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Linder N, Denecke T, Busse H. Body composition analysis by radiological imaging - methods, applications, and prospects. ROFO-FORTSCHR RONTG 2024; 196:1046-1054. [PMID: 38569516 DOI: 10.1055/a-2263-1501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
BACKGROUND This review discusses the quantitative assessment of tissue composition in the human body (body composition, BC) using radiological methods. Such analyses are gaining importance, in particular, for oncological and metabolic problems. The aim is to present the different methods and definitions in this field to a radiological readership in order to facilitate application and dissemination of BC methods. The main focus is on radiological cross-sectional imaging. METHODS The review is based on a recent literature search in the US National Library of Medicine catalog (pubmed.gov) using appropriate search terms (body composition, obesity, sarcopenia, osteopenia in conjunction with imaging and radiology, respectively), as well as our own work and experience, particularly with MRI- and CT-based analyses of abdominal fat compartments and muscle groups. RESULTS AND CONCLUSION Key post-processing methods such as segmentation of tomographic datasets are now well established and used in numerous clinical disciplines, including bariatric surgery. Validated reference values are required for a reliable assessment of radiological measures, such as fatty liver or muscle. Artificial intelligence approaches (deep learning) already enable the automated segmentation of different tissues and compartments so that the extensive datasets can be processed in a time-efficient manner - in the case of so-called opportunistic screening, even retrospectively from diagnostic examinations. The availability of analysis tools and suitable datasets for AI training is considered a limitation. KEY POINTS · Radiological imaging methods are increasingly used to determine body composition (BC).. · BC parameters are usually quantitative and well reproducible.. · CT image data from routine clinical examinations can be used retrospectively for BC analysis.. · Prospectively, MRI examinations can be used to determine organ-specific BC parameters.. · Automated and in-depth analysis methods (deep learning or radiomics) appear to become important in the future.. CITATION FORMAT · Linder N, Denecke T, Busse H. Body composition analysis by radiological imaging - methods, applications, and prospects. Fortschr Röntgenstr 2024; 196: 1046 - 1054.
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Affiliation(s)
- Nicolas Linder
- Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Leipzig, Germany
- Division of Radiology and Nuclear Medicine, Kantonsspital St. Gallen, Sankt Gallen, Switzerland
| | - Timm Denecke
- Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Leipzig, Germany
| | - Harald Busse
- Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Leipzig, Germany
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2
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Milch HS, Haramati LB. The science and practice of imaging-based screening: What the radiologist needs to know. Clin Imaging 2024; 114:110266. [PMID: 39216274 DOI: 10.1016/j.clinimag.2024.110266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 08/12/2024] [Accepted: 08/13/2024] [Indexed: 09/04/2024]
Abstract
Imaging-based screening is an important public health focus and a fundamental part of Diagnostic Radiology. Hence, radiologists should be familiar with the concepts that drive imaging-based screening practice including goals, risks, biases and clinical trials. This review article discusses an array of imaging-based screening exams including the key epidemiology and evidence that drive screening guidelines for abdominal aortic aneurysm, breast cancer, carotid artery disease, colorectal cancer, coronary artery disease, lung cancer, osteoporosis, and thyroid cancer. We will provide an overview on societal interests in screening, screening-related inequities, and opportunities to address them. Emerging evidence for opportunistic screening and the role of AI in imaging-based screening will be explored. In-depth knowledge and formalized training in imaging-based screening strengthens radiologists as clinician scientists and has the potential to broaden our public health leadership opportunities. SUMMARY SENTENCE: An overview of key screening concepts, the evidence that drives today's imaging-based screening practices, and the need for radiologist leadership in screening policies and evidence development.
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Affiliation(s)
- Hannah S Milch
- Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States of America.
| | - Linda B Haramati
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States of America
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3
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Crim J. Editorial Comment: Retrospective Opportunistic CT for Assessment of Changes in Body Composition Related to Semaglutide Therapy. AJR Am J Roentgenol 2024. [PMID: 39320356 DOI: 10.2214/ajr.24.32063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2024]
Affiliation(s)
- Julia Crim
- University of Missouri at Columbia, Department of Radiology, Columbia, MO United States
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4
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Waite S, Davenport MS, Graber ML, Banja JD, Sheppard B, Bruno M. Opportunity and Opportunism in Artificial-Intelligence-Powered Data Extraction: A Value-Centered Approach. AJR Am J Roentgenol 2024. [PMID: 39291941 DOI: 10.2214/ajr.24.31686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2024]
Abstract
Radiologists' traditional role in the diagnostic process is to respond to specific clinical questions and reduce uncertainty enough to permit treatment decisions. This charge is rapidly evolving due to forces such as artificial intelligence [AI], big data [opportunistic imaging, imaging prognostication], and advanced diagnostic technologies. A new "modernistic" paradigm is emerging whereby radiologists, in conjunction with computer algorithms, will be tasked with extracting as much information from imaging data as possible, often without a specific clinical question being posed and independent of any stated clinical need. In addition, AI algorithms are increasingly able to predict long-term outcomes using data from seemingly normal examinations, enabling AI-assisted prognostication. As these algorithms become a standard component of radiology practice, the sheer amount of information they demand will increase the need for streamlined workflows, communication, and data management techniques. In addition, the provision of such information raises reimbursement, liability, and access issues. Guidelines will be needed to ensure all patients have access to the benefits of this new technology and guarantee mined data do not inadvertently create harm. In this article, we discuss challenges and opportunities relevant to radiologists in this changing landscape, with an emphasis on ensuring that radiologists provide high-value care.
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Affiliation(s)
- Stephen Waite
- Clinical Associate Professor of Radiology and Internal Medicine, SUNY Downstate Medical Center, 450 Clarkson Avenue, Brooklyn, NY 11203
| | - Matthew S Davenport
- William Martel Collegiate Professor of Radiology and Professor of Urology, Co-Director, Ronald Weiser Center for Prostate Cancer, Service Chief for Radiology, Vice Chair (Research, Academic Affairs, Faculty Development) Michigan Medicine, Michigan Medicine, 1500 E Medical Center Dr, B2A209P, Ann Arbor, MI 48109-5030
| | - Mark L Graber
- Professor Emeritus, Stony Brook University, NY; Founder and President Emeritus, Society to Improve Diagnosis in Medicine (SIDM)
| | - John D Banja
- Professor: Department of Rehabilitation Medicine; Medical Ethicist: Center for Ethics; Associate Editor: Radiology: Artificial Intelligence; Principal Investigator: Radiology, Ethics and Artificial Intelligence Project, Emory University, 1531 Dickey Drive, Room 184
| | - Brian Sheppard
- Professor of Law, Seton Hall University, One Newark Center, Newark, NJ 07102
| | - Michael Bruno
- Professor of Radiology and Medicine, Vice-Chair for Radiology Quality and Safety, Chief Section Emergency Medicine, Penn State Milton S. Hershey Medical Center, 500 University Drive, Hershey, PA 17033
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5
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Eltorai AEM, Parris DJ, Tarrant MJ, Mayo-Smith WW, Andriole KP. AI implementation: Radiologists' perspectives on AI-enabled opportunistic CT screening. Clin Imaging 2024; 115:110282. [PMID: 39270428 DOI: 10.1016/j.clinimag.2024.110282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2024] [Revised: 09/01/2024] [Accepted: 09/03/2024] [Indexed: 09/15/2024]
Abstract
OBJECTIVE AI adoption requires perceived value by end-users. AI-enabled opportunistic CT screening (OS) detects incidental clinically meaningful imaging risk markers on CT for potential preventative health benefit. This investigation assesses radiologists' perspectives on AI and OS. METHODS An online survey was distributed to 7500 practicing radiologists among ACR membership of which 4619 opened the emails. Familiarity with and views of AI applications were queried and tabulated, as well as knowledge of OS and inducements and impediments to use. RESULTS Respondent (n = 211) demographics: mean age 55 years, 73 % male, 91 % diagnostic radiologists, 46 % in private practice. 68 % reported using AI in practice, while 52 % were only somewhat familiar with AI. 70 % viewed AI positively though only 46 % reported AI's overall accuracy met expectations. 57 % were unfamiliar with OS, with 52 % of those familiar having a positive opinion. Patient perceptions were the most commonly reported (25 %) inducement for OS use. Provider (44 %) and patient (40 %) costs were the most common impediments. Respondents reported that osteoporosis/osteopenia (81 %), fatty liver (78 %), and atherosclerotic cardiovascular disease risk (76 %) could be well assessed by OS. Most indicated OS output requires radiologist oversight/signoff and should be included in a separate "screening" section in the Radiology report. 28 % indicated willingness to spend 1-3 min reviewing AI-generated output while 18 % would not spend any time. Society guidelines/recommendations were most likely to impact OS implementation. DISCUSSION Radiologists' perspectives on AI and OS provide practical insights on AI implementation. Increasing end-user familiarity with AI-enabled applications and development of society guidelines/recommendations are likely essential prerequisites for Radiology AI adoption.
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Affiliation(s)
- Adam E M Eltorai
- Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States of America.
| | | | - Mary Jo Tarrant
- American College of Radiology, Reston, VA, United States of America
| | - William W Mayo-Smith
- Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Katherine P Andriole
- Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States of America; AI Office, Mass General Brigham, Boston, MA, United States of America
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6
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Sodickson DK. The Perils and the Promise of Whole-Body MRI: Why We May Be Debating the Wrong Things. J Am Coll Radiol 2024:S1546-1440(24)00765-8. [PMID: 39251175 DOI: 10.1016/j.jacr.2024.08.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 08/03/2024] [Accepted: 08/07/2024] [Indexed: 09/11/2024]
Affiliation(s)
- Daniel K Sodickson
- Chief of Innovation, Department of Radiology, New York University Grossman School of Medicine, New York, New York; Principal Investigator, Center for Advanced Imaging Innovation and Research; Member, National Advisory Council, National Institute of Biomedical Imaging and Bioengineering.
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7
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Nelson LW, Lee MH, Garrett JW, Pickhardt SG, Warner JD, Summers RM, Pickhardt PJ. Intrapatient Changes in CT-Based Body Composition After Initiation of Semaglutide (Glucagon-Like Peptide-1 Agonist) Therapy. AJR Am J Roentgenol 2024. [PMID: 39230989 DOI: 10.2214/ajr.24.31805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2024]
Abstract
Background: The long-acting glucagon-like peptide-1 receptor agonist semaglutide is used to treat type 2 diabetes or obesity in adults. Clinical trials have observed associations of semaglutide with weight loss, improved diabetic control, and cardiovascular risk reduction. Objective: To evaluate intrapatient changes in body composition after initiation of semaglutide therapy by applying an automated suite of CT-based artificial intelligence (AI) body composition tools. Methods: This retrospective study included adult patients with semaglutide treatment who underwent abdominopelvic CT both within 5 years before and within 5 years after semaglutide initiation, between January 2016 and November 2023. An automated suite of previously validated CT-based AI body composition tools was applied to pre-semaglutide and post-semaglutide scans to quantify visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) area, skeletal muscle area and attenuation, intermuscular adipose tissue (IMAT) area, liver volume and attenuation, and trabecular bone mineral density (BMD). Patients with ≥5-kg weight loss and ≥5-kg weight gain between scans were compared. Results: The study included 241 patients (mean age, 60.4±12.4 years; 151 women, 90 men). In the weight-loss group (n=67), the post-semaglutide scan, versus pre-semaglutide scan, showed decrease in VAT area (341.1 vs 309.4 cm2, p<.001), SAT area (371.4 vs 410.7 cm2, p<.001), muscle area (179.2 vs 193.0, p<.001), and liver volume (2379.0 vs 2578 HU, p=.009), and increase in liver attenuation (74.5 vs 67.6 HU, p=.03). In the weight-gain group (n=48), the post-semaglutide scan, versus pre-semaglutide scan, showed increase in VAT area (334.0 vs 312.8, p=.002), SAT area (485.8 vs 488.8 cm2, p=.01), and IMAT area (48.4 vs 37.6, p=.009), and decrease in muscle attenuation (5.9 vs 13.1, p<.001). Other comparisons were not significant (p>.05). Conclusion: Patients using semaglutide who lost versus gained weight demonstrated distinct patterns of changes in CT-based body composition measures. Those with weight loss exhibited overall favorable shifts in measures related to cardiometabolic risk. Muscle attenuation decrease in those with weight gain is consistent with decreased muscle quality. Clinical Impact: Automated CT-based AI tools provide biomarkers of body composition changes in patients using semaglutide beyond that which is evident by standard clinical measures.
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Affiliation(s)
- Leslie W Nelson
- The Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Matthew H Lee
- The Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - John W Garrett
- The Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Silas G Pickhardt
- The Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Joshua D Warner
- The Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Ronald M Summers
- The Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Perry J Pickhardt
- The Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
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Pickhardt PJ. Harnessing the Value of Incidental Tissue and Organ Data at Body CT. Radiology 2024; 312:e241349. [PMID: 39105643 DOI: 10.1148/radiol.241349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/07/2024]
Affiliation(s)
- Perry J Pickhardt
- From the Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252
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9
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Eltorai AEM, McKinney SE, Rockenbach MABC, Karuppiah S, Bizzo BC, Andriole KP. Primary care provider perspectives on the value of opportunistic CT screening. Clin Imaging 2024; 112:110210. [PMID: 38850710 DOI: 10.1016/j.clinimag.2024.110210] [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: 02/08/2024] [Revised: 05/10/2024] [Accepted: 05/31/2024] [Indexed: 06/10/2024]
Abstract
BACKGROUND Clinical adoption of AI applications requires stakeholders see value in their use. AI-enabled opportunistic-CT-screening (OS) capitalizes on incidentally-detected findings within CTs for potential health benefit. This study evaluates primary care providers' (PCP) perspectives on OS. METHODS A survey was distributed to US Internal and Family Medicine residencies. Assessed were familiarity with AI and OS, perspectives on potential value/costs, communication of results, and technology implementation. RESULTS 62 % of respondents (n = 71) were in Family Medicine, 64.8 % practiced in community hospitals. Although 74.6 % of respondents had heard of AI/machine learning, 95.8 % had little-to-no familiarity with OS. The majority reported little-to-no trust in AI. Reported concerns included AI accuracy (74.6 %) and unknown liability (73.2 %). 78.9 % of respondents reported that OS applications would require radiologist oversight. 53.5 % preferred OS results be included in a separate "screening" section within the Radiology report, accompanied by condition risks and management recommendations. The majority of respondents reported results would likely affect clinical management for all queried applications, and that atherosclerotic cardiovascular disease risk, abdominal aortic aneurysm, and liver fibrosis should be included within every CT report regardless of reason for examination. 70.5 % felt that PCP practices are unlikely to pay for OS. Added costs to the patient (91.5 %), the healthcare provider (77.5 %), and unknown liability (74.6 %) were the most frequently reported concerns. CONCLUSION PCP preferences and concerns around AI-enabled OS offer insights into clinical value and costs. As AI applications grow, feedback from end-users should be considered in the development of such technology to optimize implementation and adoption. Increasing stakeholder familiarity with AI may be a critical prerequisite first step before stakeholders consider implementation.
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Affiliation(s)
- Adam E M Eltorai
- Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Suzannah E McKinney
- Data Science Office, Mass General Brigham, Boston, MA, United States of America
| | | | - Saby Karuppiah
- Department of Family Medicine, HCA Healthcare, Kansas City, MO, United States of America
| | - Bernardo C Bizzo
- Data Science Office, Mass General Brigham, Boston, MA, United States of America
| | - Katherine P Andriole
- Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States of America; Data Science Office, Mass General Brigham, Boston, MA, United States of America.
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10
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Chang Y, Yoon SH, Kwon R, Kang J, Kim YH, Kim JM, Chung HJ, Choi J, Jung HS, Lim GY, Ahn J, Wild SH, Byrne CD, Ryu S. Automated Comprehensive CT Assessment of the Risk of Diabetes and Associated Cardiometabolic Conditions. Radiology 2024; 312:e233410. [PMID: 39105639 DOI: 10.1148/radiol.233410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/07/2024]
Abstract
Background CT performed for various clinical indications has the potential to predict cardiometabolic diseases. However, the predictive ability of individual CT parameters remains underexplored. Purpose To evaluate the ability of automated CT-derived markers to predict diabetes and associated cardiometabolic comorbidities. Materials and Methods This retrospective study included Korean adults (age ≥ 25 years) who underwent health screening with fluorine 18 fluorodeoxyglucose PET/CT between January 2012 and December 2015. Fully automated CT markers included visceral and subcutaneous fat, muscle, bone density, liver fat, all normalized to height (in meters squared), and aortic calcification. Predictive performance was assessed with area under the receiver operating characteristic curve (AUC) and Harrell C-index in the cross-sectional and survival analyses, respectively. Results The cross-sectional and cohort analyses included 32166 (mean age, 45 years ± 6 [SD], 28833 men) and 27 298 adults (mean age, 44 years ± 5 [SD], 24 820 men), respectively. Diabetes prevalence and incidence was 6% at baseline and 9% during the 7.3-year median follow-up, respectively. Visceral fat index showed the highest predictive performance for prevalent and incident diabetes, yielding AUC of 0.70 (95% CI: 0.68, 0.71) for men and 0.82 (95% CI: 0.78, 0.85) for women and C-index of 0.68 (95% CI: 0.67, 0.69) for men and 0.82 (95% CI: 0.77, 0.86) for women, respectively. Combining visceral fat, muscle area, liver fat fraction, and aortic calcification improved predictive performance, yielding C-indexes of 0.69 (95% CI: 0.68, 0.71) for men and 0.83 (95% CI: 0.78, 0.87) for women. The AUC for visceral fat index in identifying metabolic syndrome was 0.81 (95% CI: 0.80, 0.81) for men and 0.90 (95% CI: 0.88, 0.91) for women. CT-derived markers also identified US-diagnosed fatty liver, coronary artery calcium scores greater than 100, sarcopenia, and osteoporosis, with AUCs ranging from 0.80 to 0.95. Conclusion Automated multiorgan CT analysis identified individuals at high risk of diabetes and other cardiometabolic comorbidities. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Pickhardt in this issue.
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Affiliation(s)
- Yoosoo Chang
- From the Center for Cohort Studies (Y.C., R.K., J.K., J.H.C., H.S.J., G.Y.L., J.A., S.R.) and Department of Occupational and Environmental Medicine (Y.C., S.R.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea (Y.C., S.R.); Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Republic of Korea (S.H.Y.); Research & Science Division, MEDICAL IP, Seoul, Republic of Korea (J.M.K., H.J.C.); Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea (R.K., G.Y.L.); Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.H.K.); Usher Institute, University of Edinburgh, Edinburgh, United Kingdom (S.H.W.); Department of Nutrition and Metabolism, University of Southampton Faculty of Medicine, Southampton, United Kingdom (C.D.B.); and National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom (C.D.B.)
| | - Soon Ho Yoon
- From the Center for Cohort Studies (Y.C., R.K., J.K., J.H.C., H.S.J., G.Y.L., J.A., S.R.) and Department of Occupational and Environmental Medicine (Y.C., S.R.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea (Y.C., S.R.); Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Republic of Korea (S.H.Y.); Research & Science Division, MEDICAL IP, Seoul, Republic of Korea (J.M.K., H.J.C.); Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea (R.K., G.Y.L.); Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.H.K.); Usher Institute, University of Edinburgh, Edinburgh, United Kingdom (S.H.W.); Department of Nutrition and Metabolism, University of Southampton Faculty of Medicine, Southampton, United Kingdom (C.D.B.); and National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom (C.D.B.)
| | - Ria Kwon
- From the Center for Cohort Studies (Y.C., R.K., J.K., J.H.C., H.S.J., G.Y.L., J.A., S.R.) and Department of Occupational and Environmental Medicine (Y.C., S.R.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea (Y.C., S.R.); Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Republic of Korea (S.H.Y.); Research & Science Division, MEDICAL IP, Seoul, Republic of Korea (J.M.K., H.J.C.); Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea (R.K., G.Y.L.); Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.H.K.); Usher Institute, University of Edinburgh, Edinburgh, United Kingdom (S.H.W.); Department of Nutrition and Metabolism, University of Southampton Faculty of Medicine, Southampton, United Kingdom (C.D.B.); and National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom (C.D.B.)
| | - Jeonggyu Kang
- From the Center for Cohort Studies (Y.C., R.K., J.K., J.H.C., H.S.J., G.Y.L., J.A., S.R.) and Department of Occupational and Environmental Medicine (Y.C., S.R.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea (Y.C., S.R.); Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Republic of Korea (S.H.Y.); Research & Science Division, MEDICAL IP, Seoul, Republic of Korea (J.M.K., H.J.C.); Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea (R.K., G.Y.L.); Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.H.K.); Usher Institute, University of Edinburgh, Edinburgh, United Kingdom (S.H.W.); Department of Nutrition and Metabolism, University of Southampton Faculty of Medicine, Southampton, United Kingdom (C.D.B.); and National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom (C.D.B.)
| | - Young Hwan Kim
- From the Center for Cohort Studies (Y.C., R.K., J.K., J.H.C., H.S.J., G.Y.L., J.A., S.R.) and Department of Occupational and Environmental Medicine (Y.C., S.R.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea (Y.C., S.R.); Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Republic of Korea (S.H.Y.); Research & Science Division, MEDICAL IP, Seoul, Republic of Korea (J.M.K., H.J.C.); Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea (R.K., G.Y.L.); Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.H.K.); Usher Institute, University of Edinburgh, Edinburgh, United Kingdom (S.H.W.); Department of Nutrition and Metabolism, University of Southampton Faculty of Medicine, Southampton, United Kingdom (C.D.B.); and National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom (C.D.B.)
| | - Jong-Min Kim
- From the Center for Cohort Studies (Y.C., R.K., J.K., J.H.C., H.S.J., G.Y.L., J.A., S.R.) and Department of Occupational and Environmental Medicine (Y.C., S.R.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea (Y.C., S.R.); Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Republic of Korea (S.H.Y.); Research & Science Division, MEDICAL IP, Seoul, Republic of Korea (J.M.K., H.J.C.); Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea (R.K., G.Y.L.); Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.H.K.); Usher Institute, University of Edinburgh, Edinburgh, United Kingdom (S.H.W.); Department of Nutrition and Metabolism, University of Southampton Faculty of Medicine, Southampton, United Kingdom (C.D.B.); and National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom (C.D.B.)
| | - Han-Jae Chung
- From the Center for Cohort Studies (Y.C., R.K., J.K., J.H.C., H.S.J., G.Y.L., J.A., S.R.) and Department of Occupational and Environmental Medicine (Y.C., S.R.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea (Y.C., S.R.); Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Republic of Korea (S.H.Y.); Research & Science Division, MEDICAL IP, Seoul, Republic of Korea (J.M.K., H.J.C.); Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea (R.K., G.Y.L.); Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.H.K.); Usher Institute, University of Edinburgh, Edinburgh, United Kingdom (S.H.W.); Department of Nutrition and Metabolism, University of Southampton Faculty of Medicine, Southampton, United Kingdom (C.D.B.); and National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom (C.D.B.)
| | - JunHyeok Choi
- From the Center for Cohort Studies (Y.C., R.K., J.K., J.H.C., H.S.J., G.Y.L., J.A., S.R.) and Department of Occupational and Environmental Medicine (Y.C., S.R.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea (Y.C., S.R.); Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Republic of Korea (S.H.Y.); Research & Science Division, MEDICAL IP, Seoul, Republic of Korea (J.M.K., H.J.C.); Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea (R.K., G.Y.L.); Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.H.K.); Usher Institute, University of Edinburgh, Edinburgh, United Kingdom (S.H.W.); Department of Nutrition and Metabolism, University of Southampton Faculty of Medicine, Southampton, United Kingdom (C.D.B.); and National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom (C.D.B.)
| | - Hyun-Suk Jung
- From the Center for Cohort Studies (Y.C., R.K., J.K., J.H.C., H.S.J., G.Y.L., J.A., S.R.) and Department of Occupational and Environmental Medicine (Y.C., S.R.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea (Y.C., S.R.); Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Republic of Korea (S.H.Y.); Research & Science Division, MEDICAL IP, Seoul, Republic of Korea (J.M.K., H.J.C.); Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea (R.K., G.Y.L.); Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.H.K.); Usher Institute, University of Edinburgh, Edinburgh, United Kingdom (S.H.W.); Department of Nutrition and Metabolism, University of Southampton Faculty of Medicine, Southampton, United Kingdom (C.D.B.); and National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom (C.D.B.)
| | - Ga-Young Lim
- From the Center for Cohort Studies (Y.C., R.K., J.K., J.H.C., H.S.J., G.Y.L., J.A., S.R.) and Department of Occupational and Environmental Medicine (Y.C., S.R.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea (Y.C., S.R.); Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Republic of Korea (S.H.Y.); Research & Science Division, MEDICAL IP, Seoul, Republic of Korea (J.M.K., H.J.C.); Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea (R.K., G.Y.L.); Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.H.K.); Usher Institute, University of Edinburgh, Edinburgh, United Kingdom (S.H.W.); Department of Nutrition and Metabolism, University of Southampton Faculty of Medicine, Southampton, United Kingdom (C.D.B.); and National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom (C.D.B.)
| | - Jiin Ahn
- From the Center for Cohort Studies (Y.C., R.K., J.K., J.H.C., H.S.J., G.Y.L., J.A., S.R.) and Department of Occupational and Environmental Medicine (Y.C., S.R.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea (Y.C., S.R.); Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Republic of Korea (S.H.Y.); Research & Science Division, MEDICAL IP, Seoul, Republic of Korea (J.M.K., H.J.C.); Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea (R.K., G.Y.L.); Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.H.K.); Usher Institute, University of Edinburgh, Edinburgh, United Kingdom (S.H.W.); Department of Nutrition and Metabolism, University of Southampton Faculty of Medicine, Southampton, United Kingdom (C.D.B.); and National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom (C.D.B.)
| | - Sarah H Wild
- From the Center for Cohort Studies (Y.C., R.K., J.K., J.H.C., H.S.J., G.Y.L., J.A., S.R.) and Department of Occupational and Environmental Medicine (Y.C., S.R.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea (Y.C., S.R.); Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Republic of Korea (S.H.Y.); Research & Science Division, MEDICAL IP, Seoul, Republic of Korea (J.M.K., H.J.C.); Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea (R.K., G.Y.L.); Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.H.K.); Usher Institute, University of Edinburgh, Edinburgh, United Kingdom (S.H.W.); Department of Nutrition and Metabolism, University of Southampton Faculty of Medicine, Southampton, United Kingdom (C.D.B.); and National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom (C.D.B.)
| | - Christopher D Byrne
- From the Center for Cohort Studies (Y.C., R.K., J.K., J.H.C., H.S.J., G.Y.L., J.A., S.R.) and Department of Occupational and Environmental Medicine (Y.C., S.R.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea (Y.C., S.R.); Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Republic of Korea (S.H.Y.); Research & Science Division, MEDICAL IP, Seoul, Republic of Korea (J.M.K., H.J.C.); Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea (R.K., G.Y.L.); Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.H.K.); Usher Institute, University of Edinburgh, Edinburgh, United Kingdom (S.H.W.); Department of Nutrition and Metabolism, University of Southampton Faculty of Medicine, Southampton, United Kingdom (C.D.B.); and National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom (C.D.B.)
| | - Seungho Ryu
- From the Center for Cohort Studies (Y.C., R.K., J.K., J.H.C., H.S.J., G.Y.L., J.A., S.R.) and Department of Occupational and Environmental Medicine (Y.C., S.R.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea (Y.C., S.R.); Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Republic of Korea (S.H.Y.); Research & Science Division, MEDICAL IP, Seoul, Republic of Korea (J.M.K., H.J.C.); Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea (R.K., G.Y.L.); Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.H.K.); Usher Institute, University of Edinburgh, Edinburgh, United Kingdom (S.H.W.); Department of Nutrition and Metabolism, University of Southampton Faculty of Medicine, Southampton, United Kingdom (C.D.B.); and National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom (C.D.B.)
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11
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Chatterjee N, Duda J, Gee J, Elahi A, Martin K, Doan V, Liu H, Maclean M, Rader D, Borthakur A, Kahn C, Sagreiya H, Witschey W. A Cloud-Based System for Automated AI Image Analysis and Reporting. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01200-z. [PMID: 39085717 DOI: 10.1007/s10278-024-01200-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 06/01/2024] [Accepted: 07/05/2024] [Indexed: 08/02/2024]
Abstract
Although numerous AI algorithms have been published, the relatively small number of algorithms used clinically is partly due to the difficulty of implementing AI seamlessly into the clinical workflow for radiologists and for their healthcare enterprise. The authors developed an AI orchestrator to facilitate the deployment and use of AI tools in a large multi-site university healthcare system and used it to conduct opportunistic screening for hepatic steatosis. During the 60-day study period, 991 abdominal CTs were processed at multiple different physical locations with an average turnaround time of 2.8 min. Quality control images and AI results were fully integrated into the existing clinical workflow. All input into and output from the server was in standardized data formats. The authors describe the methodology in detail; this framework can be adapted to integrate any clinical AI algorithm.
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Affiliation(s)
- Neil Chatterjee
- Department of Radiology, University of Pennsylvania, Philadelphia, USA.
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, USA.
| | - Jeffrey Duda
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - James Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
- Perlman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Ameena Elahi
- Department of Information Services, University of Pennsylvania, Philadelphia, USA
| | - Kristen Martin
- Department of Information Services, University of Pennsylvania, Philadelphia, USA
| | - Van Doan
- Department of Information Services, University of Pennsylvania, Philadelphia, USA
| | - Hannah Liu
- Department of Bioengineering, University of Pennsylvania, Philadelphia, USA
| | - Matthew Maclean
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Daniel Rader
- Department of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Arijitt Borthakur
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Charles Kahn
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
- Perlman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Hersh Sagreiya
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
- Perlman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Walter Witschey
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
- Perlman School of Medicine, University of Pennsylvania, Philadelphia, USA
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12
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Müller L. AI-based body composition measures in abdominal CT scans: prime time for clinical implementation? Eur Radiol 2024:10.1007/s00330-024-10936-9. [PMID: 38995386 DOI: 10.1007/s00330-024-10936-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 06/03/2024] [Accepted: 06/10/2024] [Indexed: 07/13/2024]
Affiliation(s)
- Lukas Müller
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
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13
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Ko HS, Attenberger U. Medical imaging in cancer cachexia. RADIOLOGIE (HEIDELBERG, GERMANY) 2024:10.1007/s00117-024-01346-5. [PMID: 38995346 DOI: 10.1007/s00117-024-01346-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/18/2024] [Indexed: 07/13/2024]
Abstract
Cancer cachexia, often referred to as "wasting syndrome," is characterized by fatigue, weakness, and involuntary weight loss. This syndrome is concomitant with progressive skeletal muscle atrophy with or without adipose tissue loss and is frequently accompanied by systemic inflammation. Understanding the complexities of cancer cachexia is crucial for early detection and intervention, and it is also paramount for enhancing patient outcomes. Medical imaging, comprising diverse imaging modalities, plays a pivotal role in this context, facilitating the diagnosis and surveillance assessment of both the disease extent and the body composition changes that offer valuable information and insights into disease progression. This article provides a comprehensive discourse of the pathophysiological mechanisms and clinical manifestations of cancer cachexia as well as the role of medical imaging in this setting. Particular emphasis is placed on contemporary multidisciplinary and translational research efforts for the development of diagnostic and treatment tools, aiming to mitigate the devastating consequences of cancer cachexia.
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Affiliation(s)
- Hyun Soo Ko
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
| | - Ulrike Attenberger
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
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14
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Linguraru MG, Bakas S, Aboian M, Chang PD, Flanders AE, Kalpathy-Cramer J, Kitamura FC, Lungren MP, Mongan J, Prevedello LM, Summers RM, Wu CC, Adewole M, Kahn CE. Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts. Radiol Artif Intell 2024; 6:e240225. [PMID: 38984986 PMCID: PMC11294958 DOI: 10.1148/ryai.240225] [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: 04/13/2024] [Revised: 04/13/2024] [Accepted: 04/25/2024] [Indexed: 07/11/2024]
Abstract
The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and seminars focused on the present impact and future directions of artificial intelligence (AI) in radiology. These conversations have collected viewpoints from multidisciplinary experts in radiology, medical imaging, and machine learning on the current clinical penetration of AI technology in radiology and how it is impacted by trust, reproducibility, explainability, and accountability. The collective points-both practical and philosophical-define the cultural changes for radiologists and AI scientists working together and describe the challenges ahead for AI technologies to meet broad approval. This article presents the perspectives of experts from MICCAI and RSNA on the clinical, cultural, computational, and regulatory considerations-coupled with recommended reading materials-essential to adopt AI technology successfully in radiology and, more generally, in clinical practice. The report emphasizes the importance of collaboration to improve clinical deployment, highlights the need to integrate clinical and medical imaging data, and introduces strategies to ensure smooth and incentivized integration. Keywords: Adults and Pediatrics, Computer Applications-General (Informatics), Diagnosis, Prognosis © RSNA, 2024.
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Affiliation(s)
- Marius George Linguraru
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Spyridon Bakas
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Mariam Aboian
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Peter D. Chang
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Adam E. Flanders
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Jayashree Kalpathy-Cramer
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Felipe C. Kitamura
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Matthew P. Lungren
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - John Mongan
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Luciano M. Prevedello
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Ronald M. Summers
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Carol C. Wu
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Maruf Adewole
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Charles E. Kahn
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
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15
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Fumagalli IA, Le ST, Peng PD, Kipnis P, Liu VX, Caan B, Chow V, Beg MF, Popuri K, Cespedes Feliciano EM. Automated CT Analysis of Body Composition as a Frailty Biomarker in Abdominal Surgery. JAMA Surg 2024; 159:766-774. [PMID: 38598191 PMCID: PMC11007659 DOI: 10.1001/jamasurg.2024.0628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 01/13/2024] [Indexed: 04/11/2024]
Abstract
Importance Prior studies demonstrated consistent associations of low skeletal muscle mass assessed on surgical planning scans with postoperative morbidity and mortality. The increasing availability of imaging artificial intelligence enables development of more comprehensive imaging biomarkers to objectively phenotype frailty in surgical patients. Objective To evaluate the associations of body composition scores derived from multiple skeletal muscle and adipose tissue measurements from automated segmentation of computed tomography (CT) with the Hospital Frailty Risk Score (HFRS) and adverse outcomes after abdominal surgery. Design, Setting, and Participants This retrospective cohort study used CT imaging and electronic health record data from a random sample of adults who underwent abdominal surgery at 20 medical centers within Kaiser Permanente Northern California from January 1, 2010, to December 31, 2020. Data were analyzed from April 1, 2022, to December 1, 2023. Exposure Body composition derived from automated analysis of multislice abdominal CT scans. Main Outcomes and Measures The primary outcome of the study was all-cause 30-day postdischarge readmission or postoperative mortality. The secondary outcome was 30-day postoperative morbidity among patients undergoing abdominal surgery who were sampled for reporting to the National Surgical Quality Improvement Program. Results The study included 48 444 adults; mean [SD] age at surgery was 61 (17) years, and 51% were female. Using principal component analysis, 3 body composition scores were derived: body size, muscle quantity and quality, and distribution of adiposity. Higher muscle quantity and quality scores were inversely correlated (r = -0.42; 95% CI, -0.43 to -0.41) with the HFRS and associated with a reduced risk of 30-day readmission or mortality (quartile 4 vs quartile 1: relative risk, 0.61; 95% CI, 0.56-0.67) and 30-day postoperative morbidity (quartile 4 vs quartile 1: relative risk, 0.59; 95% CI, 0.52-0.67), independent of sex, age, comorbidities, body mass index, procedure characteristics, and the HFRS. In contrast to the muscle score, scores for body size and greater subcutaneous and intermuscular vs visceral adiposity had inconsistent associations with postsurgical outcomes and were attenuated and only associated with 30-day postoperative morbidity after adjustment for the HFRS. Conclusions and Relevance In this study, higher muscle quantity and quality scores were correlated with frailty and associated with 30-day readmission and postoperative mortality and morbidity, whereas body size and adipose tissue distribution scores were not correlated with patient frailty and had inconsistent associations with surgical outcomes. The findings suggest that assessment of muscle quantity and quality on CT can provide an objective measure of patient frailty that would not otherwise be clinically apparent and that may complement existing risk stratification tools to identify patients at high risk of mortality, morbidity, and readmission.
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Affiliation(s)
| | - Sidney T. Le
- Division of Research, Kaiser Permanente Northern California, Oakland
- Department of Surgery, University of California San Francisco–East Bay, Oakland
| | | | - Patricia Kipnis
- Division of Research, Kaiser Permanente Northern California, Oakland
- The Permanente Medical Group, Oakland, California
| | - Vincent X. Liu
- Division of Research, Kaiser Permanente Northern California, Oakland
- The Permanente Medical Group, Oakland, California
| | - Bette Caan
- Division of Research, Kaiser Permanente Northern California, Oakland
| | - Vincent Chow
- School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Mirza Faisal Beg
- School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Karteek Popuri
- Department of Computer Science, Faculty of Science, Memorial University of Newfoundland, St John’s, Newfoundland and Labrador, Canada
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16
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Pickhardt PJ, Blake GM, Moeller A, Garrett JW, Summers RM. Post-contrast CT liver attenuation alone is superior to the liver-spleen difference for identifying moderate hepatic steatosis. Eur Radiol 2024:10.1007/s00330-024-10816-2. [PMID: 38834787 DOI: 10.1007/s00330-024-10816-2] [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: 01/06/2024] [Revised: 04/05/2024] [Accepted: 04/20/2024] [Indexed: 06/06/2024]
Abstract
OBJECTIVE To assess the diagnostic performance of post-contrast CT for predicting moderate hepatic steatosis in an older adult cohort undergoing a uniform CT protocol, utilizing hepatic and splenic attenuation values. MATERIALS AND METHODS A total of 1676 adults (mean age, 68.4 ± 10.2 years; 1045M/631F) underwent a CT urothelial protocol that included unenhanced, portal venous, and 10-min delayed phases through the liver and spleen. Automated hepatosplenic segmentation for attenuation values (in HU) was performed using a validated deep-learning tool. Unenhanced liver attenuation < 40.0 HU, corresponding to > 15% MRI-based proton density fat, served as the reference standard for moderate steatosis. RESULTS The prevalence of moderate or severe steatosis was 12.9% (216/1676). The diagnostic performance of portal venous liver HU in predicting moderate hepatic steatosis (AUROC = 0.943) was significantly better than the liver-spleen HU difference (AUROC = 0.814) (p < 0.001). Portal venous phase liver thresholds of 80 and 90 HU had a sensitivity/specificity for moderate steatosis of 85.6%/89.6%, and 94.9%/74.7%, respectively, whereas a liver-spleen difference of -40 HU and -10 HU had a sensitivity/specificity of 43.5%/90.0% and 92.1%/52.5%, respectively. Furthermore, livers with moderate-severe steatosis demonstrated significantly less post-contrast enhancement (mean, 35.7 HU vs 47.3 HU; p < 0.001). CONCLUSION Moderate steatosis can be reliably diagnosed on standard portal venous phase CT using liver attenuation values alone. Consideration of splenic attenuation appears to add little value. Moderate steatosis not only has intrinsically lower pre-contrast liver attenuation values (< 40 HU), but also enhances less, typically resulting in post-contrast liver attenuation values of 80 HU or less. CLINICAL RELEVANCE STATEMENT Moderate steatosis can be reliably diagnosed on post-contrast CT using liver attenuation values alone. Livers with at least moderate steatosis enhance less than those with mild or no steatosis, which combines with the lower intrinsic attenuation to improve detection. KEY POINTS The liver-spleen attenuation difference is frequently utilized in routine practice but appears to have performance limitations. The liver-spleen attenuation difference is less effective than liver attenuation for moderate steatosis. Moderate and severe steatosis can be identified on standard portal venous phase CT using liver attenuation alone.
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Affiliation(s)
- Perry J Pickhardt
- The University of Wisconsin School of Medicine & Public Health, Madison, WI, USA.
| | - Glen M Blake
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Alex Moeller
- The University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - John W Garrett
- The University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
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17
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Hanneman K, Gulsin GS. Noninvasive Pressure-Volume Loops: Can Cardiac MRI Obviate the Need for Invasive Catheter Hemodynamic Measurements? JACC. ADVANCES 2024; 3:101000. [PMID: 38938855 PMCID: PMC11198569 DOI: 10.1016/j.jacadv.2024.101000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Affiliation(s)
- Kate Hanneman
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
- Joint Department of Medical Imaging, University Health Network (UHN), University Medical Imaging Toronto, Toronto, Ontario, Canada
- Toronto General Hospital Research Institute, University Health Network (UHN), University of Toronto, Toronto, Ontario, Canada
| | - Gaurav S. Gulsin
- Department of Cardiovascular Sciences, University of Leicester and the NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
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18
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Brady AP, Allen B, Chong J, Kotter E, Kottler N, Mongan J, Oakden-Rayner L, Dos Santos DP, Tang A, Wald C, Slavotinek J. Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA. Can Assoc Radiol J 2024; 75:226-244. [PMID: 38251882 DOI: 10.1177/08465371231222229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024] Open
Abstract
Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.
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Affiliation(s)
| | - Bibb Allen
- Department of Radiology, Grandview Medical Center, Birmingham, AL, USA
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Jaron Chong
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Elmar Kotter
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Nina Kottler
- Radiology Partners, El Segundo, CA, USA
- Stanford Center for Artificial Intelligence in Medicine & Imaging, Palo Alto, CA, USA
| | - John Mongan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany
| | - An Tang
- Department of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montréal, QC, Canada
| | - Christoph Wald
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA
- Tufts University Medical School, Boston, MA, USA
- American College of Radiology, Reston, VA, USA
| | - John Slavotinek
- South Australia Medical Imaging, Flinders Medical Centre Adelaide, SA, Australia
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
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19
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Ghosh A. B-mode US and Deep Learning Rivals Shear-Wave Elastography in Screening for Fibrosis. Radiology 2024; 311:e240868. [PMID: 38652032 DOI: 10.1148/radiol.240868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Affiliation(s)
- Adarsh Ghosh
- From the Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH 45229
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20
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Otero MF, Tahoces PG, Mera A, Dalstra M, Verna C, Pedersen TK, Herlin T, Mira J. Quantification of temporomandibular joint space in patients with juvenile idiopathic arthritis assessed by cone beam computerized tomography. Orthod Craniofac Res 2024; 27:203-210. [PMID: 37525623 DOI: 10.1111/ocr.12702] [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: 05/02/2023] [Revised: 07/17/2023] [Accepted: 07/18/2023] [Indexed: 08/02/2023]
Abstract
OBJECTIVE To describe a method to calculate the total intra-articular volume (inter-osseous space) of the temporomandibular joint (TMJ) determined by cone-beam computed tomography (CBCT). This could be used as a marker of tissue proliferation and different degrees of soft tissue hyperplasia in juvenile idiopathic arthritis (JIA) patients. MATERIALS AND METHODS Axial single-slice CBCT images of cross-sections of the TMJs of 11 JIA patients and 11 controls were employed. From the top of the glenoid fossa, in the caudal direction, an average of 26 slices were defined in each joint (N = 44). The interosseous space was manually delimited from each slice by using dedicated software that includes a graphic interface. TMJ volumes were calculated by adding the areas measured in each slice. Two volumes were defined: Ve-i and Vi , where Ve-i is the inter-osseous space, volume defined by the borders of the fossa and Vi is the internal volume defined by the condyle. An intra-articular volume filling index (IF) was defined as Ve-i /Vi , which represents the filling of the space. RESULTS The measured space of the intra-articular volume, corresponding to the intra-articular soft tissue and synovial fluid, was more than twice as large in the JIA group as in the control group. CONCLUSION The presented method, based on CBCT, is feasible for assessing inter-osseus joint volume of the TMJ and delimits a threshold of intra-articular changes related to intra-articular soft tissue proliferation, based on differences in volumes. Intra-articular soft tissue is found to be enlarged in JIA patients.
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Affiliation(s)
- María Florinda Otero
- Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Pablo G Tahoces
- Departamento de Electrónica e Computación, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Antonio Mera
- Rheumatology Service, Hospital Clínico Universitario, Santiago de Compostela, Spain
| | - Michel Dalstra
- Section of Orthodontics, Department of Dentistry and Oral Health, Aarhus University, Aarhus, Denmark
| | - Carlalberta Verna
- Department of Orthodontics and Pediatric Dentistry, University Center for Dental Medicine, Basel, Switzerland
| | - Thomas Klit Pedersen
- Section of Orthodontics, Department of Dentistry and Oral Health, Aarhus University, Aarhus, Denmark
- Department of Oral and Maxillofacial Surgery, Aarhus University Hospital, Aarhus, Denmark
| | - Troels Herlin
- Department of Pediatrics, Aarhus University Hospital, Aarhus, Denmark
| | - Jorge Mira
- Departamento de Física Aplicada and Instituto de Materiais (iMATUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
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21
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Liu D, Garrett JW, Perez AA, Zea R, Binkley NC, Summers RM, Pickhardt PJ. Fully automated CT imaging biomarkers for opportunistic prediction of future hip fractures. Br J Radiol 2024; 97:770-778. [PMID: 38379423 PMCID: PMC11027263 DOI: 10.1093/bjr/tqae041] [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: 04/05/2023] [Revised: 09/27/2023] [Accepted: 02/19/2024] [Indexed: 02/22/2024] Open
Abstract
OBJECTIVE Assess automated CT imaging biomarkers in patients who went on to hip fracture, compared with controls. METHODS In this retrospective case-control study, 6926 total patients underwent initial abdominal CT over a 20-year interval at one institution. A total of 1308 patients (mean age at initial CT, 70.5 ± 12.0 years; 64.4% female) went on to hip fracture (mean time to fracture, 5.2 years); 5618 were controls (mean age 70.3 ± 12.0 years; 61.2% female; mean follow-up interval 7.6 years). Validated fully automated quantitative CT algorithms for trabecular bone attenuation (at L1), skeletal muscle attenuation (at L3), and subcutaneous adipose tissue area (SAT) (at L3) were applied to all scans. Hazard ratios (HRs) comparing highest to lowest risk quartiles and receiver operating characteristic (ROC) curve analysis including area under the curve (AUC) were derived. RESULTS Hip fracture HRs (95% CI) were 3.18 (2.69-3.76) for low trabecular bone HU, 1.50 (1.28-1.75) for low muscle HU, and 2.18 (1.86-2.56) for low SAT. 10-year ROC AUC values for predicting hip fracture were 0.702, 0.603, and 0.603 for these CT-based biomarkers, respectively. Multivariate combinations of these biomarkers further improved predictive value; the 10-year ROC AUC combining bone/muscle/SAT was 0.733, while combining muscle/SAT was 0.686. CONCLUSION Opportunistic use of automated CT bone, muscle, and fat measures can identify patients at higher risk for future hip fracture, regardless of the indication for CT imaging. ADVANCES IN KNOWLEDGE CT data can be leveraged opportunistically for further patient evaluation, with early intervention as needed. These novel AI tools analyse CT data to determine a patient's future hip fracture risk.
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Affiliation(s)
- Daniel Liu
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
| | - John W Garrett
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
| | - Alberto A Perez
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
| | - Ryan Zea
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
| | - Neil C Binkley
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Potomac, MD, 20892, United States
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
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22
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Lin C, Kuo FC, Chau T, Shih JH, Lin CS, Chen CC, Lee CC, Lin SH. Artificial intelligence-enabled electrocardiography contributes to hyperthyroidism detection and outcome prediction. COMMUNICATIONS MEDICINE 2024; 4:42. [PMID: 38472334 DOI: 10.1038/s43856-024-00472-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 03/01/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Hyperthyroidism is frequently under-recognized and leads to heart failure and mortality. Timely identification of high-risk patients is a prerequisite to effective antithyroid therapy. Since the heart is very sensitive to hyperthyroidism and its electrical signature can be demonstrated by electrocardiography, we developed an artificial intelligence model to detect hyperthyroidism by electrocardiography and examined its potential for outcome prediction. METHODS The deep learning model was trained using a large dataset of 47,245 electrocardiograms from 33,246 patients at an academic medical center. Patients were included if electrocardiograms and measurements of serum thyroid-stimulating hormone were available that had been obtained within a three day period. Serum thyroid-stimulating hormone and free thyroxine were used to define overt and subclinical hyperthyroidism. We tested the model internally using 14,420 patients and externally using two additional test sets comprising 11,498 and 596 patients, respectively. RESULTS The performance of the deep learning model achieves areas under the receiver operating characteristic curves (AUCs) of 0.725-0.761 for hyperthyroidism detection, AUCs of 0.867-0.876 for overt hyperthyroidism, and AUC of 0.631-0.701 for subclinical hyperthyroidism, superior to a traditional features-based machine learning model. Patients identified as hyperthyroidism-positive by the deep learning model have a significantly higher risk (1.97-2.94 fold) of all-cause mortality and new-onset heart failure compared to hyperthyroidism-negative patients. This cardiovascular disease stratification is particularly pronounced in subclinical hyperthyroidism, surpassing that observed in overt hyperthyroidism. CONCLUSIONS An innovative algorithm effectively identifies overt and subclinical hyperthyroidism and contributes to cardiovascular risk assessment.
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Affiliation(s)
- Chin Lin
- School of Medicine, National Defense Medical Center, Taipei, Taiwan ROC
- Graduate Institute of Aerospace and Undersea Medicine, National Defense Medical Center, Taipei, Taiwan ROC
| | - Feng-Chih Kuo
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC
| | - Tom Chau
- Department of Medicine, Providence St. Vincent Medical Center, Portland, OR, USA
| | - Jui-Hu Shih
- Department of Pharmacy Practice, Tri-Service General Hospital, Taipei, Taiwan ROC
- School of Pharmacy, National Defense Medical Center, Taipei, Taiwan ROC
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC
| | - Chien-Chou Chen
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC
| | - Chia-Cheng Lee
- Department of Medical Informatics, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC
| | - Shih-Hua Lin
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC.
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Pickhardt PJ. Invited Commentary: Metabolic Syndrome: The Urgent Need for an Imaging-based Definition. Radiographics 2024; 44:e230230. [PMID: 38329902 DOI: 10.1148/rg.230230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Affiliation(s)
- Perry J Pickhardt
- From the Department of Radiology, The University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252
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24
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Lee MH, Liu D, Garrett JW, Perez A, Zea R, Summers RM, Pickhardt PJ. Comparing fully automated AI body composition measures derived from thin and thick slice CT image data. Abdom Radiol (NY) 2024; 49:985-996. [PMID: 38158424 DOI: 10.1007/s00261-023-04135-1] [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: 07/20/2023] [Revised: 11/17/2023] [Accepted: 11/22/2023] [Indexed: 01/03/2024]
Abstract
PURPOSE To compare fully automated artificial intelligence body composition measures derived from thin (1.25 mm) and thick (5 mm) slice abdominal CT data. METHODS In this retrospective study, fully automated CT-based body composition algorithms for quantifying bone attenuation, muscle attenuation, muscle area, liver attenuation, liver volume, spleen volume, visceral-to-subcutaneous fat ratio (VSR) and aortic calcium were applied to both thin (1.25 × 0.625 mm) and thick (5 × 3 mm) abdominal CT series from two patient cohorts: unenhanced scans in asymptomatic adults undergoing colorectal cancer screening, and post-contrast scans in patients with colorectal cancer. Body composition measures derived from thin and thick slice data were compared, including correlation coefficients and Bland-Altman analysis. RESULTS A total of 9882 CT scans (mean age, 57.0 years; 4527 women, 5355 men) were evaluated, including 8947 non-contrast and 935 contrast-enhanced CT exams. Very strong positive correlation was observed for all soft tissue measures: muscle attenuation (r2 = 0.97), muscle area (r2 = 0.98), liver attenuation (r2 = 0.99), liver volume (r2 = 0.98) and spleen volume (r2 = 0.99), VSR (r2 = 0.98), and aortic calcium (r2 = 0.92); (p < 0.001 for all). Moderate positive correlation was observed for bone attenuation (r2 = 0.35). Bland-Altman analysis showed strong agreement for muscle attenuation, muscle area, liver attenuation, liver volume and spleen volume. Mean percentage differences amongst body composition measures were less than 5% for VSR (4.6%), muscle area (- 0.5%), liver attenuation (0.4%) and liver volume (2.7%) and less than 10% for muscle attenuation (- 5.5%) and spleen volume (5.1%). For aortic calcium, thick slice overestimated for Agatston scores between 0 and 100 and > 400 burden in 3.1% and 0.3% relative to thin slice, respectively, but underestimated scores between 100 and 400. CONCLUSION Automated body composition measures derived from thin and thick abdominal CT data are strongly correlated and show agreement, particularly for soft tissue applications, making it feasible to use either series for these CT-based body composition algorithms.
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Affiliation(s)
- Matthew H Lee
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI, 53792, USA.
| | - Daniel Liu
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - John W Garrett
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - Alberto Perez
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - Ryan Zea
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI, 53792, USA
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25
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Ko HS, Denehy L, Edbrooke L, Albarqouni S, Attenberger U, Parker BL, Cox A, Le B, Cheng L. Enhancing oncological care: A guide to setting up a new multidisciplinary cancer cachexia clinic within a tertiary centre. J Cachexia Sarcopenia Muscle 2024; 15:4-7. [PMID: 37964737 PMCID: PMC10834344 DOI: 10.1002/jcsm.13360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2023] Open
Affiliation(s)
- Hyun Soo Ko
- Department of Cancer ImagingThe Peter MacCallum Cancer CentreMelbourneVictoriaAustralia
- The Sir Peter MacCallum Department of OncologyThe University of MelbourneParkvilleVictoriaAustralia
- Department of Diagnostic and Interventional RadiologyUniversity Hospital BonnBonnGermany
| | - Linda Denehy
- The Sir Peter MacCallum Department of OncologyThe University of MelbourneParkvilleVictoriaAustralia
- Department of PhysiotherapyThe University of MelbourneParkvilleVictoriaAustralia
- Department of Health Services ResearchThe Peter MacCallum Cancer CentreMelbourneVictoriaAustralia
| | - Lara Edbrooke
- The Sir Peter MacCallum Department of OncologyThe University of MelbourneParkvilleVictoriaAustralia
- Department of PhysiotherapyThe University of MelbourneParkvilleVictoriaAustralia
- Department of Health Services ResearchThe Peter MacCallum Cancer CentreMelbourneVictoriaAustralia
| | - Shadi Albarqouni
- Department of Diagnostic and Interventional RadiologyUniversity Hospital BonnBonnGermany
- Helmholtz Munich, Helmholtz AINeuherbergGermany
| | - Ulrike Attenberger
- Department of Diagnostic and Interventional RadiologyUniversity Hospital BonnBonnGermany
| | - Benjamin L. Parker
- Department of Anatomy and Physiology, Centre for Muscle ResearchThe University of MelbourneParkvilleVictoriaAustralia
| | - Andrew Cox
- The Sir Peter MacCallum Department of OncologyThe University of MelbourneParkvilleVictoriaAustralia
- Department of Biochemistry and PharmacologyThe University of MelbourneMelbourneVictoriaAustralia
| | - Brian Le
- The Sir Peter MacCallum Department of OncologyThe University of MelbourneParkvilleVictoriaAustralia
- Department of Medical OncologyThe Peter MacCallum Cancer CentreMelbourneVictoriaAustralia
- Department of Palliative CareThe Royal Melbourne HospitalParkvilleVictoriaAustralia
| | - Louise Cheng
- Department of Biochemistry and PharmacologyThe University of MelbourneMelbourneVictoriaAustralia
- Cheng LabThe Peter MacCallum Cancer CentreMelbourneVictoriaAustralia
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26
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Doo FX, Vosshenrich J, Cook TS, Moy L, Almeida EP, Woolen SA, Gichoya JW, Heye T, Hanneman K. Environmental Sustainability and AI in Radiology: A Double-Edged Sword. Radiology 2024; 310:e232030. [PMID: 38411520 PMCID: PMC10902597 DOI: 10.1148/radiol.232030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/21/2023] [Accepted: 11/17/2023] [Indexed: 02/28/2024]
Abstract
According to the World Health Organization, climate change is the single biggest health threat facing humanity. The global health care system, including medical imaging, must manage the health effects of climate change while at the same time addressing the large amount of greenhouse gas (GHG) emissions generated in the delivery of care. Data centers and computational efforts are increasingly large contributors to GHG emissions in radiology. This is due to the explosive increase in big data and artificial intelligence (AI) applications that have resulted in large energy requirements for developing and deploying AI models. However, AI also has the potential to improve environmental sustainability in medical imaging. For example, use of AI can shorten MRI scan times with accelerated acquisition times, improve the scheduling efficiency of scanners, and optimize the use of decision-support tools to reduce low-value imaging. The purpose of this Radiology in Focus article is to discuss this duality at the intersection of environmental sustainability and AI in radiology. Further discussed are strategies and opportunities to decrease AI-related emissions and to leverage AI to improve sustainability in radiology, with a focus on health equity. Co-benefits of these strategies are explored, including lower cost and improved patient outcomes. Finally, knowledge gaps and areas for future research are highlighted.
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Affiliation(s)
- Florence X. Doo
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Jan Vosshenrich
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Tessa S. Cook
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Linda Moy
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Eduardo P.R.P. Almeida
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Sean A. Woolen
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Judy Wawira Gichoya
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Tobias Heye
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Kate Hanneman
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
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Brady AP, Allen B, Chong J, Kotter E, Kottler N, Mongan J, Oakden-Rayner L, Pinto Dos Santos D, Tang A, Wald C, Slavotinek J. Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA. J Med Imaging Radiat Oncol 2024; 68:7-26. [PMID: 38259140 DOI: 10.1111/1754-9485.13612] [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: 11/23/2023] [Accepted: 11/23/2023] [Indexed: 01/24/2024]
Abstract
Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.
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Affiliation(s)
| | - Bibb Allen
- Department of Radiology, Grandview Medical Center, Birmingham, Alabama, USA
- American College of Radiology Data Science Institute, Reston, Virginia, USA
| | - Jaron Chong
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Elmar Kotter
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Nina Kottler
- Radiology Partners, El Segundo, California, USA
- Stanford Center for Artificial Intelligence in Medicine & Imaging, Palo Alto, California, USA
| | - John Mongan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, South Australia, Australia
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany
| | - An Tang
- Department of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Christoph Wald
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts, USA
- Tufts University Medical School, Boston, Massachusetts, USA
- Commision On Informatics, and Member, Board of Chancellors, American College of Radiology, Reston, Virginia, USA
| | - John Slavotinek
- South Australia Medical Imaging, Flinders Medical Centre Adelaide, Adelaide, South Australia, Australia
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
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28
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Brady AP, Allen B, Chong J, Kotter E, Kottler N, Mongan J, Oakden-Rayner L, Dos Santos DP, Tang A, Wald C, Slavotinek J. Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA. Insights Imaging 2024; 15:16. [PMID: 38246898 PMCID: PMC10800328 DOI: 10.1186/s13244-023-01541-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024] Open
Abstract
Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.Key points • The incorporation of artificial intelligence (AI) in radiological practice demands increased monitoring of its utility and safety.• Cooperation between developers, clinicians, and regulators will allow all involved to address ethical issues and monitor AI performance.• AI can fulfil its promise to advance patient well-being if all steps from development to integration in healthcare are rigorously evaluated.
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Affiliation(s)
| | - Bibb Allen
- Department of Radiology, Grandview Medical Center, Birmingham, AL, USA
- American College of Radiology Data Science Institute, Reston, VA, USA
| | - Jaron Chong
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Elmar Kotter
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Nina Kottler
- Radiology Partners, El Segundo, CA, USA
- Stanford Center for Artificial Intelligence in Medicine & Imaging, Palo Alto, CA, USA
| | - John Mongan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany
| | - An Tang
- Department of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada
| | - Christoph Wald
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA
- Tufts University Medical School, Boston, MA, USA
- Commision On Informatics, and Member, Board of Chancellors, American College of Radiology, Virginia, USA
| | - John Slavotinek
- South Australia Medical Imaging, Flinders Medical Centre Adelaide, Adelaide, Australia
- College of Medicine and Public Health, Flinders University, Adelaide, Australia
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Taylor SA. CT Colonography Reporting and Data System Version 2023: Updated, Robust, and Relevant. Radiology 2024; 310:e233169. [PMID: 38289211 DOI: 10.1148/radiol.233169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Affiliation(s)
- Stuart A Taylor
- From the UCL Centre for Medical Imaging, Division of Medicine, 2nd Floor Charles Bell House, 43-45 Foley St, London W1W 7TY, England
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30
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Leão Filho HM. The impact of steatosis assessment in imaging. Radiol Bras 2024; 57:e3. [PMID: 38993966 PMCID: PMC11235060 DOI: 10.1590/0100-3984.2024.57.e3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024] Open
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Brady AP, Allen B, Chong J, Kotter E, Kottler N, Mongan J, Oakden-Rayner L, dos Santos DP, Tang A, Wald C, Slavotinek J. Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA. Radiol Artif Intell 2024; 6:e230513. [PMID: 38251899 PMCID: PMC10831521 DOI: 10.1148/ryai.230513] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. This article is simultaneously published in Insights into Imaging (DOI 10.1186/s13244-023-01541-3), Journal of Medical Imaging and Radiation Oncology (DOI 10.1111/1754-9485.13612), Canadian Association of Radiologists Journal (DOI 10.1177/08465371231222229), Journal of the American College of Radiology (DOI 10.1016/j.jacr.2023.12.005), and Radiology: Artificial Intelligence (DOI 10.1148/ryai.230513). Keywords: Artificial Intelligence, Radiology, Automation, Machine Learning Published under a CC BY 4.0 license. ©The Author(s) 2024. Editor's Note: The RSNA Board of Directors has endorsed this article. It has not undergone review or editing by this journal.
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Affiliation(s)
| | - Bibb Allen
- Department of Radiology, Grandview Medical
Center, Birmingham, AL, USA
- American College of Radiology Data Science
Institute, Reston, VA, USA
| | - Jaron Chong
- Department of Medical Imaging, Schulich
School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Elmar Kotter
- Department of Diagnostic and
Interventional Radiology, Medical Center, Faculty of Medicine, University of
Freiburg, Freiburg, Germany
| | - Nina Kottler
- Radiology Partners, El Segundo, CA,
USA
- Stanford Center for Artificial
Intelligence in Medicine & Imaging, Palo Alto, CA, USA
| | - John Mongan
- Department of Radiology and Biomedical
Imaging, University of California, San Francisco, USA
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning,
University of Adelaide, Adelaide, Australia
| | - Daniel Pinto dos Santos
- Department of Radiology, University
Hospital of Cologne, Cologne, Germany
- Department of Radiology, University
Hospital of Frankfurt, Frankfurt, Germany
| | - An Tang
- Department of Radiology, Radiation
Oncology, and Nuclear Medicine, Université de Montréal,
Montréal, Québec, Canada
| | - Christoph Wald
- Department of Radiology, Lahey Hospital
& Medical Center, Burlington, MA, USA
- Tufts University Medical School, Boston,
MA, USA
- Commission On Informatics, and Member,
Board of Chancellors, American College of Radiology, Virginia, USA
| | - John Slavotinek
- South Australia Medical Imaging,
Flinders Medical Centre Adelaide, Adelaide, Australia
- College of Medicine and Public Health,
Flinders University, Adelaide, Australia
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Chu LC. Editorial Comment: Redefining the Skeletal Muscle Index and Sarcopenia. AJR Am J Roentgenol 2024; 222:e2330495. [PMID: 37937838 DOI: 10.2214/ajr.23.30495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Affiliation(s)
- Linda C Chu
- Johns Hopkins University School of Medicine, Baltimore, MD, , @LindaChuMD
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Tang Y, Hong W, Xu X, Li M, Jin L. Traumatic rib fracture patterns associated with bone mineral density statuses derived from CT images. Front Endocrinol (Lausanne) 2023; 14:1304219. [PMID: 38155951 PMCID: PMC10754511 DOI: 10.3389/fendo.2023.1304219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 11/27/2023] [Indexed: 12/30/2023] Open
Abstract
Background The impact of decreased bone mineral density (BMD) on traumatic rib fractures remains unknown. We combined computed tomography (CT) and artificial intelligence (AI) to measure BMD and explore its impact on traumatic rib fractures and their patterns. Methods The retrospective cohort comprised patients who visited our hospital from 2017-2018; the prospective cohort (control group) was consecutively recruited from the same hospital from February-June 2023. All patients had blunt chest trauma and underwent CT. Volumetric BMD of L1 vertebra was measured by using an AI software. Analyses were done by using BMD categorized as osteoporosis (<80 mg/cm3), osteopenia (80-120 mg/cm3), or normal (>120 mg/cm3). Pearson's χ2, Fisher's exact, or Kruskal-Wallis tests and Bonferroni correction were used for comparisons. Negative binomial, and logistic regression analyses were used to assess the associations and impacts of BMD status. Sensitivity analyses were also performed. Findings The retrospective cohort included 2,076 eligible patients, of whom 954 (46%) had normal BMD, 806 (38.8%) had osteopenia, and 316 (15.2%) had osteoporosis. After sex- and age-adjustment, osteoporosis was significantly associated with higher rib fracture rates, and a higher likelihood of fractures in ribs 4-7. Furthermore, both the osteopenia and osteoporosis groups demonstrated a significantly higher number of fractured ribs and fracture sites on ribs, with a higher likelihood of fractures in ribs 1-3, as well as flail chest. The prospective cohort included 205 eligible patients, of whom 92 (44.9%) had normal BMD, 74 (36.1%) had osteopenia, and 39 (19.0%) had osteoporosis. The findings observed within this cohort were in concurrence with those in the retrospective cohort. Interpretation Traumatic rib fractures are associated with decreased BMD. CT-AI can help to identify individuals who have decreased BMD and a greater rib fracture rate, along with their fracture patterns.
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Affiliation(s)
- Yilin Tang
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China
| | - Wei Hong
- Department of Geriatrics and Gerontology, Huadong Hospital, Affiliated with Fudan University, Shanghai, China
| | - Xinxin Xu
- Clinical Research Center for Geriatric Medicine, Huadong Hospital, Affiliated with Fudan University, Shanghai, China
| | - Ming Li
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China
- Diagnosis and Treatment Center of Small Lung Nodules, Huadong Hospital, Affiliated with Fudan University, Shanghai, China
| | - Liang Jin
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China
- Diagnosis and Treatment Center of Small Lung Nodules, Huadong Hospital, Affiliated with Fudan University, Shanghai, China
- Radiology Department, Huashan Hospital Affiliated with Fudan University, Shanghai, China
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Mirón-Mombiela R, Ruiz-España S, Moratal D, Borrás C. Assessment and risk prediction of frailty using texture-based muscle ultrasound image analysis and machine learning techniques. Mech Ageing Dev 2023; 215:111860. [PMID: 37666473 DOI: 10.1016/j.mad.2023.111860] [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: 04/25/2023] [Revised: 08/08/2023] [Accepted: 08/30/2023] [Indexed: 09/06/2023]
Abstract
The purpose of this study was to evaluate texture-based muscle ultrasound image analysis for the assessment and risk prediction of frailty phenotype. This retrospective study of prospectively acquired data included 101 participants who underwent ultrasound scanning of the anterior thigh. Participants were subdivided according to frailty phenotype and were followed up for two years. Primary and secondary outcome measures were death and comorbidity, respectively. Forty-three texture features were computed from the rectus femoris and the vastus intermedius muscles using statistical methods. Model performance was evaluated by computing the area under the receiver operating characteristic curve (AUC) while outcome prediction was evaluated using regression analysis. Models developed achieved a moderate to good AUC (0.67 ≤ AUC ≤ 0.79) for categorizing frailty. The stepwise multiple logistic regression analysis demonstrated that they correctly classified 70-87% of the cases. The models were associated with increased comorbidity (0.01 ≤ p ≤ 0.18) and were predictive of death for pre-frail and frail participants (0.001 ≤ p ≤ 0.016). In conclusion, texture analysis can be useful to identify frailty and assess risk prediction (i.e. mortality) using texture features extracted from muscle ultrasound images in combination with a machine learning approach.
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Affiliation(s)
- Rebeca Mirón-Mombiela
- Department of Physiology, Universitat de València/INCLIVA, Avda. Blasco Ibáñez, 15, 46010 Valencia, Spain; Hospital General Universitario de Valencia (HGUV), Valencia, Spain; Herlev og Gentofte Hospital, Herlev, Denmark.
| | - Silvia Ruiz-España
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain.
| | - David Moratal
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain.
| | - Consuelo Borrás
- Department of Physiology, Universitat de València/INCLIVA, Avda. Blasco Ibáñez, 15, 46010 Valencia, Spain; INCLIVA Health Research Institute, Av/ de Menéndez y Pelayo, 4, 46010 Valencia, Spain; Center for Biomedical Network Research on Frailty and Healthy Aging (CIBERFES), CIBER-ISCIII, Valencia, Spain.
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