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Yao Y, Yang Y, Hu Q, Xie X, Jiang W, Liu C, Li X, Wang Y, Luo L, Li J. A nomogram combining CT-based radiomic features with clinical features for the differentiation of benign and malignant cystic pulmonary nodules. J Cardiothorac Surg 2024; 19:392. [PMID: 38937772 PMCID: PMC11210004 DOI: 10.1186/s13019-024-02936-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 06/15/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND Currently, the differentiation between benign and malignant cystic pulmonary nodules poses a significant challenge for clinicians. The objective of this retrospective study was to construct a predictive model for determining the likelihood of malignancy in patients with cystic pulmonary nodules. METHODS The current study involved 129 patients diagnosed with cystic pulmonary nodules between January 2017 and June 2023 at the Neijiang First People's Hospital. The study gathered the clinical data, preoperative imaging features of chest CT, and postoperative histopathological results for both cohorts. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, from which a prediction model and nomogram were developed. In addition, The model's performance was assessed through receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA). RESULTS A cohort of 129 patients presenting with cystic pulmonary nodules, consisting of 92 malignant and 37 benign lesions, was examined. Logistic data analysis identified a cystic airspace with a mural nodule, spiculation, mural morphology, and the number of cystic cavities as significant independent predictors for discriminating between benign and malignant cystic lung nodules. The nomogram prediction model demonstrated a high level of predictive accuracy, as evidenced by an area under the ROC curve (AUC) of 0.874 (95% CI: 0.804-0.944). Furthermore, the calibration curve of the model displayed satisfactory calibration. DCA proved that the prediction model was useful for clinical application. CONCLUSION In summary, the risk prediction model for benign and malignant cystic pulmonary nodules has the potential to assist clinicians in the diagnosis of such nodules and enhance clinical decision-making processes.
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Affiliation(s)
- Yi Yao
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Yanhui Yang
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Qiuxia Hu
- Department of Obstetrics and Gynecology, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Xiaoyang Xie
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Wenjian Jiang
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Caiyang Liu
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Xiaoliang Li
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Yi Wang
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Lei Luo
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Ji Li
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China.
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2
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Azour L, Oh AS, Prosper AE, Toussie D, Villasana-Gomez G, Pourzand L. Subsolid Nodules: Significance and Current Understanding. Clin Chest Med 2024; 45:263-277. [PMID: 38816087 DOI: 10.1016/j.ccm.2024.02.003] [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: 06/01/2024]
Abstract
Subsolid nodules are heterogeneously appearing and behaving entities, commonly encountered incidentally and in high-risk populations. Accurate characterization of subsolid nodules, and application of evolving surveillance guidelines, facilitates evidence-based and multidisciplinary patient-centered management.
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Affiliation(s)
- Lea Azour
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA.
| | - Andrea S Oh
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
| | - Ashley E Prosper
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
| | - Danielle Toussie
- Department of Radiology, New York University Grossman School of Medicine, NYU Langone Health, 660 1st Avenue, New York, NY 10016, USA
| | - Geraldine Villasana-Gomez
- Department of Radiology, New York University Grossman School of Medicine, NYU Langone Health, 660 1st Avenue, New York, NY 10016, USA
| | - Lila Pourzand
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
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3
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Christensen J, Prosper AE, Wu CC, Chung J, Lee E, Elicker B, Hunsaker AR, Petranovic M, Sandler KL, Stiles B, Mazzone P, Yankelevitz D, Aberle D, Chiles C, Kazerooni E. ACR Lung-RADS v2022: Assessment Categories and Management Recommendations. J Am Coll Radiol 2024; 21:473-488. [PMID: 37820837 DOI: 10.1016/j.jacr.2023.09.009] [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: 06/05/2023] [Revised: 08/08/2023] [Accepted: 09/21/2023] [Indexed: 10/13/2023]
Abstract
The ACR created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.
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Affiliation(s)
- Jared Christensen
- Vice Chair and Professor of Radiology, Department of Radiology, Duke University, Durham, North Carolina; Chair, ACR Lung-RADS Committee.
| | - Ashley Elizabeth Prosper
- Assistant Professor and Section Chief of Cardiothoracic Imaging, Department of Radiological Sciences, University of California, Los Angeles, California
| | - Carol C Wu
- Professor of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jonathan Chung
- Professor of Radiology Vice Chair of Quality Section Chief of Cardiopulmonary Imaging, University of Chicago, Chicago, Illinois
| | - Elizabeth Lee
- Clinical Associate Professor, Radiology, Michigan Medicine, Ann Arbor, Michigan
| | - Brett Elicker
- Chief of the Cardiac & Pulmonary Imaging Section, University of California, San Francisco, California
| | - Andetta R Hunsaker
- Brigham and Women's Hospital, Boston, Massachusetts; Associate Professor Harvard Medical School Chief Division of Thoracic Imaging
| | - Milena Petranovic
- Instructor, Radiology, Harvard Medical School Divisional Quality Director, Thoracic Imaging and Intervention, Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Kim L Sandler
- Associate Professor, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Brendon Stiles
- Professor and Chair, Thoracic Surgery and Surgical Oncology, Montefiore Health System, Albert Einstein College of Medicine, Bronx, New York
| | | | | | - Denise Aberle
- Professor of Radiology, Department of Radiological Sciences; David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Caroline Chiles
- Professor of Radiology Director, Lung Screening Program, Atrium Health Wake Forest, Winston-Salem, North Carolina
| | - Ella Kazerooni
- Professor of Radiology & Internal Medicine and Associate Chief Clinical Officer for Diagnostics, Michigan Medicine/University of Michigan Medical School, Ann Arbor, Michigan; Clinical Information Management, University of Michigan Medical Group
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4
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Christensen J, Prosper AE, Wu CC, Chung J, Lee E, Elicker B, Hunsaker AR, Petranovic M, Sandler KL, Stiles B, Mazzone P, Yankelevitz D, Aberle D, Chiles C, Kazerooni E. ACR Lung-RADS v2022: Assessment Categories and Management Recommendations. Chest 2024; 165:738-753. [PMID: 38300206 DOI: 10.1016/j.chest.2023.10.028] [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: 02/02/2024] Open
Abstract
The American College of Radiology created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.
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Affiliation(s)
- Jared Christensen
- Vice Chair and Professor of Radiology, Department of Radiology, Duke University, Durham, North Carolina; Chair, ACR Lung-RADS Committee.
| | - Ashley Elizabeth Prosper
- Assistant Professor and Section Chief of Cardiothoracic Imaging, Department of Radiological Sciences, University of California, Los Angeles, California
| | - Carol C Wu
- Professor of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jonathan Chung
- Professor of Radiology Vice Chair of Quality Section Chief of Cardiopulmonary Imaging, University of Chicago, Chicago, Illinois
| | - Elizabeth Lee
- Clinical Associate Professor, Radiology, Michigan Medicine, Ann Arbor, Michigan
| | - Brett Elicker
- Chief of the Cardiac & Pulmonary Imaging Section, University of California, San Francisco, California
| | - Andetta R Hunsaker
- Brigham and Women's Hospital, Boston, Massachusetts; Associate Professor Harvard Medical School Chief Division of Thoracic Imaging
| | - Milena Petranovic
- Instructor, Radiology, Harvard Medical School Divisional Quality Director, Thoracic Imaging and Intervention, Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Kim L Sandler
- Associate Professor, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Brendon Stiles
- Professor and Chair, Thoracic Surgery and Surgical Oncology, Montefiore Health System, Albert Einstein College of Medicine, Bronx, New York
| | | | | | - Denise Aberle
- Professor of Radiology, Department of Radiological Sciences; David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Caroline Chiles
- Professor of Radiology Director, Lung Screening Program, Atrium Health Wake Forest, Winston-Salem, North Carolina
| | - Ella Kazerooni
- Professor of Radiology & Internal Medicine and Associate Chief Clinical Officer for Diagnostics, Michigan Medicine/University of Michigan Medical School, Ann Arbor, Michigan; Clinical Information Management, University of Michigan Medical Group
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5
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Liu W, Shen N, Zhang L, Wang X, Chen B, Liu Z, Yang C. Research in the application of artificial intelligence to lung cancer diagnosis. Front Med (Lausanne) 2024; 11:1343485. [PMID: 38352145 PMCID: PMC10861801 DOI: 10.3389/fmed.2024.1343485] [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: 11/23/2023] [Accepted: 01/02/2024] [Indexed: 02/16/2024] Open
Abstract
The morbidity and mortality rates in lung cancer are high worldwide. Early diagnosis and personalized treatment are important to manage this public health issue. In recent years, artificial intelligence (AI) has played increasingly important roles in early screening, auxiliary diagnosis, and prognostic assessment. AI uses algorithms to extract quantitative feature information from high-volume and high-latitude data and learn existing data to predict disease outcomes. In this review, we describe the current uses of AI in lung cancer-focused pathomics, imageomics, and genomics applications.
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Affiliation(s)
- Wenjuan Liu
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Nan Shen
- Department of Nephrology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Limin Zhang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xiaoxi Wang
- Department of Clinical Laboratory, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Bainan Chen
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Zhuo Liu
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chao Yang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
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6
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Mascalchi M, Picozzi G, Puliti D, Diciotti S, Deliperi A, Romei C, Falaschi F, Pistelli F, Grazzini M, Vannucchi L, Bisanzi S, Zappa M, Gorini G, Carozzi FM, Carrozzi L, Paci E. Lung Cancer Screening with Low-Dose CT: What We Have Learned in Two Decades of ITALUNG and What Is Yet to Be Addressed. Diagnostics (Basel) 2023; 13:2197. [PMID: 37443590 DOI: 10.3390/diagnostics13132197] [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: 04/26/2023] [Revised: 06/15/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
The ITALUNG trial started in 2004 and compared lung cancer (LC) and other-causes mortality in 55-69 years-aged smokers and ex-smokers who were randomized to four annual chest low-dose CT (LDCT) or usual care. ITALUNG showed a lower LC and cardiovascular mortality in the screened subjects after 13 years of follow-up, especially in women, and produced many ancillary studies. They included recruitment results of a population-based mimicking approach, development of software for computer-aided diagnosis (CAD) and lung nodules volumetry, LDCT assessment of pulmonary emphysema and coronary artery calcifications (CAC) and their relevance to long-term mortality, results of a smoking-cessation intervention, assessment of the radiations dose associated with screening LDCT, and the results of biomarkers assays. Moreover, ITALUNG data indicated that screen-detected LCs are mostly already present at baseline LDCT, can present as lung cancer associated with cystic airspaces, and can be multiple. However, several issues of LC screening are still unaddressed. They include the annual vs. biennial pace of LDCT, choice between opportunistic or population-based recruitment. and between uni or multi-centre screening, implementation of CAD-assisted reading, containment of false positive and negative LDCT results, incorporation of emphysema. and CAC quantification in models of personalized LC and mortality risk, validation of ultra-LDCT acquisitions, optimization of the smoking-cessation intervention. and prospective validation of the biomarkers.
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Affiliation(s)
- Mario Mascalchi
- Department of Clinical and Experimental Biomedical Sciences "Mario Serio", University of Florence, 50121 Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Giulia Picozzi
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Donella Puliti
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 47521 Cesena, Italy
| | - Annalisa Deliperi
- Radiodiagnostic Unit 2, Department of Diagnostic Imaging, Cisanello University Hospital of Pisa, 56124 Pisa, Italy
| | - Chiara Romei
- Radiodiagnostic Unit 2, Department of Diagnostic Imaging, Cisanello University Hospital of Pisa, 56124 Pisa, Italy
| | - Fabio Falaschi
- Radiodiagnostic Unit 2, Department of Diagnostic Imaging, Cisanello University Hospital of Pisa, 56124 Pisa, Italy
| | - Francesco Pistelli
- Pulmonary Unit, Cardiothoracic and Vascular Department, University Hospital of Pisa, 56124 Pisa, Italy
| | - Michela Grazzini
- Division of Pneumonology, San Jacopo Hospital Pistoia, 51100 Pistoia, Italy
| | - Letizia Vannucchi
- Division of Radiology, San Jacopo Hospital Pistoia, 51100 Pistoia, Italy
| | - Simonetta Bisanzi
- Regional Laboratory of Cancer Prevention, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Marco Zappa
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Giuseppe Gorini
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Francesca Maria Carozzi
- Regional Laboratory of Cancer Prevention, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Laura Carrozzi
- Pulmonary Unit, Cardiothoracic and Vascular Department, University Hospital of Pisa, 56124 Pisa, Italy
| | - Eugenio Paci
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
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7
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Zhu H, Zhang L, Huang Z, Chen J, Sun L, Chen Y, Huang G, Chen Q, Yu H. Lung adenocarcinoma associated with cystic airspaces: Predictive value of CT features in assessing pathologic invasiveness. Eur J Radiol 2023; 165:110947. [PMID: 37392546 DOI: 10.1016/j.ejrad.2023.110947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/10/2023] [Accepted: 06/21/2023] [Indexed: 07/03/2023]
Abstract
OBJECTIVES Lung adenocarcinoma associated with cystic airspaces (LACA) is a unique entity with limited understanding. Our aim was to evaluate the radiological characteristics of LACA and to study which criteria were predictive of invasiveness. METHODS A retrospective monocentric analysis of consecutive patients with pathologically confirmed LACA was performed. The diagnosed adenocarcinomas were classified into preinvasive (atypical adenomatous hyperplasia, adenocarcinoma in situ, or minimally invasive adenocarcinoma) and invasive adenocarcinomas. Eight clinical features and twelve CT features were evaluated. Univariable and multivariable analyses were performed to analyse the correlation between invasiveness, and CT and clinical features. The inter-observer agreement was evaluated using κ statistics and intraclass correlation coefficients. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS A total of 252 patients with 265 lesions (128 men and 124 women; mean age, 58.0 ± 11.1 years) were enrolled. Multivariable logistic regression indicated that multiple cystic airspaces (OR, 5.599; 95 % CI, 1.865-16.802), irregular shape of cystic airspace (OR, 3.236; 95 % CI, 1.073-9.761), entire tumour size (OR, 1.281; 95 % CI, 1.075-1.526), and attenuation (OR, 1.007; 95 % CI, 1.005-1.010) were independent risk factors for invasive LACA. The AUC of the logistic regression model was 0.964 (95 % CI, 0.944-0.985). CONCLUSION Multiple cystic airspaces, irregular shape of cystic airspace, entire tumour size, and attenuation were identified as independent risk factors for invasive LACA. The prediction model gives a good predictive performance, providing additional diagnostic information.
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Affiliation(s)
- Huiyuan Zhu
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lian Zhang
- Shanghai University of Traditional Chinese Medicine, Shanghai, China; Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China; Department of Radiology, Jiading Hospital of Traditional Chinese Medicine, Shanghai, China
| | - Zike Huang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jing Chen
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linlin Sun
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yinan Chen
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Gang Huang
- Shanghai University of Traditional Chinese Medicine, Shanghai, China; Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China; Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
| | - Qunhui Chen
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Hong Yu
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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8
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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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9
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Mikhael PG, Wohlwend J, Yala A, Karstens L, Xiang J, Takigami AK, Bourgouin PP, Chan P, Mrah S, Amayri W, Juan YH, Yang CT, Wan YL, Lin G, Sequist LV, Fintelmann FJ, Barzilay R. Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography. J Clin Oncol 2023; 41:2191-2200. [PMID: 36634294 PMCID: PMC10419602 DOI: 10.1200/jco.22.01345] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/10/2022] [Accepted: 11/29/2022] [Indexed: 01/13/2023] Open
Abstract
PURPOSE Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data. METHODS We developed a model called Sybil using LDCTs from the National Lung Screening Trial (NLST). Sybil requires only one LDCT and does not require clinical data or radiologist annotations; it can run in real time in the background on a radiology reading station. Sybil was validated on three independent data sets: a heldout set of 6,282 LDCTs from NLST participants, 8,821 LDCTs from Massachusetts General Hospital (MGH), and 12,280 LDCTs from Chang Gung Memorial Hospital (CGMH, which included people with a range of smoking history including nonsmokers). RESULTS Sybil achieved area under the receiver-operator curves for lung cancer prediction at 1 year of 0.92 (95% CI, 0.88 to 0.95) on NLST, 0.86 (95% CI, 0.82 to 0.90) on MGH, and 0.94 (95% CI, 0.91 to 1.00) on CGMH external validation sets. Concordance indices over 6 years were 0.75 (95% CI, 0.72 to 0.78), 0.81 (95% CI, 0.77 to 0.85), and 0.80 (95% CI, 0.75 to 0.86) for NLST, MGH, and CGMH, respectively. CONCLUSION Sybil can accurately predict an individual's future lung cancer risk from a single LDCT scan to further enable personalized screening. Future study is required to understand Sybil's clinical applications. Our model and annotations are publicly available. [Media: see text].
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Affiliation(s)
- Peter G. Mikhael
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Jeremy Wohlwend
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Adam Yala
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Ludvig Karstens
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Justin Xiang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Angelo K. Takigami
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Patrick P. Bourgouin
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - PuiYee Chan
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Sofiane Mrah
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Wael Amayri
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Yu-Hsiang Juan
- Chang Gung University, Taoyuan, Taiwan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Cheng-Ta Yang
- Chang Gung University, Taoyuan, Taiwan
- Department of Thoracic Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Yung-Liang Wan
- Chang Gung University, Taoyuan, Taiwan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Gigin Lin
- Chang Gung University, Taoyuan, Taiwan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Lecia V. Sequist
- Harvard Medical School, Boston, MA
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Florian J. Fintelmann
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Regina Barzilay
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
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10
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Yang X, Zhang L, Meng F, Song W, Li D, Zhong D. Lung adenocarcinoma associated with cystic airspaces. Chronic Dis Transl Med 2023; 9:58-62. [PMID: 36926256 PMCID: PMC10011662 DOI: 10.1002/cdt3.51] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 08/31/2022] [Accepted: 10/25/2022] [Indexed: 11/21/2022] Open
Affiliation(s)
- Xue Yang
- Department of Medical Oncology Tianjin Medical University General Hospital Tianjin China
| | - Linlin Zhang
- Department of Medical Oncology Tianjin Medical University General Hospital Tianjin China
| | - Fanlu Meng
- Department of Medical Oncology Tianjin Medical University General Hospital Tianjin China
| | - Wenjing Song
- Department of Pathology Tianjin Medical University Tianjin China.,Department of Pathology Tianjin Medical University General Hospital Tianjin China
| | - Dong Li
- Department of Radiology Tianjin Medical University General Hospital Tianjin China
| | - Diansheng Zhong
- Department of Medical Oncology Tianjin Medical University General Hospital Tianjin China
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11
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de Margerie-Mellon C, Chassagnon G. Artificial intelligence: A critical review of applications for lung nodule and lung cancer. Diagn Interv Imaging 2023; 104:11-17. [PMID: 36513593 DOI: 10.1016/j.diii.2022.11.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a broad concept that usually refers to computer programs that can learn from data and perform certain specific tasks. In the recent years, the growth of deep learning, a successful technique for computer vision tasks that does not require explicit programming, coupled with the availability of large imaging databases fostered the development of multiple applications in the medical imaging field, especially for lung nodules and lung cancer, mostly through convolutional neural networks (CNN). Some of the first applications of AI is this field were dedicated to automated detection of lung nodules on X-ray and computed tomography (CT) examinations, with performances now reaching or exceeding those of radiologists. For lung nodule segmentation, CNN-based algorithms applied to CT images show excellent spatial overlap index with manual segmentation, even for irregular and ground glass nodules. A third application of AI is the classification of lung nodules between malignant and benign, which could limit the number of follow-up CT examinations for less suspicious lesions. Several algorithms have demonstrated excellent capabilities for the prediction of the malignancy risk when a nodule is discovered. These different applications of AI for lung nodules are particularly appealing in the context of lung cancer screening. In the field of lung cancer, AI tools applied to lung imaging have been investigated for distinct aims. First, they could play a role for the non-invasive characterization of tumors, especially for histological subtype and somatic mutation predictions, with a potential therapeutic impact. Additionally, they could help predict the patient prognosis, in combination to clinical data. Despite these encouraging perspectives, clinical implementation of AI tools is only beginning because of the lack of generalizability of published studies, of an inner obscure working and because of limited data about the impact of such tools on the radiologists' decision and on the patient outcome. Radiologists must be active participants in the process of evaluating AI tools, as such tools could support their daily work and offer them more time for high added value tasks.
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Affiliation(s)
- Constance de Margerie-Mellon
- Université Paris Cité, Laboratory of Imaging Biomarkers, Center for Research on Inflammation, UMR 1149, INSERM, 75018 Paris, France; Department of Radiology, Hôpital Saint-Louis APHP, 75010 Paris, France
| | - Guillaume Chassagnon
- Université Paris Cité, Faculté de Médecine, 75006 Paris, France; Department of Radiology, Hôpital Cochin APHP, 75014 Paris, France
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12
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Shen YY, Jiang J, Zhao J, Song J. Lung squamous cell carcinoma presenting as rare clustered cystic lesions: A case report and review of literature. World J Clin Cases 2022; 10:13006-13014. [PMID: 36569005 PMCID: PMC9782924 DOI: 10.12998/wjcc.v10.i35.13006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 11/17/2022] [Accepted: 11/23/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Lung cancer is the leading cause of cancer-related death. Early diagnosis is critical to improving a patient’s chance of survival. However, lung cancer associated with cystic airspaces is often misdiagnosed or underdiagnosed due to the absence of clinical symptoms, poor imaging specificity, and high risk of biopsy-related complications.
CASE SUMMARY We report an unusual case of cancer in a 55-year-old man, in which the lesion evolved from a small solitary thin-walled cyst to lung squamous cell carcinoma (SCC) with metastases in both lungs. The SCC manifested as rare clustered cystic lesions, detected on chest computed tomography. There were air-fluid levels, compartments, and bronchial arteries in the cystic lesions. Additionally, there was no clear extrathoracic metastasis. After chemotherapy, the patient achieved a partial response, type I respiratory failure was relieved, and the lung lesions became a clustered thin-walled cyst.
CONCLUSION Pulmonary cystic lesions require regular imaging follow-up. Lung SCC should be a diagnostic consideration in cases of thin-walled cysts as well as multiple clustered cystic lesions.
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Affiliation(s)
- Yu-Yao Shen
- Department of Pulmonary and Critical Care Medicine, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai 264000, Shandong Province, China
| | - Jing Jiang
- Department of Pulmonary and Critical Care Medicine, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai 264000, Shandong Province, China
| | - Jing Zhao
- Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Jie Song
- Department of Pulmonary and Critical Care Medicine, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai 264000, Shandong Province, China
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13
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Evaluation of Simplified Diet Scores Related to C-Reactive Protein in Heavy Smokers Undergoing Lung Cancer Screening. Nutrients 2022; 14:nu14204312. [PMID: 36296996 PMCID: PMC9610125 DOI: 10.3390/nu14204312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/03/2022] [Accepted: 10/11/2022] [Indexed: 11/07/2022] Open
Abstract
The aim of this study was to assess the relationship between adherence to a healthy diet, such as the Mediterranean diet (MedDiet), and C-reactive protein (CRP) in Italian heavy smokers undergoing an LDCT screening program (bioMILD trial), using scores calculated by simple questionnaires. Simple formats of food frequency questionnaires were administered to a sample of 2438 volunteers, and the adherence to a healthy diet was measured by the validated 14-point MEDAS and by two adaptations proposed by us: 17-item revised-MEDAS and 18-item revised-MEDAS. The OR of CRP ≥ 2 mg/L for 1-point increase in 14-point MEDAS score was 0.95 (95% CI 0.91–0.99), for 17-point score was 0.94 (95% CI 0.91–0.98), and for 18-point score was 0.92 (95% CI 0.88–0.97). These inverse associations remained statistically significant also after further adjustment for body mass index. These results showed the efficacy of simplified scores and their relationship with lower levels of CRP in a population of heavy smokers. This suggests that a targeted nutritional intervention might achieve a substantial reduction in CRP levels. The findings will be prospectively tested in a new randomized study on primary prevention during lung cancer screening.
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14
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Ko JP, Bagga B, Gozansky E, Moore WH. Solitary Pulmonary Nodule Evaluation: Pearls and Pitfalls. Semin Ultrasound CT MR 2022; 43:230-245. [PMID: 35688534 DOI: 10.1053/j.sult.2022.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Lung nodules are frequently encountered while interpreting chest CTs and are challenging to detect, characterize, and manage given they can represent both benign or malignant etiologies. An understanding of features associated with malignancy and causes of interpretive pitfalls is helpful to avoid misdiagnoses. This review addresses pertinent topics related to the etiologies for missed lung nodules on radiography and CT. Additionally, CT imaging technical pitfalls and challenges in addition to issues in the evaluation of nodule morphology, attenuation, and size will be discussed. Nodule management guidelines will be addressed as well as recent investigations that further our understanding of lung nodules.
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Affiliation(s)
- Jane P Ko
- Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY.
| | - Barun Bagga
- Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY
| | - Elliott Gozansky
- Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY
| | - William H Moore
- Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY
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15
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Peters AA, Huber AT, Obmann VC, Heverhagen JT, Christe A, Ebner L. Diagnostic validation of a deep learning nodule detection algorithm in low-dose chest CT: determination of optimized dose thresholds in a virtual screening scenario. Eur Radiol 2022; 32:4324-4332. [PMID: 35059804 DOI: 10.1007/s00330-021-08511-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 12/06/2021] [Accepted: 12/09/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES This study was conducted to evaluate the effect of dose reduction on the performance of a deep learning (DL)-based computer-aided diagnosis (CAD) system regarding pulmonary nodule detection in a virtual screening scenario. METHODS Sixty-eight anthropomorphic chest phantoms were equipped with 329 nodules (150 ground glass, 179 solid) with four sizes (5 mm, 8 mm, 10 mm, 12 mm) and scanned with nine tube voltage/current combinations. The examinations were analyzed by a commercially available DL-based CAD system. The results were compared by a comparison of proportions. Logistic regression was performed to evaluate the impact of tube voltage, tube current, nodule size, nodule density, and nodule location. RESULTS The combination with the lowest effective dose (E) and unimpaired detection rate was 80 kV/50 mAs (sensitivity: 97.9%, mean false-positive rate (FPR): 1.9, mean CTDIvol: 1.2 ± 0.4 mGy, mean E: 0.66 mSv). Logistic regression revealed that tube voltage and current had the greatest impact on the detection rate, while nodule size and density had no significant influence. CONCLUSIONS The optimal tube voltage/current combination proposed in this study (80 kV/50 mAs) is comparable to the proposed combinations in similar studies, which mostly dealt with conventional CAD software. Modification of tube voltage and tube current has a significant impact on the performance of DL-based CAD software in pulmonary nodule detection regardless of their size and composition. KEY POINTS • Modification of tube voltage and tube current has a significant impact on the performance of deep learning-based CAD software. • Nodule size and composition have no significant impact on the software's performance. • The optimal tube voltage/current combination for the examined software is 80 kV/50 mAs.
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Affiliation(s)
- Alan A Peters
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland.
| | - Adrian T Huber
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland
| | - Verena C Obmann
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland
| | - Johannes T Heverhagen
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland.,Department of BioMedical Research, Experimental Radiology, University of Bern, 3008, Bern, Switzerland.,Department of Radiology, The Ohio State University, Columbus, OH, USA
| | - Andreas Christe
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland
| | - Lukas Ebner
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland
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16
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Chen K, Lai YC, Vanniarajan B, Wang PH, Wang SC, Lin YC, Ng SH, Tran P, Lin G. Clinical impact of a deep learning system for automated detection of missed pulmonary nodules on routine body computed tomography including the chest region. Eur Radiol 2022; 32:2891-2900. [PMID: 34999920 DOI: 10.1007/s00330-021-08412-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/29/2021] [Accepted: 10/13/2021] [Indexed: 11/27/2022]
Abstract
OBJECTIVES To evaluate the clinical impact of a deep learning system (DLS) for automated detection of pulmonary nodules on computed tomography (CT) images as a second reader. METHODS This single-centre retrospective study screened 21,150 consecutive body CT studies from September 2018 to February 2019. Pulmonary nodules detected by the DLS on axial CT images but not mentioned in initial radiology reports were flagged. Flagged images were scored by four board-certificated radiologists each with at least 5 years of experience. Nodules with scores of 2 (understandable miss) or 3 (should not be missed) were then categorised as unlikely to be clinically significant (2a or 3a) or likely to be clinically significant (2b or 3b) according to the 2017 Fleischner guidelines for pulmonary nodules. The miss rate was defined as the total number of studies receiving scores of 2 or 3 divided by total screened studies. RESULTS Among 172 nodules flagged by the DLS, 60 (35%) missed nodules were confirmed by the radiologists. The nodules were further categorised as 2a, 2b, 3a, and 3b in 24, 14, 10, and 12 studies, respectively, with an overall positive predictive value of 35%. Missed pulmonary nodules were identified in 0.3% of all CT images, and one-third of these lesions were considered clinically significant. CONCLUSIONS Use of DLS-assisted automated detection as a second reader can identify missed pulmonary nodules, some of which may be clinically significant. CLINICAL RELEVANCE/APPLICATION Use of DLS to help radiologists detect pulmonary lesions may improve patient care. KEY POINTS • DLS-assisted automated detection as a second reader is feasible in a large consecutive cohort. • Performance of combined radiologists and DLS was better than DLS or radiologists alone. • Pulmonary nodules were missed more frequently in abdomino-pelvis CT than the thoracic CT.
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Affiliation(s)
- Kueian Chen
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan
- Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan
| | - Ying-Chieh Lai
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan
- Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan
- Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, Fuhsing St., Taoyuan, 33382, Guishan, Taiwan
| | | | - Pieh-Hsu Wang
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan
| | - Shao-Chung Wang
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan
| | - Yu-Chun Lin
- Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan
- Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, Fuhsing St., Taoyuan, 33382, Guishan, Taiwan
| | - Shu-Hang Ng
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan
| | - Pelu Tran
- FerrumFerrum Health, Santa Clara, CA, USA
| | - Gigin Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan.
- Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan.
- Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, Fuhsing St., Taoyuan, 33382, Guishan, Taiwan.
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17
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Ziegelmayer S, Graf M, Makowski M, Gawlitza J, Gassert F. Cost-Effectiveness of Artificial Intelligence Support in Computed Tomography-Based Lung Cancer Screening. Cancers (Basel) 2022; 14:cancers14071729. [PMID: 35406501 PMCID: PMC8997030 DOI: 10.3390/cancers14071729] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/23/2022] [Accepted: 03/23/2022] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Lung cancer screening is already implemented in the USA and strongly recommended by European Radiological and Thoracic societies as well. Upon implementation, the total number of thoracic computed tomographies (CT) is likely to rise significantly. As shown in previous studies, modern artificial intelligence-based algorithms are on-par or even exceed radiologist's performance in lung nodule detection and classification. Therefore, the aim of this study was to evaluate the cost-effectiveness of an AI-based system in the context of baseline lung cancer screening. METHODS In this retrospective study, a decision model based on Markov simulation was developed to estimate the quality-adjusted life-years (QALYs) and lifetime costs of the diagnostic modalities. Literature research was performed to determine model input parameters. Model uncertainty and possible costs of the AI-system were assessed using deterministic and probabilistic sensitivity analysis. RESULTS In the base case scenario CT + AI resulted in a negative incremental cost-effectiveness ratio (ICER) as compared to CT only, showing lower costs and higher effectiveness. Threshold analysis showed that the ICER remained negative up to a threshold of USD 68 for the AI support. The willingness-to-pay of USD 100,000 was crossed at a value of USD 1240. Deterministic and probabilistic sensitivity analysis showed model robustness for varying input parameters. CONCLUSION Based on our results, the use of an AI-based system in the initial low-dose CT scan of lung cancer screening is a feasible diagnostic strategy from a cost-effectiveness perspective.
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Silva M, Picozzi G, Sverzellati N, Anglesio S, Bartolucci M, Cavigli E, Deliperi A, Falchini M, Falaschi F, Ghio D, Gollini P, Larici AR, Marchianò AV, Palmucci S, Preda L, Romei C, Tessa C, Rampinelli C, Mascalchi M. Low-dose CT for lung cancer screening: position paper from the Italian college of thoracic radiology. Radiol Med 2022; 127:543-559. [PMID: 35306638 PMCID: PMC8934407 DOI: 10.1007/s11547-022-01471-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 02/18/2022] [Indexed: 12/24/2022]
Abstract
Smoking is the main risk factor for lung cancer (LC), which is the leading cause of cancer-related death worldwide. Independent randomized controlled trials, governmental and inter-governmental task forces, and meta-analyses established that LC screening (LCS) with chest low dose computed tomography (LDCT) decreases the mortality of LC in smokers and former smokers, compared to no-screening, especially in women. Accordingly, several Italian initiatives are offering LCS by LDCT and smoking cessation to about 10,000 high-risk subjects, supported by Private or Public Health Institutions, envisaging a possible population-based screening program. Because LDCT is the backbone of LCS, Italian radiologists with LCS expertise are presenting this position paper that encompasses recommendations for LDCT scan protocol and its reading. Moreover, fundamentals for classification of lung nodules and other findings at LDCT test are detailed along with international guidelines, from the European Society of Thoracic Imaging, the British Thoracic Society, and the American College of Radiology, for their reporting and management in LCS. The Italian College of Thoracic Radiologists produced this document to provide the basics for radiologists who plan to set up or to be involved in LCS, thus fostering homogenous evidence-based approach to the LDCT test over the Italian territory and warrant comparison and analyses throughout National and International practices.
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Affiliation(s)
- Mario Silva
- Department of Medicine and Surgery (DiMeC), University of Parma, Via Gramsci 14, Parma, Italy.
- Unit of "Scienze Radiologiche", University Hospital of Parma, Pad. Barbieri, Via Gramsci 14, 43126, Parma, Italy.
| | - Giulia Picozzi
- Istituto Di Studio Prevenzione E Rete Oncologica, Firenze, Italy
| | - Nicola Sverzellati
- Department of Medicine and Surgery (DiMeC), University of Parma, Via Gramsci 14, Parma, Italy
- Unit of "Scienze Radiologiche", University Hospital of Parma, Pad. Barbieri, Via Gramsci 14, 43126, Parma, Italy
| | | | | | | | | | | | | | - Domenico Ghio
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Anna Rita Larici
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Università Cattolica del Sacro Cuore Di Roma, Roma, Italy
| | - Alfonso V Marchianò
- Department of Radiology, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, MI, Italy
| | - Stefano Palmucci
- UOC Radiologia 1, Dipartimento Scienze Mediche Chirurgiche E Tecnologie Avanzate "GF Ingrassia", Università Di Catania, AOU Policlinico "G. Rodolico-San Marco", Catania, Italy
| | - Lorenzo Preda
- IRCCS Fondazione Policlinico San Matteo, Pavia, Italy
- Dipartimento Di Scienze Clinico-Chirurgiche, Diagnostiche E Pediatriche, Università Degli Studi Di Pavia, Pavia, Italy
| | | | - Carlo Tessa
- Radiologia Apuane E Lunigiana, Azienda USL Toscana Nord Ovest, Pisa, Italy
| | | | - Mario Mascalchi
- Istituto Di Studio Prevenzione E Rete Oncologica, Firenze, Italy
- Università Di Firenze, Firenze, Italy
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Suzuki K, Otsuka Y, Nomura Y, Kumamaru KK, Kuwatsuru R, Aoki S. Development and Validation of a Modified Three-Dimensional U-Net Deep-Learning Model for Automated Detection of Lung Nodules on Chest CT Images From the Lung Image Database Consortium and Japanese Datasets. Acad Radiol 2022; 29 Suppl 2:S11-S17. [PMID: 32839096 DOI: 10.1016/j.acra.2020.07.030] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 07/13/2020] [Accepted: 07/22/2020] [Indexed: 12/17/2022]
Abstract
RATIONALE AND OBJECTIVES A more accurate lung nodule detection algorithm is needed. We developed a modified three-dimensional (3D) U-net deep-learning model for the automated detection of lung nodules on chest CT images. The purpose of this study was to evaluate the accuracy of the developed modified 3D U-net deep-learning model. MATERIALS AND METHODS In this Health Insurance Portability and Accountability Act-compliant, Institutional Review Board-approved retrospective study, the 3D U-net based deep-learning model was trained using the Lung Image Database Consortium and Image Database Resource Initiative dataset. For internal model validation, we used 89 chest CT scans that were not used for model training. For external model validation, we used 450 chest CT scans taken at an urban university hospital in Japan. Each case included at least one nodule of >5 mm identified by an experienced radiologist. We evaluated model accuracy using the competition performance metric (CPM) (average sensitivity at 1/8, 1/4, 1/2, 1, 2, 4, and 8 false-positives per scan). The 95% confidence interval (CI) was computed by bootstrapping 1000 times. RESULTS In the internal validation, the CPM was 94.7% (95% CI: 89.1%-98.6%). In the external validation, the CPM was 83.3% (95% CI: 79.4%-86.1%). CONCLUSION The modified 3D U-net deep-learning model showed high performance in both internal and external validation.
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Affiliation(s)
- Kazuhiro Suzuki
- Department of Radiology, Juntendo University Faculty of Medicine and Graduate School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo 113-8431, Japan.
| | - Yujiro Otsuka
- Department of Radiology, Juntendo University Faculty of Medicine and Graduate School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo 113-8431, Japan; Plusmann LLC, Tokyo, Japan; Milliman, Inc., Tokyo, Japan
| | - Yukihiro Nomura
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Kanako K Kumamaru
- Department of Radiology, Juntendo University Faculty of Medicine and Graduate School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo 113-8431, Japan
| | - Ryohei Kuwatsuru
- Department of Radiology, Juntendo University Faculty of Medicine and Graduate School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo 113-8431, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Faculty of Medicine and Graduate School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo 113-8431, Japan
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20
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Kim YW, Jeon M, Song MJ, Kwon BS, Lim SY, Lee YJ, Park JS, Cho YJ, Yoon HI, Lee KW, Lee JH, Lee CT. Differences in detection patterns, characteristics, and outcomes of central and peripheral lung cancers in low-dose computed tomography screening. Transl Lung Cancer Res 2022; 10:4185-4199. [PMID: 35004249 PMCID: PMC8674608 DOI: 10.21037/tlcr-21-658] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 10/09/2021] [Indexed: 12/18/2022]
Abstract
Background Although low-dose computed tomography (LDCT) screening is known to be effective for the detection of lung cancers localized in peripheral lung regions at a curable stage, limited data is available regarding the characteristics and outcomes of central lung cancers diagnosed in a screening cohort. This study aimed to determine whether LDCT screening could effectively detect central lung cancers at an early stage and offer survival benefits. Methods We analyzed 52,615 adults who underwent lung cancer screening with LDCT between May 2003 and Dec 2019 at a tertiary center in South Korea. Characteristics and outcomes of those diagnosed with lung cancer, stratified by screen-detection status and cancer location, were evaluated. Results A total of 352 individuals (281 screen-detected, 71 non-screen-detected) were diagnosed with lung cancer. Compared to screen-detected cancers, non-screen-detected cancers tended to be centrally-located (11.4% vs. 64.8%, P<0.001). Most non-screen-detected central cancers (89.1%) had a negative result on prior LDCT screening. Multivariable regression analyses revealed that for peripheral cancers, screen-detection was associated with a significantly lower probability of diagnosis at an advanced stage [III/IV, odds ratio (OR) =0.15, 95% confidence interval (CI): 0.05-0.45] and mortality [hazard ratio (HR) =0.33, 95% CI: 0.13-0.84]; however, the association was insignificant for central cancers. For screen-detected cancers, central location, compared to peripheral location, was significantly associated with a higher risk of diagnosis at an advanced stage (OR =20.83, 95% CI: 6.67-64.98) and mortality (HR =4.98, 95% CI: 2.26-10.97). Conclusions Unlike for peripheral cancers, LDCT screening did not demonstrate an improvement in outcomes of central lung cancers, indicating an important limitation of LDCT screening and the need for developing novel modalities to screen and treat central lung cancer.
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Affiliation(s)
- Yeon Wook Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Minhee Jeon
- Medical Research Collaborating Center, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Myung Jin Song
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Byoung Soo Kwon
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Sung Yoon Lim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Yeon Joo Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Jong Sun Park
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Young-Jae Cho
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Ho Il Yoon
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Kyung Won Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Jae Ho Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Choon-Taek Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
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21
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OUP accepted manuscript. Eur J Cardiothorac Surg 2022; 62:6580206. [DOI: 10.1093/ejcts/ezac297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/19/2022] [Accepted: 04/29/2022] [Indexed: 11/14/2022] Open
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22
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Byrne D, English JC, Atkar-Khattra S, Lam S, Yee J, Myers R, Bilawich AM, Mayo JR, Mets OM. Cystic Primary Lung Cancer: Evolution of Computed Tomography Imaging Morphology Over Time. J Thorac Imaging 2021; 36:373-381. [PMID: 34029281 DOI: 10.1097/rti.0000000000000594] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
PURPOSE Primary lung cancers associated with cystic airspaces are increasingly being recognized; however, there is a paucity of data on their natural history. We aimed to evaluate the prevalence, pathologic, and imaging characteristics of cystic lung cancer in a regional thoracic surgery center with a focus on the evolution of computed tomography morphology over time. MATERIALS AND METHODS Consecutive patients referred for potential surgical management of primary lung cancer between January 2016 and December 2018 were included. Clinical, imaging, and pathologic data were collected at the time of diagnosis and at the time of the oldest computed tomography showing the target lesion. Descriptive analysis was carried out. RESULTS A total of 441 cancers in 431 patients (185 males, 246 females), median age 69.6 years (interquartile range: 62.6 to 75.3 y), were assessed. Overall, 41/441 (9.3%) primary lung cancers were cystic at the time of diagnosis. The remaining showed solid (67%), part-solid (22%), and ground-glass (2%) morphologies. Histopathology of the cystic lung cancers at diagnosis included 31/41 (76%) adenocarcinomas, 8/41 (20%) squamous cell carcinomas, 1/41 (2%) adenosquamous carcinoma, and 1/41 (2%) unspecified non-small cell lung carcinoma. Overall, 8/34 (24%) cystic cancers at the time of diagnosis developed from different morphologic subtype precursor lesions, while 8/34 (24%) cystic precursor lesions also transitioned into part-solid or solid cancers at the time of diagnosis. CONCLUSIONS This study demonstrates that cystic airspaces within lung cancers are not uncommon, and may be seen transiently as cancers evolve. Increased awareness of the spectrum of cystic lung cancer morphology is important to improve diagnostic accuracy and lung cancer management.
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Affiliation(s)
- Danielle Byrne
- Departments of Cardiothoracic Radiology
- Department of Radiology, St James Hospital and Trinity College, Dublin, Ireland
| | | | - Sukhinder Atkar-Khattra
- Department of Integrative Oncology, The British Columbia Cancer Agency, Vancouver, BC, Canada
| | - Stephen Lam
- Respiratory Medicine
- Department of Integrative Oncology, The British Columbia Cancer Agency, Vancouver, BC, Canada
| | - John Yee
- Thoracic Surgery, Vancouver General Hospital and University of British Columbia
| | - Renelle Myers
- Respiratory Medicine
- Department of Integrative Oncology, The British Columbia Cancer Agency, Vancouver, BC, Canada
| | - Ana-Maria Bilawich
- Department of Radiology, St James Hospital and Trinity College, Dublin, Ireland
| | - John R Mayo
- Department of Radiology, St James Hospital and Trinity College, Dublin, Ireland
| | - Onno M Mets
- Departments of Cardiothoracic Radiology
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
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23
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Shen Y, Zhang Y, Guo Y, Li W, Huang Y, Wu T, Jiang G, Dai J. Prognosis of lung cancer associated with cystic airspaces: A propensity score matching analysis. Lung Cancer 2021; 159:111-116. [PMID: 34325317 DOI: 10.1016/j.lungcan.2021.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 07/02/2021] [Accepted: 07/07/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVE The association between the morphological characteristics and survival outcome of lung cancer associated with cystic airspaces (LCCAs) is unclear due to rarity of this disease. The current study attempted to compare the survival outcome between LCCAs and non-LCCAs and investigate the correlation between imaging features and prognosis of LCCA. METHOD Of 10,835 patients diagnosed with non-small cell lung carcinoma (NSCLC) between January 2015 and December 2016, 123 patients with LCCA were included. The non-LCCA group comprised 3136 patients with primary solitary adenocarcinoma or squamous cell lung cancer. Propensity score matching (PSM) was performed for age, sex, tumor size, tumor stage, and lymph node involvement in a 1:1 ratio between the LCCAs and non-LCCAs, and the correlation between radiological features and recurrence-free survival (RFS) was analyzed. RESULT The computed tomography (CT) lesion size was found to be higher in all LCCA subtypes, particularly in Type III (a cystic airspace with a mural nodule) and Type IV (mixed) LCCAs (3.09 and 3.65 cm, respectively), than in non-LCCAs (2 cm) after PSM. Three-year RFS in the LCCA group was higher than in the non-LCCA group (Type I- IV LCCAs: 100%, 84%, 77% and 83%, respectively vs. non-LCCAs: 77%). However, statistically significant difference was only found in comparison between LCCA Type I (thin-walled) and non-LCCA groups (P = 0.026). Type III lung cancer exhibited the worst survival among all four LCCA subtypes. CONCLUSIONS The CT lesion size and pathologic tumor size varied significantly across LCCAs. Type I LCCAs exhibited better survival than non-LCCAs, whereas Type III LCCAs exhibited the worst survival rate among the four LCCA subtypes.
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Affiliation(s)
- Yingran Shen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China
| | - Yunfei Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China
| | - Yanhua Guo
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China
| | - Weitong Li
- Department of Medical Imaging, Shishi Hospital, Fujian 362700, China
| | - Yan Huang
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China
| | - Tong Wu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China.
| | - Jie Dai
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China.
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Azour L, Ko JP, Washer SL, Lanier A, Brusca-Augello G, Alpert JB, Moore WH. Incidental Lung Nodules on Cross-sectional Imaging: Current Reporting and Management. Radiol Clin North Am 2021; 59:535-549. [PMID: 34053604 DOI: 10.1016/j.rcl.2021.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Pulmonary nodules are the most common incidental finding in the chest, particularly on computed tomographs that include a portion or all of the chest, and may be encountered more frequently with increasing utilization of cross-sectional imaging. Established guidelines address the reporting and management of incidental pulmonary nodules, both solid and subsolid, synthesizing nodule and patient features to distinguish benign nodules from those of potential clinical consequence. Standard nodule assessment is essential for the accurate reporting of nodule size, attenuation, and morphology, all features with varying risk implications and thus management recommendations.
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Affiliation(s)
- Lea Azour
- Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, Center for Biomedical Imaging, 660 First Avenue, New York, NY 10016, USA.
| | - Jane P Ko
- Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, Center for Biomedical Imaging, 660 First Avenue, New York, NY 10016, USA
| | - Sophie L Washer
- Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, Center for Biomedical Imaging, 660 First Avenue, New York, NY 10016, USA
| | - Amelia Lanier
- Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, Center for Biomedical Imaging, 660 First Avenue, New York, NY 10016, USA
| | - Geraldine Brusca-Augello
- Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, Center for Biomedical Imaging, 660 First Avenue, New York, NY 10016, USA
| | - Jeffrey B Alpert
- Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, Center for Biomedical Imaging, 660 First Avenue, New York, NY 10016, USA
| | - William H Moore
- Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, Center for Biomedical Imaging, 660 First Avenue, New York, NY 10016, USA
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25
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Snoeckx A, Franck C, Silva M, Prokop M, Schaefer-Prokop C, Revel MP. The radiologist's role in lung cancer screening. Transl Lung Cancer Res 2021; 10:2356-2367. [PMID: 34164283 PMCID: PMC8182709 DOI: 10.21037/tlcr-20-924] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Lung cancer is still the deadliest cancer in men and women worldwide. This high mortality is related to diagnosis in advanced stages, when curative treatment is no longer an option. Large randomized controlled trials have shown that lung cancer screening (LCS) with low-dose computed tomography (CT) can detect lung cancers at earlier stages and reduce lung cancer-specific mortality. The recent publication of the significant reduction of cancer-related mortality by 26% in the Dutch-Belgian NELSON LCS trial has increased the likelihood that implementation of LCS in Europe will move forward. Radiologists are important stakeholders in numerous aspects of the LCS pathway. Their role goes beyond nodule detection and nodule management. Being part of a multidisciplinary team, radiologists are key players in numerous aspects of implementation of a high quality LCS program. In this non-systematic review we discuss the multifaceted role of radiologists in LCS.
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Affiliation(s)
- Annemiek Snoeckx
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Caro Franck
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Mathias Prokop
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Marie-Pierre Revel
- Department of Radiology, Cochin Hospital, APHP Centre, Université de Paris, Paris, France
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26
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Snoeckx A, Reyntiens P, Pauwels P, Van Schil PE, Parizel PM, Van Meerbeeck JP. Molecular profiling in lung cancer associated with cystic airspaces. Acta Clin Belg 2021; 76:158-161. [PMID: 31615350 DOI: 10.1080/17843286.2019.1680134] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
'Lung cancer associated with cystic airspaces' is a rare radiological entity that is more frequently encountered on imaging studies and is gaining more attention since the widespread use of CT for lung cancer screening. Numerous aspects of this entity remain unraveled, including molecular profiling. The goal of this observational retrospective single-center case series is to investigate the molecular profile of lung cancers presenting with this specific morphology in a Caucasian population. Between January 2014 and May 2017, 13 patients were presented at the Multidisciplinary Thoracic Oncology Tumor Board with imaging findings consistent with 'lung cancer associated with cystic airspaces'. Electronic medical files were reviewed for patient characteristics, stage, histopathological findings and - in particular - molecular profiling. Histopathological diagnosis showed adenocarcinoma in 11 patients in our series. Mutational analysis in 10 showed different molecular alterations: an EGFR exon 18 mutation, ROS1 rearrangement and BRAF mutation in one patient each. Two patients showed KRAS mutations. With 5 out of 10 patients with an adenocarcinoma presenting with cystic airspace morphology showing a molecular alteration, this may indicate that in this subgroup, molecular profiling is mandatory, regardless of smoking history.
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Affiliation(s)
- Annemiek Snoeckx
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Pieter Reyntiens
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Patrick Pauwels
- Department of Pathology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Paul E. Van Schil
- Department of Thoracic and Vascular Surgery, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Paul M. Parizel
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Jan P. Van Meerbeeck
- Department of Pulmonology and Thoracic Oncology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
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Tringali G, Milanese G, Ledda RE, Pastorino U, Sverzellati N, Silva M. Lung Cancer Screening: Evidence, Risks, and Opportunities for Implementation. ROFO-FORTSCHR RONTG 2021; 193:1153-1161. [PMID: 33772489 DOI: 10.1055/a-1382-8648] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND Lung cancer is the most common cause of cancer death worldwide. Several trials with different screening approaches have recognized the role of lung cancer screening with low-dose CT for reducing lung cancer mortality. The efficacy of lung cancer screening depends on many factors and implementation is still pending in most European countries. METHODS This review aims to portray current evidence on lung cancer screening with a focus on the potential for opportunities for implementation strategies. Pillars of lung cancer screening practice will be discussed according to the most updated literature (PubMed search until November 16, 2020). RESULTS AND CONCLUSION The NELSON trial showed reduction of lung cancer mortality, thus confirming previous results of independent European studies, notably by volume of lung nodules. Heterogeneity in patient recruitment could influence screening efficacy, hence the importance of risk models and community-based screening. Recruitment strategies develop and adapt continuously to address the specific needs of the heterogeneous population of potential participants, the most updated evidence comes from the UK. The future of lung cancer screening is a tailored approach with personalized continuous stratification of risk, aimed at reducing costs and risks. KEY POINTS · Secondary prevention of lung cancer by low-dose computed tomography showed a reduction of lung cancer mortality.. · Semi-automated volume measurement and use of volume doubling time should be the reference method for optimization of risks, namely controlling measurement variability and the false-positive rate.. · A conservative approach with surveillance of subsolid nodules can be one of the strategies to reduce the risk of overdiagnosis and overtreatment.. · The goal of a tailored approach with personalized risk stratification aims to reduce costs and risks. A longer interval between rounds is one option for participants at lower risk.. CITATION FORMAT · Tringali G, Milanese G, Ledda RE et al. Lung Cancer Screening: Evidence, Risks, and Opportunities for Implementation. Fortschr Röntgenstr 2021; 193: 1153 - 1161.
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Affiliation(s)
- Giulia Tringali
- Department of Medicine and Surgery (DiMeC - Scienze Radiologiche), University of Parma, Italy
| | - Gianluca Milanese
- Department of Medicine and Surgery (DiMeC - Scienze Radiologiche), University of Parma, Italy
| | - Roberta Eufrasia Ledda
- Department of Medicine and Surgery (DiMeC - Scienze Radiologiche), University of Parma, Italy
| | - Ugo Pastorino
- Department of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
| | - Nicola Sverzellati
- Department of Medicine and Surgery (DiMeC - Scienze Radiologiche), University of Parma, Italy
| | - Mario Silva
- Department of Medicine and Surgery (DiMeC - Scienze Radiologiche), University of Parma, Italy
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Jonas DE, Reuland DS, Reddy SM, Nagle M, Clark SD, Weber RP, Enyioha C, Malo TL, Brenner AT, Armstrong C, Coker-Schwimmer M, Middleton JC, Voisin C, Harris RP. Screening for Lung Cancer With Low-Dose Computed Tomography: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA 2021; 325:971-987. [PMID: 33687468 DOI: 10.1001/jama.2021.0377] [Citation(s) in RCA: 214] [Impact Index Per Article: 71.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
IMPORTANCE Lung cancer is the leading cause of cancer-related death in the US. OBJECTIVE To review the evidence on screening for lung cancer with low-dose computed tomography (LDCT) to inform the US Preventive Services Task Force (USPSTF). DATA SOURCES MEDLINE, Cochrane Library, and trial registries through May 2019; references; experts; and literature surveillance through November 20, 2020. STUDY SELECTION English-language studies of screening with LDCT, accuracy of LDCT, risk prediction models, or treatment for early-stage lung cancer. DATA EXTRACTION AND SYNTHESIS Dual review of abstracts, full-text articles, and study quality; qualitative synthesis of findings. Data were not pooled because of heterogeneity of populations and screening protocols. MAIN OUTCOMES AND MEASURES Lung cancer incidence, lung cancer mortality, all-cause mortality, test accuracy, and harms. RESULTS This review included 223 publications. Seven randomized clinical trials (RCTs) (N = 86 486) evaluated lung cancer screening with LDCT; the National Lung Screening Trial (NLST, N = 53 454) and Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON, N = 15 792) were the largest RCTs. Participants were more likely to benefit than the US screening-eligible population (eg, based on life expectancy). The NLST found a reduction in lung cancer mortality (incidence rate ratio [IRR], 0.85 [95% CI, 0.75-0.96]; number needed to screen [NNS] to prevent 1 lung cancer death, 323 over 6.5 years of follow-up) with 3 rounds of annual LDCT screening compared with chest radiograph for high-risk current and former smokers aged 55 to 74 years. NELSON found a reduction in lung cancer mortality (IRR, 0.75 [95% CI, 0.61-0.90]; NNS to prevent 1 lung cancer death of 130 over 10 years of follow-up) with 4 rounds of LDCT screening with increasing intervals compared with no screening for high-risk current and former smokers aged 50 to 74 years. Harms of screening included radiation-induced cancer, false-positive results leading to unnecessary tests and invasive procedures, overdiagnosis, incidental findings, and increases in distress. For every 1000 persons screened in the NLST, false-positive results led to 17 invasive procedures (number needed to harm, 59) and fewer than 1 person having a major complication. Overdiagnosis estimates varied greatly (0%-67% chance that a lung cancer was overdiagnosed). Incidental findings were common, and estimates varied widely (4.4%-40.7% of persons screened). CONCLUSIONS AND RELEVANCE Screening high-risk persons with LDCT can reduce lung cancer mortality but also causes false-positive results leading to unnecessary tests and invasive procedures, overdiagnosis, incidental findings, increases in distress, and, rarely, radiation-induced cancers. Most studies reviewed did not use current nodule evaluation protocols, which might reduce false-positive results and invasive procedures for false-positive results.
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Affiliation(s)
- Daniel E Jonas
- RTI International, University of North Carolina at Chapel Hill Evidence-based Practice Center
- Department of Internal Medicine, The Ohio State University, Columbus
| | - Daniel S Reuland
- Department of Medicine, University of North Carolina at Chapel Hill
- Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill
| | - Shivani M Reddy
- RTI International, University of North Carolina at Chapel Hill Evidence-based Practice Center
- RTI International, Research Triangle Park, North Carolina
| | - Max Nagle
- Michigan Medicine, University of Michigan, Ann Arbor
| | - Stephen D Clark
- Department of Internal Medicine, Virginia Commonwealth University, Richmond
| | - Rachel Palmieri Weber
- RTI International, University of North Carolina at Chapel Hill Evidence-based Practice Center
- Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill
| | - Chineme Enyioha
- Department of Family Medicine, University of North Carolina at Chapel Hill
| | - Teri L Malo
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill
| | - Alison T Brenner
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill
| | - Charli Armstrong
- RTI International, University of North Carolina at Chapel Hill Evidence-based Practice Center
- Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill
| | - Manny Coker-Schwimmer
- RTI International, University of North Carolina at Chapel Hill Evidence-based Practice Center
- Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill
| | - Jennifer Cook Middleton
- RTI International, University of North Carolina at Chapel Hill Evidence-based Practice Center
- Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill
| | - Christiane Voisin
- RTI International, University of North Carolina at Chapel Hill Evidence-based Practice Center
- Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill
| | - Russell P Harris
- Department of Medicine, University of North Carolina at Chapel Hill
- Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill
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Aldaghlawi F, Von Holzen U, Li L, Hadid W. A case of squamous cell lung cancer presented as a cystic lesion and recurrent pneumothoraces. Respir Med Case Rep 2021; 33:101382. [PMID: 33796442 PMCID: PMC7995653 DOI: 10.1016/j.rmcr.2021.101382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 02/16/2021] [Accepted: 03/03/2021] [Indexed: 10/29/2022] Open
Abstract
We report a rare case of a 70-year-old male with recurrent pneumothoraces within one year treated with intermittent insertion of chest tube on each occasion. Diagnostic testing was notable for a cystic lesion in the left lung that was initially interpreted as bulla on chest x-ray and chest computed tomographic scan. Due to thickening and nodularity changes of the thin wall of the cystic lesion, the patient underwent left upper lobectomy. Pathology showed poorly differentiated squamous cell carcinoma of the cystic lesion wall. This case emphasizes the importance of monitoring pulmonary cystic lesions especially in patients with a history of smoking and emphysema.
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Affiliation(s)
- Fadi Aldaghlawi
- Department of Medicine, Indiana University School of Medicine-South Bend, South Bend, IN, USA
| | - Urs Von Holzen
- Department of Surgical Oncology, Indiana University School of Medicine-South Bend, USA
- Goshen Center for Cancer Care, Goshen, IN, USA
| | - Liang Li
- Department of Pathology, Goshen Health Hospital, Goshen, IN, USA
| | - Walid Hadid
- Department of Medicine, Indiana University School of Medicine-South Bend, South Bend, IN, USA
- Goshen Center for Cancer Care, Goshen, IN, USA
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Bartlett EC, Silva M, Callister ME, Devaraj A. False-Negative Results in Lung Cancer Screening-Evidence and Controversies. J Thorac Oncol 2021; 16:912-921. [PMID: 33545386 DOI: 10.1016/j.jtho.2021.01.1607] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 12/22/2020] [Accepted: 01/05/2021] [Indexed: 12/17/2022]
Abstract
Identifying false-negative cases is an important quality metric in lung cancer screening, but it has been infrequently and variably reported in previous studies. Although as a proportion of all screening participants, false-negative cases are uncommon, such cases may constitute a substantial proportion of all lung cancers diagnosed (up to 15%) within a screening program. This article reviews the impact and causes of false-negative lung cancer screening tests, including those related to radiologic evaluation, nodule management protocols, and management decisions made by multidisciplinary teams. Following a review of data from international screening studies, this article discusses the controversies within the screening literature surrounding the definition and classification of a false-negative lung cancer screening test and how data on false-negative rates should be captured and recorded. Challenges, such as avoiding overly cautious surveillance of lung nodules while minimizing overdiagnosis and investigation of indolent or benign lesions, are considered. Finally, the advantages and disadvantages of different approaches to dealing with false-negative results in lung cancer screening are discussed.
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Affiliation(s)
- Emily C Bartlett
- Department of Radiology, Royal Brompton Hospital, London, United Kingdom; National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Mario Silva
- Section of "Scienze Radiologiche," Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Matthew E Callister
- St James's University Hospital, The Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Anand Devaraj
- Department of Radiology, Royal Brompton Hospital, London, United Kingdom; National Heart and Lung Institute, Imperial College London, London, United Kingdom.
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Clinicopathologic and Longitudinal Imaging Features of Lung Cancer Associated With Cystic Airspaces: A Systematic Review and Meta-Analysis. AJR Am J Roentgenol 2020; 216:318-329. [PMID: 32755209 DOI: 10.2214/ajr.20.23835] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND. Lung cancer (LC) associated with cystic airspaces is an uncommon presentation that is underrecognized on imaging. Additionally, understanding of its underlying pathology and risk factors is limited, which can contribute to delays in diagnosis. OBJECTIVE. The purpose of this analysis was to systematically review, analyze, and synthesize the medical literature to determine the imaging features of LC associated with cystic airspaces. EVIDENCE ACQUISITION. In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we included published research reporting the clinical, pathologic, and imaging features of LC associated with cystic airspaces. We then performed a pooled analysis of continuous and categoric data with respect to patient clinical characteristics, tumor pathologic features, underlying driver mutation, CT features, and evolution of these features over time. EVIDENCE SYNTHESIS. The analysis included eight original observational studies with a combined total of 341 patients with LC associated with cystic airspaces (weighted mean age, 61.8 years; range, 30-87 years; 135 women and 206 men). Most patients were current or previous smokers (127/192 [66.1%]). The most common histologic finding was adenocarcinoma (289/328 [88.1%]) followed by squamous cell carcinoma (30/328 [9.1%]). The most common driver mutations were EGFR (46/122 [37.7%]) and KRAS (21/122 [17.2%]). The cysts in LC associated with cystic airspaces commonly had nonuniform (104/114 [91.2%]) and thick (83/222 [37.4%]) walls, irregular margins (53/142 [37.3%]), and were unilocular (173/272 [63.6%]). Most cysts had a nodular component (210/328 [64.0%]). Over time, most cysts showed development or enlargement of the nodular component (61/89 [68.5%]), approximately half showed wall thickening (43/89 [48.3%]), and a minority evolved into completely solid lesions (11/89 [12.4%]). The size of the cystic component increased in 36 of 89 patients (40.4%), decreased in 28 (31.5%), and remained stable in 24 (27.0%). CONCLUSION. LC associated with cystic airspaces occurs most commonly as adeno-carcinoma and is seen in both smokers and nonsmokers. The cysts associated with LC show wall thickening and mural nodularity, which may evolve over time. LC associated with cystic airspaces can be indolent, and long-term surveillance with imaging should be considered if cysts are not resected. CLINICAL IMPACT. Familiarity with the imaging features and temporal evolution of LC associated with cystic airspaces can minimize delays in LC diagnosis. Future management guidelines should include protocols for follow-up and management of cystic lung lesions identified during diagnostic and LC screening CT.
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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] [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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33
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Takaishi T, Ozawa Y, Bando Y, Yamamoto A, Okochi S, Suzuki H, Shibamoto Y. Incorporation of a computer-aided vessel-suppression system to detect lung nodules in CT images: effect on sensitivity and reading time in routine clinical settings. Jpn J Radiol 2020; 39:159-164. [PMID: 32940850 DOI: 10.1007/s11604-020-01043-y] [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: 07/30/2020] [Accepted: 09/10/2020] [Indexed: 11/24/2022]
Abstract
PURPOSE To evaluate whether a computer-aided vessel-suppression system improves lung nodule detection in routine clinical settings. MATERIALS AND METHODS We used computer software that automatically suppresses pulmonary vessels on chest CT while preserving pulmonary nodules. Sixty-one chest CT images were included in our study. Three radiologists independently read either standard CT images alone or both computer-aided CT and standard CT images randomly to detect a pulmonary nodule ≥ 4 mm in diameter. After an interval of at least 15 days to avoid recall bias, the three radiologists interpreted the counterpart images of the same patients. The reference standard was decided by an expert panel. The primary endpoint was sensitivity. The secondary endpoint was interpretation time. RESULTS The average sensitivity improved with computer-aided CT (72% for standard CT vs. 84% for computer-aided CT, p = 0.02). There was no difference in the false-positive rate (21% for both standard CT and computer-aided CT, p = 0.98). Although the average reading time was 9.5% longer for computer-aided plus standard CT compared with standard CT alone, the difference was not significant (p = 0.11). CONCLUSION Vessel-suppressed CT images helped radiologists to improve the sensitivity of pulmonary nodule detection without compromising the false-positive rate.
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Affiliation(s)
- Taku Takaishi
- Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan.
| | - Yoshiyuki Ozawa
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Yuya Bando
- Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan
| | - Akiko Yamamoto
- Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan
| | - Sachiko Okochi
- Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan
| | - Hirochika Suzuki
- Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan
| | - Yuta Shibamoto
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
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Chen B, Yang L, Zhang R, Luo W, Li W. Radiomics: an overview in lung cancer management-a narrative review. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:1191. [PMID: 33241040 PMCID: PMC7576016 DOI: 10.21037/atm-20-4589] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Radiomics is a novel approach for optimizing the analysis massive data from medical images to provide auxiliary guidance in clinical issues. Quantitative feature extraction is one of the critical steps of radiomics. The association between radiomics features and the clinicopathological information of diseases can be identified by several statistics methods. For instance, although significant progress has been made in the field of lung cancer, too many questions remain, especially for the individualized decisions. Radiomics offers a new tool to encode the characteristics of lung cancer which is the leading cause of cancer-related deaths worldwide. Here, we reviewed the workflow and clinical utility of radiomics in lung cancer management, including pulmonary nodules detection, classification, histopathology and genetics evaluation, clinical staging, therapy response, and prognosis prediction. Most of these studies showed positive results, indicating the potential value of radiomics in clinical practice. The implementation of radiomics is both feasible and invaluable, and has aided clinicians in ascertaining the nature of a disease with greater precision. However, it should be noted that radiomics in its current state cannot completely replace the work of therapists or tissue examination. The potential future trends of this modality were also remarked. More efforts are needed to overcome the limitations identified above in order to facilitate the widespread application of radiomics in the reasonably near future.
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Affiliation(s)
- Bojiang Chen
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Lan Yang
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Rui Zhang
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Wenxin Luo
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
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35
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Silva M, Milanese G, Kauczor HU, Revel MP, Sverzellati N. Milestones towards lung cancer screening implementation. Clin Radiol 2020; 75:881-885. [PMID: 32863024 DOI: 10.1016/j.crad.2020.07.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/27/2020] [Indexed: 01/08/2023]
Abstract
The European Society of Radiology (ESR) and European Respiratory Society (ERS) published their joint statement paper on lung cancer screening (LCS), on 12 February 2020. This document joins and completes previous recommendations on LCS with specific emphasis on the analysis of issues encountered in the practical implementation of LCS in the community. Major milestones to enable the most efficient and equal dissemination of LCS are recognised as engagement of all stakeholders (e.g. candidate/participant, general practitioners, up to the specialised LCS facility), quality assurance, and primary prevention in the form of provision of counselling for smoking cessation.
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Affiliation(s)
- M Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Italy.
| | - G Milanese
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Italy
| | - H-U Kauczor
- Dept of Diagnostic and Interventional Radiology, University Hospital Heidelberg, German Center of Lung Research, Heidelberg, Germany
| | - M-P Revel
- Radiology Department, Cochin Hospital, APHP, Paris, France
| | - N Sverzellati
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Italy
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36
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Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography. Jpn J Radiol 2020; 38:1052-1061. [PMID: 32592003 DOI: 10.1007/s11604-020-01009-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 06/18/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE To evaluate the performance of a deep learning-based computer-aided diagnosis (CAD) system at detecting pulmonary nodules on CT by comparing radiologists' readings with and without CAD. MATERIALS AND METHODS A total of 120 chest CT images were randomly selected from patients with suspected lung cancer. The gold standard of nodules ≥ 3 mm was established by a panel of three expert radiologists. Two less experienced radiologists read the images without and afterward with CAD system. Their reading times were recorded. RESULTS The radiologists' sensitivity increased from 20.9% to 38.0% with the introduction of CAD. The positive predictive value (PPV) decreased from 70.5% to 61.8%, and the F1-score increased from 32.2% to 47.0%. The sensitivity significantly increased from 13.7% to 32.4% for small nodules (3-6 mm) and from 33.3% to 47.6% for medium nodules (6-10 mm). CAD alone showed a sensitivity of 70.3%, a PPV of 57.9%, and an F1-score of 63.5%. Reading time decreased by 11.3% with the use of CAD. CONCLUSION CAD improved the less experienced radiologists' sensitivity in detecting pulmonary nodules of all sizes, especially including a significant improvement in the detection of clinically important-sized medium nodules (6-10 mm) as well as small nodules (3-6 mm) and reduced their reading time.
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37
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Wang W, Zhuang R, Ma H, Fang L, Wang Z, Lv W, Hu J. The diagnostic value of a seven-autoantibody panel and a nomogram with a scoring table for predicting the risk of non-small-cell lung cancer. Cancer Sci 2020; 111:1699-1710. [PMID: 32108977 PMCID: PMC7226194 DOI: 10.1111/cas.14371] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 02/20/2020] [Accepted: 02/22/2020] [Indexed: 12/17/2022] Open
Abstract
The early detection of non-small-cell lung cancer (NSCLC) remains a common concern. The aim of our study was to validate the diagnostic value of a seven-autoantibody (7-AAB) panel compared with radiological diagnosis for NSCLC. We constructed a nomogram and a scoring table based on the 7-AAB panel's result to predict the risk of NSCLC. We prospectively enrolled 268 patients who presented with radiological lesions and underwent both the 7-AAB panel test and pathological diagnosis by surgical resection. A comparison between the 7-AAB panel and radiological diagnosis was performed. A nomogram and a scoring table based on the 7-AAB panel's result to predict the risk of NSCLC were constructed and internally validated. The 7-AAB panel test had a specificity of 90.2% and a positive predictive value (PPV) of 92.7%, which were significantly higher than those of the radiological diagnosis. The 7-AAB panel also showed a preferable sensitivity in patients with early-stage disease. Seven factors, including the 7-AAB panel results, were integrated into the nomogram. For more convenient application, we formulated a scoring table based on the nomogram. The area under the receiver operating characteristic curve was 0.840 and 0.860 in the training group and validation group, respectively, which was higher than that using the 7-AAB panel or radiological diagnosis alone. This study reveals that our 7-AAB panel has clinical value in the diagnosis of NSCLC. The utility of our nomogram and the scoring table indicated that they have the potential to assist clinicians in avoiding unnecessary treatment or needless follow-up.
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Affiliation(s)
- Weidong Wang
- Department of Thoracic SurgeryThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhouChina
| | - Runzhou Zhuang
- Department of Thoracic SurgeryThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhouChina
| | - Honghai Ma
- Department of Thoracic SurgeryThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhouChina
| | - Likui Fang
- Department of Thoracic SurgeryThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhouChina
| | - Zhitian Wang
- Department of Thoracic SurgeryThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhouChina
| | - Wang Lv
- Department of Thoracic SurgeryThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhouChina
| | - Jian Hu
- Department of Thoracic SurgeryThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhouChina
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38
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Gierada DS, Black WC, Chiles C, Pinsky PF, Yankelevitz DF. Low-Dose CT Screening for Lung Cancer: Evidence from 2 Decades of Study. Radiol Imaging Cancer 2020; 2:e190058. [PMID: 32300760 DOI: 10.1148/rycan.2020190058] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 10/15/2019] [Accepted: 11/20/2019] [Indexed: 12/17/2022]
Abstract
Lung cancer remains the overwhelmingly greatest cause of cancer death in the United States, accounting for more annual deaths than breast, prostate, and colon cancer combined. Accumulated evidence since the mid to late 1990s, however, indicates that low-dose CT screening of high-risk patients enables detection of lung cancer at an early stage and can reduce the risk of dying from lung cancer. CT screening is now a recommended clinical service in the United States, subject to guidelines and reimbursement requirements intended to standardize practice and optimize the balance of benefits and risks. In this review, the evidence on the effectiveness of CT screening will be summarized and the current guidelines and standards will be described in the context of knowledge gained from lung cancer screening studies. In addition, an overview of the potential advances that may improve CT screening will be presented, and the need to better understand the performance in clinical practice outside of the research trial setting will be discussed. © RSNA, 2020.
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Affiliation(s)
- David S Gierada
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, St Louis, MO 63110 (D.S.G.); Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH (W.C.B.); Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC (C.C.); Division of Cancer Prevention, National Cancer Institute, Bethesda, Md (P.F.P.); and Department of Radiology, Mount Sinai School of Medicine, New York, NY (D.F.Y.)
| | - William C Black
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, St Louis, MO 63110 (D.S.G.); Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH (W.C.B.); Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC (C.C.); Division of Cancer Prevention, National Cancer Institute, Bethesda, Md (P.F.P.); and Department of Radiology, Mount Sinai School of Medicine, New York, NY (D.F.Y.)
| | - Caroline Chiles
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, St Louis, MO 63110 (D.S.G.); Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH (W.C.B.); Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC (C.C.); Division of Cancer Prevention, National Cancer Institute, Bethesda, Md (P.F.P.); and Department of Radiology, Mount Sinai School of Medicine, New York, NY (D.F.Y.)
| | - Paul F Pinsky
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, St Louis, MO 63110 (D.S.G.); Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH (W.C.B.); Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC (C.C.); Division of Cancer Prevention, National Cancer Institute, Bethesda, Md (P.F.P.); and Department of Radiology, Mount Sinai School of Medicine, New York, NY (D.F.Y.)
| | - David F Yankelevitz
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, St Louis, MO 63110 (D.S.G.); Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH (W.C.B.); Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC (C.C.); Division of Cancer Prevention, National Cancer Institute, Bethesda, Md (P.F.P.); and Department of Radiology, Mount Sinai School of Medicine, New York, NY (D.F.Y.)
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Kauczor HU, Baird AM, Blum TG, Bonomo L, Bostantzoglou C, Burghuber O, Čepická B, Comanescu A, Couraud S, Devaraj A, Jespersen V, Morozov S, Nardi Agmon I, Peled N, Powell P, Prosch H, Ravara S, Rawlinson J, Revel MP, Silva M, Snoeckx A, van Ginneken B, van Meerbeeck JP, Vardavas C, von Stackelberg O, Gaga M. ESR/ERS statement paper on lung cancer screening. Eur Respir J 2020; 55:13993003.00506-2019. [PMID: 32051182 DOI: 10.1183/13993003.00506-2019] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 08/16/2019] [Indexed: 12/18/2022]
Abstract
In Europe, lung cancer ranks third among the most common cancers, remaining the biggest killer. Since the publication of the first European Society of Radiology and European Respiratory Society joint white paper on lung cancer screening (LCS) in 2015, many new findings have been published and discussions have increased considerably. Thus, this updated expert opinion represents a narrative, non-systematic review of the evidence from LCS trials and description of the current practice of LCS as well as aspects that have not received adequate attention until now. Reaching out to the potential participants (persons at high risk), optimal communication and shared decision-making will be key starting points. Furthermore, standards for infrastructure, pathways and quality assurance are pivotal, including promoting tobacco cessation, benefits and harms, overdiagnosis, quality, minimum radiation exposure, definition of management of positive screen results and incidental findings linked to respective actions as well as cost-effectiveness. This requires a multidisciplinary team with experts from pulmonology and radiology as well as thoracic oncologists, thoracic surgeons, pathologists, family doctors, patient representatives and others. The ESR and ERS agree that Europe's health systems need to adapt to allow citizens to benefit from organised pathways, rather than unsupervised initiatives, to allow early diagnosis of lung cancer and reduce the mortality rate. Now is the time to set up and conduct demonstration programmes focusing, among other points, on methodology, standardisation, tobacco cessation, education on healthy lifestyle, cost-effectiveness and a central registry.
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Affiliation(s)
- Hans-Ulrich Kauczor
- Dept of Diagnostic and Interventional Radiology, University Hospital Heidelberg, German Center of Lung Research, Heidelberg, Germany
| | - Anne-Marie Baird
- Central Pathology Laboratory, Trinity College Dublin, St. James's Hospital, Dublin, Ireland
| | | | - Lorenzo Bonomo
- Dept of Radiology, Policlinico Universitario Agostino Gemelli, Rome, Italy
| | | | | | | | | | - Sébastien Couraud
- Service de Pneumologie et Oncologie Thoracique, Hospices Civils de Lyon, CH Lyon Sud, Pierre Bénite, France.,Faculté de Médecine et de Maïeutique Lyon Sud - Charles Mérieux, Université Claude Bernard Lyon I, Oullins, France
| | | | | | - Sergey Morozov
- Dept of Health Care of Moscow, Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Moscow, Russian Federation
| | | | - Nir Peled
- Thoracic Cancer Unit, Rabin Medical Center, Petach Tiqwa, Israel
| | | | - Helmut Prosch
- Dept of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Sofia Ravara
- Medical Sciences, Faculty of Health Sciences, University of Beira Interior, Covilha, Portugal.,Tobacco Cessation Unit, CHCB University Hospital, Covilha, Portugal
| | | | | | - Mario Silva
- Section of Radiology, Dept of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | | | - Bram van Ginneken
- Image Sciences Institute, University Medical Centre, Utrecht, The Netherlands.,Dept of Radiology, Nijmegen Medical Centre, Nijmegen, The Netherlands
| | | | - Constantine Vardavas
- Clinic of Social and Family Medicine, Faculty of Medicine, University of Crete, Heraklion, Greece.,Center for Global Tobacco Control, Department of Society, Human Development and Health, Harvard School of Public Health, Boston, MA, USA
| | - Oyunbileg von Stackelberg
- Dept of Diagnostic and Interventional Radiology, University Hospital Heidelberg, German Center of Lung Research, Heidelberg, Germany
| | - Mina Gaga
- 7th Respiratory Medicine Dept, Athens Chest Hospital Sotiria, Athens, Greece
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40
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Kauczor HU, Baird AM, Blum TG, Bonomo L, Bostantzoglou C, Burghuber O, Čepická B, Comanescu A, Couraud S, Devaraj A, Jespersen V, Morozov S, Agmon IN, Peled N, Powell P, Prosch H, Ravara S, Rawlinson J, Revel MP, Silva M, Snoeckx A, van Ginneken B, van Meerbeeck JP, Vardavas C, von Stackelberg O, Gaga M. ESR/ERS statement paper on lung cancer screening. Eur Radiol 2020; 30:3277-3294. [PMID: 32052170 DOI: 10.1007/s00330-020-06727-7] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 08/16/2019] [Indexed: 12/17/2022]
Abstract
In Europe, lung cancer ranks third among the most common cancers, remaining the biggest killer. Since the publication of the first European Society of Radiology and European Respiratory Society joint white paper on lung cancer screening (LCS) in 2015, many new findings have been published and discussions have increased considerably. Thus, this updated expert opinion represents a narrative, non-systematic review of the evidence from LCS trials and description of the current practice of LCS as well as aspects that have not received adequate attention until now. Reaching out to the potential participants (persons at high risk), optimal communication and shared decision-making will be key starting points. Furthermore, standards for infrastructure, pathways and quality assurance are pivotal, including promoting tobacco cessation, benefits and harms, overdiagnosis, quality, minimum radiation exposure, definition of management of positive screen results and incidental findings linked to respective actions as well as cost-effectiveness. This requires a multidisciplinary team with experts from pulmonology and radiology as well as thoracic oncologists, thoracic surgeons, pathologists, family doctors, patient representatives and others. The ESR and ERS agree that Europe's health systems need to adapt to allow citizens to benefit from organised pathways, rather than unsupervised initiatives, to allow early diagnosis of lung cancer and reduce the mortality rate. Now is the time to set up and conduct demonstration programmes focusing, among other points, on methodology, standardisation, tobacco cessation, education on healthy lifestyle, cost-effectiveness and a central registry.Key Points• Pulmonologists and radiologists both have key roles in the set up of multidisciplinary LCS teams with experts from many other fields.• Pulmonologists identify people eligible for LCS, reach out to family doctors, share the decision-making process and promote tobacco cessation.• Radiologists ensure appropriate image quality, minimum dose and a standardised reading/reporting algorithm, together with a clear definition of a "positive screen".• Strict algorithms define the exact management of screen-detected nodules and incidental findings.• For LCS to be (cost-)effective, it has to target a population defined by risk prediction models.
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Affiliation(s)
- Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, German Center of Lung Research, INF 110, 69120, Heidelberg, Germany.
| | - Anne-Marie Baird
- Central Pathology Laboratory, Trinity College Dublin, St. James's Hospital, Dublin, Ireland
| | | | - Lorenzo Bonomo
- Department of Radiology, Policlinico Universitario Agostino Gemelli, Rome, Italy
| | | | | | | | | | - Sébastien Couraud
- Service de Pneumologie et Oncologie Thoracique, Hospices Civils de Lyon, Sud, Pierre Bénite, Lyon, CH, France.,Faculté de Médecine et de Maïeutique Lyon Sud - Charles Mérieux, Université Claude Bernard Lyon I, Oullins, France
| | | | | | - Sergey Morozov
- Department of Health Care of Moscow, Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Moscow, Russian Federation
| | | | - Nir Peled
- Thoracic Cancer Unit, Rabin Medical Center, Petach Tiqwa, Israel
| | | | - Helmut Prosch
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Sofia Ravara
- Medical Sciences, Faculty of Health Sciences, University of Beira Interior, Covilha, Portugal.,Tobacco Cessation Unit, CHCB University Hospital, Covilha, Portugal
| | | | | | - Mario Silva
- Section of Radiology, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | | | - Bram van Ginneken
- Image Sciences Institute, University Medical Centre, Utrecht, The Netherlands.,Department of Radiology, Nijmegen Medical Centre, Nijmegen, The Netherlands
| | | | - Constantine Vardavas
- Clinic of Social and Family Medicine, Faculty of Medicine, University of Crete, Heraklion, Greece.,Center for Global Tobacco Control, Department of Society, Human Development and Health, Harvard School of Public Health, Boston, MA, USA
| | - Oyunbileg von Stackelberg
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, German Center of Lung Research, INF 110, 69120, Heidelberg, Germany
| | - Mina Gaga
- 7th Respiratory Medicine Department, Athens Chest Hospital Sotiria, Athens, Greece
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41
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Kim JY, Suh YJ, Nam K, Choi BW. Lung cancer detected on coronary artery calcium scoring computed tomography: factors delaying diagnosis and predictors of survival. Acta Radiol 2019; 60:1118-1126. [PMID: 30499307 DOI: 10.1177/0284185118815297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Jin Young Kim
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Radiology, Dongsan Medical Center, Keimyung University College of Medicine, Daegu, Republic of Korea
| | - Young Joo Suh
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kyungsun Nam
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Byoung Wook Choi
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
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42
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Pastorino U, Sverzellati N, Sestini S, Silva M, Sabia F, Boeri M, Cantarutti A, Sozzi G, Corrao G, Marchianò A. Ten-year results of the Multicentric Italian Lung Detection trial demonstrate the safety and efficacy of biennial lung cancer screening. Eur J Cancer 2019; 118:142-148. [PMID: 31336289 DOI: 10.1016/j.ejca.2019.06.009] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 06/22/2019] [Indexed: 12/17/2022]
Abstract
BACKGROUND The Multicentric Italian Lung Detection (MILD) trial demonstrated that prolonged low-dose computed tomography (LDCT) screening could achieve a 39% reduction in lung cancer (LC) mortality. We have here evaluated the long-term results of annual vs. biennial LDCT and the impact of screening intensity on overall and LC-specific mortality at 10 years. PATIENTS AND METHODS Between 2005 and 2018, the MILD trial prospectively randomised the 2376 screening arm participants to annual (n = 1190) or biennial (n = 1186) LDCT, for a median screening period of 6.2 years and 23,083 person-years of follow-up. The primary outcomes were 10-year overall and LC-specific mortality, and the secondary end-points were the frequency of advanced-stage and interval LCs. RESULTS The biennial LDCT arm showed a similar overall mortality (hazard ratio [HR] 0.80, 95% confidence interval [CI] 0.57-1.12) and LC-specific mortality at 10 years (HR 1.10, 95% CI 0.59-2.05), as compared with the annual LDCT arm. Biennial screening saved 44% of follow-up LDCTs in subjects with negative baseline LDCT, and 38% of LDCTs in all participants, with no increase in the occurrence of stage II-IV or interval LCs. CONCLUSIONS The MILD trial provides original evidence that prolonged screening beyond five years with biennial LDCT can achieve an LC mortality reduction comparable to annual LDCT, in subjects with a negative baseline examination.
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Affiliation(s)
- U Pastorino
- Thoracic Surgery Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy.
| | - N Sverzellati
- Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - S Sestini
- Thoracic Surgery Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy
| | - M Silva
- Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - F Sabia
- Thoracic Surgery Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy
| | - M Boeri
- Tumor Genomics Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy
| | - A Cantarutti
- Department of Statistics and Quantitative Methods, Division of Biostatistics, Epidemiology and Public Health, University of Milano-Bicocca, Milan, Italy
| | - G Sozzi
- Tumor Genomics Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy
| | - G Corrao
- Department of Statistics and Quantitative Methods, Division of Biostatistics, Epidemiology and Public Health, University of Milano-Bicocca, Milan, Italy
| | - A Marchianò
- Department of Radiology, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy
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43
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Pastorino U, Silva M, Sestini S, Sabia F, Boeri M, Cantarutti A, Sverzellati N, Sozzi G, Corrao G, Marchianò A. Prolonged lung cancer screening reduced 10-year mortality in the MILD trial: new confirmation of lung cancer screening efficacy. Ann Oncol 2019; 30:1162-1169. [PMID: 30937431 PMCID: PMC6637372 DOI: 10.1093/annonc/mdz117] [Citation(s) in RCA: 269] [Impact Index Per Article: 53.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The National Lung Screening Trial showed that lung cancer (LC) screening by three annual rounds of low-dose computed tomography (LDCT) reduces LC mortality. We evaluated the benefit of prolonged LDCT screening beyond 5 years, and its impact on overall and LC specific mortality at 10 years. DESIGN The Multicentric Italian Lung Detection (MILD) trial prospectively randomized 4099 participants, to a screening arm (n = 2376), with further randomization to annual (n = 1190) or biennial (n = 1186) LDCT for a median period of 6 years, or control arm (n = 1723) without intervention. Between 2005 and 2018, 39 293 person-years of follow-up were accumulated. The primary outcomes were 10-year overall and LC specific mortality. Landmark analysis was used to test the long-term effect of LC screening, beyond 5 years by exclusion of LCs and deaths that occurred in the first 5 years. RESULTS The LDCT arm showed a 39% reduced risk of LC mortality at 10 years [hazard ratio (HR) 0.61; 95% confidence interval (CI) 0.39-0.95], compared with control arm, and a 20% reduction of overall mortality (HR 0.80; 95% CI 0.62-1.03). LDCT benefit improved beyond the 5th year of screening, with a 58% reduced risk of LC mortality (HR 0.42; 95% CI 0.22-0.79), and 32% reduction of overall mortality (HR 0.68; 95% CI 0.49-0.94). CONCLUSIONS The MILD trial provides additional evidence that prolonged screening beyond 5 years can enhance the benefit of early detection and achieve a greater overall and LC mortality reduction compared with NLST trial. CLINICALTRIALS.GOV IDENTIFIER NCT02837809.
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Affiliation(s)
- U Pastorino
- Unit of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan.
| | - M Silva
- Unit of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan; Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, Parma
| | - S Sestini
- Unit of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan
| | - F Sabia
- Unit of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan
| | - M Boeri
- Tumour Genomics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan
| | - A Cantarutti
- Division of Biostatistics, Department of Statistics and Quantitative Methods, Epidemiology and Public Health, University of Milano-Bicocca, Milan
| | - N Sverzellati
- Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, Parma
| | - G Sozzi
- Tumour Genomics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan
| | - G Corrao
- Division of Biostatistics, Department of Statistics and Quantitative Methods, Epidemiology and Public Health, University of Milano-Bicocca, Milan
| | - A Marchianò
- Department of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
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44
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Ather S, Kadir T, Gleeson F. Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications. Clin Radiol 2019; 75:13-19. [PMID: 31202567 DOI: 10.1016/j.crad.2019.04.017] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 04/04/2019] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) has been present in some guise within the field of radiology for over 50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date back to the 1960s, and in the subsequent years, the main application of these techniques has been the detection and classification of pulmonary nodules. In addition, there have been other less intensely researched applications, such as the diagnosis of interstitial lung disease, chronic obstructive pulmonary disease, and the detection of pulmonary emboli. Despite extensive literature on the use of convolutional neural networks in thoracic imaging over the last few decades, we are yet to see these systems in use in clinical practice. The article reviews current state-of-the-art applications of AI and in detection, classification, and follow-up of pulmonary nodules and how deep-learning techniques might influence these going forward. Finally, we postulate the impact of these advancements on the role of radiologists and the importance of radiologists in the development and evaluation of these techniques.
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Affiliation(s)
- S Ather
- Department of Radiology, Churchill Hospital, Oxford, UK
| | - T Kadir
- Optellum Ltd, Oxford Centre of Innovation, Oxford, UK
| | - F Gleeson
- National Consortium of Intelligent Medical Imaging, UK; Department of Oncology, University of Oxford, UK.
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45
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Shen Y, Xu X, Zhang Y, Li W, Dai J, Jiang S, Wu T, Cai H, Sihoe A, Shi J, Jiang G. Lung cancers associated with cystic airspaces: CT features and pathologic correlation. Lung Cancer 2019; 135:110-115. [PMID: 31446982 DOI: 10.1016/j.lungcan.2019.05.012] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 04/23/2019] [Accepted: 05/06/2019] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Lung cancer associated with cystic airspaces (LCCA) is a rare entity. The diagnosis and treatment is often delayed due to lack of comprehension of this disease. We aimed to elucidate LCCA's clinicopathological characteristics and investigate imaging features correlated with pathological invasiveness. METHOD The preoperative computed tomographic (CT) scans of 10,835 patients diagnosed with NSCLC between January 2015 and December 2016 were reviewed by two thoracic radiologists for association with a cystic airspace. A clinicopathological and radiological feature analysis was done. RESULT A total number of 123 LCCA patients were identified and four morphologic patterns were recognized: I, thin-walled type (n = 23, 18.7%); II, thick-walled type (n = 34, 27.6%); III, a cystic airspace with a mural nodule (CWN) type (n = 43, 35.0%); and IV, mixed type (n = 23, 18.7%). A solid component in the cyst wall predicted histological invasiveness in all four types of LCCA. The proportion of moderately/poorly (M/P)-differentiated subtype in type III (85.0%) was higher than in other three patterns (which were 50.0%, 50.0%, and 69.6%, respectively). Multivariate analysis revealed that type III pattern (odds ratio [OR], 6.5; 95% confidence interval [CI], 1.1-36.4; P = 0.035), part-solid/solid component in wall (part-solid: OR, 27.2; 95% CI, 5.6-3131.6; P < 0.001; solid: OR 614.6; 95% CI, 36.4-10,368.6; P < 0.001), and irregular inner surface of cyst (OR 7.0; 95% CI 1.9-26.2; P = 0.004) were independent risk factors for the M/P-differentiated subtype. EGFR mutations were the predominant genetic alterations in each type of LCCAs, but no significant difference was found among them. CONCLUSIONS In LCCA, morphological patterns and wall components were two important predictors for determining pathological invasiveness.
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Affiliation(s)
- Yingran Shen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, 200433, China
| | - Xinnan Xu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, 200433, China
| | - Yunfei Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, 200433, China
| | - Weitong Li
- Department of Medical Imaging, Shishi Hospital, Fujian, 362700, China
| | - Jie Dai
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, 200433, China
| | - Siming Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, 200433, China
| | - Tong Wu
- Department of Medical Imaging, Shanghai Pulmonary Hospital, Tongji University, Shanghai, 200433, China
| | - Haomin Cai
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, 200433, China
| | - Alan Sihoe
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, 200433, China
| | - Jingyun Shi
- Department of Medical Imaging, Shanghai Pulmonary Hospital, Tongji University, Shanghai, 200433, China.
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, 200433, China.
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46
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Inter-observer agreement on the morphology of screening-detected lung cancer: beyond pulmonary nodules and masses. Eur Radiol 2019; 29:3862-3870. [DOI: 10.1007/s00330-019-06243-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 03/28/2019] [Accepted: 04/17/2019] [Indexed: 12/17/2022]
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47
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Mergo PJ, Rojas CA. CT Characteristics and Pathologic Basis of Solitary Cystic Lung Cancer. Radiology 2019; 291:502-503. [DOI: 10.1148/radiol.2019190329] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Patricia J. Mergo
- From the Department of Radiology, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224
| | - Carlos A. Rojas
- From the Department of Radiology, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224
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48
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Snoeckx A, Reyntiens P, Carp L, Spinhoven MJ, El Addouli H, Van Hoyweghen A, Nicolay S, Van Schil PE, Pauwels P, van Meerbeeck JP, Parizel PM. Diagnostic and clinical features of lung cancer associated with cystic airspaces. J Thorac Dis 2019; 11:987-1004. [PMID: 31019789 DOI: 10.21037/jtd.2019.02.91] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
"Lung cancer associated with cystic airspaces" is an uncommon manifestation, in which lung cancer presents on imaging studies with a cystic area with associated consolidation and/or ground glass. With the widespread use of computed tomography (CT), both in clinical practice and for lung cancer screening, these tumors are being more frequently recognized. An association of this entity with smoking has been established with the majority of cases reported being in former and current smokers. The true pathogenesis of the cystic airspace is not yet fully understood. Different causes of this cystic airspace have been described, including a check-valve mechanism obstructing the small airways, lepidic growth of adenocarcinoma on emphysematous lung parenchyma, cyst formation of tumor and tumor growth along the wall of a pre-existing bulla. Adenocarcinoma is the commonest histological type, followed by squamous cell carcinoma. Two classification systems have been described, based on morphological features of the lesion, taking into account both the cystic airspace as well as the morphology of the surrounding consolidation or ground glass. The cystic component may mislead radiologists to a benign etiology and the many different faces on imaging can make early diagnosis challenging. Special attention should be made to focal or diffuse wall thickening and consolidation or ground glass abutting or interspersed with cystic airspaces. Despite their atypical morphology, staging and management currently are still similar to that of other lung cancer types. Although the rarity of this entity will hamper larger studies, numerous aspects regarding this particular lung cancer type still need to be unraveled. This manuscript reviews the CT-imaging findings and gives an overview of available data in the English literature on pathogenesis, histopathology and clinical findings. Differential diagnosis and pitfalls are discussed as well as future directions regarding staging and management.
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Affiliation(s)
- Annemie Snoeckx
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Pieter Reyntiens
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Laurens Carp
- Department of Nuclear Medicine, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Maarten J Spinhoven
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Haroun El Addouli
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Astrid Van Hoyweghen
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Simon Nicolay
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Paul E Van Schil
- Department of Thoracic and Vascular Surgery, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Patrick Pauwels
- Department of Pathology Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Jan P van Meerbeeck
- Department of Pulmonology and Thoracic Oncology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Paul M Parizel
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
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49
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The Added Value of Computer-aided Detection of Small Pulmonary Nodules and Missed Lung Cancers. J Thorac Imaging 2019; 33:390-395. [PMID: 30239461 DOI: 10.1097/rti.0000000000000362] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Lung cancer at its earliest stage is typically manifested on computed tomography as a pulmonary nodule, which could be detected by low-dose multidetector computed tomography technology and the use of thinner collimation. Within the last 2 decades, computer-aided detection (CAD) of pulmonary nodules has been developed to meet the increasing demand for lung cancer screening computed tomography with a larger set of images per scan. This review introduced the basic techniques and then summarized the up-to-date applications of CAD systems in clinical and research programs and in the low-dose lung cancer screening trials, especially in the detection of small pulmonary nodules and missed lung cancers. Many studies have already shown that the CAD systems could increase the sensitivity and reduce the false-positive rate in the diagnosis of pulmonary nodules, especially for the small and isolated nodules. Further improvements to the current CAD schemes are needed to detect nodules accurately, particularly for subsolid nodules.
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50
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Mets OM, Schaefer-Prokop CM, de Jong PA. Cyst-related primary lung malignancies: an important and relatively unknown imaging appearance of (early) lung cancer. Eur Respir Rev 2018; 27:27/150/180079. [PMID: 30567934 DOI: 10.1183/16000617.0079-2018] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 11/11/2018] [Indexed: 12/18/2022] Open
Abstract
It is well known that lung cancer can manifest itself in imaging as solid and subsolid nodules or masses. However, in this era of increased computed tomography use another morphological computed tomography appearance of lung cancer is increasingly being recognised, presenting as a malignancy in relation to cystic airspaces. Despite the fact that it seems to be a relatively common finding in daily practice, literature on this entity is scarce and presumably the overall awareness is limited. This can lead to misinterpretation and delay in diagnosis and, therefore, increased awareness is urgently needed. This review aims to illustrate the imaging appearances of cyst-related primary lung malignancies, demonstrate its mimickers and potential pitfalls, and discuss the clinical implications based on the available literature and our own experience in four different hospitals.
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Affiliation(s)
- Onno M Mets
- Dept of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cornelia M Schaefer-Prokop
- Diagnostic Imaging Analysis Groups, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.,Dept of Radiology, Meander Medical Center, Amersfoort, The Netherlands
| | - Pim A de Jong
- Dept of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
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