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Wulaningsih W, Villamaria C, Akram A, Benemile J, Croce F, Watkins J. Deep Learning Models for Predicting Malignancy Risk in CT-Detected Pulmonary Nodules: A Systematic Review and Meta-analysis. Lung 2024; 202:625-636. [PMID: 38782779 PMCID: PMC11427562 DOI: 10.1007/s00408-024-00706-1] [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: 01/15/2024] [Accepted: 05/12/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND There has been growing interest in using artificial intelligence/deep learning (DL) to help diagnose prevalent diseases earlier. In this study we sought to survey the landscape of externally validated DL-based computer-aided diagnostic (CADx) models, and assess their diagnostic performance for predicting the risk of malignancy in computed tomography (CT)-detected pulmonary nodules. METHODS An electronic search was performed in four databases (from inception to 10 August 2023). Studies were eligible if they were peer-reviewed experimental or observational articles comparing the diagnostic performance of externally validated DL-based CADx models with models widely used in clinical practice to predict the risk of malignancy. A bivariate random-effect approach for the meta-analysis on the included studies was used. RESULTS Seventeen studies were included, comprising 8553 participants and 9884 nodules. Pooled analyses showed DL-based CADx models were 11.6% more sensitive than physician judgement alone, and 14.5% more than clinical risk models alone. They had a similar pooled specificity to physician judgement alone [0.77 (95% CI 0.68-0.84) v 0.81 (95% CI 0.71-0.88)], and were 7.4% more specific than clinical risk models alone. They had superior pooled areas under the receiver operating curve (AUC), with relative pooled AUCs of 1.03 (95% CI 1.00-1.07) and 1.10 (95% CI 1.07-1.13) versus physician judgement and clinical risk models alone, respectively. CONCLUSION DL-based models are already used in clinical practice in certain settings for nodule management. Our results show their diagnostic performance potentially justifies wider, more routine deployment alongside experienced physician readers to help inform multidisciplinary team decision-making.
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Affiliation(s)
- Wahyu Wulaningsih
- The Royal Marsden, London, UK.
- Faculty of Life Sciences & Medicine, King's College London, London, UK.
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Karera A, Davidson F, Engel-Hills P. Operational challenges and collaborative solutions in radiology image interpretation: perspectives from imaging departments in a low-resource setting. J Med Radiat Sci 2024. [PMID: 39087494 DOI: 10.1002/jmrs.815] [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: 02/22/2024] [Revised: 07/10/2024] [Accepted: 07/17/2024] [Indexed: 08/02/2024] Open
Abstract
INTRODUCTION Medical imaging's critical role in diagnosis requires prompt and precise image interpretation. Numerous radiology departments, especially in low-resourced settings, encounter challenges such as a shortage of radiologists in their operational setup. This study explored the perceptions of radiographers and radiologists from low-resourced departments in a single country regarding operational challenges and potential solutions in image interpretation. METHODS A qualitative approach was utilised, involving heads of departments, senior radiographers, and radiologists with a minimum of 5 years of experience, from three major state referral hospitals. Face-to-face, semi-structured interviews were conducted in November 2022, using an interview guide that included questions on the challenges encountered during image interpretation and the proposed solutions. Data analysis was conducted using Atlas.ti version 9.0, following the four-step content analysis method. All participants willingly provided consent to participate in the study. RESULTS Ten participants, comprising two radiologists and eight radiographers participated in the study. The research identified three main themes: image interpretation pathways, image interpretation operational challenges and proposed solutions for image interpretation. In addition, a total of 10 subthemes were generated from the three main themes. CONCLUSION The study revealed critical challenges and the need to explore the formal inclusion of radiographers in image interpretation, as a way to improve efficiency. However, a comprehensive assessment of the image interpretation system, encompassing radiographers' knowledge and competence, is recommended for context-specific, empirical-based modifications to enhance service provision.
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Affiliation(s)
- Abel Karera
- Department of Radiography, School of Allied Health Sciences, Faculty of Health Sciences and Veterinary Medicine, University of Namibia, Windhoek, Namibia
- Department of Medical Imaging and Therapeutic Sciences, Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Cape Town, South Africa
| | - Florence Davidson
- Department of Medical Imaging and Therapeutic Sciences, Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Cape Town, South Africa
| | - Penelope Engel-Hills
- Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Cape Town, South Africa
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Hall H, Ruparel M, Quaife SL, Dickson JL, Horst C, Tisi S, Batty J, Woznitza N, Ahmed A, Burke S, Shaw P, Soo MJ, Taylor M, Navani N, Bhowmik A, Baldwin DR, Duffy SW, Devaraj A, Nair A, Janes SM. The role of computer-assisted radiographer reporting in lung cancer screening programmes. Eur Radiol 2022; 32:6891-6899. [PMID: 35567604 PMCID: PMC9474336 DOI: 10.1007/s00330-022-08824-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/11/2022] [Accepted: 04/13/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVES Successful lung cancer screening delivery requires sensitive, timely reporting of low-dose computed tomography (LDCT) scans, placing a demand on radiology resources. Trained non-radiologist readers and computer-assisted detection (CADe) software may offer strategies to optimise the use of radiology resources without loss of sensitivity. This report examines the accuracy of trained reporting radiographers using CADe support to report LDCT scans performed as part of the Lung Screen Uptake Trial (LSUT). METHODS In this observational cohort study, two radiographers independently read all LDCT performed within LSUT and reported on the presence of clinically significant nodules and common incidental findings (IFs), including recommendations for management. Reports were compared against a 'reference standard' (RS) derived from nodules identified by study radiologists without CADe, plus consensus radiologist review of any additional nodules identified by the radiographers. RESULTS A total of 716 scans were included, 158 of which had one or more clinically significant pulmonary nodules as per our RS. Radiographer sensitivity against the RS was 68-73.7%, with specificity of 92.1-92.7%. Sensitivity for detection of proven cancers diagnosed from the baseline scan was 83.3-100%. The spectrum of IFs exceeded what could reasonably be covered in radiographer training. CONCLUSION Our findings highlight the complexity of LDCT reporting requirements, including the limitations of CADe and the breadth of IFs. We are unable to recommend CADe-supported radiographers as a sole reader of LDCT scans, but propose potential avenues for further research including initial triage of abnormal LDCT or reporting of follow-up surveillance scans. KEY POINTS • Successful roll-out of mass screening programmes for lung cancer depends on timely, accurate CT scan reporting, placing a demand on existing radiology resources. • This observational cohort study examines the accuracy of trained radiographers using computer-assisted detection (CADe) software to report lung cancer screening CT scans, as a potential means of supporting reporting workflows in LCS programmes. • CADe-supported radiographers were less sensitive than radiologists at identifying clinically significant pulmonary nodules, but had a low false-positive rate and good sensitivity for detection of confirmed cancers.
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Affiliation(s)
- Helen Hall
- Lungs for Living Research Centre, UCL Respiratory, Rayne Institute, University College London, 5 University Street, London, WC1E 6JF, UK
| | - Mamta Ruparel
- Lungs for Living Research Centre, UCL Respiratory, Rayne Institute, University College London, 5 University Street, London, WC1E 6JF, UK
| | - Samantha L Quaife
- Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Jennifer L Dickson
- Lungs for Living Research Centre, UCL Respiratory, Rayne Institute, University College London, 5 University Street, London, WC1E 6JF, UK
| | - Carolyn Horst
- Lungs for Living Research Centre, UCL Respiratory, Rayne Institute, University College London, 5 University Street, London, WC1E 6JF, UK
| | - Sophie Tisi
- Lungs for Living Research Centre, UCL Respiratory, Rayne Institute, University College London, 5 University Street, London, WC1E 6JF, UK
| | - James Batty
- Department of Radiology, University College London Hospital, London, UK
| | | | - Asia Ahmed
- Department of Radiology, University College London Hospital, London, UK
| | - Stephen Burke
- Department of Radiology, Homerton University Hospital, London, UK
| | - Penny Shaw
- Department of Radiology, University College London Hospital, London, UK
| | - May Jan Soo
- Department of Radiology, Homerton University Hospital, London, UK
| | - Magali Taylor
- Department of Radiology, University College London Hospital, London, UK
| | - Neal Navani
- Lungs for Living Research Centre, UCL Respiratory, Rayne Institute, University College London, 5 University Street, London, WC1E 6JF, UK
- Department of Thoracic Medicine, University College London Hospital, London, UK
| | - Angshu Bhowmik
- Department of Thoracic Medicine, Homerton University Hospital, London, UK
| | - David R Baldwin
- Respiratory Medicine Unit, David Evans Research Centre, Nottingham University Hospitals, Nottingham, UK
| | - Stephen W Duffy
- Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Anand Devaraj
- Department of Radiology, Royal Brompton Hospital, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Arjun Nair
- Department of Radiology, University College London Hospital, London, UK
| | - Sam M Janes
- Lungs for Living Research Centre, UCL Respiratory, Rayne Institute, University College London, 5 University Street, London, WC1E 6JF, UK.
- Department of Thoracic Medicine, University College London Hospital, London, UK.
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