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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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2
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Lucà S, Pagliuca F, Perrotta F, Ronchi A, Mariniello DF, Natale G, Bianco A, Fiorelli A, Accardo M, Franco R. Multidisciplinary Approach to the Diagnosis of Idiopathic Interstitial Pneumonias: Focus on the Pathologist's Key Role. Int J Mol Sci 2024; 25:3618. [PMID: 38612431 PMCID: PMC11011777 DOI: 10.3390/ijms25073618] [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: 02/01/2024] [Revised: 03/14/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
Idiopathic Interstitial Pneumonias (IIPs) are a heterogeneous group of the broader category of Interstitial Lung Diseases (ILDs), pathologically characterized by the distortion of lung parenchyma by interstitial inflammation and/or fibrosis. The American Thoracic Society (ATS)/European Respiratory Society (ERS) international multidisciplinary consensus classification of the IIPs was published in 2002 and then updated in 2013, with the authors emphasizing the need for a multidisciplinary approach to the diagnosis of IIPs. The histological evaluation of IIPs is challenging, and different types of IIPs are classically associated with specific histopathological patterns. However, morphological overlaps can be observed, and the same histopathological features can be seen in totally different clinical settings. Therefore, the pathologist's aim is to recognize the pathologic-morphologic pattern of disease in this clinical setting, and only after multi-disciplinary evaluation, if there is concordance between clinical and radiological findings, a definitive diagnosis of specific IIP can be established, allowing the optimal clinical-therapeutic management of the patient.
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Affiliation(s)
- Stefano Lucà
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (S.L.); (F.P.); (A.R.); (M.A.)
| | - Francesca Pagliuca
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (S.L.); (F.P.); (A.R.); (M.A.)
| | - Fabio Perrotta
- Department of Translational Medical Science, Università degli Studi della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (F.P.); (D.F.M.); (A.B.)
| | - Andrea Ronchi
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (S.L.); (F.P.); (A.R.); (M.A.)
| | - Domenica Francesca Mariniello
- Department of Translational Medical Science, Università degli Studi della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (F.P.); (D.F.M.); (A.B.)
| | - Giovanni Natale
- Division of Thoracic Surgery, Università degli Studi della Campania “Luigi Vanvitelli”, Piazza Miraglia, 2, 80138 Naples, Italy; (G.N.); (A.F.)
| | - Andrea Bianco
- Department of Translational Medical Science, Università degli Studi della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (F.P.); (D.F.M.); (A.B.)
| | - Alfonso Fiorelli
- Division of Thoracic Surgery, Università degli Studi della Campania “Luigi Vanvitelli”, Piazza Miraglia, 2, 80138 Naples, Italy; (G.N.); (A.F.)
| | - Marina Accardo
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (S.L.); (F.P.); (A.R.); (M.A.)
| | - Renato Franco
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (S.L.); (F.P.); (A.R.); (M.A.)
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3
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Rea G, Bocchino M, Lieto R, Ledda RE, D’Alto M, Sperandeo M, Lucci R, Pasquinelli P, Sanduzzi Zamparelli S, Bocchini G, Valente T, Sica G. The Unveiled Triad: Clinical, Radiological and Pathological Insights into Hypersensitivity Pneumonitis. J Clin Med 2024; 13:797. [PMID: 38337490 PMCID: PMC10856167 DOI: 10.3390/jcm13030797] [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: 11/28/2023] [Revised: 01/10/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Hypersensitivity pneumonitis (HP) is a diffuse parenchymal lung disease (DLPD) characterized by complex interstitial lung damage with polymorphic and protean inflammatory aspects affecting lung tissue targets including small airways, the interstitium, alveolar compartments and vascular structures. HP shares clinical and often radiological features with other lung diseases in acute or chronic forms. In its natural temporal evolution, if specific therapy is not initiated promptly, HP leads to progressive fibrotic damage with reduced lung volumes and impaired gas exchange. The prevalence of HP varies considerably worldwide, influenced by factors like imprecise disease classification, diagnostic method limitations for obtaining a confident diagnosis, diagnostic limitations in the correct processing of high-resolution computed tomography (HRCT) radiological parameters, unreliable medical history, diverse geographical conditions, heterogeneous agricultural and industrial practices and occasionally ineffective individual protections regarding occupational exposures and host risk factors. The aim of this review is to present an accurate and detailed 360-degree analysis of HP considering HRCT patterns and the role of the broncho-alveolar lavage (BAL), without neglecting biopsy and anatomopathological aspects and future technological developments that could make the diagnosis of this disease less challenging.
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Affiliation(s)
- Gaetano Rea
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy; (G.R.); (R.L.); (G.B.); (T.V.)
| | - 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; (G.R.); (R.L.); (G.B.); (T.V.)
| | - Roberta Eufrasia Ledda
- 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;
| | - Marco Sperandeo
- Interventional Ultrasound Unit, Department of Internal Medicine, IRCCS “Casa Sollievo Della Sofferenza” Hospital, San Giovanni Rotondo, 71013 Foggia, Italy;
| | - Raffaella Lucci
- Department of Pathology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy;
| | - Patrizio Pasquinelli
- Italian Federation of Pulmonary Fibrosis and Rare Pulmonary Diseases “FIMARP”, 00185 Rome, Italy;
- Department of Pulmonary Diseases, San Camillo-Forlanini Hospital, 00152 Rome, Italy
| | | | - Giorgio Bocchini
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy; (G.R.); (R.L.); (G.B.); (T.V.)
| | - Tullio Valente
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy; (G.R.); (R.L.); (G.B.); (T.V.)
| | - Giacomo Sica
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy; (G.R.); (R.L.); (G.B.); (T.V.)
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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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5
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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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6
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Ding D, Gao R, Xue Q, Luan R, Yang J. Genomic Fingerprint Associated with Familial Idiopathic Pulmonary Fibrosis: A Review. Int J Med Sci 2023; 20:329-345. [PMID: 36860670 PMCID: PMC9969503 DOI: 10.7150/ijms.80358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/12/2023] [Indexed: 02/04/2023] Open
Abstract
Idiopathic pulmonary fibrosis (IPF) is a severe interstitial lung disease; although the recent introduction of two anti-fibrosis drugs, pirfenidone and Nidanib, have resulted in a significant reduction in lung function decline, IPF is still not curable. Approximately 2-20% of patients with IPF have a family history of the disease, which is considered the strongest risk factor for idiopathic interstitial pneumonia. However, the genetic predispositions of familial IPF (f-IPF), a particular type of IPF, remain largely unknown. Genetics affect the susceptibility and progression of f-IPF. Genomic markers are increasingly being recognized for their contribution to disease prognosis and drug therapy outcomes. Existing data suggest that genomics may help identify individuals at risk for f-IPF, accurately classify patients, elucidate key pathways involved in disease pathogenesis, and ultimately develop more effective targeted therapies. Since several genetic variants associated with the disease have been found in f-IPF, this review systematically summarizes the latest progress in the gene spectrum of the f-IPF population and the underlying mechanisms of f-IPF. The genetic susceptibility variation related to the disease phenotype is also illustrated. This review aims to improve the understanding of the IPF pathogenesis and facilitate his early detection.
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Affiliation(s)
- Dongyan Ding
- Department of Respiratory Medicine, The Second Hospital of Jilin University, Changchun, China
| | - Rong Gao
- Department of Respiratory Medicine, The Second Hospital of Jilin University, Changchun, China
| | - Qianfei Xue
- Hospital of Jilin University, Changchun, China
| | - Rumei Luan
- Department of Respiratory Medicine, The Second Hospital of Jilin University, Changchun, China
| | - Junling Yang
- Department of Respiratory Medicine, The Second Hospital of Jilin University, Changchun, China
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7
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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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8
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Yang CC, Chen CY, Kuo YT, Ko CC, Wu WJ, Liang CH, Yun CH, Huang WM. Radiomics for the Prediction of Response to Antifibrotic Treatment in Patients with Idiopathic Pulmonary Fibrosis: A Pilot Study. Diagnostics (Basel) 2022; 12:diagnostics12041002. [PMID: 35454050 PMCID: PMC9028756 DOI: 10.3390/diagnostics12041002] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 02/06/2023] Open
Abstract
Antifibrotic therapy has changed the treatment paradigm for idiopathic pulmonary fibrosis (IPF); however, a subset of patients still experienced rapid disease progression despite treatment. This study aimed to determine whether CT-based radiomic features can predict therapeutic response to antifibrotic agents. In this retrospective study, 35 patients with IPF on antifibrotic treatment enrolled from two centers were divided into training (n = 26) and external validation (n = 9) sets. Clinical and pulmonary function data were collected. The patients were categorized into stable disease (SD) and progressive disease (PD) groups based on functional or radiologic criteria. From pretreatment non-enhanced high-resolution CT (HRCT) images, twenty-six radiomic features were extracted through whole-lung texture analysis, and six parenchymal patterns were quantified using dedicated imaging platforms. The predictive factors for PD were determined via univariate and multivariate logistic regression analyses. In the training set (SD/PD: 12/14), univariate analysis identified eight radiomic features and ground-glass opacity percentage (GGO%) as potential predicators of PD. However, multivariate analysis found that the single independent predictor was the sum entropy (accuracy, 80.77%; AUC, 0.75). The combined sum entropy-GGO% model improved the predictive performance in the training set (accuracy, 88.46%; AUC, 0.77). The overall accuracy of the combined model in the validation set (SD/PD: 7/2) was 66.67%. Our preliminary results demonstrated that radiomic features based on pretreatment HRCT could predict the response of patients with IPF to antifibrotic treatment.
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Affiliation(s)
- Cheng-Chun Yang
- Department of Medical Imaging, Chi Mei Hospital, Tainan 710, Taiwan; (C.-C.Y.); (C.-Y.C.); (Y.-T.K.); (C.-C.K.)
| | - Chin-Yu Chen
- Department of Medical Imaging, Chi Mei Hospital, Tainan 710, Taiwan; (C.-C.Y.); (C.-Y.C.); (Y.-T.K.); (C.-C.K.)
| | - Yu-Ting Kuo
- Department of Medical Imaging, Chi Mei Hospital, Tainan 710, Taiwan; (C.-C.Y.); (C.-Y.C.); (Y.-T.K.); (C.-C.K.)
- Department of Radiology, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi Mei Hospital, Tainan 710, Taiwan; (C.-C.Y.); (C.-Y.C.); (Y.-T.K.); (C.-C.K.)
- Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan 717, Taiwan
- Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 804, Taiwan
| | - Wen-Jui Wu
- Division of Pulmonary and Critical Care Medicine, Mackay Memorial Hospital, Taipei 104, Taiwan;
| | - Chia-Hao Liang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan;
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Chun-Ho Yun
- Department of Radiology, Mackay Memorial Hospital, Taipei 104, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City 252, Taiwan
- Mackay Junior College of Medicine, Nursing, and Management, New Taipei City 252, Taiwan
- Correspondence: (C.-H.Y.); (W.-M.H.)
| | - Wei-Ming Huang
- Department of Radiology, Mackay Memorial Hospital, Taipei 104, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City 252, Taiwan
- Mackay Junior College of Medicine, Nursing, and Management, New Taipei City 252, Taiwan
- Correspondence: (C.-H.Y.); (W.-M.H.)
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