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Hardavella G, Frille A, Sreter KB, Atrafi F, Yousaf-Khan U, Beyaz F, Kyriakou F, Bellou E, Mullin ML, Janes SM. Lung cancer screening: where do we stand? Breathe (Sheff) 2024; 20:230190. [PMID: 39193459 PMCID: PMC11348916 DOI: 10.1183/20734735.0190-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 06/19/2024] [Indexed: 08/29/2024] Open
Abstract
Lung cancer screening (LCS) programmes have emerged over recent years around the world. LCS programmes present differences in delivery, inclusion criteria and resource allocation. On a national scale, only a few LCS programmes have been fully established, but more are anticipated to follow. Evidence has shown that, in combination with a low-dose chest computed tomography scan, smoking cessation should be offered as part of a LCS programme for improved patient outcomes. Promising tools in LCS include further refined risk prediction models, the use of biomarkers, artificial intelligence and radiomics. However, these tools require further study and clinical validation is required prior to routine implementation.
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Affiliation(s)
- Georgia Hardavella
- 4th–9th Department of Respiratory Medicine, ‘Sotiria’ Athens’ Chest Diseases Hospital, Greece
| | - Armin Frille
- Department of Respiratory Medicine, University of Leipzig, Leipzig, Germany
| | | | - Florence Atrafi
- Amphia Hospital, Department of Pulmonary Medicine, Breda, The Netherlands
| | - Uraujh Yousaf-Khan
- Amphia Hospital, Department of Pulmonary Medicine, Breda, The Netherlands
| | - Ferhat Beyaz
- Department of Pulmonary Diseases, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Fotis Kyriakou
- 4th–9th Department of Respiratory Medicine, ‘Sotiria’ Athens’ Chest Diseases Hospital, Greece
| | - Elena Bellou
- 4th–9th Department of Respiratory Medicine, ‘Sotiria’ Athens’ Chest Diseases Hospital, Greece
| | - Monica L. Mullin
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Sam M. Janes
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
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2
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Catarata MJ, Van Geffen WH, Banka R, Ferraz B, Sidhu C, Carew A, Viola L, Gijtenbeek R, Hardavella G. ERS International Congress 2022: highlights from the Thoracic Oncology Assembly. ERJ Open Res 2023; 9:00579-2022. [PMID: 37583965 PMCID: PMC10423989 DOI: 10.1183/23120541.00579-2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 05/31/2023] [Indexed: 08/17/2023] Open
Abstract
Thoracic malignancies are associated with a substantial public health burden. Lung cancer is the leading cause of cancer-related mortality worldwide, with significant impact on patients' quality of life. Following 2 years of virtual European Respiratory Society (ERS) Congresses due to the COVID-19 pandemic, the 2022 hybrid ERS Congress in Barcelona, Spain allowed peers from all over the world to meet again and present their work. Thoracic oncology experts presented best practices and latest developments in lung cancer screening, lung cancer diagnosis and management. Early lung cancer diagnosis, subsequent pros and cons of aggressive management, identification and management of systemic treatments' side-effects, and the application of artificial intelligence and biomarkers across all aspects of the thoracic oncology pathway were among the areas that triggered specific interest and will be summarised here.
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Affiliation(s)
- Maria Joana Catarata
- Pulmonology Department, Hospital de Braga, Braga, Portugal
- Tumour & Microenvironment Interactions Group, I3S-Institute for Health Research & Innovation, University of Porto, Porto, Portugal
| | - Wouter H. Van Geffen
- Department of Respiratory Medicine, Medical Center Leeuwarden, Leeuwarden, The Netherlands
| | - Radhika Banka
- P.D. Hinduja National Hospital and Medical Research Centre, Mumbai, India
| | - Beatriz Ferraz
- Pulmonology Department, Centro Hospitalar e Universitário do Porto, Porto, Portugal
- ICBAS School of Medicine and Biomedical Sciences, University of Porto, Porto, Portugal
| | | | - Alan Carew
- Queensland Lung Transplant Service, Department of Thoracic Medicine, Prince Charles Hospital, Brisbane, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Australia
| | - Lucia Viola
- Thoracic Oncology Service, Fundación Neumológica Colombiana, Bogotá, Colombia
- Thoracic Clinic, Luis Carlos Sarmiento Angulo Cancer Treatment and Research Center (Fundación CTIC), Bogotá, Colombia
| | - Rolof Gijtenbeek
- Department of Respiratory Medicine, Medical Center Leeuwarden, Leeuwarden, The Netherlands
| | - Georgia Hardavella
- 9th Department of Respiratory Medicine, “Sotiria” Athens Chest Diseases Hospital, Athens, Greece
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3
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Ewals LJS, van der Wulp K, van den Borne BEEM, Pluyter JR, Jacobs I, Mavroeidis D, van der Sommen F, Nederend J. The Effects of Artificial Intelligence Assistance on the Radiologists' Assessment of Lung Nodules on CT Scans: A Systematic Review. J Clin Med 2023; 12:jcm12103536. [PMID: 37240643 DOI: 10.3390/jcm12103536] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/19/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
To reduce the number of missed or misdiagnosed lung nodules on CT scans by radiologists, many Artificial Intelligence (AI) algorithms have been developed. Some algorithms are currently being implemented in clinical practice, but the question is whether radiologists and patients really benefit from the use of these novel tools. This study aimed to review how AI assistance for lung nodule assessment on CT scans affects the performances of radiologists. We searched for studies that evaluated radiologists' performances in the detection or malignancy prediction of lung nodules with and without AI assistance. Concerning detection, radiologists achieved with AI assistance a higher sensitivity and AUC, while the specificity was slightly lower. Concerning malignancy prediction, radiologists achieved with AI assistance generally a higher sensitivity, specificity and AUC. The radiologists' workflows of using the AI assistance were often only described in limited detail in the papers. As recent studies showed improved performances of radiologists with AI assistance, AI assistance for lung nodule assessment holds great promise. To achieve added value of AI tools for lung nodule assessment in clinical practice, more research is required on the clinical validation of AI tools, impact on follow-up recommendations and ways of using AI tools.
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Affiliation(s)
- Lotte J S Ewals
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Kasper van der Wulp
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Ben E E M van den Borne
- Department of Pulmonology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Jon R Pluyter
- Department of Experience Design, Royal Philips, 5656 AE Eindhoven, The Netherlands
| | - Igor Jacobs
- Department of Hospital Services and Informatics, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Dimitrios Mavroeidis
- Department of Data Science, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Joost Nederend
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
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4
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Paudyal R, Shah AD, Akin O, Do RKG, Konar AS, Hatzoglou V, Mahmood U, Lee N, Wong RJ, Banerjee S, Shin J, Veeraraghavan H, Shukla-Dave A. Artificial Intelligence in CT and MR Imaging for Oncological Applications. Cancers (Basel) 2023; 15:cancers15092573. [PMID: 37174039 PMCID: PMC10177423 DOI: 10.3390/cancers15092573] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients.
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Affiliation(s)
- Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Akash D Shah
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Amaresha Shridhar Konar
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Usman Mahmood
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Nancy Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Richard J Wong
- Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | | | | | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
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5
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Computer-Aided Detection of Subsolid Nodules on Chest Computed Tomography: Assessment of Visualization on Vessel-Suppressed Images. J Comput Assist Tomogr 2023; 47:412-417. [PMID: 36877791 DOI: 10.1097/rct.0000000000001444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
OBJECTIVES This study aimed to clarify the performance of automatic detection of subsolid nodules by commercially available software on computed tomography (CT) images of various slice thicknesses and compare it with visualization on the accompanying vessel-suppression CT (VS-CT) images. METHODS A total of 95 subsolid nodules from 84 CT examinations of 84 patients were included. The reconstructed CT image series of each case with 3-, 2-, and 1-mm slice thicknesses were loaded into a commercially available software application (ClearRead CT) for automatic detection of subsolid nodules and generation of VS-CT images. Automatic nodule detection sensitivity was assessed for 95 nodules on each series of images acquired at 3 slice thicknesses. Four radiologists subjectively evaluated visual assessment of the nodules on VS-CT. RESULTS ClearRead CT automatically detected 69.5% (66/95 nodules), 68.4% (65/95 nodules), and 70.5% (67/95 nodules) of all subsolid nodules in 3-, 2-, and 1-mm slices, respectively. The detection rate was higher for part-solid nodules than for pure ground-glass nodules at all slice thicknesses. In the visualization assessment on VS-CT, 3 nodules at each slice thickness (3.2%) were judged as invisible, while 26 of 29 (89.7%), 27 of 30 (90.0%), and 25 of 28 (89.3%) nodules, which were missed by computer-aided detection, were judged as visible in 3-, 2-, and 1-mm slices, respectively. CONCLUSIONS The automatic detection rate of subsolid nodules by ClearRead CT was approximately 70% at all slice thicknesses. More than 95% of subsolid nodules were visualized on VS-CT, including nodules undetected by the automated software. Computed tomography acquisition at slices thinner than 3 mm did not confer any benefits.
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6
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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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7
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[Low-dose Spiral Computed Tomography in Lung Cancer Screening]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2022; 25:678-683. [PMID: 36172733 PMCID: PMC9549430 DOI: 10.3779/j.issn.1009-3419.2022.101.40] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Lung cancer is one of the malignant tumors with the highest morbidity and mortality in the world. The low early diagnosis rate and poor prognosis of patients have caused serious social burden. Regular screening of high-risk population by low-dose spiral computed tomography (LDCT) can significantly improve the early diagnosis rate of lung cancer and bring new opportunities for the diagnosis and treatment of lung cancer. In recent years, LDCT lung cancer screening programs have been carried out in many countries around the world and achieved good results, but there are still some controversies in the selection of screening subjects, screening frequency, cost effectiveness and other aspects. In this paper, the key factors of LDCT lung cancer screening, screening effect, pulmonary nodule management and artificial intelligence contribution to the development of LDCT will be reviewed, and the application progress of LDCT in lung cancer screening will be discussed.
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8
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Shu J, Wen D, Xu Z, Meng X, Zhang Z, Lin S, Zheng M. Improved interobserver agreement on nodule type and Lung-RADS classification of subsolid nodules using computer-aided solid component measurement. Eur J Radiol 2022; 152:110339. [DOI: 10.1016/j.ejrad.2022.110339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 04/06/2022] [Accepted: 05/01/2022] [Indexed: 11/16/2022]
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9
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Lancaster HL, Heuvelmans MA, Oudkerk M. Low-dose computed tomography lung cancer screening: Clinical evidence and implementation research. J Intern Med 2022; 292:68-80. [PMID: 35253286 PMCID: PMC9311401 DOI: 10.1111/joim.13480] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Lung cancer causes more deaths than breast, cervical, and colorectal cancer combined. Nevertheless, population-based lung cancer screening is still not considered standard practice in most countries worldwide. Early lung cancer detection leads to better survival outcomes: patients diagnosed with stage 1A lung cancer have a >75% 5-year survival rate, compared to <5% at stage 4. Low-dose computed tomography (LDCT) thorax imaging for the secondary prevention of lung cancer has been studied at length, and has been shown to significantly reduce lung cancer mortality in high-risk populations. The US National Lung Screening Trial reported a 20% overall reduction in lung cancer mortality when comparing LDCT to chest X-ray, and the Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) trial more recently reported a 24% reduction when comparing LDCT to no screening. Hence, the focus has now shifted to implementation research. Consequently, the 4-IN-THE-LUNG-RUN consortium based in five European countries, has set up a large-scale multicenter implementation trial. Successful implementation of and accessibility to LDCT lung cancer screening are dependent on many factors, not limited to population selection, recruitment strategy, computed tomography screening frequency, lung-nodule management, participant compliance, and cost effectiveness. This review provides an overview of current evidence for LDCT lung cancer screening, and draws attention to major factors that need to be addressed to successfully implement standardized, effective, and accessible screening throughout Europe. Evidence shows that through the appropriate use of risk-prediction models and a more personalized approach to screening, efficacy could be improved. Furthermore, extending the screening interval for low-risk individuals to reduce costs and associated harms is a possibility, and through the use of volumetric-based measurement and follow-up, false positive results can be greatly reduced. Finally, smoking cessation programs could be a valuable addition to screening programs and artificial intelligence could offer a solution to the added workload pressures radiologists are facing.
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Affiliation(s)
- Harriet L Lancaster
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Institute for Diagnostic Accuracy, Groningen, The Netherlands
| | - Marjolein A Heuvelmans
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Institute for Diagnostic Accuracy, Groningen, The Netherlands
| | - Matthijs Oudkerk
- Institute for Diagnostic Accuracy, Groningen, The Netherlands.,Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands
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10
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Müller FC, Raaschou H, Akhtar N, Brejnebøl M, Collatz L, Andersen MB. Impact of Concurrent Use of Artificial Intelligence Tools on Radiologists Reading Time: A Prospective Feasibility Study. Acad Radiol 2022; 29:1085-1090. [PMID: 34801345 DOI: 10.1016/j.acra.2021.10.008] [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: 08/10/2021] [Revised: 09/27/2021] [Accepted: 10/09/2021] [Indexed: 12/01/2022]
Abstract
RATIONAL AND OBJECTIVES This study investigated how an AI tool impacted radiologists reading time for non-contrast chest CT exams. MATERIALS AND METHODS An AI tool was implemented into the PACS reading workflow of non-contrast chest CT exams between April and May 2020. The reading time was recorded for one CONSULTANT RADIOLOGIST and one RADIOLOGY RESIDENT by an external observer. After each case radiologists answered questions regarding additional findings and perceived case overview. Reading times were recorded for 25 cases without and 20 cases with AI tool assistance for each reader. Differences in reading time with and without the AI tool were assessed using Welch's t-test for non-inferiority with non-inferiority limits defined as 100 seconds for the consultant and 200 seconds for the resident. RESULTS The mean reading time for the radiology resident was not significantly affected by the AI tool (without AI 370s vs with AI 437s; +67s 95% CI -28s to +163s, p = 0.16). The reading time for the radiology consultant was also not significantly affected by the AI tool (without AI 366s vs with AI 380s; +13s (95% CI - -57s to 84s, p = 0.70). The AI tool led to additional actionable findings in 5/40 (12.5%) studies and better overview in 18/20 (90%) of studies for the resident. CONCLUSION A PACS based implementation of an AI tool for concurrent reading of chest CT exams did not increase reading time with additional actionable findings made as well as a perceived better case overview for the radiology resident.
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Affiliation(s)
- Felix C Müller
- Department of Radiology, Herlev and Gentofte Hospital, Herlev, Denmark.
| | | | - Naurien Akhtar
- Department of Radiology, Herlev and Gentofte Hospital, Herlev, Denmark
| | - Mathias Brejnebøl
- Department of Radiology, Herlev and Gentofte Hospital, Herlev, Denmark; Department of Radiology, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
| | - Lene Collatz
- Department of Radiology, Herlev and Gentofte Hospital, Herlev, Denmark
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11
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Silva M, Milanese G, Ledda RE, Nayak SM, Pastorino U, Sverzellati N. European lung cancer screening: valuable trial evidence for optimal practice implementation. Br J Radiol 2022; 95:20200260. [PMID: 34995141 PMCID: PMC10993986 DOI: 10.1259/bjr.20200260] [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: 03/18/2020] [Revised: 11/25/2021] [Accepted: 12/07/2021] [Indexed: 11/05/2022] Open
Abstract
Lung cancer screening (LCS) by low-dose computed tomography is a strategy for secondary prevention of lung cancer. In the last two decades, LCS trials showed several options to practice secondary prevention in association with primary prevention, however, the translation from trial to practice is everything but simple. In 2020, the European Society of Radiology and European Respiratory Society published their joint statement paper on LCS. This commentary aims to provide the readership with detailed description about hurdles and potential solutions that could be encountered in the practice of LCS.
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Affiliation(s)
- Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery
(DiMeC), University of Parma,
Parma, Italy
| | - Gianluca Milanese
- Scienze Radiologiche, Department of Medicine and Surgery
(DiMeC), University of Parma,
Parma, Italy
| | - Roberta E Ledda
- Scienze Radiologiche, Department of Medicine and Surgery
(DiMeC), University of Parma,
Parma, Italy
| | - Sundeep M Nayak
- Department of Radiology, Kaiser Permanente Northern
California, San Leandro,
California, USA
| | - Ugo Pastorino
- Section of Thoracic Surgery, IRCCS Istituto Nazionale
Tumori, Milano,
Italy
| | - Nicola Sverzellati
- Scienze Radiologiche, Department of Medicine and Surgery
(DiMeC), University of Parma,
Parma, Italy
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12
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[Chinese Experts Consensus on Artificial Intelligence Assisted Management for
Pulmonary Nodule (2022 Version)]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2022; 25:219-225. [PMID: 35340198 PMCID: PMC9051301 DOI: 10.3779/j.issn.1009-3419.2022.102.08] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Low-dose computed tomography (CT) for lung cancer screening has been proven to reduce lung cancer deaths in the screening group compared with the control group. The increasing number of pulmonary nodules being detected by CT scans significantly increase the workload of the radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of pulmonary nodule discrimination and has been tested in preliminary studies for nodule management. As more and more artificial AI products are commercialized, the consensus statement has been organized in a collaborative effort by Thoracic Surgery Committee, Department of Simulated Medicine, Wu Jieping Medical Foundation to aid clinicians in the application of AI-assisted management for pulmonary nodules.
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13
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Barufaldi B, Vent TL, Bakic PR, Maidment ADA. Computer Simulations of Case Difficulty in Digital Breast Tomosynthesis Using Virtual Clinical Trials. Med Phys 2022; 49:2220-2232. [PMID: 35212403 DOI: 10.1002/mp.15553] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 02/07/2022] [Accepted: 02/13/2022] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Virtual clinical trials (VCTs) require computer simulations of representative patients and images to evaluate and compare changes in performance of imaging technologies. The simulated images are usually interpreted by model observers whose performance depends upon the selection of imaging cases used in training evaluation models. This work proposes an efficient method to simulate and calibrate soft tissue lesions, which matches the detectability threshold of virtual and human readings. METHODS Anthropomorphic breast phantoms were used to evaluate the simulation of four mass models (I-IV) that vary in shape and composition of soft tissue. Ellipsoidal (I) and spiculated (II-IV) masses were simulated using composite voxels with partial volumes. Digital breast tomosynthesis projections and reconstructions of a clinical system were simulated. Channelized Hotelling observers (CHOs) were evaluated using reconstructed slices of masses that varied in shape, composition, and density of surrounded tissue. The detectability threshold of each mass model was evaluated using receiver operating characteristic (ROC) curves calculated with the CHO's scores. RESULTS The area under the curve (AUC) of each calibrated mass model were within the 95% confidence interval (mean AUC [95% CI]) reported in a previous reader study (0.93 [0.89, 0.97]). The mean AUC [95% CI] obtained were 0.94 [0.93, 0.96], 0.92 [0.90, 0.93], 0.92 [0.90, 0.94], 0.93 [0.92, 0.95] for models I to IV, respectively. The mean AUC results varied substantially as a function of shape, composition, and density of surrounded tissue. CONCLUSIONS For successful VCTs, lesions composed of soft tissue should be calibrated to simulate imaging cases that match the case difficulty predicted by human readers. Lesion composition, shape, and size are parameters that should be carefully selected to calibrate VCTs. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Bruno Barufaldi
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, United States
| | - Trevor L Vent
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, United States
| | - Predrag R Bakic
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, United States.,Department of Translational Medicine, Lund University, Malmö, 20502, Sweden
| | - Andrew D A Maidment
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, United States
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Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective. Pediatr Radiol 2022; 52:2120-2130. [PMID: 34471961 PMCID: PMC8409695 DOI: 10.1007/s00247-021-05146-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/22/2021] [Accepted: 06/28/2021] [Indexed: 12/19/2022]
Abstract
Artificial intelligence (AI) applications for chest radiography and chest CT are among the most developed applications in radiology. More than 40 certified AI products are available for chest radiography or chest CT. These AI products cover a wide range of abnormalities, including pneumonia, pneumothorax and lung cancer. Most applications are aimed at detecting disease, complemented by products that characterize or quantify tissue. At present, none of the thoracic AI products is specifically designed for the pediatric population. However, some products developed to detect tuberculosis in adults are also applicable to children. Software is under development to detect early changes of cystic fibrosis on chest CT, which could be an interesting application for pediatric radiology. In this review, we give an overview of current AI products in thoracic radiology and cover recent literature about AI in chest radiography, with a focus on pediatric radiology. We also discuss possible pediatric applications.
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15
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Wang D, Cao L, Li B. Computer-aided diagnosis system versus conventional reading system in low-dose (< 2 mSv) computed tomography: comparative study for patients at risk of lung cancer. SAO PAULO MED J 2022; 141:89-97. [PMID: 36472867 PMCID: PMC10005467 DOI: 10.1590/1516-3180.2022.0130.r1.29042022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 04/29/2022] [Indexed: 03/12/2023] Open
Abstract
BACKGROUND Computer-aided diagnosis in low-dose (≤ 3 mSv) computed tomography (CT) is a potential screening tool for lung nodules, with quality interpretation and less inter-observer variability among readers. Therefore, we aimed to determine the screening potential of CT using a radiation dose that does not exceed 2 mSv. OBJECTIVE We aimed to compare the diagnostic parameters of low-dose (< 2 mSv) CT interpretation results using a computer-aided diagnosis system for lung cancer screening with those of a conventional reading system used by radiologists. DESIGN AND SETTING We conducted a comparative study of chest CT images for lung cancer screening at three private institutions. METHODS A database of low-dose (< 2 mSv) chest CT images of patients at risk of lung cancer was viewed with the conventional reading system (301 patients and 226 nodules) or computer-aided diagnosis system without any subsequent radiologist review (944 patients and 1,048 nodules). RESULTS The numbers of detected and solid nodules per patient (both P < 0.0001) were higher using the computer-aided diagnosis system than those using the conventional reading system. The nodule size was reported as the maximum size in any plane in the computer-aided diagnosis system. Higher numbers of patients (102 [11%] versus 20 [7%], P = 0.0345) and nodules (154 [15%] versus 17 [8%], P = 0.0035) were diagnosed with cancer using the computer-aided diagnosis system. CONCLUSIONS The computer-aided diagnosis system facilitates the diagnosis of cancerous nodules, especially solid nodules, in low-dose (< 2 mSv) CT among patients at risk for lung cancer.
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Affiliation(s)
- Dong Wang
- MD. Physician, Department of Medical Imaging, Xianyang Cai-Hong Hospital, Xianyang, Shaanxi, China
| | - Lina Cao
- MD. Physician, Department of Medical Imaging, Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Boya Li
- MD. Physician, Department of Medical Imaging, Jiangxi provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
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16
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Schreuder A, Scholten ET, van Ginneken B, Jacobs C. Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice? Transl Lung Cancer Res 2021; 10:2378-2388. [PMID: 34164285 PMCID: PMC8182724 DOI: 10.21037/tlcr-2020-lcs-06] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Lung cancer computed tomography (CT) screening trials using low-dose CT have repeatedly demonstrated a reduction in the number of lung cancer deaths in the screening group compared to a control group. With various countries currently considering the implementation of lung cancer screening, recurring discussion points are, among others, the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of lung cancer screening. We discuss the performance levels of AI algorithms for various tasks related to the interpretation of lung screening CT scans, how they compare to human experts, and how AI and humans may complement each other. We discuss how AI may be used in the lung cancer CT screening workflow according to the current evidence and describe the additional research that will be required before AI can take a more prominent role in the analysis of lung screening CT scans.
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Affiliation(s)
- Anton Schreuder
- Department of Radiology, Nuclear Medicine, and Anatomy, Radboudumc, Nijmegen, The Netherlands
| | - Ernst T Scholten
- Department of Radiology, Nuclear Medicine, and Anatomy, Radboudumc, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Department of Radiology, Nuclear Medicine, and Anatomy, Radboudumc, Nijmegen, The Netherlands.,Fraunhofer MEVIS, Bremen, Germany
| | - Colin Jacobs
- Department of Radiology, Nuclear Medicine, and Anatomy, Radboudumc, Nijmegen, The Netherlands
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17
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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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18
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Silva M, Milanese G, Ledda RE, Pastorino U, Sverzellati N. Screen-detected solid nodules: from detection of nodule to structured reporting. Transl Lung Cancer Res 2021; 10:2335-2346. [PMID: 34164281 PMCID: PMC8182712 DOI: 10.21037/tlcr-20-296] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Lung cancer screening (LCS) is gaining some interest worldwide after positive results from International trials. Unlike other screening practices, LCS is performed by an extremely sensitive test, namely low-dose computed tomography (LDCT) that can detect the smallest nodules in lung parenchyma. Up-to-date detection approaches, such as computer aided detection systems, have been increasingly employed for lung nodule automatic identification and are largely used in most LCS programs as a complementary tool to visual reading. Solid nodules of any size are represented in the vast majority of subjects undergoing LDCT. However, less than 1% of solid nodules will be diagnosed lung cancer. This fact calls for specific characterization of nodules to avoid false positives, overinvestigation, and reduce the risks associated with nodule work up. Recent research has been exploring the potential of artificial intelligence, including deep learning techniques, to enhance the accuracy of both detection and characterisation of lung nodule. Computer aided detection and diagnosis algorithms based on artificial intelligence approaches have demonstrated the ability to accurately detect and characterize parenchymal nodules, reducing the number of false positives, and to outperform some of the currently used risk models for prediction of lung cancer risk, potentially reducing the proportion of surveillance CT scans. These forthcoming approaches will eventually integrate a new reasoning for development of future guidelines, which are expected to evolve into precision and personalized stratification of lung cancer risk stratification by continuous fashion, as opposed to the current format with a limited number of risk classes within fixed thresholds of nodule size. This review aims to detail the standard of reference for optimal management of solid nodules by low-dose computed and its projection into the fine selection of candidates for work up.
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Affiliation(s)
- Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Gianluca Milanese
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Roberta E Ledda
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Ugo Pastorino
- Section of Thoracic Surgery, IRCCS Istituto Nazionale Tumori, Milano, Italy
| | - Nicola Sverzellati
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
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19
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Cheung HMC, Rubin D. Challenges and opportunities for artificial intelligence in oncological imaging. Clin Radiol 2021; 76:728-736. [PMID: 33902889 DOI: 10.1016/j.crad.2021.03.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/15/2021] [Indexed: 02/08/2023]
Abstract
Imaging plays a key role in oncology, including the diagnosis and detection of cancer, determining clinical management, assessing treatment response, and complications of treatment or disease. The current use of clinical oncology is predominantly qualitative in nature with some relatively crude size-based measurements of tumours for assessment of disease progression or treatment response; however, it is increasingly understood that there may be significantly more information about oncological disease that can be obtained from imaging that is not currently utilized. Artificial intelligence (AI) has the potential to harness quantitative techniques to improve oncological imaging. These may include improving the efficiency or accuracy of traditional roles of imaging such as diagnosis or detection. These may also include new roles for imaging such as risk-stratifying patients for different types of therapy or determining biological tumour subtypes. This review article outlines several major areas in oncological imaging where there may be opportunities for AI technology. These include (1) screening and detection of cancer, (2) diagnosis and risk stratification, (3) tumour segmentation, (4) precision oncology, and (5) predicting prognosis and assessing treatment response. This review will also address some of the potential barriers to AI research in oncological imaging.
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Affiliation(s)
- H M C Cheung
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Canada
| | - D Rubin
- Department of Radiology, Stanford University, CA, USA.
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20
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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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21
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Park S, Lee SM, Kim W, Park H, Jung KH, Do KH, Seo JB. Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning-based CT Section Thickness Reduction. Radiology 2021; 299:211-219. [PMID: 33560190 DOI: 10.1148/radiol.2021203387] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Studies on the optimal CT section thickness for detecting subsolid nodules (SSNs) with computer-aided detection (CAD) are lacking. Purpose To assess the effect of CT section thickness on CAD performance in the detection of SSNs and to investigate whether deep learning-based super-resolution algorithms for reducing CT section thickness can improve performance. Materials and Methods CT images obtained with 1-, 3-, and 5-mm-thick sections were obtained in patients who underwent surgery between March 2018 and December 2018. Patients with resected synchronous SSNs and those without SSNs (negative controls) were retrospectively evaluated. The SSNs, which ranged from 6 to 30 mm, were labeled ground-truth lesions. A deep learning-based CAD system was applied to SSN detection on CT images of each section thickness and those converted from 3- and 5-mm section thickness into 1-mm section thickness by using the super-resolution algorithm. The CAD performance on each section thickness was evaluated and compared by using the jackknife alternative free response receiver operating characteristic figure of merit. Results A total of 308 patients (mean age ± standard deviation, 62 years ± 10; 183 women) with 424 SSNs (310 part-solid and 114 nonsolid nodules) and 182 patients without SSNs (mean age, 65 years ± 10; 97 men) were evaluated. The figures of merit differed across the three section thicknesses (0.92, 0.90, and 0.89 for 1, 3, and 5 mm, respectively; P = .04) and between 1- and 5-mm sections (P = .04). The figures of merit varied for nonsolid nodules (0.78, 0.72, and 0.66 for 1, 3, and 5 mm, respectively; P < .001) but not for part-solid nodules (range, 0.93-0.94; P = .76). The super-resolution algorithm improved CAD sensitivity on 3- and 5-mm-thick sections (P = .02 for 3 mm, P < .001 for 5 mm). Conclusion Computer-aided detection (CAD) of subsolid nodules performed better at 1-mm section thickness CT than at 3- and 5-mm section thickness CT, particularly with nonsolid nodules. Application of a super-resolution algorithm improved the sensitivity of CAD at 3- and 5-mm section thickness CT. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Goo in this issue.
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Affiliation(s)
- Sohee Park
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea (S.P., S.M.L., W.K., K.H.D., J.B.S.); and VUNO, Seoul, South Korea (H.P., K.H.J.)
| | - Sang Min Lee
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea (S.P., S.M.L., W.K., K.H.D., J.B.S.); and VUNO, Seoul, South Korea (H.P., K.H.J.)
| | - Wooil Kim
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea (S.P., S.M.L., W.K., K.H.D., J.B.S.); and VUNO, Seoul, South Korea (H.P., K.H.J.)
| | - Hyunho Park
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea (S.P., S.M.L., W.K., K.H.D., J.B.S.); and VUNO, Seoul, South Korea (H.P., K.H.J.)
| | - Kyu-Hwan Jung
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea (S.P., S.M.L., W.K., K.H.D., J.B.S.); and VUNO, Seoul, South Korea (H.P., K.H.J.)
| | - Kyung-Hyun Do
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea (S.P., S.M.L., W.K., K.H.D., J.B.S.); and VUNO, Seoul, South Korea (H.P., K.H.J.)
| | - Joon Beom Seo
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea (S.P., S.M.L., W.K., K.H.D., J.B.S.); and VUNO, Seoul, South Korea (H.P., K.H.J.)
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22
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Blazis SP, Dickerscheid DBM, Linsen PVM, Martins Jarnalo CO. Effect of CT reconstruction settings on the performance of a deep learning based lung nodule CAD system. Eur J Radiol 2021; 136:109526. [PMID: 33453573 DOI: 10.1016/j.ejrad.2021.109526] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 12/23/2020] [Accepted: 01/04/2021] [Indexed: 01/08/2023]
Abstract
PURPOSE To study the effect of different reconstruction parameter settings on the performance of a commercially available deep learning based pulmonary nodule CAD system. MATERIALS AND METHODS We performed a retrospective analysis of 24 chest CT scans, reconstructed at 16 different reconstruction settings for two different iterative reconstruction algorithms (SAFIRE and ADMIRE) varying in slice thickness, kernel size and iterative reconstruction level strength using a commercially available deep learning pulmonary nodule CAD system. The DL-CAD software was evaluated at 25 different sensitivity threshold settings and nodules detected by the DL-CAD software were matched against a reference standard based on the consensus reading of three radiologists. RESULTS A total of 384 CT reconstructions was analysed from 24 patients, resulting in a total of 5786 found nodules. We matched the detected nodules against the reference standard, defined by a team of thoracic radiologists, and showed a gradual drop in recall, and an improvement in precision when the iterative strength levels were increased for a constant kernel size. The optimal DL-CAD threshold setting for use in our clinical workflow was found to be 0.88 with an F2 of 0.73 ± 0.053. CONCLUSIONS The DL-CAD system behaves differently on IR data than on FBP data, there is a gradual drop in recall, and growth in precision when the iterative strength levels are increased. As a result, caution should be taken when implementing deep learning software in a hospital with multiple CT scanners and different reconstruction protocols. To the best of our knowledge, this is the first study that demonstrates this result from a DL-CAD system on clinical data.
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Affiliation(s)
- Stephan P Blazis
- Department of Clinical Physics, Albert Schweitzer Ziekenhuis, Dordrecht, the Netherlands.
| | | | - Philip V M Linsen
- Department of Radiology, Albert Schweitzer Ziekenhuis, Dordrecht, the Netherlands
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23
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Ye FY, Lyu GR, Li SQ, You JH, Wang KJ, Cai ML, Su QC. Diagnostic Performance of Ultrasound Computer-Aided Diagnosis Software Compared with That of Radiologists with Different Levels of Expertise for Thyroid Malignancy: A Multicenter Prospective Study. ULTRASOUND IN MEDICINE & BIOLOGY 2021; 47:114-124. [PMID: 33239154 DOI: 10.1016/j.ultrasmedbio.2020.09.019] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 09/19/2020] [Accepted: 09/22/2020] [Indexed: 06/11/2023]
Abstract
The aim of the work described here was to evaluate the diagnostic performance of ultrasound thyroid computer-aided diagnosis (CAD) software. This multicenter prospective study included 494 patients (565 thyroid nodules) who underwent surgery or biopsy after ultrasonography at four hospitals from January 2019 to September 2019. The diagnostic performance metrics of different readers were calculated and compared with the pathologic results. The sensitivity of CAD was outstanding and was equivalent to that of a senior radiologist (90.51% vs. 88.47%, p > 0.05). The area under the curve of CAD was equivalent to that of a junior radiologist (0.748 vs. 0.739, p > 0.05). However, the specificity was only 49.63%, which was lower than those of the three radiologists (75.56%, 85.93% and 90.37% for the junior, intermediate and senior radiologists, respectively). The diagnostic performance of the junior radiologist was significantly improved with the aid of CAD (junior + CAD). The sensitivity and area under the curve of junior + CAD were improved from 72.20% to 89.93% and from 0.739 to 0.816, respectively (both p values <0.05), and the positive predictive value, negative predictive value and κ coefficient improved from 76.3% to 78.6%, 82.0% to 86.8% and 0.394 to 0.511, respectively. Though specificity slightly decreased from 75.56% to 73.33%, the difference was not statistically significant (p > 0.05). In general, the clinical application value of CAD is promising, and its instrumental value for junior radiologists is significant.
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Affiliation(s)
- Feng-Ying Ye
- Department of Ultrasound, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Guo-Rong Lyu
- Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, China.
| | - Shang-Qing Li
- Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, China
| | - Jian-Hong You
- Department of Ultrasound, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China
| | - Kang-Jian Wang
- Department of Ultrasound, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou, China
| | - Ming-Li Cai
- Department of Ultrasound, Jinjiang City Hospital, Jinjiang, China
| | - Qi-Chen Su
- Department of Ultrasound, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
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24
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Azour L, Ko JP, Naidich DP, Moore WH. Shades of Gray: Subsolid Nodule Considerations and Management. Chest 2020; 159:2072-2089. [PMID: 33031828 PMCID: PMC7534873 DOI: 10.1016/j.chest.2020.09.252] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 09/16/2020] [Accepted: 09/27/2020] [Indexed: 12/15/2022] Open
Abstract
Subsolid nodules are common on chest CT imaging and may be either benign or malignant. Their varied features and broad differential diagnoses present management challenges. Although subsolid nodules often represent lung adenocarcinomas, other possibilities are common and influence management. Practice guidelines exist for subsolid nodule management for both incidentally and screening-detected nodules, incorporating patient and nodule characteristics. This review highlights the similarities and differences among these algorithms, with the intent of providing a resource for comparison and aid in choosing management options.
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Affiliation(s)
- Lea Azour
- Department of Radiology, NYU Grossman School of Medicine, New York, NY; and NYU Langone Health, New York, NY.
| | - Jane P Ko
- Department of Radiology, NYU Grossman School of Medicine, New York, NY; and NYU Langone Health, New York, NY
| | - David P Naidich
- Department of Radiology, NYU Grossman School of Medicine, New York, NY; and NYU Langone Health, New York, NY
| | - William H Moore
- Department of Radiology, NYU Grossman School of Medicine, New York, NY; and NYU Langone Health, New York, NY
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25
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Lung cancer screening by nodule volume in Lung-RADS v1.1: negative baseline CT yields potential for increased screening interval. Eur Radiol 2020; 31:1956-1968. [PMID: 32997182 PMCID: PMC7979670 DOI: 10.1007/s00330-020-07275-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 07/21/2020] [Accepted: 09/09/2020] [Indexed: 12/17/2022]
Abstract
Objectives The 2019 Lung CT Screening Reporting & Data System version 1.1 (Lung-RADS v1.1) introduced volumetric categories for nodule management. The aims of this study were to report the distribution of Lung-RADS v1.1 volumetric categories and to analyse lung cancer (LC) outcomes within 3 years for exploring personalized algorithm for lung cancer screening (LCS). Methods Subjects from the Multicentric Italian Lung Detection (MILD) trial were retrospectively selected by National Lung Screening Trial (NLST) criteria. Baseline characteristics included selected pre-test metrics and nodule characterization according to the volume-based categories of Lung-RADS v1.1. Nodule volume was obtained by segmentation with dedicated semi-automatic software. Primary outcome was diagnosis of LC, tested by univariate and multivariable models. Secondary outcome was stage of LC. Increased interval algorithms were simulated for testing rate of delayed diagnosis (RDD) and reduction of low-dose computed tomography (LDCT) burden. Results In 1248 NLST-eligible subjects, LC frequency was 1.2% at 1 year, 1.8% at 2 years and 2.6% at 3 years. Nodule volume in Lung-RADS v1.1 was a strong predictor of LC: positive LDCT showed an odds ratio (OR) of 75.60 at 1 year (p < 0.0001), and indeterminate LDCT showed an OR of 9.16 at 2 years (p = 0.0068) and an OR of 6.35 at 3 years (p = 0.0042). In the first 2 years after negative LDCT, 100% of resected LC was stage I. The simulations of low-frequency screening showed a RDD of 13.6–21.9% and a potential reduction of LDCT burden of 25.5–41%. Conclusions Nodule volume by semi-automatic software allowed stratification of LC risk across Lung-RADS v1.1 categories. Personalized screening algorithm by increased interval seems feasible in 80% of NLST eligible. Key Points • Using semi-automatic segmentation of nodule volume, Lung-RADS v1.1 selected 10.8% of subjects with positive CT and 96.87 relative risk of lung cancer at 1 year, compared to negative CT. • Negative low-dose CT by Lung-RADS v1.1 was found in 80.6% of NLST eligible and yielded 40 times lower relative risk of lung cancer at 2 years, compared to positive low-dose CT; annual screening could be preference sensitive in this group. • Semi-automatic segmentation of nodule volume and increased screening interval by volumetric Lung-RADS v1.1 could retrospectively suggest a 25.5–41% reduction of LDCT burden, at the cost of 13.6–21.9% rate of delayed diagnosis. Electronic supplementary material The online version of this article (10.1007/s00330-020-07275-w) contains supplementary material, which is available to authorized users.
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Abstract
OBJECTIVES The objective of this study is to assess the performance of a computer-aided diagnosis (CAD) system (INTACT system) for the automatic classification of high-resolution computed tomography images into 4 radiological diagnostic categories and to compare this with the performance of radiologists on the same task. MATERIALS AND METHODS For the comparison, a total of 105 cases of pulmonary fibrosis were studied (54 cases of nonspecific interstitial pneumonia and 51 cases of usual interstitial pneumonia). All diagnoses were interstitial lung disease board consensus diagnoses (radiologically or histologically proven cases) and were retrospectively selected from our database. Two subspecialized chest radiologists made a consensual ground truth radiological diagnosis, according to the Fleischner Society recommendations. A comparison analysis was performed between the INTACT system and 2 other radiologists with different years of experience (readers 1 and 2). The INTACT system consists of a sequential pipeline in which first the anatomical structures of the lung are segmented, then the various types of pathological lung tissue are identified and characterized, and this information is then fed to a random forest classifier able to recommend a radiological diagnosis. RESULTS Reader 1, reader 2, and INTACT achieved similar accuracy for classifying pulmonary fibrosis into the original 4 categories: 0.6, 0.54, and 0.56, respectively, with P > 0.45. The INTACT system achieved an F-score (harmonic mean for precision and recall) of 0.56, whereas the 2 readers, on average, achieved 0.57 (P = 0.991). For the pooled classification (2 groups, with and without the need for biopsy), reader 1, reader 2, and CAD had similar accuracies of 0.81, 0.70, and 0.81, respectively. The F-score was again similar for the CAD system and the radiologists. The CAD system and the average reader reached F-scores of 0.80 and 0.79 (P = 0.898). CONCLUSIONS We found that a computer-aided detection algorithm based on machine learning was able to classify idiopathic pulmonary fibrosis with similar accuracy to a human reader.
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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: 54] [Impact Index Per Article: 13.5] [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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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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Chassagnon G, Vakalopoulou M, Paragios N, Revel MP. Artificial intelligence applications for thoracic imaging. Eur J Radiol 2019; 123:108774. [PMID: 31841881 DOI: 10.1016/j.ejrad.2019.108774] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 11/13/2019] [Accepted: 11/21/2019] [Indexed: 02/06/2023]
Abstract
Artificial intelligence is a hot topic in medical imaging. The development of deep learning methods and in particular the use of convolutional neural networks (CNNs), have led to substantial performance gain over the classic machine learning techniques. Multiple usages are currently being evaluated, especially for thoracic imaging, such as such as lung nodule evaluation, tuberculosis or pneumonia detection or quantification of diffuse lung diseases. Chest radiography is a near perfect domain for the development of deep learning algorithms for automatic interpretation, requiring large annotated datasets, in view of the high number of procedures and increasing data availability. Current algorithms are able to detect up to 14 common anomalies, when present as isolated findings. Chest computed tomography is another major field of application for artificial intelligence, especially in the perspective of large scale lung cancer screening. It is important for radiologists to apprehend, contribute actively and lead this new era of radiology powered by artificial intelligence. Such a perspective requires understanding new terms and concepts associated with machine learning. The objective of this paper is to provide useful definitions for understanding the methods used and their possibilities, and report current and future developments for thoracic imaging. Prospective validation of AI tools will be required before reaching routine clinical implementation.
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Affiliation(s)
- Guillaume Chassagnon
- Radiology Department, Groupe Hospitalier Cochin Broca Hôtel-Dieu - Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France; Laboratoire Mathématiques et Informatique pour la Complexité et les Systèmes, Ecole CentraleSupelec, 3 Rue Joliot Curie, 91190 Gif-sur-Yvette, France; Center for Visual Computing, Ecole CentraleSupelec, 3 Rue Joliot Curie, 91190, Gif-sur-Yvette, France
| | - Maria Vakalopoulou
- Laboratoire Mathématiques et Informatique pour la Complexité et les Systèmes, Ecole CentraleSupelec, 3 Rue Joliot Curie, 91190 Gif-sur-Yvette, France; Center for Visual Computing, Ecole CentraleSupelec, 3 Rue Joliot Curie, 91190, Gif-sur-Yvette, France
| | - Nikos Paragios
- Laboratoire Mathématiques et Informatique pour la Complexité et les Systèmes, Ecole CentraleSupelec, 3 Rue Joliot Curie, 91190 Gif-sur-Yvette, France; TheraPanacea, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France; Center for Visual Computing, Ecole CentraleSupelec, 3 Rue Joliot Curie, 91190, Gif-sur-Yvette, France
| | - Marie-Pierre Revel
- Radiology Department, Groupe Hospitalier Cochin Broca Hôtel-Dieu - Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France; Center for Visual Computing, Ecole CentraleSupelec, 3 Rue Joliot Curie, 91190, Gif-sur-Yvette, France.
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Silva M, Milanese G, Pastorino U, Sverzellati N. Lung cancer screening: tell me more about post-test risk. J Thorac Dis 2019; 11:3681-3688. [PMID: 31656638 PMCID: PMC6790433 DOI: 10.21037/jtd.2019.09.28] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Accepted: 08/28/2019] [Indexed: 12/18/2022]
Affiliation(s)
- Mario Silva
- Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Gianluca Milanese
- Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Ugo Pastorino
- Department of Thoracic Surgery, IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Nicola Sverzellati
- Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
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Evaluation of an AI-Powered Lung Nodule Algorithm for Detection and 3D Segmentation of Primary Lung Tumors. CONTRAST MEDIA & MOLECULAR IMAGING 2019; 2019:1545747. [PMID: 31354393 PMCID: PMC6636561 DOI: 10.1155/2019/1545747] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 05/26/2019] [Indexed: 01/12/2023]
Abstract
Automated detection and segmentation is a prerequisite for the deployment of image-based secondary analyses, especially for lung tumors. However, currently only applications for lung nodules ≤3 cm exist. Therefore, we tested the performance of a fully automated AI-based lung nodule algorithm for detection and 3D segmentation of primary lung tumors in the context of tumor staging using the CT component of FDG-PET/CT and including all T-categories (T1-T4). FDG-PET/CTs of 320 patients with histologically confirmed lung cancer performed between 01/2010 and 06/2016 were selected. First, the main primary lung tumor within each scan was manually segmented using the CT component of the PET/CTs as reference. Second, the CT series were transferred to a platform with AI-based algorithms trained on chest CTs for detection and segmentation of lung nodules. Detection and segmentation performance were analyzed. Factors influencing detection rates were explored with binominal logistic regression and radiomic analysis. We also processed 94 PET/CTs negative for pulmonary nodules to investigate frequency and reasons of false-positive findings. The ratio of detected tumors was best in the T1-category (90.4%) and decreased continuously: T2 (70.8%), T3 (29.4%), and T4 (8.8%). Tumor contact with the pleura was a strong predictor of misdetection. Segmentation performance was excellent for T1 tumors (r = 0.908, p < 0.001) and tumors without pleural contact (r = 0.971, p < 0.001). Volumes of larger tumors were systematically underestimated. There were 0.41 false-positive findings per exam. The algorithm tested facilitates a reliable detection and 3D segmentation of T1/T2 lung tumors on FDG-PET/CTs. The detection and segmentation of more advanced lung tumors is currently imprecise due to the conception of the algorithm for lung nodules <3 cm. Future efforts should therefore focus on this collective to facilitate segmentation of all tumor types and sizes to bridge the gap between CAD applications for screening and staging of lung cancer.
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