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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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2
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De Muzio F, Pellegrino F, Fusco R, Tafuto S, Scaglione M, Ottaiano A, Petrillo A, Izzo F, Granata V. Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics. Diagnostics (Basel) 2023; 13:2877. [PMID: 37761243 PMCID: PMC10529975 DOI: 10.3390/diagnostics13182877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
Neuroendocrine neoplasms (NENs) are a group of lesions originating from cells of the diffuse neuroendocrine system. NENs may involve different sites, including the gastrointestinal tract (GEP-NENs). The incidence and prevalence of GEP-NENs has been constantly rising thanks to the increased diagnostic power of imaging and immuno-histochemistry. Despite the plethora of biochemical markers and imaging techniques, the prognosis and therapeutic choice in GEP-NENs still represents a challenge, mainly due to the great heterogeneity in terms of tumor lesions and clinical behavior. The concept that biomedical images contain information about tissue heterogeneity and pathological processes invisible to the human eye is now well established. From this substrate comes the idea of radiomics. Computational analysis has achieved promising results in several oncological settings, and the use of radiomics in different types of GEP-NENs is growing in the field of research, yet with conflicting results. The aim of this narrative review is to provide a comprehensive update on the role of radiomics on GEP-NEN management, focusing on the main clinical aspects analyzed by most existing reports: predicting tumor grade, distinguishing NET from other tumors, and prognosis assessment.
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Affiliation(s)
- Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | | | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy;
| | - Salvatore Tafuto
- Unit of Sarcomi e Tumori Rari, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Mariano Scaglione
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy
| | - Alessandro Ottaiano
- Unit for Innovative Therapies of Abdominal Metastastes, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Francesco Izzo
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
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3
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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: 3] [Impact Index Per Article: 3.0] [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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Borgheresi A, Agostini A, Pierpaoli L, Bruno A, Valeri T, Danti G, Bicci E, Gabelloni M, De Muzio F, Brunese MC, Bruno F, Palumbo P, Fusco R, Granata V, Gandolfo N, Miele V, Barile A, Giovagnoni A. Tips and Tricks in Thoracic Radiology for Beginners: A Findings-Based Approach. Tomography 2023; 9:1153-1186. [PMID: 37368547 DOI: 10.3390/tomography9030095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/03/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
This review has the purpose of illustrating schematically and comprehensively the key concepts for the beginner who approaches chest radiology for the first time. The approach to thoracic imaging may be challenging for the beginner due to the wide spectrum of diseases, their overlap, and the complexity of radiological findings. The first step consists of the proper assessment of the basic imaging findings. This review is divided into three main districts (mediastinum, pleura, focal and diffuse diseases of the lung parenchyma): the main findings will be discussed in a clinical scenario. Radiological tips and tricks, and relative clinical background, will be provided to orient the beginner toward the differential diagnoses of the main thoracic diseases.
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Affiliation(s)
- Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
- Department of Radiology, University Hospital "Azienda Ospedaliero Universitaria delle Marche", Via Conca 71, 60126 Ancona, Italy
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
- Department of Radiology, University Hospital "Azienda Ospedaliero Universitaria delle Marche", Via Conca 71, 60126 Ancona, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Luca Pierpaoli
- School of Radiology, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
| | - Alessandra Bruno
- School of Radiology, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
| | - Tommaso Valeri
- School of Radiology, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
| | - Ginevra Danti
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Eleonora Bicci
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Michela Gabelloni
- Nuclear Medicine Unit, Department of Translational Research, University of Pisa, 56126 Pisa, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health, Unit 1, 67100 L'Aquila, Italy
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health, Unit 1, 67100 L'Aquila, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131 Naples, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, 16149 Genoa, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, 67100 L'Aquila, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
- Department of Radiology, University Hospital "Azienda Ospedaliero Universitaria delle Marche", Via Conca 71, 60126 Ancona, Italy
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Shalmon T, Salazar P, Horie M, Hanneman K, Pakkal M, Anwari V, Fratesi J. Predefined and data driven CT densitometric features predict critical illness and hospital length of stay in COVID-19 patients. Sci Rep 2022; 12:8143. [PMID: 35581369 PMCID: PMC9114017 DOI: 10.1038/s41598-022-12311-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 05/09/2022] [Indexed: 11/09/2022] Open
Abstract
The aim of this study was to compare whole lung CT density histograms to predict critical illness outcome and hospital length of stay in a cohort of 80 COVID-19 patients. CT chest images on segmented lungs were retrospectively analyzed. Functional Principal Component Analysis (FPCA) was used to find the main modes of variations on CT density histograms. CT density features, the CT severity score, the COVID-GRAM score and the patient clinical data were assessed for predicting the patient outcome using logistic regression models and survival analysis. ROC analysis predictors of critically ill status: 87.5th percentile CT density (Q875)—AUC 0.88 95% CI (0.79 0.94), F1-CT—AUC 0.87 (0.77 0.93) Standard Deviation (SD-CT)—AUC 0.86 (0.73, 0.93). Multivariate models combining CT-density predictors and Neutrophil–Lymphocyte Ratio showed the highest accuracy. SD-CT, Q875 and F1 score were significant predictors of hospital length of stay (LOS) while controlling for hospital death using competing risks models. Moreover, two multivariate Fine-Gray regression models combining the clinical variables: age, NLR, Contrast CT factor with either Q875 or F1 CT-density predictors revealed significant effects for the prediction of LOS incidence in presence of a competing risk (death) and acceptable predictive performances (Bootstrapped C-index 0.74 [0.70 0.78]).
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Affiliation(s)
- Tamar Shalmon
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,University Health Network, Toronto General Hospital, 200 Elizabeth St, Toronto, ON, M5G 2C4, Canada
| | | | - Miho Horie
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,University Health Network, Toronto General Hospital, 200 Elizabeth St, Toronto, ON, M5G 2C4, Canada
| | - Kate Hanneman
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,University Health Network, Toronto General Hospital, 200 Elizabeth St, Toronto, ON, M5G 2C4, Canada
| | - Mini Pakkal
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,University Health Network, Toronto General Hospital, 200 Elizabeth St, Toronto, ON, M5G 2C4, Canada
| | - Vahid Anwari
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,University Health Network, Toronto General Hospital, 200 Elizabeth St, Toronto, ON, M5G 2C4, Canada
| | - Jennifer Fratesi
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada. .,University Health Network, Toronto General Hospital, 200 Elizabeth St, Toronto, ON, M5G 2C4, Canada.
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Rea G, Bocchino M. The challenge of diagnosing interstitial lung disease by HRCT: state of the art and future perspectives. J Bras Pneumol 2021; 47:e20210199. [PMID: 34190867 PMCID: PMC8332719 DOI: 10.36416/1806-3756/e20210199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Affiliation(s)
- Gaetano Rea
- . Dipartimento di Radiologia, A.O. dei Colli, Ospedale Monaldi, Napoli, Italia
| | - Marialuisa Bocchino
- . Sezione di Malattie Respiratorie, Dipartimento di Medicina e Chirurgia Clinica, Università Federico II, Napoli, Italia
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Ground-glass opacity (GGO): a review of the differential diagnosis in the era of COVID-19. Jpn J Radiol 2021; 39:721-732. [PMID: 33900542 PMCID: PMC8071755 DOI: 10.1007/s11604-021-01120-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 04/07/2021] [Indexed: 02/06/2023]
Abstract
Thoracic imaging is fundamental in the diagnostic route of Coronavirus disease 2019 (COVID-19) especially in patients admitted to hospitals. In particular, chest computed tomography (CT) has a key role in identifying the typical features of the infection. Ground-glass opacities (GGO) are one of the main CT findings, but their presence is not specific for this viral pneumonia. In fact, GGO is a radiological sign of different pathologies with both acute and subacute/chronic clinical manifestations. In the evaluation of a subject with focal or diffuse GGO, the radiologist has to know the patient’s medical history to obtain a valid diagnostic hypothesis. The authors describe the various CT appearance of GGO, related to the onset of symptoms, focusing also on the ancillary signs that can help radiologist to obtain a correct and prompt diagnosis.
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