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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 2024:10.1007/s00408-024-00706-1. [PMID: 38782779 DOI: 10.1007/s00408-024-00706-1] [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: 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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Adams SJ, Flores EJ, Little BP, Sharma A, Lennes IT, Shepard JAO, Fintelmann FJ. RadioGraphics Update: The 10 Pillars of Lung Cancer Screening-Rationale and Logistics of a Lung Cancer Screening Program. Radiographics 2024; 44:e230057. [PMID: 38329900 PMCID: PMC10878164 DOI: 10.1148/rg.230057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Editor's Note.-RadioGraphics Update articles supplement or update information found in full-length articles previously published in RadioGraphics. These updates, written by at least one author of the previous article, provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes.
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Affiliation(s)
- Scott J Adams
- From the Departments of Radiology (S.J.A., E.J.F., A.S., J.O.S., F.J.F.) and Medicine (I.T.L.), Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02111; and Department of Radiology, Mayo Clinic Florida, Jacksonville, Fla (B.P.L.)
| | - Efren J Flores
- From the Departments of Radiology (S.J.A., E.J.F., A.S., J.O.S., F.J.F.) and Medicine (I.T.L.), Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02111; and Department of Radiology, Mayo Clinic Florida, Jacksonville, Fla (B.P.L.)
| | - Brent P Little
- From the Departments of Radiology (S.J.A., E.J.F., A.S., J.O.S., F.J.F.) and Medicine (I.T.L.), Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02111; and Department of Radiology, Mayo Clinic Florida, Jacksonville, Fla (B.P.L.)
| | - Amita Sharma
- From the Departments of Radiology (S.J.A., E.J.F., A.S., J.O.S., F.J.F.) and Medicine (I.T.L.), Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02111; and Department of Radiology, Mayo Clinic Florida, Jacksonville, Fla (B.P.L.)
| | - Inga T Lennes
- From the Departments of Radiology (S.J.A., E.J.F., A.S., J.O.S., F.J.F.) and Medicine (I.T.L.), Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02111; and Department of Radiology, Mayo Clinic Florida, Jacksonville, Fla (B.P.L.)
| | - Jo-Anne O Shepard
- From the Departments of Radiology (S.J.A., E.J.F., A.S., J.O.S., F.J.F.) and Medicine (I.T.L.), Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02111; and Department of Radiology, Mayo Clinic Florida, Jacksonville, Fla (B.P.L.)
| | - Florian J Fintelmann
- From the Departments of Radiology (S.J.A., E.J.F., A.S., J.O.S., F.J.F.) and Medicine (I.T.L.), Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02111; and Department of Radiology, Mayo Clinic Florida, Jacksonville, Fla (B.P.L.)
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Fan W, Liu H, Zhang Y, Chen X, Huang M, Xu B. Diagnostic value of artificial intelligence based on computed tomography (CT) density in benign and malignant pulmonary nodules: a retrospective investigation. PeerJ 2024; 12:e16577. [PMID: 38188164 PMCID: PMC10768667 DOI: 10.7717/peerj.16577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 11/13/2023] [Indexed: 01/09/2024] Open
Abstract
Objective To evaluate the diagnostic value of artificial intelligence (AI) in the detection and management of benign and malignant pulmonary nodules (PNs) using computed tomography (CT) density. Methods A retrospective analysis was conducted on the clinical data of 130 individuals diagnosed with PNs based on pathological confirmation. The utilization of AI and physicians has been employed in the diagnostic process of distinguishing benign and malignant PNs. The CT images depicting PNs were integrated into AI-based software. The gold standard for evaluating the accuracy of AI diagnosis software and physician interpretation was the pathological diagnosis. Results Out of 226 PNs screened from 130 patients diagnosed by AI and physician reading based on CT, 147 were confirmed by pathology. AI had a sensitivity of 94.69% and radiologists had a sensitivity of 85.40% in identifying PNs. The chi-square analysis indicated that the screening capacity of AI was superior to that of physician reading, with statistical significance (p < 0.05). 195 of the 214 PNs suggested by AI were confirmed pathologically as malignant, and 19 were identified as benign; among the 29 PNs suggested by AI as low risk, 13 were confirmed pathologically as malignant, and 16 were identified as benign. From the physician reading, 193 PNs were identified as malignant, 183 were confirmed malignant by pathology, and 10 appeared benign. Physician reading also identified 30 low-risk PNs, 19 of which were pathologically malignant and 11 benign. The physician readings and AI had kappa values of 0.432 and 0.547, respectively. The physician reading and AI area under curves (AUCs) were 0.814 and 0.798, respectively. Both of the diagnostic techniques had worthy diagnostic value, as indicated by their AUCs of >0.7. Conclusion It is anticipated that the use of AI-based CT diagnosis in the detection of PNs would increase the precision in early detection of lung carcinoma, as well as yield more precise evidence for clinical management.
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Affiliation(s)
- Wei Fan
- Department of Radiology, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Huitong Liu
- Department of Orthopaedics, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Yan Zhang
- Department of Radiology, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Xiaolong Chen
- Department of Radiology, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Minggang Huang
- Department of Radiology, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Bingqiang Xu
- Department of Radiology, Shaanxi Provincial People’s Hospital, Xi’an, China
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Farič N, Hinder S, Williams R, Ramaesh R, Bernabeu MO, van Beek E, Cresswell K. Early experiences of integrating an artificial intelligence-based diagnostic decision support system into radiology settings: a qualitative study. J Am Med Inform Assoc 2023; 31:24-34. [PMID: 37748456 PMCID: PMC10746311 DOI: 10.1093/jamia/ocad191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 08/23/2023] [Accepted: 09/13/2023] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVES Artificial intelligence (AI)-based clinical decision support systems to aid diagnosis are increasingly being developed and implemented but with limited understanding of how such systems integrate with existing clinical work and organizational practices. We explored the early experiences of stakeholders using an AI-based imaging software tool Veye Lung Nodules (VLN) aiding the detection, classification, and measurement of pulmonary nodules in computed tomography scans of the chest. MATERIALS AND METHODS We performed semistructured interviews and observations across early adopter deployment sites with clinicians, strategic decision-makers, suppliers, patients with long-term chest conditions, and academics with expertise in the use of diagnostic AI in radiology settings. We coded the data using the Technology, People, Organizations, and Macroenvironmental factors framework. RESULTS We conducted 39 interviews. Clinicians reported VLN to be easy to use with little disruption to the workflow. There were differences in patterns of use between experts and novice users with experts critically evaluating system recommendations and actively compensating for system limitations to achieve more reliable performance. Patients also viewed the tool positively. There were contextual variations in tool performance and use between different hospital sites and different use cases. Implementation challenges included integration with existing information systems, data protection, and perceived issues surrounding wider and sustained adoption, including procurement costs. DISCUSSION Tool performance was variable, affected by integration into workflows and divisions of labor and knowledge, as well as technical configuration and infrastructure. CONCLUSION The socio-organizational factors affecting performance of diagnostic AI are under-researched and require attention and further research.
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Affiliation(s)
- Nuša Farič
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Sue Hinder
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Robin Williams
- Institute for the Study of Science, Technology and Innovation, University of Edinburgh, Edinburgh, United Kingdom
| | - Rishi Ramaesh
- Department of Radiology, Royal Infirmary Hospital, Edinburgh, United Kingdom
| | - Miguel O Bernabeu
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
- The Bayes Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Edwin van Beek
- Centre for Cardiovascular Science, Edinburgh Imaging and Neuroscience, University of Edinburgh, Edinburgh, United Kingdom
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Adams SJ, Mikhael P, Wohlwend J, Barzilay R, Sequist LV, Fintelmann FJ. Artificial Intelligence and Machine Learning in Lung Cancer Screening. Thorac Surg Clin 2023; 33:401-409. [PMID: 37806742 DOI: 10.1016/j.thorsurg.2023.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Recent advances in artificial intelligence and machine learning (AI/ML) hold substantial promise to address some of the current challenges in lung cancer screening and improve health equity. This article reviews the status and future directions of AI/ML tools in the lung cancer screening workflow, focusing on determining screening eligibility, radiation dose reduction and image denoising for low-dose chest computed tomography (CT), lung nodule detection, lung nodule classification, and determining optimal screening intervals. AI/ML tools can assess for chronic diseases on CT, which creates opportunities to improve population health through opportunistic screening.
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Affiliation(s)
- Scott J Adams
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Peter Mikhael
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeremy Wohlwend
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Regina Barzilay
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lecia V Sequist
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA; Harvard Medical School, Boston, MA, USA.
| | - Florian J Fintelmann
- Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.
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Vithlani J, Hawksworth C, Elvidge J, Ayiku L, Dawoud D. Economic evaluations of artificial intelligence-based healthcare interventions: a systematic literature review of best practices in their conduct and reporting. Front Pharmacol 2023; 14:1220950. [PMID: 37693892 PMCID: PMC10486896 DOI: 10.3389/fphar.2023.1220950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 07/25/2023] [Indexed: 09/12/2023] Open
Abstract
Objectives: Health economic evaluations (HEEs) help healthcare decision makers understand the value of new technologies. Artificial intelligence (AI) is increasingly being used in healthcare interventions. We sought to review the conduct and reporting of published HEEs for AI-based health interventions. Methods: We conducted a systematic literature review with a 15-month search window (April 2021 to June 2022) on 17th June 2022 to identify HEEs of AI health interventions and update a previous review. Records were identified from 3 databases (Medline, Embase, and Cochrane Central). Two reviewers screened papers against predefined study selection criteria. Data were extracted from included studies using prespecified data extraction tables. Included studies were quality assessed using the National Institute for Health and Care Excellence (NICE) checklist. Results were synthesized narratively. Results: A total of 21 studies were included. The most common type of AI intervention was automated image analysis (9/21, 43%) mainly used for screening or diagnosis in general medicine and oncology. Nearly all were cost-utility (10/21, 48%) or cost-effectiveness analyses (8/21, 38%) that took a healthcare system or payer perspective. Decision-analytic models were used in 16/21 (76%) studies, mostly Markov models and decision trees. Three (3/16, 19%) used a short-term decision tree followed by a longer-term Markov component. Thirteen studies (13/21, 62%) reported the AI intervention to be cost effective or dominant. Limitations tended to result from the input data, authorship conflicts of interest, and a lack of transparent reporting, especially regarding the AI nature of the intervention. Conclusion: Published HEEs of AI-based health interventions are rapidly increasing in number. Despite the potentially innovative nature of AI, most have used traditional methods like Markov models or decision trees. Most attempted to assess the impact on quality of life to present the cost per QALY gained. However, studies have not been comprehensively reported. Specific reporting standards for the economic evaluation of AI interventions would help improve transparency and promote their usefulness for decision making. This is fundamental for reimbursement decisions, which in turn will generate the necessary data to develop flexible models better suited to capturing the potentially dynamic nature of AI interventions.
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Affiliation(s)
- Jai Vithlani
- National Institute for Health and Care Excellence, London, United Kingdom
| | - Claire Hawksworth
- National Institute for Health and Care Excellence, Manchester, United Kingdom
| | - Jamie Elvidge
- National Institute for Health and Care Excellence, Manchester, United Kingdom
| | - Lynda Ayiku
- National Institute for Health and Care Excellence, Manchester, United Kingdom
| | - Dalia Dawoud
- National Institute for Health and Care Excellence, London, United Kingdom
- Faculty of Pharmacy, Cairo University, Cairo, Egypt
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Zhao ZR, Yu YH, Lin ZC, Ma DH, Lin YB, Hu J, Luo QQ, Li GF, Chen C, Yang YL, Yang JC, Lin YB, Long H. Invasiveness assessment by artificial intelligence against intraoperative frozen section for pulmonary nodules ≤ 3 cm. J Cancer Res Clin Oncol 2023; 149:7759-7765. [PMID: 37016100 DOI: 10.1007/s00432-023-04713-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/18/2023] [Indexed: 04/06/2023]
Abstract
PURPOSE To investigate the performance of an artificial intelligence (AI) algorithm for assessing the malignancy and invasiveness of pulmonary nodules in a multicenter cohort. METHODS A previously developed deep learning system based on a 3D convolutional neural network was used to predict tumor malignancy and invasiveness. Dataset of pulmonary nodules no more than 3 cm was integrated with CT images and pathologic information. Receiver operating characteristic curve analysis was used to evaluate the performance of the system. RESULTS A total of 466 resected pulmonary nodules were included in this study. The areas under the curves (AUCs) of the deep learning system in the prediction of malignancy as compared with pathological reports were 0.80, 0.80, and 0.75 for all, subcentimeter, and solid nodules, respectively. Additionally, the AUC in the AI-assisted prediction of invasive adenocarcinoma (IA) among subsolid lesions (n = 184) was 0.88. Most malignancies that were misdiagnosed by the AI system as benign diseases with a diameter measuring greater than 1 cm (26/250, 10.4%) presented as solid nodules (19/26, 73.1%) on CT. In an exploratory analysis involving nodules underwent intraoperative pathologic examination, the concordance rate in identifying IA between the AI model and frozen section examination was 0.69, with a sensitivity of 0.50 and specificity of 0.97. CONCLUSION The deep learning system can discriminate malignant diseases for pulmonary nodules measuring no more than 3 cm. The AI model has a high positive predictive value for invasive adenocarcinoma with respect to intraoperative frozen section examination, which might help determine the individualized surgical strategy.
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Affiliation(s)
- Ze-Rui Zhao
- State Key Laboratory of Oncology in Southern China, Department of Thoracic Surgery, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, Guangdong, People's Republic of China
| | | | - Zhi-Chao Lin
- Department of Thoracic Surgery, Jiangmen Central Hospital, Jiangmen, China
| | - De-Hua Ma
- Department of Thoracic Surgery, Taizhou Hospital, Taizhou, China
| | - Yao-Bin Lin
- State Key Laboratory of Oncology in Southern China, Department of Thoracic Surgery, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, Guangdong, People's Republic of China
| | - Jian Hu
- Department of Thoracic Surgery, School of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Qing-Quan Luo
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Gao-Feng Li
- Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chun Chen
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yu-Lun Yang
- Department of Thoracic Surgery, Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-Cheng Yang
- Dianei Technology, Shanghai, China.
- Shanghai Jiao Tong University, 800# Dong Chuan Road, Shanghai, 200240, People's Republic of China.
- EPFL, Lausanne, Switzerland.
| | - Yong-Bin Lin
- State Key Laboratory of Oncology in Southern China, Department of Thoracic Surgery, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, Guangdong, People's Republic of China.
| | - Hao Long
- State Key Laboratory of Oncology in Southern China, Department of Thoracic Surgery, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, Guangdong, People's Republic of China.
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8
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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: 0] [Impact Index Per Article: 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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Silva-Aravena F, Núñez Delafuente H, Gutiérrez-Bahamondes JH, Morales J. A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making. Cancers (Basel) 2023; 15:cancers15092443. [PMID: 37173910 PMCID: PMC10177162 DOI: 10.3390/cancers15092443] [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: 03/02/2023] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023] Open
Abstract
Worldwide, the coronavirus has intensified the management problems of health services, significantly harming patients. Some of the most affected processes have been cancer patients' prevention, diagnosis, and treatment. Breast cancer is the most affected, with more than 20 million cases and at least 10 million deaths by 2020. Various studies have been carried out to support the management of this disease globally. This paper presents a decision support strategy for health teams based on machine learning (ML) tools and explainability algorithms (XAI). The main methodological contributions are: first, the evaluation of different ML algorithms that allow classifying patients with and without cancer from the available dataset; and second, an ML methodology mixed with an XAI algorithm, which makes it possible to predict the disease and interpret the variables and how they affect the health of patients. The results show that first, the XGBoost Algorithm has a better predictive capacity, with an accuracy of 0.813 for the train data and 0.81 for the test data; and second, with the SHAP algorithm, it is possible to know the relevant variables and their level of significance in the prediction, and to quantify the impact on the clinical condition of the patients, which will allow health teams to offer early and personalized alerts for each patient.
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Affiliation(s)
- Fabián Silva-Aravena
- Facultad de Ciencias Sociales y Económicas, Universidad Católica del Maule, Avenida San Miguel 3605, Talca 3460000, Chile
| | - Hugo Núñez Delafuente
- Doctorado en Sistemas de Ingeniería, Facultad de Ingeniería, Universidad de Talca, Camino Los Niches Km 1, Curicó 3340000, Chile
| | - Jimmy H Gutiérrez-Bahamondes
- Doctorado en Sistemas de Ingeniería, Facultad de Ingeniería, Universidad de Talca, Camino Los Niches Km 1, Curicó 3340000, Chile
| | - Jenny Morales
- Facultad de Ciencias Sociales y Económicas, Universidad Católica del Maule, Avenida San Miguel 3605, Talca 3460000, Chile
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10
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Morgan H, Baldwin DR. Important parameters for cost-effective implementation of lung cancer screening. Br J Radiol 2023; 96:20220489. [PMID: 36607805 PMCID: PMC10161917 DOI: 10.1259/bjr.20220489] [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: 05/06/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 01/07/2023] Open
Abstract
It is now widely accepted that lung cancer screening through low-dose computed tomography (LDCT) results in fewer diagnoses at a late stage, and decreased lung cancer mortality. Whilst reducing deaths from lung cancer is an essential prerequisite, this must be balanced against the considerable economic costs accumulated in screening. Multiple health economic models have shown substantial variation in cost per Quality-Adjusted Life Year (QALY), partly driven by the healthcare costs in the country concerned and partly by other modifiable programme components. Recent modelling using UK costs and a targeted approach suggest that most scenarios are within the willingness to pay threshold for the UK. However, identifying the most clinically and cost-effective programme is a priority to minimise the total financial impact. Programme components that influence cost-effectiveness include the method of selection of the eligible population, the participation rate, the interval between rounds of screening, the method of pulmonary nodule management, and the approach to clinical work up. Future research will clarify if a personalised approach to screening, using baseline and subsequent risk to define screening intervals is more cost-effective. The burden of LDCT screening on the medical infrastructure and workforce has to be quantified and carefully managed during implementation.
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Affiliation(s)
- Helen Morgan
- Roy Castle Clinical Research Fellow, Division of Lifespan and Population Health, University of Nottingham, Nottingham, United Kingdom
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11
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Adams SJ, Stone E, Baldwin DR, Vliegenthart R, Lee P, Fintelmann FJ. Lung cancer screening. Lancet 2023; 401:390-408. [PMID: 36563698 DOI: 10.1016/s0140-6736(22)01694-4] [Citation(s) in RCA: 53] [Impact Index Per Article: 53.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/26/2022] [Accepted: 08/25/2022] [Indexed: 12/24/2022]
Abstract
Randomised controlled trials, including the National Lung Screening Trial (NLST) and the NELSON trial, have shown reduced mortality with lung cancer screening with low-dose CT compared with chest radiography or no screening. Although research has provided clarity on key issues of lung cancer screening, uncertainty remains about aspects that might be critical to optimise clinical effectiveness and cost-effectiveness. This Review brings together current evidence on lung cancer screening, including an overview of clinical trials, considerations regarding the identification of individuals who benefit from lung cancer screening, management of screen-detected findings, smoking cessation interventions, cost-effectiveness, the role of artificial intelligence and biomarkers, and current challenges, solutions, and opportunities surrounding the implementation of lung cancer screening programmes from an international perspective. Further research into risk models for patient selection, personalised screening intervals, novel biomarkers, integrated cardiovascular disease and chronic obstructive pulmonary disease assessments, smoking cessation interventions, and artificial intelligence for lung nodule detection and risk stratification are key opportunities to increase the efficiency of lung cancer screening and ensure equity of access.
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Affiliation(s)
- Scott J Adams
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Emily Stone
- Faculty of Medicine, University of New South Wales and Department of Lung Transplantation and Thoracic Medicine, St Vincent's Hospital, Sydney, NSW, Australia
| | - David R Baldwin
- Respiratory Medicine Unit, David Evans Research Centre, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | | | - Pyng Lee
- Division of Respiratory and Critical Care Medicine, National University Hospital and National University of Singapore, Singapore
| | - Florian J Fintelmann
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
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12
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Adams SJ, Madtes DK, Burbridge B, Johnston J, Goldberg IG, Siegel EL, Babyn P, Nair VS, Calhoun ME. Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT. J Am Coll Radiol 2023; 20:232-242. [PMID: 36064040 DOI: 10.1016/j.jacr.2022.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 08/19/2022] [Accepted: 08/29/2022] [Indexed: 11/17/2022]
Abstract
OBJECTIVE To evaluate whether an imaging classifier for radiology practice can improve lung nodule classification and follow-up. METHODS A machine learning classifier was developed and trained using imaging data from the National Lung Screening Trial (NSLT) to produce a malignancy risk score (malignancy Similarity Index [mSI]) for individual lung nodules. In addition to NLST cohorts, external cohorts were developed from a tertiary referral lung cancer screening program data set and an external nonscreening data set of all nodules detected on CT. Performance of the mSI combined with Lung-RADS was compared with Lung-RADS alone and the Mayo and Brock risk calculators. RESULTS We analyzed 963 subjects and 1,331 nodules across these cohorts. The mSI was comparable in accuracy (area under the curve = 0.89) to existing clinical risk models (area under the curve = 0.86-0.88) and independently predictive in the NLST cohort of 704 nodules. When compared with Lung-RADS, the mSI significantly increased sensitivity across all cohorts (25%-117%), with significant increases in specificity in the screening cohorts (17%-33%). When used in conjunction with Lung-RADS, use of mSI would result in earlier diagnoses and reduced follow-up across cohorts, including the potential for early diagnosis in 42% of malignant NLST nodules from prior-year CT scans. CONCLUSION A computer-assisted diagnosis software improved risk classification from chest CTs of screening and incidentally detected lung nodules compared with Lung-RADS. mSI added predictive value independent of existing radiological and clinical variables. These results suggest the generalizability and potential clinical impact of a tool that is straightforward to implement in practice.
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Affiliation(s)
- Scott J Adams
- Department of Medical Imaging, University of Saskatchewan, Saskatoon, Canada; Scientific Director of the National Medical Imaging Clinic in Saskatoon
| | - David K Madtes
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Brent Burbridge
- Department of Medical Imaging, University of Saskatchewan, Saskatoon, Canada
| | | | | | - Eliot L Siegel
- Professor and Vice Chair, Department of Diagnostic Radiology, University of Maryland School of Medicine; Chief of Radiology and Nuclear Medicine for the Veterans Affairs Maryland Healthcare System; and Fellow of the American College of Radiology
| | - Paul Babyn
- Department of Medical Imaging, University of Saskatchewan, Saskatoon, Canada; recently retired as Physician Executive, Provincial Programs for the Saskatchewan Health Authority
| | - Viswam S Nair
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington; Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington School of Medicine, Seattle, Washington
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13
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Choe J, Lee SM, Hwang HJ, Lee SM, Yun J, Kim N, Seo JB. Artificial Intelligence in Lung Imaging. Semin Respir Crit Care Med 2022; 43:946-960. [PMID: 36174647 DOI: 10.1055/s-0042-1755571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging. Some of these have been approved by governments and are now commercially available in the marketplace. In the field of chest radiology, there are various tasks and purposes that are suitable for AI: initial evaluation/triage of certain diseases, detection and diagnosis, quantitative assessment of disease severity and monitoring, and prediction for decision support. While AI is a powerful technology that can be applied to medical imaging and is expected to improve our current clinical practice, some obstacles must be addressed for the successful implementation of AI in workflows. Understanding and becoming familiar with the current status and potential clinical applications of AI in chest imaging, as well as remaining challenges, would be essential for radiologists and clinicians in the era of AI. This review introduces the potential clinical applications of AI in chest imaging and also discusses the challenges for the implementation of AI in daily clinical practice and future directions in chest imaging.
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Affiliation(s)
- Jooae Choe
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Hye Jeon Hwang
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jihye Yun
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.,Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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14
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Istasy P, Lee WS, Iansavichene A, Upshur R, Gyawali B, Burkell J, Sadikovic B, Lazo-Langner A, Chin-Yee B. The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review. J Med Internet Res 2022; 24:e39748. [PMID: 36005841 PMCID: PMC9667381 DOI: 10.2196/39748] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 08/11/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity. OBJECTIVE We aimed to conduct a scoping review of the literature to address the question, "What are the current and potential impacts of AI technologies on health equity in oncology?" METHODS Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology. RESULTS Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes. CONCLUSIONS Our scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI's ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.
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Affiliation(s)
- Paul Istasy
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Rotman Institute of Philosophy, Western University, London, ON, Canada
| | - Wen Shen Lee
- Department of Pathology & Laboratory Medicine, Schulich School of Medicine, Western University, London, ON, Canada
| | | | - Ross Upshur
- Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Bridgepoint Collaboratory for Research and Innovation, Lunenfeld Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Bishal Gyawali
- Division of Cancer Care and Epidemiology, Department of Oncology, Queen's University, Kingston, ON, Canada
- Division of Cancer Care and Epidemiology, Department of Public Health Sciences, Queen's University, Kingston, ON, Canada
| | - Jacquelyn Burkell
- Faculty of Information and Media Studies, Western University, London, ON, Canada
| | - Bekim Sadikovic
- Department of Pathology & Laboratory Medicine, Schulich School of Medicine, Western University, London, ON, Canada
| | - Alejandro Lazo-Langner
- Division of Hematology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Benjamin Chin-Yee
- Rotman Institute of Philosophy, Western University, London, ON, Canada
- Division of Hematology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Division of Hematology, Department of Medicine, London Health Sciences Centre, London, ON, Canada
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15
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Lyu PF, Wang Y, Meng QX, Fan PM, Ma K, Xiao S, Cao XC, Lin GX, Dong SY. Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis. Front Oncol 2022; 12:955668. [PMID: 36212413 PMCID: PMC9535738 DOI: 10.3389/fonc.2022.955668] [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: 05/29/2022] [Accepted: 08/25/2022] [Indexed: 11/23/2022] Open
Abstract
Background Artificial intelligence (AI) is more and more widely used in cancer, which is of great help to doctors in diagnosis and treatment. This study aims to summarize the current research hotspots in the Application of Artificial Intelligence in Cancer (AAIC) and to assess the research trends in AAIC. Methods Scientific publications for AAIC-related research from 1 January 1998 to 1 July 2022 were obtained from the Web of Science database. The metrics analyses using bibliometrics software included publication, keyword, author, journal, institution, and country. In addition, the blustering analysis on the binary matrix was performed on hot keywords. Results The total number of papers in this study is 1592. The last decade of AAIC research has been divided into a slow development phase (2013-2018) and a rapid development phase (2019-2022). An international collaboration centered in the USA is dedicated to the development and application of AAIC. Li J is the most prolific writer in AAIC. Through clustering analysis and high-frequency keyword research, it has been shown that AI plays a significantly important role in the prediction, diagnosis, treatment and prognosis of cancer. Classification, diagnosis, carcinogenesis, risk, and validation are developing topics. Eight hotspot fields of AAIC were also identified. Conclusion AAIC can benefit cancer patients in diagnosing cancer, assessing the effectiveness of treatment, making a decision, predicting prognosis and saving costs. Future AAIC research may be dedicated to optimizing AI calculation tools, improving accuracy, and promoting AI.
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Affiliation(s)
- Peng-fei Lyu
- Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Yu Wang
- Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Qing-Xiang Meng
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Ping-ming Fan
- Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Ke Ma
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Sha Xiao
- International School of Public Health and One Health, Heinz Mehlhorn Academician Workstation, Hainan Medical University, Haikou, China
| | - Xun-chen Cao
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Guang-Xun Lin
- Department of Orthopedics, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- *Correspondence: Guang-Xun Lin, ; Si-yuan Dong,
| | - Si-yuan Dong
- Thoracic Department, The First Hospital of China Medical University, Shenyang, China
- *Correspondence: Guang-Xun Lin, ; Si-yuan Dong,
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16
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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] [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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