic of Korea (Y.N.)
| | - Hyunna Lee
- Bigdata Research Center, Asan Institute for Life Science, Asan Medical Center, Seoul, Republic of Korea (H.L.)
| | - Jooae Choe
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 138-736, Republic of Korea (Y.A., S.M.L., J.C., K.-H.D., J.B.S.)
| | - Kyung-Hyun Do
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 138-736, Republic of Korea (Y.A., S.M.L., J.C., K.-H.D., J.B.S.)
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 138-736, Republic of Korea (Y.A., S.M.L., J.C., K.-H.D., J.B.S.)
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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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10
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Park S, Choe J, Hwang HJ, Noh HN, Jung YJ, Lee JB, Do KH, Chae EJ, Seo JB. Long-Term Follow-Up of Interstitial Lung Abnormality: Implication in Follow-Up Strategy and Risk Thresholds. Am J Respir Crit Care Med 2023; 208:858-867. [PMID: 37590877 DOI: 10.1164/rccm.202303-0410oc] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 08/17/2023] [Indexed: 08/19/2023] Open
Abstract
Rationale: The optimal follow-up computed tomography (CT) interval for detecting the progression of interstitial lung abnormality (ILA) is unknown. Objectives: To identify optimal follow-up strategies and extent thresholds on CT relevant to outcomes. Methods: This retrospective study included self-referred screening participants aged 50 years or older, including nonsmokers, who had imaging findings relevant to ILA on chest CT scans. Consecutive CT scans were evaluated to determine the dates of the initial CT showing ILA and the CT showing progression. Deep learning-based ILA quantification was performed. Cox regression was used to identify risk factors for the time to ILA progression and progression to usual interstitial pneumonia (UIP). Measurements and Main Results: Of the 305 participants with a median follow-up duration of 11.3 years (interquartile range, 8.4-14.3 yr), 239 (78.4%) had ILA on at least one CT scan. In participants with serial follow-up CT studies, ILA progression was observed in 80.5% (161 of 200), and progression to UIP was observed in 17.3% (31 of 179), with median times to progression of 3.2 years (95% confidence interval [CI], 3.0-3.4 yr) and 11.8 years (95% CI, 10.8-13.0 yr), respectively. The extent of fibrosis on CT was an independent risk factor for ILA progression (hazard ratio, 1.12 [95% CI, 1.02-1.23]) and progression to UIP (hazard ratio, 1.39 [95% CI, 1.07-1.80]). Risk groups based on honeycombing and extent of fibrosis (1% in the whole lung or 5% per lung zone) showed significant differences in 10-year overall survival (P = 0.02). Conclusions: For individuals with initially detected ILA, follow-up CT at 3-year intervals may be appropriate to monitor radiologic progression; however, those at high risk of adverse outcomes on the basis of the quantified extent of fibrotic ILA and the presence of honeycombing may benefit from shortening the interval for follow-up scans.
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Affiliation(s)
- Sohee Park
- Department of Radiology and Research Institute of Radiology
| | - Jooae Choe
- Department of Radiology and Research Institute of Radiology
| | - Hye Jeon Hwang
- Department of Radiology and Research Institute of Radiology
| | - Han Na Noh
- Health Screening and Promotion Center, and
| | | | - Jung-Bok Lee
- Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Kyung-Hyun Do
- Department of Radiology and Research Institute of Radiology
| | - Eun Jin Chae
- Department of Radiology and Research Institute of Radiology
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology
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11
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Park D, Jang R, Chung MJ, An HJ, Bak S, Choi E, Hwang D. Development and validation of a hybrid deep learning-machine learning approach for severity assessment of COVID-19 and other pneumonias. Sci Rep 2023; 13:13420. [PMID: 37591967 PMCID: PMC10435445 DOI: 10.1038/s41598-023-40506-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: 06/10/2023] [Accepted: 08/11/2023] [Indexed: 08/19/2023] Open
Abstract
The Coronavirus Disease 2019 (COVID-19) is transitioning into the endemic phase. Nonetheless, it is crucial to remain mindful that pandemics related to infectious respiratory diseases (IRDs) can emerge unpredictably. Therefore, we aimed to develop and validate a severity assessment model for IRDs, including COVID-19, influenza, and novel influenza, using CT images on a multi-centre data set. Of the 805 COVID-19 patients collected from a single centre, 649 were used for training and 156 were used for internal validation (D1). Additionally, three external validation sets were obtained from 7 cohorts: 1138 patients with COVID-19 (D2), and 233 patients with influenza and novel influenza (D3). A hybrid model, referred to as Hybrid-DDM, was constructed by combining two deep learning models and a machine learning model. Across datasets D1, D2, and D3, the Hybrid-DDM exhibited significantly improved performance compared to the baseline model. The areas under the receiver operating curves (AUCs) were 0.830 versus 0.767 (p = 0.036) in D1, 0.801 versus 0.753 (p < 0.001) in D2, and 0.774 versus 0.668 (p < 0.001) in D3. This study indicates that the Hybrid-DDM model, trained using COVID-19 patient data, is effective and can also be applicable to patients with other types of viral pneumonia.
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Affiliation(s)
- Doohyun Park
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | | | - Myung Jin Chung
- Medical AI Research Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | | | | | - Euijoon Choi
- Department of Artificial Intelligence, Yonsei University, Seoul, Republic of Korea
| | - Dosik Hwang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
- Center for Healthcare Robotics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea.
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea.
- Department of Radiology and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, Republic of Korea.
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12
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Hwang HJ, Kim H, Seo JB, Ye JC, Oh G, Lee SM, Jang R, Yun J, Kim N, Park HJ, Lee HY, Yoon SH, Shin KE, Lee JW, Kwon W, Sun JS, You S, Chung MH, Gil BM, Lim JK, Lee Y, Hong SJ, Choi YW. Generative Adversarial Network-Based Image Conversion Among Different Computed Tomography Protocols and Vendors: Effects on Accuracy and Variability in Quantifying Regional Disease Patterns of Interstitial Lung Disease. Korean J Radiol 2023; 24:807-820. [PMID: 37500581 PMCID: PMC10400368 DOI: 10.3348/kjr.2023.0088] [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: 01/31/2023] [Revised: 06/12/2023] [Accepted: 06/18/2023] [Indexed: 07/29/2023] Open
Abstract
OBJECTIVE To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. MATERIALS AND METHODS This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. RESULTS Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT. CONCLUSION CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.
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Affiliation(s)
- Hye Jeon Hwang
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyunjong Kim
- Robotics Program, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Jong Chul Ye
- Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Gyutaek Oh
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ryoungwoo Jang
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jihye Yun
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hee Jun Park
- Coreline Soft, Co., Ltd, Seoul, Republic of Korea
| | - Ho Yun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Kyung Eun Shin
- Department of Radiology, Soonchunhyang University Bucheon Hospital, Bucheon, Republic of Korea
| | - Jae Wook Lee
- Department of Radiology, Soonchunhyang University Bucheon Hospital, Bucheon, Republic of Korea
| | - Woocheol Kwon
- Department of Radiology, Ewha Womans University Seoul Hospital, Seoul, Republic of Korea
- Department of Radiology, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Joo Sung Sun
- Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Seulgi You
- Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Myung Hee Chung
- Department of Radiology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Bo Mi Gil
- Department of Radiology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jae-Kwang Lim
- Department of Radiology, Kyungpook National University School of Medicine, Daegu, Republic of Korea
| | - Youkyung Lee
- Department of Radiology, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri, Republic of Korea
| | - Su Jin Hong
- Department of Radiology, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri, Republic of Korea
| | - Yo Won Choi
- Department of Radiology, Hanyang University Seoul Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea
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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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Kim DH, Sun S, Cho SI, Kong HJ, Lee JW, Lee JH, Suh DH. Automated Facial Acne Lesion Detecting and Counting Algorithm for Acne Severity Evaluation and Its Utility in Assisting Dermatologists. Am J Clin Dermatol 2023:10.1007/s40257-023-00777-5. [PMID: 37160644 DOI: 10.1007/s40257-023-00777-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/27/2023] [Indexed: 05/11/2023]
Abstract
BACKGROUND Although lesion counting is an evaluation method that effectively analyzes facial acne severity, its usage is limited because of difficult implementation. OBJECTIVES We aimed to develop and validate an automated algorithm that detects and counts acne lesions by type, and to evaluate its clinical applicability as an assistance tool through a reader test. METHODS A total of 20,699 lesions (closed and open comedones, papules, nodules/cysts, and pustules) were manually labeled on 1213 facial images of 398 facial acne photography sets (frontal and both lateral views) acquired from 258 patients and used for training and validating algorithms based on a convolutional neural network for classifying five classes of acne lesions or for binary classification into noninflammatory and inflammatory lesions. RESULTS In the validation dataset, the highest mean average precision was 28.48 for the binary classification algorithm. Pearson's correlation of lesion counts between algorithm and ground-truth was 0.72 (noninflammatory) and 0.90 (inflammatory), respectively. In the reader test, eight readers (100.0%) detected and counted lesions more accurately using the algorithm compared with the reader-alone evaluation. CONCLUSIONS Overall, our algorithm demonstrated clinically applicable performance in detecting and counting facial acne lesions by type and its utility as an assistance tool for evaluating acne severity.
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Affiliation(s)
- Dong Hyo Kim
- Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea
- Acne, Rosacea, Seborrheic Dermatitis and Hidradenitis Suppurativa Research Laboratory, Seoul National University Hospital, Seoul, South Korea
| | - Sukkyu Sun
- Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea
| | - Soo Ick Cho
- Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyoun-Joong Kong
- Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Ji Won Lee
- Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea
| | - Jun Hyo Lee
- Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea
- Acne, Rosacea, Seborrheic Dermatitis and Hidradenitis Suppurativa Research Laboratory, Seoul National University Hospital, Seoul, South Korea
| | - Dae Hun Suh
- Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea.
- Acne, Rosacea, Seborrheic Dermatitis and Hidradenitis Suppurativa Research Laboratory, Seoul National University Hospital, Seoul, South Korea.
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Pugashetti JV, Khanna D, Kazerooni EA, Oldham J. Clinically Relevant Biomarkers in Connective Tissue Disease-Associated Interstitial Lung Disease. Immunol Allergy Clin North Am 2023; 43:411-433. [PMID: 37055096 PMCID: PMC10584384 DOI: 10.1016/j.iac.2023.01.012] [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: 03/06/2023]
Abstract
Interstitial lung disease (ILD) complicates connective tissue disease (CTD) with variable incidence and is a leading cause of death in these patients. To improve CTD-ILD outcomes, early recognition and management of ILD is critical. Blood-based and radiologic biomarkers that assist in the diagnosis CTD-ILD have long been studied. Recent studies, including -omic investigations, have also begun to identify biomarkers that may help prognosticate such patients. This review provides an overview of clinically relevant biomarkers in patients with CTD-ILD, highlighting recent advances to assist in the diagnosis and prognostication of CTD-ILD.
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Affiliation(s)
- Janelle Vu Pugashetti
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan.
| | - Dinesh Khanna
- Scleroderma Program, Division of Rheumatology, Department of Internal Medicine, University of Michigan
| | - Ella A Kazerooni
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan; Division of Cardiothoracic Radiology, Department of Radiology, University of Michigan
| | - Justin Oldham
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan; Department of Epidemiology, University of Michigan
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Chae KJ, Lim S, Seo JB, Hwang HJ, Choi H, Lynch D, Jin GY. Interstitial Lung Abnormalities at CT in the Korean National Lung Cancer Screening Program: Prevalence and Deep Learning-based Texture Analysis. Radiology 2023; 307:e222828. [PMID: 37097142 DOI: 10.1148/radiol.222828] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
Background Interstitial lung abnormalities (ILAs) are associated with worse clinical outcomes, but ILA with lung cancer screening CT has not been quantitatively assessed. Purpose To determine the prevalence of ILA at CT examinations from the Korean National Lung Cancer Screening Program and define an optimal lung area threshold for ILA detection with CT with use of deep learning-based texture analysis. Materials and Methods This retrospective study included participants who underwent chest CT between April 2017 and December 2020 at two medical centers participating in the Korean National Lung Cancer Screening Program. CT findings were classified by three radiologists into three groups: no ILA, equivocal ILA, and ILA (fibrotic and nonfibrotic). Progression was evaluated between baseline and last follow-up CT scan. The extent of ILA was assessed visually and quantitatively with use of deep learning-based texture analysis. The Youden index was used to determine an optimal cutoff value for detecting ILA with use of texture analysis. Demographics and ILA subcategories were compared between participants with progressive and nonprogressive ILA. Results A total of 3118 participants were included in this study, and ILAs were observed with the CT scans of 120 individuals (4%). The median extent of ILA calculated by the quantitative system was 5.8% for the ILA group, 0.7% for the equivocal ILA group, and 0.1% for the no ILA group (P < .001). A 1.8% area threshold in a lung zone for quantitative detection of ILA showed 100% sensitivity and 99% specificity. Progression was observed in 48% of visually assessed fibrotic ILAs (15 of 31), and quantitative extent of ILA increased by 3.1% in subjects with progression. Conclusion ILAs were detected in 4% of the Korean lung cancer screening population. Deep learning-based texture analysis showed high sensitivity and specificity for detecting ILA with use of a 1.8% lung area cutoff value. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Egashira and Nishino in this issue.
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Affiliation(s)
- Kum Ju Chae
- From the Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, 20 Geonjiro Deokjin-gu, Jeonju-si, Jeollabuk-do, Korea 54907 (K.J.C., G.Y.J.); Department of Radiology, Jeonbuk National University Medical School, Jeonju, Korea (K.J.C., G.Y.J.); Department of Radiology, National Jewish Health, Denver, Colo (K.J.C., H.J.H., D.L.); Department of Radiology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea (S.L.); Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea (J.B.S., H.J.H.); and Department of Statistics and Institute of Applied Statistics, Jeonbuk National University, Jeonju, Republic of Korea (H.C.)
| | - Soyeoun Lim
- From the Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, 20 Geonjiro Deokjin-gu, Jeonju-si, Jeollabuk-do, Korea 54907 (K.J.C., G.Y.J.); Department of Radiology, Jeonbuk National University Medical School, Jeonju, Korea (K.J.C., G.Y.J.); Department of Radiology, National Jewish Health, Denver, Colo (K.J.C., H.J.H., D.L.); Department of Radiology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea (S.L.); Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea (J.B.S., H.J.H.); and Department of Statistics and Institute of Applied Statistics, Jeonbuk National University, Jeonju, Republic of Korea (H.C.)
| | - Joon Beom Seo
- From the Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, 20 Geonjiro Deokjin-gu, Jeonju-si, Jeollabuk-do, Korea 54907 (K.J.C., G.Y.J.); Department of Radiology, Jeonbuk National University Medical School, Jeonju, Korea (K.J.C., G.Y.J.); Department of Radiology, National Jewish Health, Denver, Colo (K.J.C., H.J.H., D.L.); Department of Radiology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea (S.L.); Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea (J.B.S., H.J.H.); and Department of Statistics and Institute of Applied Statistics, Jeonbuk National University, Jeonju, Republic of Korea (H.C.)
| | - Hye Jeon Hwang
- From the Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, 20 Geonjiro Deokjin-gu, Jeonju-si, Jeollabuk-do, Korea 54907 (K.J.C., G.Y.J.); Department of Radiology, Jeonbuk National University Medical School, Jeonju, Korea (K.J.C., G.Y.J.); Department of Radiology, National Jewish Health, Denver, Colo (K.J.C., H.J.H., D.L.); Department of Radiology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea (S.L.); Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea (J.B.S., H.J.H.); and Department of Statistics and Institute of Applied Statistics, Jeonbuk National University, Jeonju, Republic of Korea (H.C.)
| | - Hyemi Choi
- From the Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, 20 Geonjiro Deokjin-gu, Jeonju-si, Jeollabuk-do, Korea 54907 (K.J.C., G.Y.J.); Department of Radiology, Jeonbuk National University Medical School, Jeonju, Korea (K.J.C., G.Y.J.); Department of Radiology, National Jewish Health, Denver, Colo (K.J.C., H.J.H., D.L.); Department of Radiology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea (S.L.); Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea (J.B.S., H.J.H.); and Department of Statistics and Institute of Applied Statistics, Jeonbuk National University, Jeonju, Republic of Korea (H.C.)
| | - David Lynch
- From the Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, 20 Geonjiro Deokjin-gu, Jeonju-si, Jeollabuk-do, Korea 54907 (K.J.C., G.Y.J.); Department of Radiology, Jeonbuk National University Medical School, Jeonju, Korea (K.J.C., G.Y.J.); Department of Radiology, National Jewish Health, Denver, Colo (K.J.C., H.J.H., D.L.); Department of Radiology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea (S.L.); Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea (J.B.S., H.J.H.); and Department of Statistics and Institute of Applied Statistics, Jeonbuk National University, Jeonju, Republic of Korea (H.C.)
| | - Gong Yong Jin
- From the Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, 20 Geonjiro Deokjin-gu, Jeonju-si, Jeollabuk-do, Korea 54907 (K.J.C., G.Y.J.); Department of Radiology, Jeonbuk National University Medical School, Jeonju, Korea (K.J.C., G.Y.J.); Department of Radiology, National Jewish Health, Denver, Colo (K.J.C., H.J.H., D.L.); Department of Radiology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea (S.L.); Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea (J.B.S., H.J.H.); and Department of Statistics and Institute of Applied Statistics, Jeonbuk National University, Jeonju, Republic of Korea (H.C.)
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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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Hochhegger B, Pasini R, Roncally Carvalho A, Rodrigues R, Altmayer S, Kayat Bittencourt L, Marchiori E, Forghani R. Artificial Intelligence for Cardiothoracic Imaging: Overview of Current and Emerging Applications. Semin Roentgenol 2023; 58:184-195. [PMID: 37087139 DOI: 10.1053/j.ro.2023.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 03/07/2023]
Abstract
Artificial intelligence algorithms can learn by assimilating information from large datasets in order to decipher complex associations, identify previously undiscovered pathophysiological states, and construct prediction models. There has been tremendous interest and increased incorporation of artificial intelligence into various industries, including healthcare. As a result, there has been an exponential rise in the number of research articles and industry participants producing models intended for a variety of applications in medical imaging, which can be challenging to navigate for radiologists. In thoracic imaging, multiple applications are being evaluated for chest radiography and computed tomography and include applications for lung nodule evaluation and cancer imaging, quantifying diffuse lung disorders, and cardiac imaging, to name a few. This review aims to provide an overview of current clinical AI models, focusing on the most common clinical applications of AI in cardiothoracic imaging.
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Deep Transfer Learning Techniques-Based Automated Classification and Detection of Pulmonary Fibrosis from Chest CT Images. Processes (Basel) 2023. [DOI: 10.3390/pr11020443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Pulmonary Fibrosis (PF) is a non-curable chronic lung disease. Therefore, a quick and accurate PF diagnosis is imperative. In the present study, we aim to compare the performance of the six state-of-the-art Deep Transfer Learning techniques to classify patients accurately and perform abnormality localization in Computer Tomography (CT) scan images. A total of 2299 samples comprising normal and PF-positive CT images were preprocessed. The preprocessed images were split into training (75%), validation (15%), and test data (10%). These transfer learning models were trained and validated by optimizing the hyperparameters, such as the learning rate and the number of epochs. The optimized architectures have been evaluated with different performance metrics to demonstrate the consistency of the optimized model. At epoch 26, using an optimized learning rate of 0.0000625, the ResNet50v2 model achieved the highest training and validation accuracy (training = 99.92%, validation = 99.22%) and minimum loss (training = 0.00428, validation = 0.00683) for CT images. The experimental evaluation on the independent testing data confirms that optimized ResNet50v2 outperformed every other optimized architecture under consideration achieving a perfect score of 1.0 in each of the standard performance measures such as accuracy, precision, recall, F1-score, Mathew Correlation Coefficient (MCC), Area under the Receiver Operating Characteristic (ROC-AUC) curve, and the Area under the Precision recall (AUC_PR) curve. Therefore, we can propose that the optimized ResNet50v2 is a reliable diagnostic model for automatically classifying PF-positive patients using chest CT images.
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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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21
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Choe J, Lee SM, Hwang HJ, Lee SM, Yun J, Kim N, Seo JB. Artificial Intelligence in Lung Imaging. Semin Respir Crit Care Med 2022; 43:946-960. [PMID: 36174647 DOI: 10.1055/s-0042-1755571] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging. Some of these have been approved by governments and are now commercially available in the marketplace. In the field of chest radiology, there are various tasks and purposes that are suitable for AI: initial evaluation/triage of certain diseases, detection and diagnosis, quantitative assessment of disease severity and monitoring, and prediction for decision support. While AI is a powerful technology that can be applied to medical imaging and is expected to improve our current clinical practice, some obstacles must be addressed for the successful implementation of AI in workflows. Understanding and becoming familiar with the current status and potential clinical applications of AI in chest imaging, as well as remaining challenges, would be essential for radiologists and clinicians in the era of AI. This review introduces the potential clinical applications of AI in chest imaging and also discusses the challenges for the implementation of AI in daily clinical practice and future directions in chest imaging.
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Affiliation(s)
- Jooae Choe
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Hye Jeon Hwang
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jihye Yun
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.,Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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22
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Interstitial lung abnormalities (ILA) on routine chest CT: Comparison of radiologists’ visual evaluation and automated quantification. Eur J Radiol 2022; 157:110564. [DOI: 10.1016/j.ejrad.2022.110564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 10/06/2022] [Accepted: 10/11/2022] [Indexed: 11/21/2022]
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23
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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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24
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Koo CW, Williams JM, Liu G, Panda A, Patel PP, Frota Lima LMM, Karwoski RA, Moua T, Larson NB, Bratt A. Quantitative CT and machine learning classification of fibrotic interstitial lung diseases. Eur Radiol 2022; 32:8152-8161. [PMID: 35678861 DOI: 10.1007/s00330-022-08875-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 05/02/2022] [Accepted: 05/12/2022] [Indexed: 01/01/2023]
Abstract
OBJECTIVES To evaluate quantitative computed tomography (QCT) features and QCT feature-based machine learning (ML) models in classifying interstitial lung diseases (ILDs). To compare QCT-ML and deep learning (DL) models' performance. METHODS We retrospectively identified 1085 patients with pathologically proven usual interstitial pneumonitis (UIP), nonspecific interstitial pneumonitis (NSIP), and chronic hypersensitivity pneumonitis (CHP) who underwent peri-biopsy chest CT. Kruskal-Wallis test evaluated QCT feature associations with each ILD. QCT features, patient demographics, and pulmonary function test (PFT) results trained eXtreme Gradient Boosting (training/validation set n = 911) yielding 3 models: M1 = QCT features only; M2 = M1 plus age and sex; M3 = M2 plus PFT results. A DL model was also developed. ML and DL model areas under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs) were compared for multiclass (UIP vs. NSIP vs. CHP) and binary (UIP vs. non-UIP) classification performances. RESULTS The majority (69/78 [88%]) of QCT features successfully differentiated the 3 ILDs (adjusted p ≤ 0.05). All QCT-ML models achieved higher AUC than the DL model (multiclass AUC micro-averages 0.910, 0.910, 0.925, and 0.798 and macro-averages 0.895, 0.893, 0.925, and 0.779 for M1, M2, M3, and DL respectively; binary AUC 0.880, 0.899, 0.898, and 0.869 for M1, M2, M3, and DL respectively). M3 demonstrated statistically significant better performance compared to M2 (∆AUC: 0.015, CI: [0.002, 0.029]) for multiclass prediction. CONCLUSIONS QCT features successfully differentiated pathologically proven UIP, NSIP, and CHP. While QCT-based ML models outperformed a DL model for classifying ILDs, further investigations are warranted to determine if QCT-ML, DL, or a combination will be superior in ILD classification. KEY POINTS • Quantitative CT features successfully differentiated pathologically proven UIP, NSIP, and CHP. • Our quantitative CT-based machine learning models demonstrated high performance in classifying UIP, NSIP, and CHP histopathology, outperforming a deep learning model. • While our quantitative CT-based machine learning models performed better than a DL model, additional investigations are needed to determine whether either or a combination of both approaches delivers superior diagnostic performance.
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Affiliation(s)
- Chi Wan Koo
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - James M Williams
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Grace Liu
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Ananya Panda
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Parth P Patel
- Mayo Clinic Alix School of Medicine, Mayo Clinic, Jacksonville, FL, USA
| | | | - Ronald A Karwoski
- Department of Information Technology, Division of Biomedical Imaging Resources, Mayo Clinic, Rochester, MN, USA
| | - Teng Moua
- Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Mayo Clinic, Rochester, MN, USA
| | - Nicholas B Larson
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, USA
| | - Alex Bratt
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
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Soffer S, Morgenthau AS, Shimon O, Barash Y, Konen E, Glicksberg BS, Klang E. Artificial Intelligence for Interstitial Lung Disease Analysis on Chest Computed Tomography: A Systematic Review. Acad Radiol 2022; 29 Suppl 2:S226-S235. [PMID: 34219012 DOI: 10.1016/j.acra.2021.05.014] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 12/22/2022]
Abstract
RATIONALE AND OBJECTIVES High-resolution computed tomography (HRCT) is paramount in the assessment of interstitial lung disease (ILD). Yet, HRCT interpretation of ILDs may be hampered by inter- and intra-observer variability. Recently, artificial intelligence (AI) has revolutionized medical image analysis. This technology has the potential to advance patient care in ILD. We aimed to systematically evaluate the application of AI for the analysis of ILD in HRCT. MATERIALS AND METHODS We searched MEDLINE/PubMed databases for original publications of deep learning for ILD analysis on chest CT. The search included studies published up to March 1, 2021. The risk of bias evaluation included tailored Quality Assessment of Diagnostic Accuracy Studies and the modified Joanna Briggs Institute Critical Appraisal checklist. RESULTS Data was extracted from 19 retrospective studies. Deep learning techniques included detection, segmentation, and classification of ILD on HRCT. Most studies focused on the classification of ILD into different morphological patterns. Accuracies of 78%-91% were achieved. Two studies demonstrated near-expert performance for the diagnosis of idiopathic pulmonary fibrosis (IPF). The Quality Assessment of Diagnostic Accuracy Studies tool identified a high risk of bias in 15/19 (78.9%) of the studies. CONCLUSION AI has the potential to contribute to the radiologic diagnosis and classification of ILD. However, the accuracy performance is still not satisfactory, and research is limited by a small number of retrospective studies. Hence, the existing published data may not be sufficiently reliable. Only well-designed prospective controlled studies can accurately assess the value of existing AI tools for ILD evaluation.
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Yousef R, Gupta G, Yousef N, Khari M. A holistic overview of deep learning approach in medical imaging. MULTIMEDIA SYSTEMS 2022; 28:881-914. [PMID: 35079207 PMCID: PMC8776556 DOI: 10.1007/s00530-021-00884-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 12/23/2021] [Indexed: 05/07/2023]
Abstract
Medical images are a rich source of invaluable necessary information used by clinicians. Recent technologies have introduced many advancements for exploiting the most of this information and use it to generate better analysis. Deep learning (DL) techniques have been empowered in medical images analysis using computer-assisted imaging contexts and presenting a lot of solutions and improvements while analyzing these images by radiologists and other specialists. In this paper, we present a survey of DL techniques used for variety of tasks along with the different medical image's modalities to provide critical review of the recent developments in this direction. We have organized our paper to provide significant contribution of deep leaning traits and learn its concepts, which is in turn helpful for non-expert in medical society. Then, we present several applications of deep learning (e.g., segmentation, classification, detection, etc.) which are commonly used for clinical purposes for different anatomical site, and we also present the main key terms for DL attributes like basic architecture, data augmentation, transfer learning, and feature selection methods. Medical images as inputs to deep learning architectures will be the mainstream in the coming years, and novel DL techniques are predicted to be the core of medical images analysis. We conclude our paper by addressing some research challenges and the suggested solutions for them found in literature, and also future promises and directions for further developments.
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Affiliation(s)
- Rammah Yousef
- Yogananda School of AI Computer and Data Sciences, Shoolini University, Solan, 173229 Himachal Pradesh India
| | - Gaurav Gupta
- Yogananda School of AI Computer and Data Sciences, Shoolini University, Solan, 173229 Himachal Pradesh India
| | - Nabhan Yousef
- Electronics and Communication Engineering, Marwadi University, Rajkot, Gujrat India
| | - Manju Khari
- Jawaharlal Nehru University, New Delhi, India
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Identification of glomerulosclerosis using IBM Watson and shallow neural networks. J Nephrol 2022; 35:1235-1242. [PMID: 35041197 PMCID: PMC8765108 DOI: 10.1007/s40620-021-01200-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 11/03/2021] [Indexed: 11/26/2022]
Abstract
Background Advanced stages of different renal diseases feature glomerular sclerosis at a histological level which is observed by light microscopy on tissue samples obtained by performing a kidney biopsy. Computer-aided diagnosis (CAD) systems leverage the potential of artificial intelligence (AI) in healthcare to support physicians in the diagnostic process. Methods We propose a novel CAD system that processes histological images and discriminates between sclerotic and non-sclerotic glomeruli. To this goal, we designed, tested, and compared two artificial neural network (ANN) classifiers. The former implements a shallow ANN classifying hand-crafted features extracted from Regions of Interest (ROIs) by means of image-processing procedures. The latter, instead, employs the IBM Watson Visual Recognition System, which uses a deep artificial neural network making decisions taking the images as input, without the need to design any procedure for describing images with features. The input dataset consisted of 428 sclerotic glomeruli and 2344 non-sclerotic glomeruli derived from images of kidney biopsies scanned by the Aperio ScanScope System. Results Both AI approaches allowed to very accurately distinguish (mean MCC 0.95 and mean Accuracy 0.99) between sclerotic and non-sclerotic glomeruli. Although the systems may seem interchangeable, the approach based on feature extraction and classification would allow clinicians to gain information on the most discriminating features. In fact, further procedures could explain the classifier’s decision by analysing which subset of features impacted the most on the final decision. Conclusions We developed a customizable support system that can facilitate the work of renal pathologists both in clinical and research settings. Graphical abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1007/s40620-021-01200-0.
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Tanabe N, Kaji S, Shima H, Shiraishi Y, Maetani T, Oguma T, Sato S, Hirai T. Kernel Conversion for Robust Quantitative Measurements of Archived Chest Computed Tomography Using Deep Learning-Based Image-to-Image Translation. Front Artif Intell 2022; 4:769557. [PMID: 35112080 PMCID: PMC8801695 DOI: 10.3389/frai.2021.769557] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/23/2021] [Indexed: 01/19/2023] Open
Abstract
Chest computed tomography (CT) is used to screen for lung cancer and evaluate pulmonary and extra-pulmonary abnormalities such as emphysema and coronary artery calcification, particularly in smokers. In real-world practice, lung abnormalities are visually assessed using high-contrast thin-slice images which are generated from raw scan data using sharp reconstruction kernels with the sacrifice of increased image noise. In contrast, accurate CT quantification requires low-contrast thin-slice images with low noise, which are generated using soft reconstruction kernels. However, only sharp-kernel thin-slice images are archived in many medical facilities due to limited data storage space. This study aimed to establish deep neural network (DNN) models to convert sharp-kernel images to soft-kernel-like images with a final goal to reuse historical chest CT images for robust quantitative measurements, particularly in completed previous longitudinal studies. By using pairs of sharp-kernel (input) and soft-kernel (ground-truth) images from 30 patients with chronic obstructive pulmonary disease (COPD), DNN models were trained. Then, the accuracy of kernel conversion based on the established DNN models was evaluated using CT from independent 30 smokers with and without COPD. Consequently, differences in CT values between new images converted from sharp-kernel images using the established DNN models and ground-truth soft-kernel images were comparable with the inter-scans variability derived from repeated phantom scans (6 times), showing that the conversion error was the same level as the measurement error of the CT device. Moreover, the Dice coefficients to quantify the similarity between low attenuation voxels on given images and the ground-truth soft-kernel images were significantly higher on the DNN-converted images than the Gaussian-filtered, median-filtered, and sharp-kernel images (p < 0.001). There were good agreements in quantitative measurements of emphysema, intramuscular adipose tissue, and coronary artery calcification between the converted and the ground-truth soft-kernel images. These findings demonstrate the validity of the new DNN model for kernel conversion and the clinical applicability of soft-kernel-like images converted from archived sharp-kernel images in previous clinical studies. The presented method to evaluate the validity of the established DNN model using repeated scans of phantom could be applied to various deep learning-based image conversions for robust quantitative evaluation.
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Affiliation(s)
- Naoya Tanabe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- *Correspondence: Naoya Tanabe
| | - Shizuo Kaji
- Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan
| | - Hiroshi Shima
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yusuke Shiraishi
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tomoki Maetani
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tsuyoshi Oguma
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Susumu Sato
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Toyohiro Hirai
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Zhang H, Guo W, Zhang S, Lu H, Zhao X. Unsupervised Deep Anomaly Detection for Medical Images Using an Improved Adversarial Autoencoder. J Digit Imaging 2022; 35:153-161. [PMID: 35013826 PMCID: PMC8921374 DOI: 10.1007/s10278-021-00558-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 11/28/2021] [Accepted: 11/29/2021] [Indexed: 12/12/2022] Open
Abstract
Anomaly detection has been applied in the various disease of medical practice, such as breast cancer, retinal, lung lesion, and skin disease. However, in real-world anomaly detection, there exist a large number of healthy samples, and but very few sick samples. To alleviate the problem of data imbalance in anomaly detection, this paper proposes an unsupervised learning method for deep anomaly detection based on an improved adversarial autoencoder, in which a module called chain of convolutional block (CCB) is employed instead of the conventional skip-connections used in adversarial autoencoder. Such CCB connections provide considerable advantages via direct connections, not only preserving both global and local information but also alleviating the problem of semantic disparity between the encoding features and the corresponding decoding features. The proposed method is thus able to capture the distribution of normal samples within both image space and latent vector space. By means of minimizing the reconstruction error within both spaces during training phase, higher reconstruction error during test phase is indicative of an anomaly. Our method is trained only on the healthy persons in order to learn the distribution of normal samples and can detect sick samples based on high deviation from the distribution of normality in an unsupervised way. Experimental results for multiple datasets from different fields demonstrate that the proposed method yields superior performance to state-of-the-art methods.
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Affiliation(s)
- Haibo Zhang
- Taizhou Central Hospital (Taizhou University Hospital), Taizhou University, Zhejiang, 318000, China
| | - Wenping Guo
- Taizhou Central Hospital (Taizhou University Hospital), Taizhou University, Zhejiang, 318000, China
- College of Computer and Information, Hohai University, Nanjing, 210098, China
| | - Shiqing Zhang
- Taizhou Central Hospital (Taizhou University Hospital), Taizhou University, Zhejiang, 318000, China
| | - Hongsheng Lu
- Taizhou Central Hospital (Taizhou University Hospital), Taizhou University, Zhejiang, 318000, China.
| | - Xiaoming Zhao
- Taizhou Central Hospital (Taizhou University Hospital), Taizhou University, Zhejiang, 318000, China.
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Wang B, Zhang W. MARnet: multi-scale adaptive residual neural network for chest X-ray images recognition of lung diseases. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:331-350. [PMID: 34902994 DOI: 10.3934/mbe.2022017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Chest X-ray image is an important clinical diagnostic reference to lung diseases that is a serious threat to human health. At present, with the rapid development of computer vision and deep learning technology, many scholars have carried out the fruitful research on how to build a valid model for chest X-ray images recognition of lung diseases. While some efforts are still expected to improve the performance of the recognition model and enhance the interpretability of the recognition results. In this paper, we construct a multi-scale adaptive residual neural network (MARnet) to identify chest X-ray images of lung diseases. To make the model better extract image features, we cross-transfer the information extracted by residual block and the information extracted by adaptive structure to different layer, avoiding the reduction effect of residual structure on adaptive function. We compare MARnet with some classical neural networks, and the results show that MARnet achieves accuracy (ACC) of 83.3% and the area under ROC curve (AUC) of 0.97 in the identification of 4 kinds of typical lung X-ray images including nodules, atelectasis, normal and infection, which are higher than those of other methods. Moreover, to avoid the randomness of the train-test-split method, 5-fold cross-validation method is used to verify the generalization ability of the MARnet model and the results are satisfactory. Finally, the technique called Gradient-weighted Class Activation Mapping (Grad-CAM), is adopted to display significantly the discriminative regions of the images in the form of the heat map, which provides an explainable and more direct clinical diagnostic reference to lung diseases.
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Affiliation(s)
- Boyang Wang
- School of Computer Science and Software Engineering, University of Science and Technology, Liaoning 114044, China
| | - Wenyu Zhang
- School of Computer Science and Software Engineering, University of Science and Technology, Liaoning 114044, China
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31
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Cheng X, Wen H, You H, Hua L, Xiaohua W, Qiuting C, Jiabao L. Recognition of Peripheral Lung Cancer and Focal Pneumonia on Chest Computed Tomography Images Based on Convolutional Neural Network. Technol Cancer Res Treat 2022; 21:15330338221085375. [PMID: 35293240 PMCID: PMC8935416 DOI: 10.1177/15330338221085375] [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] [Indexed: 11/16/2022] Open
Abstract
Introduction: Chest computed tomography (CT) is important for the early screening of lung diseases and clinical diagnosis, particularly during the COVID-19 pandemic. We propose a method for classifying peripheral lung cancer and focal pneumonia on chest CT images and undertake 5 window settings to study the effect on the artificial intelligence processing results. Methods: A retrospective collection of CT images from 357 patients with peripheral lung cancer having solitary solid nodule or focal pneumonia with a solitary consolidation was applied. We segmented and aligned the lung parenchyma based on some morphological methods and cropped this region of the lung parenchyma with the minimum 3D bounding box. Using these 3D cropped volumes of all cases, we designed a 3D neural network to classify them into 2 categories. We also compared the classification results of the 3 physicians with different experience levels on the same dataset. Results: We conducted experiments using 5 window settings. After cropping and alignment based on an automatic preprocessing procedure, our neural network achieved an average classification accuracy of 91.596% under a 5-fold cross-validation in the full window, in which the area under the curve (AUC) was 0.946. The classification accuracy and AUC value were 90.48% and 0.957 for the junior physician, 94.96% and 0.989 for the intermediate physician, and 96.92% and 0.980 for the senior physician, respectively. After removing the error prediction, the accuracy improved significantly, reaching 98.79% in the self-defined window2. Conclusion: Using the proposed neural network, in separating peripheral lung cancer and focal pneumonia in chest CT data, we achieved an accuracy competitive to that of a junior physician. Through a data ablation study, the proposed 3D CNN can achieve a slightly higher accuracy compared with senior physicians in the same subset. The self-defined window2 was the best for data training and evaluation.
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Affiliation(s)
- Xiaoyue Cheng
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - He Wen
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Hao You
- Key laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Li Hua
- Key laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Wu Xiaohua
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Cao Qiuting
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Liu Jiabao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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32
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Lee S, Summers RM. Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen. Radiol Clin North Am 2021; 59:987-1002. [PMID: 34689882 DOI: 10.1016/j.rcl.2021.07.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Organ segmentation, chest radiograph classification, and lung and liver nodule detections are some of the popular artificial intelligence (AI) tasks in chest and abdominal radiology due to the wide availability of public datasets. AI algorithms have achieved performance comparable to humans in less time for several organ segmentation tasks, and some lesion detection and classification tasks. This article introduces the current published articles of AI applied to chest and abdominal radiology, including organ segmentation, lesion detection, classification, and predicting prognosis.
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Affiliation(s)
- Sungwon Lee
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD 20892-1182, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD 20892-1182, USA.
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Choe J, Hwang HJ, Seo JB, Lee SM, Yun J, Kim MJ, Jeong J, Lee Y, Jin K, Park R, Kim J, Jeon H, Kim N, Yi J, Yu D, Kim B. Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT. Radiology 2021; 302:187-197. [PMID: 34636634 DOI: 10.1148/radiol.2021204164] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Evaluation of interstitial lung disease (ILD) at CT is a challenging task that requires experience and is subject to substantial interreader variability. Purpose To investigate whether a proposed content-based image retrieval (CBIR) of similar chest CT images by using deep learning can aid in the diagnosis of ILD by readers with different levels of experience. Materials and methods This retrospective study included patients with confirmed ILD after multidisciplinary discussion and available CT images identified between January 2000 and December 2015. Database was composed of four disease classes: usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), cryptogenic organizing pneumonia, and chronic hypersensitivity pneumonitis. Eighty patients were selected as queries from the database. The proposed CBIR retrieved the top three similar CT images with diagnosis from the database by comparing the extent and distribution of different regional disease patterns quantified by a deep learning algorithm. Eight readers with varying experience interpreted the query CT images and provided their most probable diagnosis in two reading sessions 2 weeks apart, before and after applying CBIR. Diagnostic accuracy was analyzed by using McNemar test and generalized estimating equation, and interreader agreement was analyzed by using Fleiss κ. Results A total of 288 patients were included (mean age, 58 years ± 11 [standard deviation]; 145 women). After applying CBIR, the overall diagnostic accuracy improved in all readers (before CBIR, 46.1% [95% CI: 37.1, 55.3]; after CBIR, 60.9% [95% CI: 51.8, 69.3]; P < .001). In terms of disease category, the diagnostic accuracy improved after applying CBIR in UIP (before vs after CBIR, 52.4% vs 72.8%, respectively; P < .001) and NSIP cases (before vs after CBIR, 42.9% vs 61.6%, respectively; P < .001). Interreader agreement improved after CBIR (before vs after CBIR Fleiss κ, 0.32 vs 0.47, respectively; P = .005). Conclusion The proposed content-based image retrieval system for chest CT images with deep learning improved the diagnostic accuracy of interstitial lung disease and interreader agreement in readers with different levels of experience. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Wielpütz in this issue.
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Affiliation(s)
- Jooae Choe
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Hye Jeon Hwang
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Joon Beom Seo
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Sang Min Lee
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Jihye Yun
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Min-Ju Kim
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Jewon Jeong
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Youngsoo Lee
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Kiok Jin
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Rohee Park
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Jihoon Kim
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Howook Jeon
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Namkug Kim
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Jaeyoun Yi
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Donghoon Yu
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Byeongsoo Kim
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
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Aznar-Gimeno R, Esteban LM, Labata-Lezaun G, del-Hoyo-Alonso R, Abadia-Gallego D, Paño-Pardo JR, Esquillor-Rodrigo MJ, Lanas Á, Serrano MT. A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine Learning Algorithms. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8677. [PMID: 34444425 PMCID: PMC8394359 DOI: 10.3390/ijerph18168677] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/09/2021] [Accepted: 08/11/2021] [Indexed: 12/24/2022]
Abstract
The purpose of the study was to build a predictive model for estimating the risk of ICU admission or mortality among patients hospitalized with COVID-19 and provide a user-friendly tool to assist clinicians in the decision-making process. The study cohort comprised 3623 patients with confirmed COVID-19 who were hospitalized in the SALUD hospital network of Aragon (Spain), which includes 23 hospitals, between February 2020 and January 2021, a period that includes several pandemic waves. Up to 165 variables were analysed, including demographics, comorbidity, chronic drugs, vital signs, and laboratory data. To build the predictive models, different techniques and machine learning (ML) algorithms were explored: multilayer perceptron, random forest, and extreme gradient boosting (XGBoost). A reduction dimensionality procedure was used to minimize the features to 20, ensuring feasible use of the tool in practice. Our model was validated both internally and externally. We also assessed its calibration and provide an analysis of the optimal cut-off points depending on the metric to be optimized. The best performing algorithm was XGBoost. The final model achieved good discrimination for the external validation set (AUC = 0.821, 95% CI 0.787-0.854) and accurate calibration (slope = 1, intercept = -0.12). A cut-off of 0.4 provides a sensitivity and specificity of 0.71 and 0.78, respectively. In conclusion, we built a risk prediction model from a large amount of data from several pandemic waves, which had good calibration and discrimination ability. We also created a user-friendly web application that can aid rapid decision-making in clinical practice.
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Affiliation(s)
- Rocío Aznar-Gimeno
- Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain; (G.L.-L.); (D.A.-G.)
| | - Luis M. Esteban
- Escuela Universitaria Politécnica de La Almunia, Universidad de Zaragoza, Calle Mayor, 5, 50100 La Almunia de Doña Godina, Spain;
| | - Gorka Labata-Lezaun
- Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain; (G.L.-L.); (D.A.-G.)
| | - Rafael del-Hoyo-Alonso
- Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain; (G.L.-L.); (D.A.-G.)
| | - David Abadia-Gallego
- Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain; (G.L.-L.); (D.A.-G.)
| | - J. Ramón Paño-Pardo
- Infectious Disease Department, University Clinic Hospital Lozano Blesa, San Juan Bosco 15, 50009 Zaragoza, Spain;
- Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, 50009 Zaragoza, Spain; (Á.L.); (M.T.S.)
- Aragon Health Research Institute (IIS Aragon), 50009 Zaragoza, Spain
| | - M. José Esquillor-Rodrigo
- Internal Medicine Department, University Clinic Hospital Lozano Blesa, San Juan Bosco 15, 50009 Zaragoza, Spain;
| | - Ángel Lanas
- Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, 50009 Zaragoza, Spain; (Á.L.); (M.T.S.)
- Aragon Health Research Institute (IIS Aragon), 50009 Zaragoza, Spain
- Digestive Diseases Department, University Clinic Hospital Lozano Blesa, San Juan Bosco 15, 50009 Zaragoza, Spain
- CIBEREHD, 28029 Madrid, Spain
| | - M. Trinidad Serrano
- Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, 50009 Zaragoza, Spain; (Á.L.); (M.T.S.)
- Aragon Health Research Institute (IIS Aragon), 50009 Zaragoza, Spain
- Digestive Diseases Department, University Clinic Hospital Lozano Blesa, San Juan Bosco 15, 50009 Zaragoza, Spain
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Hata A, Schiebler ML, Lynch DA, Hatabu H. Interstitial Lung Abnormalities: State of the Art. Radiology 2021; 301:19-34. [PMID: 34374589 DOI: 10.1148/radiol.2021204367] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
The clinical importance of interstitial lung abnormality (ILA) is increasingly recognized. In July 2020, the Fleischner Society published a position paper about ILA. The purposes of this article are to summarize the definition, existing evidence, clinical management, and unresolved issues for ILA from a radiologic standpoint and to provide a practical guide for radiologists. ILA is a common incidental finding at CT and is often progressive and associated with worsened clinical outcomes. The hazard ratios for mortality range from 1.3 to 2.7 in large cohorts. Risk factors for ILA include age, smoking status, other inhalational exposures, and genetic factors (eg, gene encoding mucin 5B variant). Radiologists should systematically record the presence, morphologic characteristics, distribution, and subcategories of ILA (ie, nonsubpleural, subpleural nonfibrotic, and subpleural fibrotic), as these are informative for predicting progression and mortality. Clinically significant interstitial lung disease should not be considered ILA. Individuals with ILA are triaged into higher- and lower-risk groups depending on their risk factors for progression, and systematic follow-up, including CT, should be considered for the higher-risk group. Artificial intelligence-based automated analysis for ILA may be helpful, but further validation and improvement are needed. Radiologists have a central role in clinical management and research on ILA.
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Affiliation(s)
- Akinori Hata
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02115 (A.H., H.H.); Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan (A.H.); Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wis (M.L.S.); and Department of Radiology, National Jewish Health, Denver, Colo (D.A.L.)
| | - Mark L Schiebler
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02115 (A.H., H.H.); Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan (A.H.); Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wis (M.L.S.); and Department of Radiology, National Jewish Health, Denver, Colo (D.A.L.)
| | - David A Lynch
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02115 (A.H., H.H.); Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan (A.H.); Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wis (M.L.S.); and Department of Radiology, National Jewish Health, Denver, Colo (D.A.L.)
| | - Hiroto Hatabu
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02115 (A.H., H.H.); Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan (A.H.); Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wis (M.L.S.); and Department of Radiology, National Jewish Health, Denver, Colo (D.A.L.)
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CT Image Analysis and Clinical Diagnosis of New Coronary Pneumonia Based on Improved Convolutional Neural Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:7259414. [PMID: 34335865 PMCID: PMC8321746 DOI: 10.1155/2021/7259414] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/08/2021] [Accepted: 06/24/2021] [Indexed: 02/05/2023]
Abstract
In this paper, based on the improved convolutional neural network, in-depth analysis of the CT image of the new coronary pneumonia, using the U-Net series of deep neural networks to semantically segment the CT image of the new coronary pneumonia, to obtain the new coronary pneumonia area as the foreground and the remaining areas as the background of the binary image, provides a basis for subsequent image diagnosis. Secondly, the target-detection framework Faster RCNN extracts features from the CT image of the new coronary pneumonia tumor, obtains a higher-level abstract representation of the data, determines the lesion location of the new coronary pneumonia tumor, and gives its bounding box in the image. By generating an adversarial network to diagnose the lesion area of the CT image of the new coronary pneumonia tumor, obtaining a complete image of the new coronary pneumonia, achieving the effect of the CT image diagnosis of the new coronary pneumonia tumor, and three-dimensionally reconstructing the complete new coronary pneumonia model, filling the current the gap in this aspect, provide a basis to produce new coronary pneumonia prosthesis and improve the accuracy of diagnosis.
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Lung Segmentation on HRCT and Volumetric CT for Diffuse Interstitial Lung Disease Using Deep Convolutional Neural Networks. J Digit Imaging 2021; 32:1019-1026. [PMID: 31396776 DOI: 10.1007/s10278-019-00254-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
A robust lung segmentation method using a deep convolutional neural network (CNN) was developed and evaluated on high-resolution computed tomography (HRCT) and volumetric CT of various types of diffuse interstitial lung disease (DILD). Chest CT images of 617 patients with various types of DILD, including cryptogenic organizing pneumonia (COP), usual interstitial pneumonia (UIP), and nonspecific interstitial pneumonia (NSIP), were scanned using HRCT (1-2-mm slices, 5-10-mm intervals) and volumetric CT (sub-millimeter thickness without intervals). Each scan was segmented using a conventional image processing method and then manually corrected by an expert thoracic radiologist to create gold standards. The lung regions in the HRCT images were then segmented using a two-dimensional U-Net architecture with the deep CNN, using separate training, validation, and test sets. In addition, 30 independent volumetric CT images of UIP patients were used to further evaluate the model. The segmentation results for both conventional and deep-learning methods were compared quantitatively with the gold standards using four accuracy metrics: the Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), mean surface distance (MSD), and Hausdorff surface distance (HSD). The mean and standard deviation values of those metrics for the HRCT images were 98.84 ± 0.55%, 97.79 ± 1.07%, 0.27 ± 0.18 mm, and 25.47 ± 13.63 mm, respectively. Our deep-learning method showed significantly better segmentation performance (p < 0.001), and its segmentation accuracies for volumetric CT were similar to those for HRCT. We have developed an accurate and robust U-Net-based DILD lung segmentation method that can be used for patients scanned with different clinical protocols, including HRCT and volumetric CT.
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Drukker K, Yan P, Sibley A, Wang G. Biomedical imaging and analysis through deep learning. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00004-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Tandon YK, Bartholmai BJ, Koo CW. Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules. J Thorac Dis 2020; 12:6954-6965. [PMID: 33282401 PMCID: PMC7711413 DOI: 10.21037/jtd-2019-cptn-03] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 07/10/2020] [Indexed: 12/18/2022]
Abstract
Lung cancer remains the leading cause of cancer related death world-wide despite advances in treatment. This largely relates to the fact that many of these patients already have advanced diseases at the time of initial diagnosis. As most lung cancers present as nodules initially, an accurate classification of pulmonary nodules as early lung cancers is critical to reducing lung cancer morbidity and mortality. There have been significant recent advances in artificial intelligence (AI) for lung nodule evaluation. Deep learning (DL) and convolutional neural networks (CNNs) have shown promising results in pulmonary nodule detection and have also excelled in segmentation and classification of pulmonary nodules. This review aims to provide an overview of progress that has been made in AI recently for pulmonary nodule detection and characterization with the ultimate goal of lung cancer prediction and classification while outlining some of the pitfalls and challenges that remain to bring such advancements to routine clinical use.
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Affiliation(s)
| | | | - Chi Wan Koo
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
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Hwang HJ, Seo JB, Lee SM, Kim EY, Park B, Bae HJ, Kim N. Content-Based Image Retrieval of Chest CT with Convolutional Neural Network for Diffuse Interstitial Lung Disease: Performance Assessment in Three Major Idiopathic Interstitial Pneumonias. Korean J Radiol 2020; 22:281-290. [PMID: 33169547 PMCID: PMC7817627 DOI: 10.3348/kjr.2020.0603] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 05/08/2020] [Accepted: 06/03/2020] [Indexed: 12/02/2022] Open
Abstract
Objective To assess the performance of content-based image retrieval (CBIR) of chest CT for diffuse interstitial lung disease (DILD). Materials and Methods The database was comprised by 246 pairs of chest CTs (initial and follow-up CTs within two years) from 246 patients with usual interstitial pneumonia (UIP, n = 100), nonspecific interstitial pneumonia (NSIP, n = 101), and cryptogenic organic pneumonia (COP, n = 45). Sixty cases (30-UIP, 20-NSIP, and 10-COP) were selected as the queries. The CBIR retrieved five similar CTs as a query from the database by comparing six image patterns (honeycombing, reticular opacity, emphysema, ground-glass opacity, consolidation and normal lung) of DILD, which were automatically quantified and classified by a convolutional neural network. We assessed the rates of retrieving the same pairs of query CTs, and the number of CTs with the same disease class as query CTs in top 1–5 retrievals. Chest radiologists evaluated the similarity between retrieved CTs and queries using a 5-scale grading system (5-almost identical; 4-same disease; 3-likelihood of same disease is half; 2-likely different; and 1-different disease). Results The rate of retrieving the same pairs of query CTs in top 1 retrieval was 61.7% (37/60) and in top 1–5 retrievals was 81.7% (49/60). The CBIR retrieved the same pairs of query CTs more in UIP compared to NSIP and COP (p = 0.008 and 0.002). On average, it retrieved 4.17 of five similar CTs from the same disease class. Radiologists rated 71.3% to 73.0% of the retrieved CTs with a similarity score of 4 or 5. Conclusion The proposed CBIR system showed good performance for retrieving chest CTs showing similar patterns for DILD.
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Affiliation(s)
- Hye Jeon Hwang
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Eun Young Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Beomhee Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hyun Jin Bae
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Hatabu H, Hunninghake GM, Richeldi L, Brown KK, Wells AU, Remy-Jardin M, Verschakelen J, Nicholson AG, Beasley MB, Christiani DC, San José Estépar R, Seo JB, Johkoh T, Sverzellati N, Ryerson CJ, Graham Barr R, Goo JM, Austin JHM, Powell CA, Lee KS, Inoue Y, Lynch DA. Interstitial lung abnormalities detected incidentally on CT: a Position Paper from the Fleischner Society. THE LANCET RESPIRATORY MEDICINE 2020; 8:726-737. [PMID: 32649920 DOI: 10.1016/s2213-2600(20)30168-5] [Citation(s) in RCA: 295] [Impact Index Per Article: 73.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 03/20/2020] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
Abstract
The term interstitial lung abnormalities refers to specific CT findings that are potentially compatible with interstitial lung disease in patients without clinical suspicion of the disease. Interstitial lung abnormalities are increasingly recognised as a common feature on CT of the lung in older individuals, occurring in 4-9% of smokers and 2-7% of non-smokers. Identification of interstitial lung abnormalities will increase with implementation of lung cancer screening, along with increased use of CT for other diagnostic purposes. These abnormalities are associated with radiological progression, increased mortality, and the risk of complications from medical interventions, such as chemotherapy and surgery. Management requires distinguishing interstitial lung abnormalities that represent clinically significant interstitial lung disease from those that are subclinical. In particular, it is important to identify the subpleural fibrotic subtype, which is more likely to progress and to be associated with mortality. This multidisciplinary Position Paper by the Fleischner Society addresses important issues regarding interstitial lung abnormalities, including standardisation of the definition and terminology; predisposing risk factors; clinical outcomes; options for initial evaluation, monitoring, and management; the role of quantitative evaluation; and future research needs.
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Affiliation(s)
- Hiroto Hatabu
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Gary M Hunninghake
- Department of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Luca Richeldi
- Unitá Operativa Complessa di Pneumologia, Universitá Cattolica del Sacro Cuore, Fondazione Policlinico A Gemelli IRCCS, Rome, Italy
| | - Kevin K Brown
- Department of Medicine, Denver, CO, USA; National Jewish Health, Denver, CO, USA
| | - Athol U Wells
- Department of Respiratory Medicine, Royal Brompton and Hospital NHS Foundation Trust, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Martine Remy-Jardin
- Department of Thoracic Imaging, Hospital Calmette, University Centre of Lille, Lille, France
| | | | - Andrew G Nicholson
- Department of Histopathology, Royal Brompton and Hospital NHS Foundation Trust, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Mary B Beasley
- Department of Pathology, Icahn School of Medicine at Mount, New York, NY, USA
| | - David C Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Raúl San José Estépar
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Joon Beom Seo
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Takeshi Johkoh
- Department of Radiology, Kansai Rosai Hospital, Hyogo, Japan
| | | | - Christopher J Ryerson
- Department of Medicine, University of British Columbia and Centre for Heart Lung Innovations, St Paul's Hospital, Vancouver, BC, Canada
| | - R Graham Barr
- Department of Medicine and Department of Epidemiology, Columbia University Medical Center, New York, NY, USA
| | - Jin Mo Goo
- Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea
| | - John H M Austin
- Department of Radiology, Columbia University Medical Center, New York, NY, USA
| | - Charles A Powell
- Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount, New York, NY, USA
| | - Kyung Soo Lee
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Yoshikazu Inoue
- Clinical Research Center, National Hospital Organization Kinki-Chuo Chest Medical Center, Osaka, Japan
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Farhat H, Sakr GE, Kilany R. Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19. MACHINE VISION AND APPLICATIONS 2020; 31:53. [PMID: 32834523 PMCID: PMC7386599 DOI: 10.1007/s00138-020-01101-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 06/21/2020] [Accepted: 07/07/2020] [Indexed: 05/07/2023]
Abstract
Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers. This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, highlighting various deep learning tasks such as classification, segmentation, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.
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Affiliation(s)
- Hanan Farhat
- Saint Joseph University of Beirut, Mar Roukos, Beirut, Lebanon
| | - George E. Sakr
- Saint Joseph University of Beirut, Mar Roukos, Beirut, Lebanon
| | - Rima Kilany
- Saint Joseph University of Beirut, Mar Roukos, Beirut, Lebanon
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Trusculescu AA, Manolescu D, Tudorache E, Oancea C. Deep learning in interstitial lung disease-how long until daily practice. Eur Radiol 2020; 30:6285-6292. [PMID: 32537728 PMCID: PMC7554005 DOI: 10.1007/s00330-020-06986-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 03/28/2020] [Accepted: 05/27/2020] [Indexed: 12/19/2022]
Abstract
Interstitial lung diseases are a diverse group of disorders that involve inflammation and fibrosis of interstitium, with clinical, radiological, and pathological overlapping features. These are an important cause of morbidity and mortality among lung diseases. This review describes computer-aided diagnosis systems centered on deep learning approaches that improve the diagnostic of interstitial lung diseases. We highlighted the challenges and the implementation of important daily practice, especially in the early diagnosis of idiopathic pulmonary fibrosis (IPF). Developing a convolutional neuronal network (CNN) that could be deployed on any computer station and be accessible to non-academic centers is the next frontier that needs to be crossed. In the future, early diagnosis of IPF should be possible. CNN might not only spare the human resources but also will reduce the costs spent on all the social and healthcare aspects of this deadly disease. Key Points • Deep learning algorithms are used in pattern recognition of different interstitial lung diseases. • High-resolution computed tomography plays a central role in the diagnosis and in the management of all interstitial lung diseases, especially fibrotic lung disease. • Developing an accessible algorithm that could be deployed on any computer station and be used in non-academic centers is the next frontier in the early diagnosis of idiopathic pulmonary fibrosis.
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Affiliation(s)
- Ana Adriana Trusculescu
- Department of Pulmonology, University of Medicine and Pharmacy "Victor Babes", Timisoara, Romania
| | - Diana Manolescu
- Department of Radiology, University of Medicine and Pharmacy "Victor Babes", Eftimie Murgu Square, Number 2, Timisoara, Romania.
| | - Emanuela Tudorache
- Department of Pulmonology, University of Medicine and Pharmacy "Victor Babes", Timisoara, Romania
| | - Cristian Oancea
- Department of Pulmonology, University of Medicine and Pharmacy "Victor Babes", Timisoara, Romania
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Hwang EJ, Park CM. Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges. Korean J Radiol 2020; 21:511-525. [PMID: 32323497 PMCID: PMC7183830 DOI: 10.3348/kjr.2019.0821] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 01/31/2020] [Indexed: 12/25/2022] Open
Abstract
Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic radiology, are under active investigation with deep learning technology, which has shown promising performance in various tasks, including detection, classification, segmentation, and image synthesis, outperforming conventional methods and suggesting its potential for clinical implementation. However, the implementation of deep learning in daily clinical practice is in its infancy and facing several challenges, such as its limited ability to explain the output results, uncertain benefits regarding patient outcomes, and incomplete integration in daily workflow. In this review article, we will introduce the potential clinical applications of deep learning technology in thoracic radiology and discuss several challenges for its implementation in daily clinical practice.
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Affiliation(s)
- Eui Jin Hwang
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Chang Min Park
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
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Prayer F, Röhrich S, Pan J, Hofmanninger J, Langs G, Prosch H. [Artificial intelligence in lung imaging]. Radiologe 2020; 60:42-47. [PMID: 31754738 DOI: 10.1007/s00117-019-00611-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
CLINICAL/METHODICAL ISSUE Artificial intelligence (AI) has the potential to improve diagnostic accuracy and management in patients with lung disease through automated detection, quantification, classification, and prediction of disease progression. STANDARD RADIOLOGICAL METHODS Owing to unspecific symptoms, few well-defined CT disease patterns, and varying prognosis, interstitial lungs disease represents a focus of AI-based research. METHODICAL INNOVATIONS Supervised and unsupervised machine learning can identify CT disease patterns using features which may allow the analysis of associations with specific diseases and outcomes. PERFORMANCE Machine learning on the one hand improves computer-aided detection of pulmonary nodules. On the other hand it enables further characterization of pulmonary nodules, which may improve resource effectiveness regarding lung cancer screening programs. ACHIEVEMENTS There are several challenges regarding AI-based CT data analysis. Besides the need for powerful algorithms, expert annotations and extensive training data sets that reflect physiologic and pathologic variability are required for effective machine learning. Comparability and reproducibility of AI research deserve consideration due to a lack of standardization in this emerging field. PRACTICAL RECOMMENDATIONS This review article presents the state of the art and the challenges concerning AI in lung imaging with special consideration of interstitial lung disease, and detection and consideration of pulmonary nodules.
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Affiliation(s)
- F Prayer
- Universitätsklinik für Radiologie und Nuklearmedizin, Medizinische Universität Wien, Währinger Gürtel 18-20, 1090, Wien, Österreich
| | - S Röhrich
- Universitätsklinik für Radiologie und Nuklearmedizin, Medizinische Universität Wien, Währinger Gürtel 18-20, 1090, Wien, Österreich
| | - J Pan
- Computational Imaging and Research Lab, Universitätsklinik für Radiologie und Nuklearmedizin, Medizinische Universität Wien, Wien, Österreich
| | - J Hofmanninger
- Computational Imaging and Research Lab, Universitätsklinik für Radiologie und Nuklearmedizin, Medizinische Universität Wien, Wien, Österreich
| | - G Langs
- Computational Imaging and Research Lab, Universitätsklinik für Radiologie und Nuklearmedizin, Medizinische Universität Wien, Wien, Österreich
| | - H Prosch
- Universitätsklinik für Radiologie und Nuklearmedizin, Medizinische Universität Wien, Währinger Gürtel 18-20, 1090, Wien, Österreich.
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Bermejo-Peláez D, Ash SY, Washko GR, San José Estépar R, Ledesma-Carbayo MJ. Classification of Interstitial Lung Abnormality Patterns with an Ensemble of Deep Convolutional Neural Networks. Sci Rep 2020; 10:338. [PMID: 31941918 PMCID: PMC6962320 DOI: 10.1038/s41598-019-56989-5] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 12/12/2019] [Indexed: 12/31/2022] Open
Abstract
Subtle interstitial changes in the lung parenchyma of smokers, known as Interstitial Lung Abnormalities (ILA), have been associated with clinical outcomes, including mortality, even in the absence of Interstitial Lung Disease (ILD). Although several methods have been proposed for the automatic identification of more advanced Interstitial Lung Disease (ILD) patterns, few have tackled ILA, which likely precedes the development ILD in some cases. In this context, we propose a novel methodology for automated identification and classification of ILA patterns in computed tomography (CT) images. The proposed method is an ensemble of deep convolutional neural networks (CNNs) that detect more discriminative features by incorporating two, two-and-a-half and three- dimensional architectures, thereby enabling more accurate classification. This technique is implemented by first training each individual CNN, and then combining its output responses to form the overall ensemble output. To train and test the system we used 37424 radiographic tissue samples corresponding to eight different parenchymal feature classes from 208 CT scans. The resulting ensemble performance including an average sensitivity of 91,41% and average specificity of 98,18% suggests it is potentially a viable method to identify radiographic patterns that precede the development of ILD.
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Affiliation(s)
- David Bermejo-Peláez
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain.
| | - Samuel Y Ash
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - George R Washko
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Raúl San José Estépar
- Applied Chest Imaging Laboratory, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - María J Ledesma-Carbayo
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
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Deep Learning Applications in Chest Radiography and Computed Tomography: Current State of the Art. J Thorac Imaging 2019; 34:75-85. [PMID: 30802231 DOI: 10.1097/rti.0000000000000387] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Deep learning is a genre of machine learning that allows computational models to learn representations of data with multiple levels of abstraction using numerous processing layers. A distinctive feature of deep learning, compared with conventional machine learning methods, is that it can generate appropriate models for tasks directly from the raw data, removing the need for human-led feature extraction. Medical images are particularly suited for deep learning applications. Deep learning techniques have already demonstrated high performance in the detection of diabetic retinopathy on fundoscopic images and metastatic breast cancer cells on pathologic images. In radiology, deep learning has the opportunity to provide improved accuracy of image interpretation and diagnosis. Many groups are exploring the possibility of using deep learning-based applications to solve unmet clinical needs. In chest imaging, there has been a large effort to develop and apply computer-aided detection systems for the detection of lung nodules on chest radiographs and chest computed tomography. The essential limitation to computer-aided detection is an inability to learn from new information. To overcome these deficiencies, many groups have turned to deep learning approaches with promising results. In addition to nodule detection, interstitial lung disease recognition, lesion segmentation, diagnosis and patient outcomes have been addressed by deep learning approaches. The purpose of this review article was to cover the current state of the art for deep learning approaches and its limitations, and some of the potential impact on the field of radiology, with specific reference to chest imaging.
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Chassagnon G, Vakalopoulou M, Paragios N, Revel MP. Artificial intelligence applications for thoracic imaging. Eur J Radiol 2019; 123:108774. [PMID: 31841881 DOI: 10.1016/j.ejrad.2019.108774] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 11/13/2019] [Accepted: 11/21/2019] [Indexed: 02/06/2023]
Abstract
Artificial intelligence is a hot topic in medical imaging. The development of deep learning methods and in particular the use of convolutional neural networks (CNNs), have led to substantial performance gain over the classic machine learning techniques. Multiple usages are currently being evaluated, especially for thoracic imaging, such as such as lung nodule evaluation, tuberculosis or pneumonia detection or quantification of diffuse lung diseases. Chest radiography is a near perfect domain for the development of deep learning algorithms for automatic interpretation, requiring large annotated datasets, in view of the high number of procedures and increasing data availability. Current algorithms are able to detect up to 14 common anomalies, when present as isolated findings. Chest computed tomography is another major field of application for artificial intelligence, especially in the perspective of large scale lung cancer screening. It is important for radiologists to apprehend, contribute actively and lead this new era of radiology powered by artificial intelligence. Such a perspective requires understanding new terms and concepts associated with machine learning. The objective of this paper is to provide useful definitions for understanding the methods used and their possibilities, and report current and future developments for thoracic imaging. Prospective validation of AI tools will be required before reaching routine clinical implementation.
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Affiliation(s)
- Guillaume Chassagnon
- Radiology Department, Groupe Hospitalier Cochin Broca Hôtel-Dieu - Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France; Laboratoire Mathématiques et Informatique pour la Complexité et les Systèmes, Ecole CentraleSupelec, 3 Rue Joliot Curie, 91190 Gif-sur-Yvette, France; Center for Visual Computing, Ecole CentraleSupelec, 3 Rue Joliot Curie, 91190, Gif-sur-Yvette, France
| | - Maria Vakalopoulou
- Laboratoire Mathématiques et Informatique pour la Complexité et les Systèmes, Ecole CentraleSupelec, 3 Rue Joliot Curie, 91190 Gif-sur-Yvette, France; Center for Visual Computing, Ecole CentraleSupelec, 3 Rue Joliot Curie, 91190, Gif-sur-Yvette, France
| | - Nikos Paragios
- Laboratoire Mathématiques et Informatique pour la Complexité et les Systèmes, Ecole CentraleSupelec, 3 Rue Joliot Curie, 91190 Gif-sur-Yvette, France; TheraPanacea, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France; Center for Visual Computing, Ecole CentraleSupelec, 3 Rue Joliot Curie, 91190, Gif-sur-Yvette, France
| | - Marie-Pierre Revel
- Radiology Department, Groupe Hospitalier Cochin Broca Hôtel-Dieu - Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France; Center for Visual Computing, Ecole CentraleSupelec, 3 Rue Joliot Curie, 91190, Gif-sur-Yvette, France.
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Mathai SK, Humphries S, Kropski JA, Blackwell TS, Powers J, Walts AD, Markin C, Woodward J, Chung JH, Brown KK, Steele MP, Loyd JE, Schwarz MI, Fingerlin T, Yang IV, Lynch DA, Schwartz DA. MUC5B variant is associated with visually and quantitatively detected preclinical pulmonary fibrosis. Thorax 2019; 74:1131-1139. [PMID: 31558622 DOI: 10.1136/thoraxjnl-2018-212430] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 08/20/2019] [Accepted: 08/25/2019] [Indexed: 11/03/2022]
Abstract
BACKGROUND Relatives of patients with familial interstitial pneumonia (FIP) are at increased risk for pulmonary fibrosis. We assessed the prevalence and risk factors for preclinical pulmonary fibrosis (PrePF) in first-degree relatives of patients with FIP and determined the utility of deep learning in detecting PrePF on CT. METHODS First-degree relatives of patients with FIP over 40 years of age who believed themselves to be unaffected by pulmonary fibrosis underwent CT scans of the chest. Images were visually reviewed, and a deep learning algorithm was used to quantify lung fibrosis. Genotyping for common idiopathic pulmonary fibrosis risk variants in MUC5B and TERT was performed. FINDINGS In 494 relatives of patients with FIP from 263 families of patients with FIP, the prevalence of PrePF on visual CT evaluation was 15.6% (95% CI 12.6 to 19.0). Compared with visual CT evaluation, deep learning quantitative CT analysis had 84% sensitivity (95% CI 0.72 to 0.89) and 86% sensitivity (95% CI 0.83 to 0.89) for discriminating subjects with visual PrePF diagnosis. Subjects with PrePF were older (65.9, SD 10.1 years) than subjects without fibrosis (55.8 SD 8.7 years), more likely to be male (49% vs 37%), more likely to have smoked (44% vs 27%) and more likely to have the MUC5B promoter variant rs35705950 (minor allele frequency 0.29 vs 0.21). MUC5B variant carriers had higher quantitative CT fibrosis scores (mean difference of 0.36%), a difference that remains significant when controlling for age and sex. INTERPRETATION PrePF is common in relatives of patients with FIP. Its prevalence increases with age and the presence of a common MUC5B promoter variant. Quantitative CT analysis can detect these imaging abnormalities.
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Affiliation(s)
- Susan K Mathai
- Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States .,Center for Advanced Heart & Lung Disease, Baylor University Medical Center, Dallas, Texas, United States
| | - Stephen Humphries
- Department of Radiology, National Jewish Health, Denver, Colorado, United States
| | - Jonathan A Kropski
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Timothy S Blackwell
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States.,Department of Veterans Affairs Medical Center, Vanderbilt, Nashville, Tennessee, United States
| | - Julia Powers
- Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
| | - Avram D Walts
- Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
| | - Cheryl Markin
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Julia Woodward
- Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
| | - Jonathan H Chung
- Department of Radiology, National Jewish Health, Denver, Colorado, United States.,Department of Radiology, University of Chicago, Chicago, Illinois, United States
| | - Kevin K Brown
- Department of Medicine, National Jewish Health, Denver, Colorado, United States
| | - Mark P Steele
- Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
| | - James E Loyd
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Marvin I Schwarz
- Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
| | - Tasha Fingerlin
- Center for Genes, Environment & Health, National Jewish Health, Denver, Colorado, United States
| | - Ivana V Yang
- Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
| | - David A Lynch
- Department of Radiology, National Jewish Health, Denver, Colorado, United States
| | - David A Schwartz
- Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
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Porcu M, De Silva P, Solinas C, Battaglia A, Schena M, Scartozzi M, Bron D, Suri JS, Willard-Gallo K, Sangiolo D, Saba L. Immunotherapy Associated Pulmonary Toxicity: Biology Behind Clinical and Radiological Features. Cancers (Basel) 2019; 11:cancers11030305. [PMID: 30841554 PMCID: PMC6468855 DOI: 10.3390/cancers11030305] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 02/17/2019] [Accepted: 02/26/2019] [Indexed: 12/22/2022] Open
Abstract
The broader use of immune checkpoint blockade in clinical routine challenges clinicians in the diagnosis and management of side effects which are caused by inflammation generated by the activation of the immune response. Nearly all organs can be affected by immune-related toxicities. However, the most frequently reported are: fatigue, rash, pruritus, diarrhea, nausea/vomiting, arthralgia, decreased appetite and abdominal pain. Although these adverse events are usually mild, reversible and not frequent, an early diagnosis is crucial. Immune-related pulmonary toxicity was most frequently observed in trials of lung cancer and of melanoma patients treated with the combination of the anti-cytotoxic T lymphocyte antigen (CTLA)-4 and the anti-programmed cell death-1 (PD-1) antibodies. The most frequent immune-related adverse event in the lung is represented by pneumonitis due to the development of infiltrates in the interstitium and in the alveoli. Clinical symptoms and radiological patterns are the key elements to be considered for an early diagnosis, rendering the differential diagnosis crucial. Diagnosis of immune-related pneumonitis may imply the temporary or definitive suspension of immunotherapy, along with the start of immuno-suppressive treatments. The aim of this work is to summarize the biological bases, clinical and radiological findings of lung toxicity under immune checkpoint blockade, underlining the importance of multidisciplinary teams for an optimal early diagnosis of this side effect, with the aim to reach an improved patient care.
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Affiliation(s)
- Michele Porcu
- Department of Radiology, University Hospital of Cagliari, 09042 Monserrato (Cagliari), Italy.
| | - Pushpamali De Silva
- Molecular Immunology Unit, Institut Jules Bordet, Universitè Libre de Bruxelles (ULB), 1000 Brussels, Belgium.
- Clinical and Experimental Hematology, Institute Jules Bordet, Universitè Libre de Bruxelles (ULB), 1000 Brussels, Belgium.
| | - Cinzia Solinas
- Molecular Immunology Unit, Institut Jules Bordet, Universitè Libre de Bruxelles (ULB), 1000 Brussels, Belgium.
- Department of Medical Oncology and Hematology, Regional Hospital of Aosta, 11100 Aosta, Italy.
| | - Angelo Battaglia
- Department of Medical Oncology and Hematology, Regional Hospital of Aosta, 11100 Aosta, Italy.
| | - Marina Schena
- Department of Medical Oncology and Hematology, Regional Hospital of Aosta, 11100 Aosta, Italy.
| | - Mario Scartozzi
- Department of Medical Oncology, University Hospital of Cagliari, 09042 Monserrato (Cagliari), Italy.
| | - Dominique Bron
- Clinical and Experimental Hematology, Institute Jules Bordet, Universitè Libre de Bruxelles (ULB), 1000 Brussels, Belgium.
| | - Jasjit S Suri
- Lung Diagnostic Division, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA.
- AtheroPoint™ LLC, Roseville, CA 95661, USA.
| | - Karen Willard-Gallo
- Molecular Immunology Unit, Institut Jules Bordet, Universitè Libre de Bruxelles (ULB), 1000 Brussels, Belgium.
| | - Dario Sangiolo
- Department of Oncology, University of Torino, 10043 Orbassano (Torino), Italy.
- Division of Medical Oncology, Experimental Cell Therapy, Candiolo Cancer Institute FPO-IRCCS, 10060 Candiolo (Torino), Italy.
| | - Luca Saba
- Department of Radiology, University Hospital of Cagliari, 09042 Monserrato (Cagliari), Italy.
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