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Han YE, Cho Y, Park BJ, Kim MJ, Sim KC, Sung DJ, Han NY, Lee J, Park YS, Yeom SK, Kim J, An H, Oh K. Development and multicenter validation of deep convolutional neural network-based detection of colorectal cancer on abdominal CT. Eur Radiol 2024; 34:6182-6192. [PMID: 38300293 DOI: 10.1007/s00330-023-10452-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/12/2023] [Accepted: 11/07/2023] [Indexed: 02/02/2024]
Abstract
OBJECTIVES This study aims to develop computer-aided detection (CAD) for colorectal cancer (CRC) using abdominal CT based on a deep convolutional neural network. METHODS This retrospective study included consecutive patients with colorectal adenocarcinoma who underwent abdominal CT before CRC resection surgery (training set = 379, test set = 103). We customized the 3D U-Net of nnU-Net (CUNET) for CRC detection, which was trained with fivefold cross-validation using annotated CT images. CUNET was validated using datasets covering various clinical situations and institutions: an internal test set (n = 103), internal patients with CRC first determined by CT (n = 54) and asymptomatic CRC (n = 51), and an external validation set from two institutions (n = 60). During each validation, data from the healthy population were added (internal = 60; external = 130). CUNET was compared with other deep CNNs: residual U-Net and EfficientDet. The CAD performances were evaluated using per-CRC sensitivity (true positive/all CRCs), free-response receiver operating characteristic (FROC), and jackknife alternative FROC (JAFROC) curves. RESULTS CUNET showed a higher maximum per-CRC sensitivity than residual U-Net and EfficientDet (internal test set 91.3% vs. 61.2%, and 64.1%). The per-CRC sensitivity of CUNET at false-positive rates of 3.0 was as follows: internal CRC determined by CT, 89.3%; internal asymptomatic CRC, 87.3%; and external validation, 89.6%. CUNET detected 69.2% (9/13) of CRCs missed by radiologists and 89.7% (252/281) of CRCs from all validation sets. CONCLUSIONS CUNET can detect CRC on abdominal CT in patients with various clinical situations and from external institutions. KEY POINTS • Customized 3D U-Net of nnU-Net (CUNET) can be applied to the opportunistic detection of colorectal cancer (CRC) in abdominal CT, helping radiologists detect unexpected CRC. • CUNET showed the best performance at false-positive rates ≥ 3.0, and 30.1% of false-positives were in the colorectum. CUNET detected 69.2% (9/13) of CRCs missed by radiologists and 87.3% (48/55) of asymptomatic CRCs. • CUNET detected CRCs in multiple validation sets composed of varying clinical situations and from different institutions, and CUNET detected 89.7% (252/281) of CRCs from all validation sets.
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Affiliation(s)
- Yeo Eun Han
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-Ro, Seongbuk-Gu, Seoul, 02841, Republic of Korea
| | - Yongwon Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-Ro, Seongbuk-Gu, Seoul, 02841, Republic of Korea
- AI Center, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-Ro, Seongbuk-Gu, Seoul, 02841, Republic of Korea
| | - Beom Jin Park
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-Ro, Seongbuk-Gu, Seoul, 02841, Republic of Korea.
| | - Min Ju Kim
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-Ro, Seongbuk-Gu, Seoul, 02841, Republic of Korea
| | - Ki Choon Sim
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-Ro, Seongbuk-Gu, Seoul, 02841, Republic of Korea
| | - Deuk Jae Sung
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-Ro, Seongbuk-Gu, Seoul, 02841, Republic of Korea
| | - Na Yeon Han
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-Ro, Seongbuk-Gu, Seoul, 02841, Republic of Korea
| | - Jongmee Lee
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, 148, Gurodong-Ro, Guro-Gu, Seoul, 08308, Republic of Korea
| | - Yang Shin Park
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, 148, Gurodong-Ro, Guro-Gu, Seoul, 08308, Republic of Korea
| | - Suk Keu Yeom
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123, Jeokgeum-Ro, Danwon-Gu, Ansan, 15355, Republic of Korea
| | - Jin Kim
- Department of Surgery, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-Ro, Seongbuk-Gu, Seoul, 02841, Republic of Korea
| | - Hyonggin An
- Department of Biostatistics, Korea University College of Medicine, 73 Goryeodae-Ro, Seongbuk-Gu, Seoul, 02841, Republic of Korea
| | - Kyuhyup Oh
- Bio & Medical Health Division, Korea Testing Laboratory, 87 Digital-Ro 26Gil, Guro-Gu, Seoul, 08389, Republic of Korea
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Reid J, McCrosson M, Tobin J, Rivas G, Rothwell S, Hartsock L, Reid K. Opportunistic CT screening demonstrates increased risk for peri-articular fractures in osteoporotic patients. Orthop Traumatol Surg Res 2024:103935. [PMID: 39155159 DOI: 10.1016/j.otsr.2024.103935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 06/11/2024] [Accepted: 07/10/2024] [Indexed: 08/20/2024]
Abstract
BACKGROUND Underdiagnosis or undertreatment of osteoporosis consequently impacts individual morbidity and mortality, as well as on healthcare systems and communities as a whole. Dual-energy x-ray absorptiometry (DXA) is the gold standard method for identifying osteoporosis, however, opportunistic CT screening is capable of precisely estimating bone mineral density (BMD) in abdominopelvic imaging with no additional cost, radiation exposure or inconvenience to patients. This study uses opportunistic CT screening to determine the prevalence of osteoporosis and anatomic distribution patterns in patients presenting with lower extremity fractures at our institution. HYPOTHESIS Trauma patients with low bone mineral density (BMD) are more likely to present with peri-articular versus shaft fractures. PATIENTS AND METHODS We conducted a retrospective review of 721 patients presenting as trauma activations to the emergency department (ED) of a Level 1 Trauma Center with lower extremity fractures. Patients were excluded if under the age of 18 or lacking a CT scan upon arrival in the ED. Hounsfield Units (HU) were measured at the L1 vertebral level on CT scans to determine bone mineral density. Values of ≤100 HU were consistent with osteoporosis, whereas 101-150 HU were consistent with osteopenia. RESULTS The final cohort included 416 patients, with mean age of 49 ± 21 years. Average bone density was 203.9 ± 73.4 HU. 15.9% of patients were diagnosed as osteopenic and 9.9% as osteoporotic. 64.2% of fractures were peri-articular, 25.7% were shaft, and 10.1% were a combination. Peri-articular fractures were significantly more likely to have lower average BMD than shaft fractures (189 ± 74.7 HU vs. 230.6 ± 66.1 HU, p < 0.001). DISCUSSION Our study demonstrates a significant relationship between low bone mineral density and lower extremity fracture pattern, however, likely influenced by other factors such as sex. Opportunistic CT screening for osteoporosis in trauma settings provides ample opportunity for early detection of low BMD and implementation of highly effective lifestyle modification and pharmacotherapy intervention. Reduction in the overall incidence of peri-articular fracture with widespread adoption of opportunistic CT screening may lessen the morbidity, mortality, and total cost currently afflicting patients, healthcare systems, and communities. LEVEL OF EVIDENCE III, therapeutic.
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Affiliation(s)
- Jared Reid
- Medical University of South Carolina, Department of Orthopaedics and Physical Medicine, 96 Jonathan Lucas St, CSB 708, Charleston, SC 29425, United States
| | - Matthew McCrosson
- Medical University of South Carolina, Department of Orthopaedics and Physical Medicine, 96 Jonathan Lucas St, CSB 708, Charleston, SC 29425, United States
| | - Jacqueline Tobin
- Medical University of South Carolina, Department of Orthopaedics and Physical Medicine, 96 Jonathan Lucas St, CSB 708, Charleston, SC 29425, United States
| | - Gabriella Rivas
- Medical University of South Carolina, Department of Orthopaedics and Physical Medicine, 96 Jonathan Lucas St, CSB 708, Charleston, SC 29425, United States
| | - Stacey Rothwell
- Medical University of South Carolina, Department of Orthopaedics and Physical Medicine, 96 Jonathan Lucas St, CSB 708, Charleston, SC 29425, United States
| | - Langdon Hartsock
- Medical University of South Carolina, Department of Orthopaedics and Physical Medicine, 96 Jonathan Lucas St, CSB 708, Charleston, SC 29425, United States
| | - Kristoff Reid
- Medical University of South Carolina, Department of Orthopaedics and Physical Medicine, 96 Jonathan Lucas St, CSB 708, Charleston, SC 29425, United States.
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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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Cho SW, Baek S, Han S, Kim CO, Kim HC, Rhee Y, Hong N. Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults. J Cachexia Sarcopenia Muscle 2024; 15:1418-1429. [PMID: 38649795 PMCID: PMC11294037 DOI: 10.1002/jcsm.13487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 03/06/2024] [Accepted: 03/21/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Computed tomography (CT) body compositions reflect age-related metabolic derangements. We aimed to develop a multi-outcome deep learning model using CT multi-level body composition parameters to detect metabolic syndrome (MS), osteoporosis and sarcopenia by identifying metabolic clusters simultaneously. We also investigated the prognostic value of metabolic phenotyping by CT model for long-term mortality. METHODS The derivation set (n = 516; 75% train set, 25% internal test set) was constructed using age- and sex-stratified random sampling from two community-based cohorts. Data from participants in the individual health assessment programme (n = 380) were used as the external test set 1. Semi-automatic quantification of body compositions at multiple levels of abdominal CT scans was performed to train a multi-layer perceptron (MLP)-based multi-label classification model. External test set 2 to test the prognostic value of the model output for mortality was built using data from individuals who underwent abdominal CT in a tertiary-level institution (n = 10 141). RESULTS The mean ages of the derivation and external sets were 62.8 and 59.7 years, respectively, without difference in sex distribution (women 50%) or body mass index (BMI; 23.9 kg/m2). Skeletal muscle density (SMD) and bone density (BD) showed a more linear decrement across age than skeletal muscle area. Alternatively, an increase in visceral fat area (VFA) was observed in both men and women. Hierarchical clustering based on multi-level CT body composition parameters revealed three distinctive phenotype clusters: normal, MS and osteosarcopenia clusters. The L3 CT-parameter-based model, with or without clinical variables (age, sex and BMI), outperformed clinical model predictions of all outcomes (area under the receiver operating characteristic curve: MS, 0.76 vs. 0.55; osteoporosis, 0.90 vs. 0.79; sarcopenia, 0.85 vs. 0.81 in external test set 1; P < 0.05 for all). VFA contributed the most to the MS predictions, whereas SMD, BD and subcutaneous fat area were features of high importance for detecting osteoporosis and sarcopenia. In external test set 2 (mean age 63.5 years, women 79%; median follow-up 4.9 years), a total of 907 individuals (8.9%) died during follow-up. Among model-predicted metabolic phenotypes, sarcopenia alone (adjusted hazard ratio [aHR] 1.55), MS + sarcopenia (aHR 1.65), osteoporosis + sarcopenia (aHR 1.83) and all three combined (aHR 1.87) remained robust predictors of mortality after adjustment for age, sex and comorbidities. CONCLUSIONS A CT body composition-based MLP model detected MS, osteoporosis and sarcopenia simultaneously in community-dwelling and hospitalized adults. Metabolic phenotypes predicted by the CT MLP model were associated with long-term mortality, independent of covariates.
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Affiliation(s)
- Sang Wouk Cho
- Department of Internal Medicine, Endocrine Research InstituteSeverance Hospital, Yonsei University College of MedicineSeoulSouth Korea
- Institue for Innovation in Digital Healthcare (IIDH)Yonsei University Health SystemSeoulSouth Korea
| | - Seungjin Baek
- Department of Internal Medicine, Endocrine Research InstituteSeverance Hospital, Yonsei University College of MedicineSeoulSouth Korea
| | - Sookyeong Han
- Department of Internal Medicine, Endocrine Research InstituteSeverance Hospital, Yonsei University College of MedicineSeoulSouth Korea
- Institue for Innovation in Digital Healthcare (IIDH)Yonsei University Health SystemSeoulSouth Korea
| | - Chang Oh Kim
- Division of Geriatric Medicine, Department of Internal MedicineYonsei University College of MedicineSeoulSouth Korea
| | - Hyeon Chang Kim
- Institue for Innovation in Digital Healthcare (IIDH)Yonsei University Health SystemSeoulSouth Korea
- Department of Preventive MedicineYonsei University College of MedicineSeoulSouth Korea
| | - Yumie Rhee
- Department of Internal Medicine, Endocrine Research InstituteSeverance Hospital, Yonsei University College of MedicineSeoulSouth Korea
- Institue for Innovation in Digital Healthcare (IIDH)Yonsei University Health SystemSeoulSouth Korea
| | - Namki Hong
- Department of Internal Medicine, Endocrine Research InstituteSeverance Hospital, Yonsei University College of MedicineSeoulSouth Korea
- Institue for Innovation in Digital Healthcare (IIDH)Yonsei University Health SystemSeoulSouth Korea
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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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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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7
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Müller L, Mähringer-Kunz A, Auer TA, Fehrenbach U, Gebauer B, Haubold J, Schaarschmidt BM, Kim MS, Hosch R, Nensa F, Kleesiek J, Diallo TD, Eisenblätter M, Kuzior H, Roehlen N, Bettinger D, Steinle V, Mayer P, Zopfs D, Pinto Dos Santos D, Kloeckner R. AI-derived body composition parameters as prognostic factors in patients with HCC undergoing TACE in a multicenter study. JHEP Rep 2024; 6:101125. [PMID: 39139458 PMCID: PMC11321290 DOI: 10.1016/j.jhepr.2024.101125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 05/17/2024] [Accepted: 05/23/2024] [Indexed: 08/15/2024] Open
Abstract
Background & Aims Body composition assessment (BCA) parameters have recently been identified as relevant prognostic factors for patients with hepatocellular carcinoma (HCC). Herein, we aimed to investigate the role of BCA parameters for prognosis prediction in patients with HCC undergoing transarterial chemoembolization (TACE). Methods This retrospective multicenter study included a total of 754 treatment-naïve patients with HCC who underwent TACE at six tertiary care centers between 2010-2020. Fully automated artificial intelligence-based quantitative 3D volumetry of abdominal cavity tissue composition was performed to assess skeletal muscle volume (SM), total adipose tissue (TAT), intra- and intermuscular adipose tissue, visceral adipose tissue, and subcutaneous adipose tissue (SAT) on pre-intervention computed tomography scans. BCA parameters were normalized to the slice number of the abdominal cavity. We assessed the influence of BCA parameters on median overall survival and performed multivariate analysis including established estimates of survival. Results Univariate survival analysis revealed that impaired median overall survival was predicted by low SM (p <0.001), high TAT volume (p = 0.013), and high SAT volume (p = 0.006). In multivariate survival analysis, SM remained an independent prognostic factor (p = 0.039), while TAT and SAT volumes no longer showed predictive ability. This predictive role of SM was confirmed in a subgroup analysis of patients with BCLC stage B. Conclusions SM is an independent prognostic factor for survival prediction. Thus, the integration of SM into novel scoring systems could potentially improve survival prediction and clinical decision-making. Fully automated approaches are needed to foster the implementation of this imaging biomarker into daily routine. Impact and implications Body composition assessment parameters, especially skeletal muscle volume, have been identified as relevant prognostic factors for many diseases and treatments. In this study, skeletal muscle volume has been identified as an independent prognostic factor for patients with hepatocellular carcinoma undergoing transarterial chemoembolization. Therefore, skeletal muscle volume as a metaparameter could play a role as an opportunistic biomarker in holistic patient assessment and be integrated into decision support systems. Workflow integration with artificial intelligence is essential for automated, quantitative body composition assessment, enabling broad availability in multidisciplinary case discussions.
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Affiliation(s)
- Lukas Müller
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Aline Mähringer-Kunz
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Timo Alexander Auer
- Department of Radiology, Charité - University Medicine Berlin, Berlin, Germany
- Berlin Institute of Health at Charité - University Medicine Berlin, Berlin, Germany
| | - Uli Fehrenbach
- Department of Radiology, Charité - University Medicine Berlin, Berlin, Germany
| | - Bernhard Gebauer
- Department of Radiology, Charité - University Medicine Berlin, Berlin, Germany
| | - Johannes Haubold
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
| | | | - Moon-Sung Kim
- Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
| | - René Hosch
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
| | - Felix Nensa
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
| | - Jens Kleesiek
- Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
| | - Thierno D. Diallo
- Department of Diagnostic and Interventional Radiology, Freiburg University Hospital, Freiburg, Germany
| | - Michel Eisenblätter
- Department of Diagnostic and Interventional Radiology, Freiburg University Hospital, Freiburg, Germany
- Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany
| | - Hanna Kuzior
- Department of Diagnostic and Interventional Radiology, Freiburg University Hospital, Freiburg, Germany
| | - Natascha Roehlen
- Department of Medicine II, Freiburg University Hospital, Freiburg, Germany
| | - Dominik Bettinger
- Department of Medicine II, Freiburg University Hospital, Freiburg, Germany
| | - Verena Steinle
- Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany
| | - Philipp Mayer
- Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany
| | - David Zopfs
- Department of Radiology, University Hospital Cologne, Cologne, Germany
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital Cologne, Cologne, Germany
- Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany
| | - Roman Kloeckner
- Institute of Interventional Radiology, University Hospital of Schleswig-Holstein – Campus Lübeck, Lübeck, Germany
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8
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Huang XC, Ma JY, Gao C, Chen JX, Li CL, Huang YL, He B. Diffusion-tensor magnetic resonance imaging as a non-invasive assessment of extracellular matrix remodeling in lumbar paravertebral muscles of rats with sarcopenia. BMC Musculoskelet Disord 2024; 25:540. [PMID: 38997743 PMCID: PMC11245810 DOI: 10.1186/s12891-024-07654-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 07/02/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND Extracellular matrix (ECM) remodeling in skeletal muscle is a significant factor in the development of sarcopenia. This study aims to evaluate changes in ECM remodeling in the lumbar paravertebral muscles of sarcopenic rats using diffusion-tensor magnetic resonance imaging (DT-MRI) and compare them with histology. METHODS Twenty 6-month-old female Sprague Dawley rats were randomly divided into the dexamethasone (DEX) group and the control (CON) group. Both groups underwent 3.0T MRI scanning, including Mensa, T2WI, and DT-MRI sequences. The changes in muscle fibers and extracellular matrix (ECM) of the erector spinal muscle were observed using hematoxylineosin and sirius red staining. The expressions of collagen I, III, and fibronectin in the erector spinae were detected by western blot. Pearson correlation analysis was employed to assess the correlation between MRI quantitative parameters and corresponding histopathology markers. RESULTS The cross-sectional area and fractional anisotropy values of the erector spinae in the DEX group rats were significantly lower than those in the CON group (p < 0.05). Hematoxylin eosin staining revealed muscle fiber atrophy and disordered arrangement in the DEX group, while sirius red staining showed a significant increase in collagen volume fraction in the DEX group. The western blot results indicate a significant increase in the expression of collagen I, collagen III, and fibronectin in the DEX group (p < 0.001 for all). Correlation coefficients between fractional anisotropy values and collagen volume fraction, collagen I, collagen III, and fibronectin were - 0.71, -0.94, -0.85, and - 0.88, respectively (p < 0.05 for all). CONCLUSIONS The fractional anisotropy value is strongly correlated with the pathological collagen volume fraction, collagen I, collagen III, and fibronectin. This indicates that DT-MRI can non-invasively evaluate the changes in extracellular matrix remodeling in the erector spinal muscle of sarcopenia. It provides a potential imaging biomarker for the diagnosis of sarcopenia.
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Affiliation(s)
- Xin-Chen Huang
- Department of Radiology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Ji-Yao Ma
- Department of Radiology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Chao Gao
- Department of Radiology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Jia-Xin Chen
- Department of Radiology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Chun-Li Li
- Department of Radiology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yi-Long Huang
- Department of Radiology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
| | - Bo He
- Department of Radiology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
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9
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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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10
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Lee MH, Zea R, Garrett JW, Summers RM, Pickhardt PJ. AI-based abdominal CT measurements of orthotopic and ectopic fat predict mortality and cardiometabolic disease risk in adults. Eur Radiol 2024:10.1007/s00330-024-10935-w. [PMID: 38995381 DOI: 10.1007/s00330-024-10935-w] [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: 01/04/2024] [Revised: 04/27/2024] [Accepted: 05/31/2024] [Indexed: 07/13/2024]
Abstract
OBJECTIVES To evaluate the utility of CT-based abdominal fat measures for predicting the risk of death and cardiometabolic disease in an asymptomatic adult screening population. METHODS Fully automated AI tools quantifying abdominal adipose tissue (L3 level visceral [VAT] and subcutaneous [SAT] fat area, visceral-to-subcutaneous fat ratio [VSR], VAT attenuation), muscle attenuation (L3 level), and liver attenuation were applied to non-contrast CT scans in asymptomatic adults undergoing CT colonography (CTC). Longitudinal follow-up documented subsequent deaths, cardiovascular events, and diabetes. ROC and time-to-event analyses were performed to generate AUCs and hazard ratios (HR) binned by octile. RESULTS A total of 9223 adults (mean age, 57 years; 4071:5152 M:F) underwent screening CTC from April 2004 to December 2016. 549 patients died on follow-up (median, nine years). Fat measures outperformed BMI for predicting mortality risk-5-year AUCs for muscle attenuation, VSR, and BMI were 0.721, 0.661, and 0.499, respectively. Higher visceral, muscle, and liver fat were associated with increased mortality risk-VSR > 1.53, HR = 3.1; muscle attenuation < 15 HU, HR = 5.4; liver attenuation < 45 HU, HR = 2.3. Higher VAT area and VSR were associated with increased cardiovascular event and diabetes risk-VSR > 1.59, HR = 2.6 for cardiovascular event; VAT area > 291 cm2, HR = 6.3 for diabetes (p < 0.001). A U-shaped association was observed for SAT with a higher risk of death for very low and very high SAT. CONCLUSION Fully automated CT-based measures of abdominal fat are predictive of mortality and cardiometabolic disease risk in asymptomatic adults and uncover trends that are not reflected in anthropomorphic measures. CLINICAL RELEVANCE STATEMENT Fully automated CT-based measures of abdominal fat soundly outperform anthropometric measures for mortality and cardiometabolic risk prediction in asymptomatic patients. KEY POINTS Abdominal fat depots associated with metabolic dysregulation and cardiovascular disease can be derived from abdominal CT. Fully automated AI body composition tools can measure factors associated with increased mortality and cardiometabolic risk. CT-based abdominal fat measures uncover trends in mortality and cardiometabolic risk not captured by BMI in asymptomatic outpatients.
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Affiliation(s)
- Matthew H Lee
- Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA.
| | - Ryan Zea
- Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - John W Garrett
- Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Perry J Pickhardt
- Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA
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Chang JY, Makary MS. Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging. Diagnostics (Basel) 2024; 14:1456. [PMID: 39001346 PMCID: PMC11240935 DOI: 10.3390/diagnostics14131456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 07/01/2024] [Accepted: 07/06/2024] [Indexed: 07/16/2024] Open
Abstract
The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.
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Affiliation(s)
- Jin Y Chang
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Mina S Makary
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
- Division of Vascular and Interventional Radiology, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
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12
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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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Somasundaram E, Taylor Z, Alves VV, Qiu L, Fortson BL, Mahalingam N, Dudley JA, Li H, Brady SL, Trout AT, Dillman JR. Deep Learning Models for Abdominal CT Organ Segmentation in Children: Development and Validation in Internal and Heterogeneous Public Datasets. AJR Am J Roentgenol 2024; 223:e2430931. [PMID: 38691411 DOI: 10.2214/ajr.24.30931] [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: 05/03/2024]
Abstract
BACKGROUND. Deep learning abdominal organ segmentation algorithms have shown excellent results in adults; validation in children is sparse. OBJECTIVE. The purpose of this article is to develop and validate deep learning models for liver, spleen, and pancreas segmentation on pediatric CT examinations. METHODS. This retrospective study developed and validated deep learning models for liver, spleen, and pancreas segmentation using 1731 CT examinations (1504 training, 221 testing), derived from three internal institutional pediatric (age ≤ 18 years) datasets (n = 483) and three public datasets comprising pediatric and adult examinations with various pathologies (n = 1248). Three deep learning model architectures (SegResNet, DynUNet, and SwinUNETR) from the Medical Open Network for Artificial Intelligence (MONAI) framework underwent training using native training (NT), relying solely on institutional datasets, and transfer learning (TL), incorporating pretraining on public datasets. For comparison, TotalSegmentator, a publicly available segmentation model, was applied to test data without further training. Segmentation performance was evaluated using mean Dice similarity coefficient (DSC), with manual segmentations as reference. RESULTS. For internal pediatric data, the DSC for TotalSegmentator, NT models, and TL models for normal liver was 0.953, 0.964-0.965, and 0.965-0.966, respectively; for normal spleen, 0.914, 0.942-0.945, and 0.937-0.945; for normal pancreas, 0.733, 0.774-0.785, and 0.775-0.786; and for pancreas with pancreatitis, 0.703, 0.590-0.640, and 0.667-0.711. For public pediatric data, the DSC for TotalSegmentator, NT models, and TL models for liver was 0.952, 0.871-0.908, and 0.941-0.946, respectively; for spleen, 0.905, 0.771-0.827, and 0.897-0.926; and for pancreas, 0.700, 0.577-0.648, and 0.693-0.736. For public primarily adult data, the DSC for TotalSegmentator, NT models, and TL models for liver was 0.991, 0.633-0.750, and 0.926-0.952, respectively; for spleen, 0.983, 0.569-0.604, and 0.923-0.947; and for pancreas, 0.909, 0.148-0.241, and 0.699-0.775. The DynUNet TL model was selected as the best-performing NT or TL model considering DSC values across organs and test datasets and was made available as an open-source MONAI bundle (https://github.com/cchmc-dll/pediatric_abdominal_segmentation_bundle.git). CONCLUSION. TL models trained on heterogeneous public datasets and fine-tuned using institutional pediatric data outperformed internal NT models and Total-Segmentator across internal and external pediatric test data. Segmentation performance was better in liver and spleen than in pancreas. CLINICAL IMPACT. The selected model may be used for various volumetry applications in pediatric imaging.
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Affiliation(s)
- Elanchezhian Somasundaram
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Zachary Taylor
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
| | - Vinicius V Alves
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
| | - Lisa Qiu
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
| | - Benjamin L Fortson
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
| | - Neeraja Mahalingam
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
| | - Jonathan A Dudley
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
| | - Hailong Li
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Samuel L Brady
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Andrew T Trout
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Jonathan R Dillman
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
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14
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Chaunzwa TL, Qian JM, Li Q, Ricciuti B, Nuernberg L, Johnson JW, Weiss J, Zhang Z, MacKay J, Kagiampakis I, Bikiel D, Di Federico A, Alessi JV, Mak RH, Jacob E, Awad MM, Aerts HJWL. Body Composition in Advanced Non-Small Cell Lung Cancer Treated With Immunotherapy. JAMA Oncol 2024; 10:773-783. [PMID: 38780929 PMCID: PMC11117154 DOI: 10.1001/jamaoncol.2024.1120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 12/13/2023] [Indexed: 05/25/2024]
Abstract
Importance The association between body composition (BC) and cancer outcomes is complex and incompletely understood. Previous research in non-small-cell lung cancer (NSCLC) has been limited to small, single-institution studies and yielded promising, albeit heterogeneous, results. Objectives To evaluate the association of BC with oncologic outcomes in patients receiving immunotherapy for advanced or metastatic NSCLC. Design, Setting, and Participants This comprehensive multicohort analysis included clinical data from cohorts receiving treatment at the Dana-Farber Brigham Cancer Center (DFBCC) who received immunotherapy given alone or in combination with chemotherapy and prospectively collected data from the phase 1/2 Study 1108 and the chemotherapy arm of the phase 3 MYSTIC trial. Baseline and follow-up computed tomography (CT) scans were collected and analyzed using deep neural networks for automatic L3 slice selection and body compartment segmentation (skeletal muscle [SM], subcutaneous adipose tissue [SAT], and visceral adipose tissue). Outcomes were compared based on baseline BC measures or their change at the first follow-up scan. The data were analyzed between July 2022 and April 2023. Main Outcomes and Measures Hazard ratios (HRs) for the association of BC measurements with overall survival (OS) and progression-free survival (PFS). Results A total of 1791 patients (878 women [49%]) with NSCLC were analyzed, of whom 487 (27.2%) received chemoimmunotherapy at DFBCC (DFBCC-CIO), 825 (46.1%) received ICI monotherapy at DFBCC (DFBCC-IO), 222 (12.4%) were treated with durvalumab monotherapy on Study 1108, and 257 (14.3%) were treated with chemotherapy on MYSTIC; median (IQR) ages were 65 (58-74), 66 (57-71), 65 (26-87), and 63 (30-84) years, respectively. A loss in SM mass, as indicated by a change in the L3 SM area, was associated with worse oncologic outcome across patient groups (HR, 0.59 [95% CI, 0.43-0.81] and 0.61 [95% CI, 0.47-0.79] for OS and PFS, respectively, in DFBCC-CIO; HR, 0.74 [95% CI, 0.60-0.91] for OS in DFBCC-IO; HR, 0.46 [95% CI, 0.33-0.64] and 0.47 [95% CI, 0.34-0.64] for OS and PFS, respectively, in Study 1108; HR, 0.76 [95% CI, 0.61-0.96] for PFS in the MYSTIC trial). This association was most prominent among male patients, with a nonsignificant association among female patients in the MYSTIC trial and DFBCC-CIO cohorts on Kaplan-Meier analysis. An increase of more than 5% in SAT density, as quantified by the average CT attenuation in Hounsfield units of the SAT compartment, was associated with poorer OS in 3 patient cohorts (HR, 0.61 [95% CI, 0.43-0.86] for DFBCC-CIO; HR, 0.62 [95% CI, 0.49-0.79] for DFBCC-IO; and HR, 0.56 [95% CI, 0.40-0.77] for Study 1108). The change in SAT density was also associated with PFS for DFBCC-CIO (HR, 0.73; 95% CI, 0.54-0.97). This was primarily observed in female patients on Kaplan-Meier analysis. Conclusions and Relevance The results of this multicohort study suggest that loss in SM mass during systemic therapy for NSCLC is a marker of poor outcomes, especially in male patients. SAT density changes are also associated with prognosis, particularly in female patients. Automated CT-derived BC measurements should be considered in determining NSCLC prognosis.
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Affiliation(s)
- Tafadzwa L. Chaunzwa
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jack M. Qian
- Department of Radiation Oncology, Dana Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Qin Li
- AstraZeneca, Cambridge, England and Waltham, Massachusetts
| | - Biagio Ricciuti
- Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Leonard Nuernberg
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands
| | - Justin W. Johnson
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
| | - Jakob Weiss
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Diagnostic and Interventional Radiology, University Freiburg, Freiburg im Breisgau, Germany
| | - Zhongyi Zhang
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
| | - Jamie MacKay
- AstraZeneca, Cambridge, England and Waltham, Massachusetts
| | | | - Damian Bikiel
- AstraZeneca, Cambridge, England and Waltham, Massachusetts
| | | | - Joao V. Alessi
- Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Raymond H. Mak
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Etai Jacob
- AstraZeneca, Cambridge, England and Waltham, Massachusetts
| | - Mark M. Awad
- Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Hugo J. W. L. Aerts
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands
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15
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Varghese AP, Naik S, Asrar Up Haq Andrabi S, Luharia A, Tivaskar S. Enhancing Radiological Diagnosis: A Comprehensive Review of Image Quality Assessment and Optimization Strategies. Cureus 2024; 16:e63016. [PMID: 39050319 PMCID: PMC11268977 DOI: 10.7759/cureus.63016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 06/24/2024] [Indexed: 07/27/2024] Open
Abstract
Image quality plays a pivotal role in the accurate diagnosis and effective management of diseases in radiology. This review explores the principles, methodologies, and strategies for assessing and optimizing image quality across various imaging modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and nuclear medicine. We discuss key factors influencing image quality, such as spatial resolution, noise, contrast, and artifacts, and highlight techniques for quality assurance, image optimization, and dose reduction in clinical practice.
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Affiliation(s)
- Albert P Varghese
- Department of Radiology, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Shreya Naik
- Department of Radiology, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | | | - Anurag Luharia
- Department of Radiology, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Suhas Tivaskar
- Department of Radiology, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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16
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Yao L, Li S, Tao Q, Mao Y, Dong J, Lu C, Han C, Qiu B, Huang Y, Huang X, Liang Y, Lin H, Guo Y, Liang Y, Chen Y, Lin J, Chen E, Jia Y, Chen Z, Zheng B, Ling T, Liu S, Tong T, Cao W, Zhang R, Chen X, Liu Z. Deep learning for colorectal cancer detection in contrast-enhanced CT without bowel preparation: a retrospective, multicentre study. EBioMedicine 2024; 104:105183. [PMID: 38848616 PMCID: PMC11192791 DOI: 10.1016/j.ebiom.2024.105183] [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: 10/27/2023] [Revised: 04/30/2024] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists. METHODS We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists' detection performance. FINDINGS In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p < 0.0001; 94.9% vs 85.3%, p < 0.0001), and significantly improved the accuracy of radiologists (93.4% vs 86.0%, p < 0.0001; 93.6% vs 85.3%, p < 0.0001). In the real-world test set, the DL model delivered sensitivity comparable to that of radiologists who had been informed about clinical indications for most cancer cases (94.3% vs 96.2%, p > 0.99), and it detected 2 cases that had been missed by radiologists. INTERPRETATION The developed DL model can accurately detect colorectal cancer and improve radiologists' detection performance, showing its potential as an effective computer-aided detection tool. FUNDING This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).
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Affiliation(s)
- Lisha Yao
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; School of Medicine, South China University of Technology, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Suyun Li
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; School of Medicine, South Medical University, Guangzhou, China
| | - Quan Tao
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yun Mao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Dong
- Department of Radiology, Shanxi Bethune Hospital (Shanxi Academy of Medical Sciences), The Third Affiliated Hospital of Shanxi Medical University, Taiyuan, China
| | - Cheng Lu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Chu Han
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Bingjiang Qiu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Sciences), Guangzhou, China
| | - Yanqi Huang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Xin Huang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; School of Medicine, Shantou University Medical College, Shantou, China
| | - Yanting Liang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; School of Medicine, South Medical University, Guangzhou, China
| | - Huan Lin
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; School of Medicine, South China University of Technology, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Yongmei Guo
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China
| | - Yingying Liang
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China
| | - Yizhou Chen
- Department of Radiology, Puning People's Hospital, Southern Medical University, Jieyang, China
| | - Jie Lin
- Department of Radiology, Puning People's Hospital, Southern Medical University, Jieyang, China
| | - Enyan Chen
- Department of Radiology, Puning People's Hospital, Southern Medical University, Jieyang, China
| | - Yanlian Jia
- Department of Radiology, Liaobu Hospital of Guangdong, Dongguan, China
| | - Zhihong Chen
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
| | - Bochi Zheng
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Tong Ling
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Shunli Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wuteng Cao
- Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ruiping Zhang
- Department of Radiology, Shanxi Bethune Hospital (Shanxi Academy of Medical Sciences), The Third Affiliated Hospital of Shanxi Medical University, Taiyuan, China.
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; School of Medicine, South China University of Technology, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
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17
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Pickhardt PJ. Abdominal CT-Based Body Composition Biomarkers for Phenotypic Biologic Aging. Mayo Clin Proc 2024; 99:858-860. [PMID: 38839185 DOI: 10.1016/j.mayocp.2024.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 04/19/2024] [Indexed: 06/07/2024]
Affiliation(s)
- Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI.
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18
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Lorenzon M, Zabotti A, Cereser L. Cancer screening before treatments in rheumatoid arthritis: lights and shadows. Rheumatology (Oxford) 2024; 63:1475-1476. [PMID: 37707539 DOI: 10.1093/rheumatology/kead478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 09/15/2023] Open
Affiliation(s)
- Michele Lorenzon
- Department of Medicine, Institute of Radiology, University of Udine-University Hospital 'S. Maria della Misericordia', Udine, Italy
| | - Alen Zabotti
- Department of Medicine, Rheumatology Institute, University of Udine-University Hospital 'S. Maria della Misericordia', Udine, Italy
| | - Lorenzo Cereser
- Department of Medicine, Institute of Radiology, University of Udine-University Hospital 'S. Maria della Misericordia', Udine, Italy
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19
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Ge Y, You Q, Gao F, Liu G, Wang L, Li B, Tian M, Yang M, Wu X. Muscle density, but not size, is independently associated with cognitive health in older adults with hip fractures. JBMR Plus 2024; 8:ziae047. [PMID: 38665314 PMCID: PMC11044827 DOI: 10.1093/jbmrpl/ziae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024] Open
Abstract
Emerging evidence indicates a complex interplay between skeletal muscle and cognitive function. Despite the known differences between muscle quantity and quality, which can be measured via computed tomography (CT), the precise nature of their associations with cognitive performance remain underexplored. To investigate the links between muscle size and density and cognitive impairment (CI) in the older adults with hip fractures, we conducted a post hoc, cross-sectional analysis within a prospective cohort study on 679 patients with hip fractures over 65. Mini-Mental State Examination (MMSE) and routine hip CT imaging were utilized to assess cognition function and muscle characteristics in older adults with hip fractures. The CT scans provided data on cross-sectional area and attenuation for the gluteus maximus (G.MaxM) and the combined gluteus medius and minimus (G.Med/MinM). Participants were categorized into CI and non-CI groups based on education levels and MMSE scores. Multivariate logistic regressions, propensity score (PS) methods, and subgroup analysis were employed to analyze associations and validate findings. This study included 123 participants (81.6 ± 6.8 years, 74% female) with CI and 556 participants (78.5 ± 7.7 years, 72% female) without. Compared to the non-CI group, muscle parameters, especially density, were significantly lower in the CI group. Specifically, G.Med/Min muscle density, but not size was robustly associated with CI (odds ratio (OR) = 0.77, 95% confidence interval = 0.62-0.96, P = 0.02), independent of other medical situations. Sensitivity analysis corroborated that G.Med/Min muscle density was consistently lower in the CI group than the non-CI group, as evidenced in the PS matched (P = 0.024) and weighted cohort (P = 0.033). Enhanced muscle parameters, particularly muscle density in the G.Med/MinM muscle, correlate with a lower risk of CI. Muscle density demonstrates a stronger association with cognitive performance than muscle size, highlighting its potential as a key focus in future cognitive health research.
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Affiliation(s)
- Yufeng Ge
- Department of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China
| | - Qian You
- Department of Neurology, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China
| | - Feng Gao
- Department of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China
| | - Gang Liu
- Department of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China
| | - Ling Wang
- Department of Radiology, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China
- JST Sarcopenia Research Center, Beijing Research Institute of Traumatology and Orthopaedics, Beijing 100035, China
| | - Bo Li
- Department of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China
| | - Maoyi Tian
- The George Institute for Global Health, Peking University Health Science Centre, Beijing 100191, China
| | - Minghui Yang
- Department of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China
| | - Xinbao Wu
- Department of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China
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20
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Galata C, Schiller P, Müller L, Karampinis I, Stamenovic D, Buhl R, Kreuter M, Roessner ED. Thoracic skeletal muscle mass predicts mortality in patients with surgery for pleural empyema: A case control study. Thorac Cancer 2024; 15:1201-1207. [PMID: 38597111 PMCID: PMC11128367 DOI: 10.1111/1759-7714.15307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/23/2024] [Accepted: 03/31/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND This study investigated the role of the thoracic skeletal muscle mass as a marker of sarcopenia on postoperative mortality in pleural empyema. METHODS All consecutive patients (n = 103) undergoing surgery for pleural empyema in a single tertiary referral center between January 2020 and December 2022 were eligible for this study. Thoracic skeletal muscle mass index (TSMI) was determined from preoperative computed tomography scans. The impact of TSMI and other potential risk factors on postoperative in-hospital mortality was retrospectively analyzed. RESULTS A total of 97 patients were included in this study. The in-hospital mortality rate was 13.4%. In univariable analysis, low values for preoperative TSMI (p = 0.020), low preoperative levels of thrombocytes (p = 0.027) and total serum protein (p = 0.046) and higher preoperative American Society of Anesthesiologists (ASA) category (p = 0.007) were statistically significant risk factors for mortality. In multivariable analysis, only TSMI (p = 0.038, OR 0.933, 95% CI: 0.875-0.996) and low thrombocytes (p = 0.031, OR 0.944, 95% CI: 0.988-0.999) remained independent prognostic factors for mortality. CONCLUSIONS TSMI was a significant prognostic risk factor for postoperative mortality in patients with pleural empyema. TSMI may be suitable for risk stratification in this disease with high morbidity and mortality, which may have further implications for the selection of the best treatment strategy.
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Affiliation(s)
- Christian Galata
- Department of Thoracic Surgery, Center for Thoracic Diseases, University Medical Center MainzJohannes Gutenberg University MainzMainzGermany
| | - Philipp Schiller
- Department of Thoracic Surgery, Center for Thoracic Diseases, University Medical Center MainzJohannes Gutenberg University MainzMainzGermany
- Department of Surgery, RoMed Hospital RosenheimRosenheimGermany
| | - Lukas Müller
- Department of Radiology, University Medical Center MainzJohannes Gutenberg University MainzMainzGermany
| | - Ioannis Karampinis
- Department of Thoracic Surgery, Center for Thoracic Diseases, University Medical Center MainzJohannes Gutenberg University MainzMainzGermany
| | - Davor Stamenovic
- Department of Thoracic Surgery, Center for Thoracic Diseases, University Medical Center MainzJohannes Gutenberg University MainzMainzGermany
| | - Roland Buhl
- Department for Pulmonology, Center for Thoracic Diseases, University Medical Center MainzJohannes Gutenberg University MainzMainzGermany
| | - Michael Kreuter
- Center for Pulmonary Medicine, Department for Pulmonology, Center for Thoracic DiseasesUniversity Medical Center Mainz, Critical Care & Sleep Medicine, Marienhaus Clinic MainzMainzGermany
| | - Eric Dominic Roessner
- Department of Thoracic Surgery, Center for Thoracic Diseases, University Medical Center MainzJohannes Gutenberg University MainzMainzGermany
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21
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Linder N, Denecke T, Busse H. Body composition analysis by radiological imaging - methods, applications, and prospects. ROFO-FORTSCHR RONTG 2024. [PMID: 38569516 DOI: 10.1055/a-2263-1501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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; DOI: 10.1055/a-2263-1501.
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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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22
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Kim A, Lee CM, Kang BK, Kim M, Choi JW. Myosteatosis and aortic calcium score on abdominal CT as prognostic markers in non-dialysis chronic kidney disease patients. Sci Rep 2024; 14:7718. [PMID: 38565556 PMCID: PMC10987640 DOI: 10.1038/s41598-024-58293-3] [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: 10/16/2023] [Accepted: 03/27/2024] [Indexed: 04/04/2024] Open
Abstract
We aimed to examine the relationship between abdominal computed tomography (CT)-based body composition data and both renal function decline and all-cause mortality in patients with non-dialysis chronic kidney disease (CKD). This retrospective study comprised non-dialysis CKD patients who underwent consecutive unenhanced abdominal CT between January 2010 and December 2011. CT-based body composition was measured using semiautomated method that included visceral fat, subcutaneous fat, skeletal muscle area and density, and abdominal aortic calcium score (AAS). Sarcopenia and myosteatosis were defined by decreased skeletal muscle index (SMI) and decreased skeletal muscle density, respectively, each with specific cutoffs. Risk factors for CKD progression and survival were identified using logistic regression and Cox proportional hazard regression models. Survival between groups based on myosteatosis and AAS was compared using the Kaplan-Meier curve. 149 patients (median age: 70 years) were included; 79 (53.0%) patients had sarcopenia and 112 (75.2%) had myosteatosis. The median AAS was 560.9 (interquartile range: 55.7-1478.3)/m2. The prognostic factors for CKD progression were myosteatosis [odds ratio (OR) = 4.31, p = 0.013] and high AAS (OR = 1.03, p = 0.001). Skeletal muscle density [hazard ratio (HR) = 0.93, p = 0.004] or myosteatosis (HR = 4.87, p = 0.032) and high AAS (HR = 1.02, p = 0.001) were independent factors for poor survival outcomes. The presence of myosteatosis and the high burden of aortic calcium were significant factors for CKD progression and survival in patients with non-dialysis CKD.
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Affiliation(s)
- Ahyun Kim
- Department of Radiology, Hanyang University Medical Center, 222-1 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Chul-Min Lee
- Department of Radiology, Hanyang University Medical Center, 222-1 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Bo-Kyeong Kang
- Department of Radiology, Hanyang University Medical Center, 222-1 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Mimi Kim
- Department of Radiology, Hanyang University Medical Center, 222-1 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea.
| | - Jong Wook Choi
- Department of Internal Medicine, Hanyang University Medical Center, 222-1 Wangsimni-ro, Seongdong-gu, Seoul, Republic of Korea.
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23
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Weston AD, Grossardt BR, Garner HW, Kline TL, Chamberlain AM, Allen AM, Erickson BJ, Rocca WA, Rule AD, St. Sauver JL. Abdominal Body Composition Reference Ranges and Association With Chronic Conditions in an Age- and Sex-Stratified Representative Sample of a Geographically Defined American Population. J Gerontol A Biol Sci Med Sci 2024; 79:glae055. [PMID: 38373180 PMCID: PMC10949446 DOI: 10.1093/gerona/glae055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Body composition can be accurately quantified from abdominal computed tomography (CT) exams and is a predictor for the development of aging-related conditions and for mortality. However, reference ranges for CT-derived body composition measures of obesity, sarcopenia, and bone loss have yet to be defined in the general population. METHODS We identified a population-representative sample of 4 900 persons aged 20 to 89 years who underwent an abdominal CT exam from 2010 to 2020. The sample was constructed using propensity score matching an age and sex stratified sample of persons residing in the 27-county region of Southern Minnesota and Western Wisconsin. The matching included race, ethnicity, education level, region of residence, and the presence of 20 chronic conditions. We used a validated deep learning based algorithm to calculate subcutaneous adipose tissue area, visceral adipose tissue area, skeletal muscle area, skeletal muscle density, vertebral bone area, and vertebral bone density from a CT abdominal section. RESULTS We report CT-based body composition reference ranges on 4 649 persons representative of our geographic region. Older age was associated with a decrease in skeletal muscle area and density, and an increase in visceral adiposity. All chronic conditions were associated with a statistically significant difference in at least one body composition biomarker. The presence of a chronic condition was generally associated with greater subcutaneous and visceral adiposity, and lower muscle density and vertebrae bone density. CONCLUSIONS We report reference ranges for CT-based body composition biomarkers in a population-representative cohort of 4 649 persons by age, sex, body mass index, and chronic conditions.
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Affiliation(s)
- Alexander D Weston
- Digital Innovation Lab, Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida, USA
| | - Brandon R Grossardt
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Hillary W Garner
- Division of Musculoskeletal Radiology, Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA
| | - Timothy L Kline
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Alanna M Chamberlain
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Alina M Allen
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Bradley J Erickson
- Mayo Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, Rochester Minnesota, USA
| | - Walter A Rocca
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Andrew D Rule
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
- Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Jennifer L St. Sauver
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
- The Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, USA
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24
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Lee MH, Zea R, Garrett JW, Summers RM, Pickhardt PJ. AI-generated CT body composition biomarkers associated with increased mortality risk in socioeconomically disadvantaged individuals. Abdom Radiol (NY) 2024; 49:1330-1340. [PMID: 38280049 DOI: 10.1007/s00261-023-04161-z] [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: 09/26/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 01/29/2024]
Abstract
PURPOSE To evaluate the relationship between socioeconomic disadvantage using national area deprivation index (ADI) and CT-based body composition measures derived from fully automated artificial intelligence (AI) tools to identify body composition measures associated with increased risk for all-cause mortality and adverse cardiovascular events. METHODS Fully automated AI body composition tools quantifying abdominal aortic calcium, abdominal fat (visceral [VAT], visceral-to-subcutaneous ratio [VSR]), and muscle attenuation (muscle HU) were applied to non-contrast CT examinations in adults undergoing screening CT colonography (CTC). Patients were partitioned into 5 socioeconomic groups based on the national ADI rank at the census block group level. Pearson correlation analysis was performed to determine the association between national ADI and body composition measures. One-way analysis of variance was used to compare means across groups. Odds ratios (ORs) were generated using high-risk, high specificity (90% specificity) body composition thresholds with the most disadvantaged groups being compared to the least disadvantaged group (ADI < 20). RESULTS 7785 asymptomatic adults (mean age, 57 years; 4361:3424 F:M) underwent screening CTC from April 2004-December 2016. ADI rank data were available in 7644 patients. Median ADI was 31 (IQR 22-43). Aortic calcium, VAT, and VSR had positive correlation with ADI and muscle attenuation had a negative correlation with ADI (all p < .001). Compared with the least disadvantaged group, mean differences for the most disadvantaged group (ADI > 80) were: Aortic calcium (Agatston) = 567, VAT = 27 cm2, VSR = 0.1, and muscle HU = -6 HU (all p < .05). Compared with the least disadvantaged group, the most disadvantaged group had significantly higher odds of having high-risk body composition measures: Aortic calcium OR = 3.8, VAT OR = 2.5, VSR OR = 2.0, and muscle HU OR = 3.1(all p < .001). CONCLUSION Fully automated CT body composition tools show that socioeconomic disadvantage is associated with high-risk body composition measures and can be used to identify individuals at increased risk for all-cause mortality and adverse cardiovascular events.
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Affiliation(s)
- Matthew H Lee
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA.
| | - Ryan Zea
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - John W Garrett
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA
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25
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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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26
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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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27
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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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28
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Kalisz K, Navin PJ, Itani M, Agarwal AK, Venkatesh SK, Rajiah PS. Multimodality Imaging in Metabolic Syndrome: State-of-the-Art Review. Radiographics 2024; 44:e230083. [PMID: 38329901 DOI: 10.1148/rg.230083] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Metabolic syndrome comprises a set of risk factors that include abdominal obesity, impaired glucose tolerance, hypertriglyceridemia, low high-density lipoprotein levels, and high blood pressure, at least three of which must be fulfilled for diagnosis. Metabolic syndrome has been linked to an increased risk of cardiovascular disease and type 2 diabetes mellitus. Multimodality imaging plays an important role in metabolic syndrome, including diagnosis, risk stratification, and assessment of complications. CT and MRI are the primary tools for quantification of excess fat, including subcutaneous and visceral adipose tissue, as well as fat around organs, which are associated with increased cardiovascular risk. PET has been shown to detect signs of insulin resistance and may detect ectopic sites of brown fat. Cardiovascular disease is an important complication of metabolic syndrome, resulting in subclinical or symptomatic coronary artery disease, alterations in cardiac structure and function with potential progression to heart failure, and systemic vascular disease. CT angiography provides comprehensive evaluation of the coronary and systemic arteries, while cardiac MRI assesses cardiac structure, function, myocardial ischemia, and infarction. Liver damage results from a spectrum of nonalcoholic fatty liver disease ranging from steatosis to fibrosis and possible cirrhosis. US, CT, and MRI are useful in assessing steatosis and can be performed to detect and grade hepatic fibrosis, particularly using elastography techniques. Metabolic syndrome also has deleterious effects on the pancreas, kidney, gastrointestinal tract, and ovaries, including increased risk for several malignancies. Metabolic syndrome is associated with cerebral infarcts, best evaluated with MRI, and has been linked with cognitive decline. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Pickhardt in this issue.
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Affiliation(s)
- Kevin Kalisz
- From the Duke University School of Medicine, Durham, NC (K.K.); Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 559905 (P.J.N., S.K.V., P.S.R.); Mallinckrodt Institute of Radiology, Washington University, St. Louis, Mo (M.I.); and Mayo Clinic, Jacksonville, Fla (A.K.A.)
| | - Patrick J Navin
- From the Duke University School of Medicine, Durham, NC (K.K.); Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 559905 (P.J.N., S.K.V., P.S.R.); Mallinckrodt Institute of Radiology, Washington University, St. Louis, Mo (M.I.); and Mayo Clinic, Jacksonville, Fla (A.K.A.)
| | - Malak Itani
- From the Duke University School of Medicine, Durham, NC (K.K.); Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 559905 (P.J.N., S.K.V., P.S.R.); Mallinckrodt Institute of Radiology, Washington University, St. Louis, Mo (M.I.); and Mayo Clinic, Jacksonville, Fla (A.K.A.)
| | - Amit Kumar Agarwal
- From the Duke University School of Medicine, Durham, NC (K.K.); Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 559905 (P.J.N., S.K.V., P.S.R.); Mallinckrodt Institute of Radiology, Washington University, St. Louis, Mo (M.I.); and Mayo Clinic, Jacksonville, Fla (A.K.A.)
| | - Sudhakar K Venkatesh
- From the Duke University School of Medicine, Durham, NC (K.K.); Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 559905 (P.J.N., S.K.V., P.S.R.); Mallinckrodt Institute of Radiology, Washington University, St. Louis, Mo (M.I.); and Mayo Clinic, Jacksonville, Fla (A.K.A.)
| | - Prabhakar Shantha Rajiah
- From the Duke University School of Medicine, Durham, NC (K.K.); Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 559905 (P.J.N., S.K.V., P.S.R.); Mallinckrodt Institute of Radiology, Washington University, St. Louis, Mo (M.I.); and Mayo Clinic, Jacksonville, Fla (A.K.A.)
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29
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Tonnesen PE, Mercaldo ND, Tahir I, Dietrich ASW, Amayri W, Graur A, Allaire B, Bouxsein ML, Samelson EJ, Kiel DP, Fintelmann FJ. Muscle Reference Values From Thoracic and Abdominal CT for Sarcopenia Assessment: The Framingham Heart Study. Invest Radiol 2024; 59:259-270. [PMID: 37725490 PMCID: PMC10920396 DOI: 10.1097/rli.0000000000001012] [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] [Indexed: 09/21/2023]
Abstract
BACKGROUND Loss of muscle mass is a known feature of sarcopenia and predicts poor clinical outcomes. Although muscle metrics can be derived from routine computed tomography (CT) images, sex-specific reference values at multiple vertebral levels over a wide age range are lacking. OBJECTIVE The aim of this study was to provide reference values for skeletal muscle mass and attenuation on thoracic and abdominal CT scans in the community-based Framingham Heart Study cohort to aid in the identification of sarcopenia. MATERIALS AND METHODS This secondary analysis of a prospective trial describes muscle metrics by age and sex for participants from the Framingham Heart Study without prior history of cancer who underwent at least 1 CT scan between 2002 and 2011. Using 2 previously validated machine learning algorithms followed by human quality assurance, skeletal muscle was analyzed on a single axial CT image per level at the 5th, 8th, 10th thoracic, and 3rd lumbar vertebral body (T5, T8, T10, L3). Cross-sectional muscle area (cm 2 ), mean skeletal muscle radioattenuation (SMRA, in Hounsfield units), skeletal muscle index (SMI, in cm 2 /m 2 ), and skeletal muscle gauge (SMRA·SMI) were calculated. Measurements were summarized by age group (<45, 45-54, 55-64, 65-74, ≥75 years), sex, and vertebral level. Models enabling the calculation of age-, sex-, and vertebral-level-specific reference values were created and embedded into an open access online Web application. RESULTS The cohort consisted of 3804 participants (1917 [50.4%] males; mean age, 55.6 ± 11.8 years; range, 33-92 years) and 7162 CT scans. Muscle metrics qualitatively decreased with increasing age and female sex. CONCLUSIONS This study established age- and sex-specific reference values for CT-based muscle metrics at thoracic and lumbar vertebral levels. These values may be used in future research investigating the role of muscle mass and attenuation in health and disease, and to identify sarcopenia.
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Affiliation(s)
- P. Erik Tonnesen
- Department of Radiology, Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Nathaniel D. Mercaldo
- Department of Radiology, Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Ismail Tahir
- Department of Radiology, Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA
| | - Anna-Sophia W. Dietrich
- Department of Radiology, Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA
| | - Wael Amayri
- Department of Radiology, Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA
| | - Alexander Graur
- Department of Radiology, Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA
| | - Brett Allaire
- Center for Advanced Orthopaedic Studies, Beth Israel Deaconess Medical Center, Boston, MA
| | - Mary L. Bouxsein
- Center for Advanced Orthopaedic Studies, Beth Israel Deaconess Medical Center, Boston, MA
- Department of Orthopedic Surgery, Harvard Medical School, Boston, MA
- Endocrine Division, Massachusetts General Hospital, Boston, MA
| | - Elizabeth J. Samelson
- Harvard Medical School, Boston, MA
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Douglas P. Kiel
- Harvard Medical School, Boston, MA
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Florian J. Fintelmann
- Department of Radiology, Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
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30
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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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31
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Ahn TR, Yoon YC, Kim HS, Kim K, Lee JH. Correlation of body composition metrics with bone mineral density and computed tomography-based trabecular attenuation. Eur J Radiol 2024; 171:111323. [PMID: 38241852 DOI: 10.1016/j.ejrad.2024.111323] [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: 11/02/2023] [Revised: 12/08/2023] [Accepted: 01/12/2024] [Indexed: 01/21/2024]
Abstract
PURPOSE To investigate the relationship of body composition metrics with bone mineral density (BMD) and trabecular attenuation in a cohort of healthy individuals. METHODS We retrospectively analyzed data of consecutively evaluated individuals who underwent dual-energy X-ray absorptiometry (DXA) and abdominopelvic computed tomography (CT) on the same day during routine medical check-ups between January 2021 and December 2021. Trabecular attenuation was measured at L1 level, while body composition metrics, including skeletal muscle index (SMI), skeletal muscle attenuation (SMA), visceral fat index (VFI), and subcutaneous fat index (SFI), were measured at L3 level. The association of body composition metrics with BMD and trabecular attenuation was analyzed using partial correlation analysis. RESULTS A total of 634 patients (median age, 56 years; range 50-62 years; 392 men) were included. In men, the SMI and SMA were positively correlated with BMD and trabecular attenuation, both before (r, 0.157-0.344; p < 0.05) and after (r, 0.103-0.246; p < 0.05) adjusting for age and body mass index. The VFI showed negative correlations with trabecular attenuation in both men (r, -0.170; p = 0.001) and women (r, -0.394; p < 0.001), which remained significant after adjusting for age and body mass index (r, -0.181 to -0.122; p < 0.05). CONCLUSION Low skeletal muscle mass and attenuation were significantly correlated with low BMD and trabecular attenuation in men. Visceral adiposity was associated with reduced BMD and trabecular attenuation in both men and women, demonstrating a stronger correlation with trabecular attenuation.
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Affiliation(s)
- Tae Ran Ahn
- Department of Radiology, Gil Medical Center, Gachon University School of Medicine, Incheon, Republic of Korea
| | - Young Cheol Yoon
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hyun Su Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyunga Kim
- Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Ji Hyun Lee
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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32
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Stehlé T, Wei F, Brabant S, Luciani A, Grimbert P, Prié D, Reizine E, Durrbach A, Mulé S, Hulin A, Boueilh A, Blain M, Champy CM, Ingels A, Matignon M, Brasseur P, Canouï-Poitrine F, Pigneur F. Glomerular Filtration Rate Measured Based on Iomeprol Clearance Assessed at CT Urography in Living Kidney Donor Candidates. Radiology 2023; 309:e230567. [PMID: 38085083 DOI: 10.1148/radiol.230567] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Background Estimating glomerular filtration rate (GFR) from serum creatinine can be inaccurate, and current procedures for measuring GFR are time-consuming and cumbersome. Purpose To develop a method for measuring GFR based on iomeprol clearance assessed at CT urography in kidney donor candidates and compare this with iohexol clearance (reference standard for measuring GFR). Materials and Methods This cross-sectional retrospective study included data from kidney donor candidates who underwent both iohexol clearance and CT urography between July 2016 and October 2022. CT-measured GFR was calculated as the iomeprol excretion rate in the urinary system between arterial and excretory phases (Hounsfield units times milliliters per minute) divided by a surrogate for serum iomeprol concentration in the aorta at the midpoint (in Hounsfield units). Performance of CT-measured GFR was assessed with use of mean bias (mean difference between CT-measured GFR and iohexol clearance), precision (the distance between quartile 1 and quartile 3 of the bias [quartile 3 minus quartile 1], with a small value indicating high precision), and accuracy (percentage of CT-measured GFR values falling within 10%, 20%, and 30% of iohexol clearance values). Intraobserver agreement was assessed for 30 randomly selected individuals with the Lin concordance correlation coefficient. Results A total of 75 kidney donor candidates were included (mean age, 51 years ± 13 [SD]; 45 female). The CT-measured GFR was unbiased (1.1 mL/min/1.73 m2 [95% CI: -1.9, 4.1]) and highly precise (16.2 mL/min/1.73 m2 [quartiles 1 to 3, -6.6 to 9.6]). The accuracy of CT-measured GFR within 10%, 20%, and 30% was 61.3% (95% CI: 50.3, 72.4), 88.0% (95% CI: 80.7, 95.4), and 100%, respectively. Concordance between CT-based GFR measurements taken 2 months apart was almost perfect (correlation coefficient, 0.99 [95% CI: 0.98, 0.99]). Conclusion In living kidney donors, GFR measured based on iomeprol clearance assessed at CT urography showed good agreement with GFR measured based on iohexol clearance. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Davenport in this issue.
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Affiliation(s)
- Thomas Stehlé
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Félix Wei
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Séverine Brabant
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Alain Luciani
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Philippe Grimbert
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Dominique Prié
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Edouard Reizine
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Antoine Durrbach
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Sébastien Mulé
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Anne Hulin
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Anna Boueilh
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Maxime Blain
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Cécile-Maud Champy
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Alexandre Ingels
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Marie Matignon
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Paul Brasseur
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Florence Canouï-Poitrine
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
| | - Frédéric Pigneur
- From the Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Université Paris Est Créteil, Institut Mondor de Recherche Biomédicale, Créteil, France (T.S., F.W., A.L., P.G., E.R., S.M., A.H., A.B., M.B., C.M.C., A.I., M.M., P.B., F.C.P., F.P.); Service de Néphrologie et Transplantation (T.S., P.G., A.D., A.B., M.M.), Fédération Hospitalo-Universitaire Innovative Therapy for Immune Disorders (T.S., P.G., A.D., A.B., M.M.), Service d'Imagerie Médicale (F.W., A.L., E.R., S.M., M.B., F.P.), Laboratoire de Pharmacologie (A.H.), Service d'Urologie (C.M.C., A.I.), Service de Santé Publique (P.B., F.C.P.), and Unité de Recherche Clinique (P.B., F.C.P.), Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Créteil, France; Service de Physiologie et Explorations Fonctionnelles, AP-HP, Groupe Hospitalier Necker Enfants Malades, Paris, France (S.B., D.P.); Faculté de Médecine, Université de Paris Cité, INSERM U1151, Paris, France (D.P.); Faculté de Médecine, Université Paris-Saclay, Orsay, France (A.D.); and INSERM UMR 1186, Institut Gustave Roussy, Villejuif, France (A.D.)
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33
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Pickhardt PJ, Blake GM, Kimmel Y, Weinstock E, Shaanan K, Hassid S, Abbas A, Fox MA. Detection of Moderate Hepatic Steatosis on Portal Venous Phase Contrast-Enhanced CT: Evaluation Using an Automated Artificial Intelligence Tool. AJR Am J Roentgenol 2023; 221:748-758. [PMID: 37466185 DOI: 10.2214/ajr.23.29651] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
BACKGROUND. Precontrast CT is an established means of evaluating for hepatic steatosis; postcontrast CT has historically been limited for this purpose. OBJECTIVE. The purpose of this study was to evaluate the diagnostic performance of portal venous phase postcontrast CT in detecting at least moderate hepatic steatosis using liver and spleen attenuation measurements determined by an automated artificial intelligence (AI) tool. METHODS. This retrospective study included 2917 patients (1381 men, 1536 women; mean age, 56.8 years) who underwent a CT examination that included at least two series through the liver. Examinations were obtained from an AI vendor's data lake of data from 24 centers in one U.S. health care network and 29 centers in one Israeli health care network. An automated deep learning tool extracted liver and spleen attenuation measurements. The reference for at least moderate steatosis was precontrast liver attenuation of less than 40 HU (i.e., estimated liver fat > 15%). A radiologist manually reviewed examinations with outlier AI results to confirm portal venous timing and identify issues impacting attenuation measurements. RESULTS. After outlier review, analysis included 2777 patients with portal venous phase images. Prevalence of at least moderate steatosis was 13.9% (387/2777). Patients without and with at least moderate steatosis, respectively, had mean postcontrast liver attenuation of 104.5 ± 18.1 (SD) HU and 67.1 ± 18.6 HU (p < .001); a mean difference in postcontrast attenuation between the liver and the spleen (hereafter, postcontrast liver-spleen attenuation difference) of -7.6 ± 16.4 (SD) HU and -31.8 ± 20.3 HU (p < .001); and mean liver enhancement of 49.3 ± 15.9 (SD) HU versus 38.6 ± 13.6 HU (p < .001). Diagnostic performance for the detection of at least moderate steatosis was higher for postcontrast liver attenuation (AUC = 0.938) than for the postcontrast liver-spleen attenuation difference (AUC = 0.832) (p < .001). For detection of at least moderate steatosis, postcontrast liver attenuation had sensitivity and specificity of 77.8% and 93.2%, respectively, at less than 80 HU and 90.5% and 78.4%, respectively, at less than 90 HU; the postcontrast liver-spleen attenuation difference had sensitivity and specificity of 71.4% and 79.3%, respectively, at less than -20 HU and 87.0% and 62.1%, respectively, at less than -10 HU. CONCLUSION. Postcontrast liver attenuation outperformed the postcontrast liver-spleen attenuation difference for detecting at least moderate steatosis in a heterogeneous patient sample, as evaluated using an automated AI tool. Splenic attenuation likely is not needed to assess for at least moderate steatosis on postcontrast images. CLINICAL IMPACT. The technique could promote early detection of clinically significant nonalcoholic fatty liver disease through individualized or large-scale opportunistic evaluation.
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Affiliation(s)
- Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252
| | - Glen M Blake
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom
| | | | | | | | | | - Ahmad Abbas
- Department of Radiology, Barzilai University Medical Center, Ashkelon, Israel
| | - Matthew A Fox
- Nanox-AI, Ltd., Neve Ilan, Israel
- Department of Radiology, Samson Assuta Ashdod University Hospital, Ashdod, Israel
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34
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Cao K, Xia Y, Yao J, Han X, Lambert L, Zhang T, Tang W, Jin G, Jiang H, Fang X, Nogues I, Li X, Guo W, Wang Y, Fang W, Qiu M, Hou Y, Kovarnik T, Vocka M, Lu Y, Chen Y, Chen X, Liu Z, Zhou J, Xie C, Zhang R, Lu H, Hager GD, Yuille AL, Lu L, Shao C, Shi Y, Zhang Q, Liang T, Zhang L, Lu J. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat Med 2023; 29:3033-3043. [PMID: 37985692 PMCID: PMC10719100 DOI: 10.1038/s41591-023-02640-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 10/12/2023] [Indexed: 11/22/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986-0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening.
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Affiliation(s)
- Kai Cao
- Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China
| | - Yingda Xia
- DAMO Academy, Alibaba Group, New York, NY, USA
| | - Jiawen Yao
- Hupan Laboratory, Hangzhou, China
- Damo Academy, Alibaba Group, Hangzhou, China
| | - Xu Han
- Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital of Zhejiang University, Hangzhou, China
| | - Lukas Lambert
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Tingting Zhang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Tang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Gang Jin
- Department of Surgery, Shanghai Institution of Pancreatic Disease, Shanghai, China
| | - Hui Jiang
- Department of Pathology, Shanghai Institution of Pancreatic Disease, Shanghai, China
| | - Xu Fang
- Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China
| | - Isabella Nogues
- Department of Biostatistics, Harvard University T.H. Chan School of Public Health, Cambridge, MA, USA
| | - Xuezhou Li
- Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China
| | - Wenchao Guo
- Hupan Laboratory, Hangzhou, China
- Damo Academy, Alibaba Group, Hangzhou, China
| | - Yu Wang
- Hupan Laboratory, Hangzhou, China
- Damo Academy, Alibaba Group, Hangzhou, China
| | - Wei Fang
- Hupan Laboratory, Hangzhou, China
- Damo Academy, Alibaba Group, Hangzhou, China
| | - Mingyan Qiu
- Hupan Laboratory, Hangzhou, China
- Damo Academy, Alibaba Group, Hangzhou, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Tomas Kovarnik
- Department of Invasive Cardiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Michal Vocka
- Department of Oncology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Yimei Lu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yingli Chen
- Department of Surgery, Shanghai Institution of Pancreatic Disease, Shanghai, China
| | - Xin Chen
- Department of Radiology, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Jian Zhou
- Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Chuanmiao Xie
- Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Rong Zhang
- Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Hong Lu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Gregory D Hager
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Alan L Yuille
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Le Lu
- DAMO Academy, Alibaba Group, New York, NY, USA
| | - Chengwei Shao
- Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China.
| | - Yu Shi
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
| | - Qi Zhang
- Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital of Zhejiang University, Hangzhou, China.
| | - Tingbo Liang
- Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital of Zhejiang University, Hangzhou, China.
| | - Ling Zhang
- DAMO Academy, Alibaba Group, New York, NY, USA.
| | - Jianping Lu
- Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China.
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Ong W, Liu RW, Makmur A, Low XZ, Sng WJ, Tan JH, Kumar N, Hallinan JTPD. Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography. Bioengineering (Basel) 2023; 10:1364. [PMID: 38135954 PMCID: PMC10741220 DOI: 10.3390/bioengineering10121364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.
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Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
| | - Ren Wei Liu
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Weizhong Jonathan Sng
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore; (J.H.T.); (N.K.)
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore; (J.H.T.); (N.K.)
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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Kim MW, Noh YM, Jung YS, Jeon SY, Lee DH. Elucidating the Correlation between Bone Mineral Density and Multifidus Muscle Characteristics: A Cross-Modal Study with Dual-Energy X-ray Absorptiometry and Spinal Computed Tomography Texture Analysis. Diagnostics (Basel) 2023; 13:3466. [PMID: 37998602 PMCID: PMC10670274 DOI: 10.3390/diagnostics13223466] [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: 09/12/2023] [Revised: 10/23/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Recent research underscores the clinical relevance of muscle conditions such as sarcopenia and their links to bone mineral density (BMD), yet notable gaps persist in the understanding of their interconnections. Our study addresses this by introducing a novel approach to decipher the correlation between BMD and the texture of the multifidus muscle, utilizing spinal computed tomography (CT) and dual-energy X-ray absorptiometry (DXA) to evaluate muscle texture, BMD, and bone mineral content (BMC) at the total lumbar vertebra and total hip. METHODS Our single-institution study examined 395 cases collected from 6 May 2012 to 30 November 2021. Each patient underwent a spinal CT scan and a DXA scan within a one-month interval. BMD and BMC at the total lumbar vertebra and total hip were measured. The texture features of the multifidus muscle from the axial cuts of T12 to S1 vertebrae were assessed via gray-level co-occurrence matrices. CT texture analysis values at angles of 45 + 45 and 90 degrees were calculated and correlated with BMD and BMC. A regression model was then constructed to predict BMD values, and the precision of these correlations was evaluated using mean square error (MSE) analysis. RESULTS Total lumbar BMC showed a correlation of 0.583-0.721 (MSE 1.568-1.842) and lumbar BMD of 0.632-0.756 (MSE 0.068-0.097). Total hip BMC had a correlation of 0.556-0.690 (MSE 0.448-0.495), while hip BMD ranged from 0.585 to 0.746 (MSE 0.072-0.092). CONCLUSIONS The analysis of spinal CT texture alongside BMD and BMC measures provides a new approach to understanding the relationship between bone and muscle health. The strong correlations expected from our research affirm the importance of integrating bone and muscle measures in the prevention, diagnosis, and management of conditions such as sarcopenia and osteoporosis.
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Affiliation(s)
- Min-Woo Kim
- Department of Orthopedic Surgery, Busan Medical Center, 62, Yangjeong-ro, Busanjin-gu, Busan 47227, Republic of Korea; (M.-W.K.); (Y.-M.N.); (Y.-S.J.)
| | - Young-Min Noh
- Department of Orthopedic Surgery, Busan Medical Center, 62, Yangjeong-ro, Busanjin-gu, Busan 47227, Republic of Korea; (M.-W.K.); (Y.-M.N.); (Y.-S.J.)
| | - Yun-Sung Jung
- Department of Orthopedic Surgery, Busan Medical Center, 62, Yangjeong-ro, Busanjin-gu, Busan 47227, Republic of Korea; (M.-W.K.); (Y.-M.N.); (Y.-S.J.)
| | - Se-Yeong Jeon
- Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea;
| | - Dong-Ha Lee
- Department of Orthopedic Surgery, Busan Medical Center, 62, Yangjeong-ro, Busanjin-gu, Busan 47227, Republic of Korea; (M.-W.K.); (Y.-M.N.); (Y.-S.J.)
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Sim JZT, Bhanu Prakash KN, Huang WM, Tan CH. Harnessing artificial intelligence in radiology to augment population health. FRONTIERS IN MEDICAL TECHNOLOGY 2023; 5:1281500. [PMID: 38021439 PMCID: PMC10663302 DOI: 10.3389/fmedt.2023.1281500] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
This review article serves to highlight radiological services as a major cost driver for the healthcare sector, and the potential improvements in productivity and cost savings that can be generated by incorporating artificial intelligence (AI) into the radiology workflow, referencing Singapore healthcare as an example. More specifically, we will discuss the opportunities for AI in lowering healthcare costs and supporting transformational shifts in our care model in the following domains: predictive analytics for optimising throughput and appropriate referrals, computer vision for image enhancement (to increase scanner efficiency and decrease radiation exposure) and pattern recognition (to aid human interpretation and worklist prioritisation), natural language processing and large language models for optimising reports and text data-mining. In the context of preventive health, we will discuss how AI can support population level screening for major disease burdens through opportunistic screening and democratise expertise to increase access to radiological services in primary and community care.
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Affiliation(s)
- Jordan Z. T. Sim
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore, Singapore
| | - K. N. Bhanu Prakash
- Clinical Data Analytics & Radiomics, Cellular Image Informatics, Bioinformatics Institute, Singapore, Singapore
| | - Wei Min Huang
- Healthcare-MedTech Division & Visual Intelligence Department, Institute for Infocomm Research, Singapore, Singapore
| | - Cher Heng Tan
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
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Kim MW, Huh JW, Noh YM, Seo HE, Lee DH. Assessing bone mineral density in non-weight bearing regions of the body: a texture analysis approach using abdomen and pelvis computed tomography Hounsfield units-a cross-sectional study. Quant Imaging Med Surg 2023; 13:7484-7493. [PMID: 37969628 PMCID: PMC10644143 DOI: 10.21037/qims-23-512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 08/29/2023] [Indexed: 11/17/2023]
Abstract
Background Highlighting a gap in comprehending bone microarchitecture's intricacies using dual-energy X-ray absorptiometry (DXA), this study aims to bridge this chasm by analyzing texture in non-weight bearing regions on axial computed tomography (CT) scans. Our goal is to enrich osteoporosis patient management by enhancing bone quality and microarchitecture insights. Methods Conducted at Busan Medical Center from March 1, 2013, to August 30, 2022, 1,320 cases (782 patients) were screened. After applying exclusion criteria, 458 samples (296 patients) underwent bone mineral density (BMD) assessment with both CT and DXA. Regions of interest (ROIs) included spine pedicle's maximum trabecular area, sacrum Zone 1, superior/inferior pubic ramus, and femur's greater/lesser trochanters. Texture features (n=45) were extracted from ROIs using gray-level co-occurrence matrices. A regression model predicted BMD, spotlighting the top five influential texture features. Results Correlation coefficients ranged from 0.709 (lowest for total femur BMD) to 0.804 (highest for femur intertrochanter BMD). Mean squared error (MSE) values were also provided for lumbar and femur BMD/bone mineral content (BMC) metrics. The most influential texture features included contrast_32, correlation_32_v, and three other metrics. Conclusions By melding traditional DXA and CT texture analysis, our approach presents a comprehensive bone health perspective, potentially revolutionizing osteoporosis diagnostics.
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Salyapongse AM, Rose SD, Pickhardt PJ, Lubner MG, Toia GV, Bujila R, Yin Z, Slavic S, Szczykutowicz TP. CT Number Accuracy and Association With Object Size: A Phantom Study Comparing Energy-Integrating Detector CT and Deep Silicon Photon-Counting Detector CT. AJR Am J Roentgenol 2023; 221:539-547. [PMID: 37255042 DOI: 10.2214/ajr.23.29463] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
BACKGROUND. Variable beam hardening based on patient size causes variation in CT numbers for energy-integrating detector (EID) CT. Photon-counting detector (PCD) CT more accurately determines effective beam energy, potentially improving CT number reliability. OBJECTIVE. The purpose of the present study was to compare EID CT and deep silicon PCD CT in terms of both the effect of changes in object size on CT number and the overall accuracy of CT numbers. METHODS. A phantom with polyethylene rings of varying sizes (mimicking patient sizes) as well as inserts of different materials was scanned on an EID CT scanner in single-energy (SE) mode (120-kV images) and in rapid-kilovoltage-switching dual-energy (DE) mode (70-keV images) and on a prototype deep silicon PCD CT scanner (70-keV images). ROIs were placed to measure the CT numbers of the materials. Slopes of CT number as a function of object size were computed. Materials' ideal CT number at 70 keV was computed using the National Institute of Standards and Technology XCOM Photon Cross Sections Database. The root mean square error (RMSE) between measured and ideal numbers was calculated across object sizes. RESULTS. Slope (expressed as Hounsfield units per centimeter) was significantly closer to zero (i.e., less variation in CT number as a function of size) for PCD CT than for SE EID CT for air (1.2 vs 2.4 HU/cm), water (-0.3 vs -1.0 HU/cm), iodine (-1.1 vs -4.5 HU/cm), and bone (-2.5 vs -10.1 HU/cm) and for PCD CT than for DE EID CT for air (1.2 vs 2.8 HU/cm), water (-0.3 vs -1.0 HU/cm), polystyrene (-0.2 vs -0.9 HU/cm), iodine (-1.1 vs -1.9 HU/cm), and bone (-2.5 vs -6.2 HU/cm) (p < .05). For all tested materials, PCD CT had the smallest RMSE, indicating CT numbers closest to ideal numbers; specifically, RMSE (expressed as Hounsfield units) for SE EID CT, DE EID CT, and PCD CT was 32, 44, and 17 HU for air; 7, 8, and 3 HU for water; 9, 10, and 4 HU for polystyrene; 31, 37, and 13 HU for iodine; and 69, 81, and 20 HU for bone, respectively. CONCLUSION. For numerous materials, deep silicon PCD CT, in comparison with SE EID CT and DE EID CT, showed lower CT number variability as a function of size and CT numbers closer to ideal numbers. CLINICAL IMPACT. Greater reliability of CT numbers for PCD CT is important given the dependence of diagnostic pathways on CT numbers.
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Affiliation(s)
- Aria M Salyapongse
- Department of Radiology, University of Wisconsin Madison, 1005 Wisconsin Institute for Medical Research, 1111 Highland Ave, Madison, WI 53705
| | - Sean D Rose
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston, Houston, TX
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin Madison, 1005 Wisconsin Institute for Medical Research, 1111 Highland Ave, Madison, WI 53705
- University of Wisconsin Carbone Cancer Center, University of Wisconsin Madison, Madison, WI
| | - Meghan G Lubner
- Department of Radiology, University of Wisconsin Madison, 1005 Wisconsin Institute for Medical Research, 1111 Highland Ave, Madison, WI 53705
| | - Giuseppe V Toia
- Department of Radiology, University of Wisconsin Madison, 1005 Wisconsin Institute for Medical Research, 1111 Highland Ave, Madison, WI 53705
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI
| | | | | | | | - Timothy P Szczykutowicz
- Department of Radiology, University of Wisconsin Madison, 1005 Wisconsin Institute for Medical Research, 1111 Highland Ave, Madison, WI 53705
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI
- Department of Biomedical Engineering, University of Wisconsin Madison, Madison, WI
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Dako F, Cook T, Zafar H, Schnall M. Population Health Management in Radiology: Economic Considerations. J Am Coll Radiol 2023; 20:962-968. [PMID: 37597716 DOI: 10.1016/j.jacr.2023.07.016] [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: 05/17/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 08/21/2023]
Abstract
There is a growing emphasis on population health management (PHM) in the United States, in part because it has the worst health outcomes indices among high-income countries despite spending by far the most on health care. Successful PHM is expected to lead to a healthier population with reduced health care utilization and cost. The role of radiology in PHM is increasingly being recognized, including efforts in care coordination, secondary prevention, and appropriate imaging utilization, among others. To further discuss economic considerations for PHM, we must understand the evolving health care payer environment, which combines fee-for-service and increasingly, an alternative payment model framework developed by the Health Care Payment Learning and Action Network. In considering the term "value-based care," perceived value needs to accrue to those who ultimately pay for care, which is more commonly employers and the government. This perspective drives the design of alternative payment models and thus should be taken into consideration to ensure sustainable practice models.
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Affiliation(s)
- Farouk Dako
- Director of the Center for Global and Population Health Research in Radiology, Department of Radiology, Senior Fellow, Leonard Davis Institute of Health Economics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
| | - Tessa Cook
- Vice Chair, Practice Transformation, Department of Radiology, Senior Fellow, Leonard Davis Institute of Health Economics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Hanna Zafar
- Vice Chair, Quality, Department of Radiology, Senior Fellow, Leonard Davis Institute of Health Economics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Mitchell Schnall
- Chairman and Eugene P. Pendergrass Professor of Radiology, Department of Radiology, Senior Fellow, Leonard Davis Institute of Health Economics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
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Dundamadappa SK. AI tools in Emergency Radiology reading room: a new era of Radiology. Emerg Radiol 2023; 30:647-657. [PMID: 37420044 DOI: 10.1007/s10140-023-02154-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/20/2023] [Indexed: 07/09/2023]
Abstract
Artificial intelligence tools in radiology practices have surged, with modules developed to target specific findings becoming increasingly prevalent and proving valuable in the daily emergency room radiology practice. The number of US Food and Drug Administration-cleared radiology-related algorithms has soared from just 10 in early 2017 to over 200 presently. This review will concentrate on the present utilization of AI tools in clinical ER radiology setting, including a brief discussion of the limitations of the technique. As radiologists, it is essential that we embrace this technology, comprehend its constraints, and use it to improve patient care.
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Langlotz CP. The Future of AI and Informatics in Radiology: 10 Predictions. Radiology 2023; 309:e231114. [PMID: 37874234 PMCID: PMC10623186 DOI: 10.1148/radiol.231114] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 10/25/2023]
Affiliation(s)
- Curtis P. Langlotz
- From the Departments of Radiology, Medicine, and Biomedical Data
Science, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA
94305
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Mervak BM, Fried JG, Wasnik AP. A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging. Diagnostics (Basel) 2023; 13:2889. [PMID: 37761253 PMCID: PMC10529018 DOI: 10.3390/diagnostics13182889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/23/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
Artificial intelligence (AI) has been a topic of substantial interest for radiologists in recent years. Although many of the first clinical applications were in the neuro, cardiothoracic, and breast imaging subspecialties, the number of investigated and real-world applications of body imaging has been increasing, with more than 30 FDA-approved algorithms now available for applications in the abdomen and pelvis. In this manuscript, we explore some of the fundamentals of artificial intelligence and machine learning, review major functions that AI algorithms may perform, introduce current and potential future applications of AI in abdominal imaging, provide a basic understanding of the pathways by which AI algorithms can receive FDA approval, and explore some of the challenges with the implementation of AI in clinical practice.
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Affiliation(s)
| | | | - Ashish P. Wasnik
- Department of Radiology, University of Michigan—Michigan Medicine, 1500 E. Medical Center Dr., Ann Arbor, MI 48109, USA; (B.M.M.); (J.G.F.)
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Ge Y, Chen Y, Liu G, Zhu S, Li B, Tian M, Zhang J, Wu X, Yang M. Association Between Hip Bone Mineral Density and Mortality Risk After Hip Fracture: A Prospective Cohort Study. Calcif Tissue Int 2023; 113:295-303. [PMID: 37347299 PMCID: PMC10449952 DOI: 10.1007/s00223-023-01109-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 06/09/2023] [Indexed: 06/23/2023]
Abstract
Factors related to mortality after osteoporotic hip fracture (HF) have been investigated intensively, except for proximal femoral bone mineral density (BMD), which is also the primary cause of osteoporosis. In this study, we aimed to investigate the association of hip BMD with mortality risk after HF. Four hundred and eleven elderly patients with HF in Beijing, China, were included and prospectively followed up with a median time of 3 years. At baseline, quantitative CT technique (QCT) was used to measure areal BMD (aBMD) of the unaffected hip. Areal BMDs of the total hip (TH), femoral neck (FN), trochanter (TR), and intertrochanter were analyzed with postoperative mortality as the primary outcome. A total of 394 patients (78.59 ± 7.59 years, 75.4% female) were included in our final analysis, with 86 (82.23 ± 7.00 years, 81.4% female) dead. All hip bone densities demonstrated a significant association with mortality risks in the unadjusted model, but only TR aBMD remained significantly correlated after adjusting for all covariates. Compared to the lower TR aBMD group, the higher TR aBMD group yielded significantly lower death risks (HR 0.21 95% CI 0.05-0.9, P = 0.036). Higher survival probabilities were observed for higher TH and TR aBMD in survival analysis (P < 0.001). Hip BMD, especially TR BMD assessed by QCT, is an independent risk factor for postoperative mortality following HF. QCT may present a promising avenue for opportunistic analysis in immobilized patients, providing valuable information for early detection and personalized interventions to enhance patient outcomes.
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Affiliation(s)
- Yufeng Ge
- Department of Orthopaedics and Traumatology, Peking University Fourth School of Clinical Medicine, Beijing Jishuitan Hospital, Beijing, China
| | - Yimin Chen
- Department of Orthopaedics and Traumatology, Peking University Fourth School of Clinical Medicine, Beijing Jishuitan Hospital, Beijing, China
| | - Gang Liu
- Department of Orthopaedics and Traumatology, Peking University Fourth School of Clinical Medicine, Beijing Jishuitan Hospital, Beijing, China
| | - Shiwen Zhu
- Department of Orthopaedics and Traumatology, Peking University Fourth School of Clinical Medicine, Beijing Jishuitan Hospital, Beijing, China
| | - Bo Li
- Department of Orthopaedics and Traumatology, Peking University Fourth School of Clinical Medicine, Beijing Jishuitan Hospital, Beijing, China
| | - Maoyi Tian
- The George Institute for Global Health at Peking University Health Science Centre, Beijing, China
| | - Jing Zhang
- School of Population Health, University of New South Wales, Sydney, NSW, Australia
| | - Xinbao Wu
- Department of Orthopaedics and Traumatology, Peking University Fourth School of Clinical Medicine, Beijing Jishuitan Hospital, Beijing, China
| | - Minghui Yang
- Department of Orthopaedics and Traumatology, Peking University Fourth School of Clinical Medicine, Beijing Jishuitan Hospital, Beijing, China.
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Babyn PS, Adams SJ. AI analysis of chest radiographs as a biomarker of biological age. THE LANCET. HEALTHY LONGEVITY 2023; 4:e446-e447. [PMID: 37597531 DOI: 10.1016/s2666-7568(23)00143-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 08/21/2023] Open
Affiliation(s)
- Paul S Babyn
- Department of Medical Imaging, University of Saskatchewan, Saskatoon S7N 0W8, SK, Canada.
| | - Scott J Adams
- Department of Medical Imaging, University of Saskatchewan, Saskatoon S7N 0W8, SK, Canada
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Bott KN, Matheson BE, Smith ACJ, Tse JJ, Boyd SK, Manske SL. Addressing Challenges of Opportunistic Computed Tomography Bone Mineral Density Analysis. Diagnostics (Basel) 2023; 13:2572. [PMID: 37568935 PMCID: PMC10416827 DOI: 10.3390/diagnostics13152572] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/20/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023] Open
Abstract
Computed tomography (CT) offers advanced biomedical imaging of the body and is broadly utilized for clinical diagnosis. Traditionally, clinical CT scans have not been used for volumetric bone mineral density (vBMD) assessment; however, computational advances can now leverage clinically obtained CT data for the secondary analysis of bone, known as opportunistic CT analysis. Initial applications focused on using clinically acquired CT scans for secondary osteoporosis screening, but opportunistic CT analysis can also be applied to answer research questions related to vBMD changes in response to various disease states. There are several considerations for opportunistic CT analysis, including scan acquisition, contrast enhancement, the internal calibration technique, and bone segmentation, but there remains no consensus on applying these methods. These factors may influence vBMD measures and therefore the robustness of the opportunistic CT analysis. Further research and standardization efforts are needed to establish a consensus and optimize the application of opportunistic CT analysis for accurate and reliable assessment of vBMD in clinical and research settings. This review summarizes the current state of opportunistic CT analysis, highlighting its potential and addressing the associated challenges.
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Affiliation(s)
- Kirsten N. Bott
- Department of Radiology, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.N.B.); (S.K.B.)
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB T2N 4Z6, Canada
| | - Bryn E. Matheson
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB T2N 4Z6, Canada
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Ainsley C. J. Smith
- Department of Radiology, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.N.B.); (S.K.B.)
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB T2N 4Z6, Canada
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Justin J. Tse
- Department of Radiology, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.N.B.); (S.K.B.)
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB T2N 4Z6, Canada
| | - Steven K. Boyd
- Department of Radiology, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.N.B.); (S.K.B.)
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB T2N 4Z6, Canada
| | - Sarah L. Manske
- Department of Radiology, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.N.B.); (S.K.B.)
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB T2N 4Z6, Canada
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Xu K, Li T, Khan MS, Gao R, Antic SL, Huo Y, Sandler KL, Maldonado F, Landman BA. Body composition assessment with limited field-of-view computed tomography: A semantic image extension perspective. Med Image Anal 2023; 88:102852. [PMID: 37276799 PMCID: PMC10527087 DOI: 10.1016/j.media.2023.102852] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 01/30/2023] [Accepted: 05/23/2023] [Indexed: 06/07/2023]
Abstract
Field-of-view (FOV) tissue truncation beyond the lungs is common in routine lung screening computed tomography (CT). This poses limitations for opportunistic CT-based body composition (BC) assessment as key anatomical structures are missing. Traditionally, extending the FOV of CT is considered as a CT reconstruction problem using limited data. However, this approach relies on the projection domain data which might not be available in application. In this work, we formulate the problem from the semantic image extension perspective which only requires image data as inputs. The proposed two-stage method identifies a new FOV border based on the estimated extent of the complete body and imputes missing tissues in the truncated region. The training samples are simulated using CT slices with complete body in FOV, making the model development self-supervised. We evaluate the validity of the proposed method in automatic BC assessment using lung screening CT with limited FOV. The proposed method effectively restores the missing tissues and reduces BC assessment error introduced by FOV tissue truncation. In the BC assessment for large-scale lung screening CT datasets, this correction improves both the intra-subject consistency and the correlation with anthropometric approximations. The developed method is available at https://github.com/MASILab/S-EFOV.
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Affiliation(s)
- Kaiwen Xu
- Vanderbilt University, 2301 Vanderbilt Place, Nashville, 37235, United States.
| | - Thomas Li
- Vanderbilt University, 2301 Vanderbilt Place, Nashville, 37235, United States
| | - Mirza S Khan
- Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, 37232, United States
| | - Riqiang Gao
- Vanderbilt University, 2301 Vanderbilt Place, Nashville, 37235, United States
| | - Sanja L Antic
- Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, 37232, United States
| | - Yuankai Huo
- Vanderbilt University, 2301 Vanderbilt Place, Nashville, 37235, United States
| | - Kim L Sandler
- Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, 37232, United States
| | - Fabien Maldonado
- Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, 37232, United States
| | - Bennett A Landman
- Vanderbilt University, 2301 Vanderbilt Place, Nashville, 37235, United States; Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, 37232, United States
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Stehlé T, Ouamri Y, Morel A, Vidal-Petiot E, Fellahi S, Segaux L, Prié D, Grimbert P, Luciani A, Audard V, Haymann JP, Mulé S, De Kerviler E, Peraldi MN, Boutten A, Matignon M, Canouï-Poitrine F, Flamant M, Pigneur F. Development and validation of a new equation based on plasma creatinine and muscle mass assessed by CT scan to estimate glomerular filtration rate: a cross-sectional study. Clin Kidney J 2023; 16:1265-1277. [PMID: 37529645 PMCID: PMC10387393 DOI: 10.1093/ckj/sfad012] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Indexed: 08/03/2023] Open
Abstract
Background Inter-individual variations of non-glomerular filtration rate (GFR) determinants of serum creatinine, such as muscle mass, account for the imperfect performance of estimated GFR (eGFR) equations. We aimed to develop an equation based on creatinine and total lumbar muscle cross-sectional area measured by unenhanced computed tomography scan at the third lumbar vertebra. Methods The muscle mass-based eGFR (MMB-eGFR) equation was developed in 118 kidney donor candidates (iohexol clearance) using linear regression. Validation cohorts included 114 healthy subjects from another center (51Cr-EDTA clearance, validation population 1), 55 patients with chronic diseases (iohexol, validation population 2), and 60 patients with highly discordant creatinine and cystatin C-based eGFR, thus presumed to have atypical non-GFR determinants of creatinine (51Cr-EDTA, validation population 3). Mean bias was the mean difference between eGFR and measured GFR, precision the standard deviation (SD) of the bias, and accuracy the percentage of eGFR values falling within 20% and 30% of measured GFR. Results In validation population 1, performance of MMB-eGFR was not different from those of CKD-EPICr2009 and CKD-EPICr2021. In validation population 2, MMB-eGFR was unbiased and displayed better precision than CKD-EPICr2009, CKD-EPICr2021 and EKFC (SD of the biases: 13.1 vs 16.5, 16.8 and 15.9 mL/min/1.73 m2). In validation population 3, MMB-eGFR had better precision and accuracy {accuracy within 30%: 75.0% [95% confidence interval (CI) 64.0-86.0] vs 51.5% (95% CI 39.0-64.3) for CKD-EPICr2009, 43.3% (95% CI 31.0-55.9) for CKD-EPICr2021, and 53.3% (95% CI 40.7-66.0) for EKFC}. Difference in bias between Black and white subjects was -2.1 mL/min/1.73 m2 (95% CI -7.2 to 3.0), vs -8.4 mL/min/1.73 m2 (95% CI -13.2 to -3.6) for CKD-EPICr2021. Conclusion MMB-eGFR displayed better performances than equations based on demographics, and could be applied to subjects of various ethnic backgrounds.
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Affiliation(s)
| | - Yaniss Ouamri
- Univ. Paris Est Créteil, Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Institut Mondor de Recherche Biomédicale (IMRB), Créteil, France
- Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Service d'Imagerie Médicale, Créteil, France
| | - Antoine Morel
- Univ. Paris Est Créteil, Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Institut Mondor de Recherche Biomédicale (IMRB), Créteil, France
- Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Service de Santé Publique, Créteil, France
| | - Emmanuelle Vidal-Petiot
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), U1149, Paris, France
- Assistance Publique des Hôpitaux de Paris (AP-HP), Département de Physiologie-Explorations Fonctionnelles, Hôpital Bichat, Paris, France
| | - Soraya Fellahi
- Université Pierre et Marie Curie Paris 6, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris (APHP), Paris, France
- Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, Département de Biochimie, Créteil, France
| | - Lauriane Segaux
- Univ. Paris Est Créteil, Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Institut Mondor de Recherche Biomédicale (IMRB), Créteil, France
- Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Service de Santé Publique, Créteil, France
| | - Dominique Prié
- Université de Paris Cité, Faculté de Médecine, Institut National de la Santé et de la Recherche Médicale (INSERM) U1151, Paris, France
- Assistance Publique des Hôpitaux de Paris (AP-HP), Groupe Hospitalier Necker Enfants Malades, Service de Physiologie et Explorations Fonctionnelles, Paris, France
| | - Philippe Grimbert
- Univ. Paris Est Créteil, Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Institut Mondor de Recherche Biomédicale (IMRB), Créteil, France
- Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, Service de Néphrologie et Transplantation, Fédération Hospitalo-Universitaire « Innovative therapy for immune disorders », Créteil, France
| | - Alain Luciani
- Univ. Paris Est Créteil, Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Institut Mondor de Recherche Biomédicale (IMRB), Créteil, France
- Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Service d'Imagerie Médicale, Créteil, France
| | - Vincent Audard
- Univ. Paris Est Créteil, Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Institut Mondor de Recherche Biomédicale (IMRB), Créteil, France
- Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, Service de Néphrologie et Transplantation, Fédération Hospitalo-Universitaire « Innovative therapy for immune disorders », Créteil, France
| | - Jean Philippe Haymann
- Univ. Paris Diderot, Sorbonne Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), U1155
- Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux de Paris, hôpital Tenon, Département de Physiologie-Explorations Fonctionnelles, Paris, France
| | - Sébastien Mulé
- Univ. Paris Est Créteil, Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Institut Mondor de Recherche Biomédicale (IMRB), Créteil, France
- Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Service d'Imagerie Médicale, Créteil, France
| | - Eric De Kerviler
- Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux de Paris, Hôpital Tenon, Département de Physiologie-Explorations Fonctionnelles, Paris, France
| | - Marie-Noëlle Peraldi
- Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpital Saint Louis, Service de Néphrologie, Paris, France
| | - Anne Boutten
- Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux de Paris, hôpital Bichat, Département de Biochimie Clinique, Paris, France
| | - Marie Matignon
- Univ. Paris Est Créteil, Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Institut Mondor de Recherche Biomédicale (IMRB), Créteil, France
- Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, Service de Néphrologie et Transplantation, Fédération Hospitalo-Universitaire « Innovative therapy for immune disorders », Créteil, France
| | - Florence Canouï-Poitrine
- Univ. Paris Est Créteil, Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Institut Mondor de Recherche Biomédicale (IMRB), Créteil, France
- Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Service de Santé Publique, Créteil, France
| | - Martin Flamant
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), U1149, Paris, France
- Assistance Publique des Hôpitaux de Paris (AP-HP), Département de Physiologie-Explorations Fonctionnelles, Hôpital Bichat, Paris, France
| | - Frédéric Pigneur
- Univ. Paris Est Créteil, Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Institut Mondor de Recherche Biomédicale (IMRB), Créteil, France
- Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Service d'Imagerie Médicale, Créteil, France
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Xu K, Khan MS, Li TZ, Gao R, Terry JG, Huo Y, Lasko TA, Carr JJ, Maldonado F, Landman BA, Sandler KL. AI Body Composition in Lung Cancer Screening: Added Value Beyond Lung Cancer Detection. Radiology 2023; 308:e222937. [PMID: 37489991 PMCID: PMC10374937 DOI: 10.1148/radiol.222937] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 05/25/2023] [Accepted: 05/26/2023] [Indexed: 07/26/2023]
Abstract
Background An artificial intelligence (AI) algorithm has been developed for fully automated body composition assessment of lung cancer screening noncontrast low-dose CT of the chest (LDCT) scans, but the utility of these measurements in disease risk prediction models has not been assessed. Purpose To evaluate the added value of CT-based AI-derived body composition measurements in risk prediction of lung cancer incidence, lung cancer death, cardiovascular disease (CVD) death, and all-cause mortality in the National Lung Screening Trial (NLST). Materials and Methods In this secondary analysis of the NLST, body composition measurements, including area and attenuation attributes of skeletal muscle and subcutaneous adipose tissue, were derived from baseline LDCT examinations by using a previously developed AI algorithm. The added value of these measurements was assessed with sex- and cause-specific Cox proportional hazards models with and without the AI-derived body composition measurements for predicting lung cancer incidence, lung cancer death, CVD death, and all-cause mortality. Models were adjusted for confounding variables including age; body mass index; quantitative emphysema; coronary artery calcification; history of diabetes, heart disease, hypertension, and stroke; and other PLCOM2012 lung cancer risk factors. Goodness-of-fit improvements were assessed with the likelihood ratio test. Results Among 20 768 included participants (median age, 61 years [IQR, 57-65 years]; 12 317 men), 865 were diagnosed with lung cancer and 4180 died during follow-up. Including the AI-derived body composition measurements improved risk prediction for lung cancer death (male participants: χ2 = 23.09, P < .001; female participants: χ2 = 15.04, P = .002), CVD death (males: χ2 = 69.94, P < .001; females: χ2 = 16.60, P < .001), and all-cause mortality (males: χ2 = 248.13, P < .001; females: χ2 = 94.54, P < .001), but not for lung cancer incidence (male participants: χ2 = 2.53, P = .11; female participants: χ2 = 1.73, P = .19). Conclusion The body composition measurements automatically derived from baseline low-dose CT examinations added predictive value for lung cancer death, CVD death, and all-cause death, but not for lung cancer incidence in the NLST. Clinical trial registration no. NCT00047385 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Fintelmann in this issue.
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Affiliation(s)
- Kaiwen Xu
- From the Department of Computer Science (K.X., Y.H., T.A.L., B.A.L.),
Department of Biomedical Engineering (T.Z.L., B.A.L.), School of Medicine
(T.Z.L.), and Department of Electrical and Computer Engineering (Y.H., B.A.L.),
Vanderbilt University, 2301 Vanderbilt Pl, Nashville, TN 37235; University of
Missouri–Kansas City, Kansas City, Mo (M.S.K.); Saint Luke’s Mid
America Heart Institute, Kansas City, Mo (M.S.K.); Siemens Healthineers,
Princeton, NJ (R.G.); Department of Radiology (J.G.T., J.J.C., B.A.L., K.L.S.),
Department of Biomedical Informatics (T.A.L., J.J.C., B.A.L.), Division of
Cardiovascular Medicine (J.J.C.), Division of Allergy, Pulmonary and Critical
Care Medicine (F.M.), Vanderbilt University Institute of Imaging Science
(B.A.L.), Vanderbilt Brain Institute (B.A.L.), Department of Psychiatry and
Behavioral Sciences (B.A.L.), Department of Neurology (B.A.L.), and Vanderbilt
Memory & Alzheimer’s Center (B.A.L.), Vanderbilt University
Medical Center, Nashville, Tenn
| | - Mirza S. Khan
- From the Department of Computer Science (K.X., Y.H., T.A.L., B.A.L.),
Department of Biomedical Engineering (T.Z.L., B.A.L.), School of Medicine
(T.Z.L.), and Department of Electrical and Computer Engineering (Y.H., B.A.L.),
Vanderbilt University, 2301 Vanderbilt Pl, Nashville, TN 37235; University of
Missouri–Kansas City, Kansas City, Mo (M.S.K.); Saint Luke’s Mid
America Heart Institute, Kansas City, Mo (M.S.K.); Siemens Healthineers,
Princeton, NJ (R.G.); Department of Radiology (J.G.T., J.J.C., B.A.L., K.L.S.),
Department of Biomedical Informatics (T.A.L., J.J.C., B.A.L.), Division of
Cardiovascular Medicine (J.J.C.), Division of Allergy, Pulmonary and Critical
Care Medicine (F.M.), Vanderbilt University Institute of Imaging Science
(B.A.L.), Vanderbilt Brain Institute (B.A.L.), Department of Psychiatry and
Behavioral Sciences (B.A.L.), Department of Neurology (B.A.L.), and Vanderbilt
Memory & Alzheimer’s Center (B.A.L.), Vanderbilt University
Medical Center, Nashville, Tenn
| | - Thomas Z. Li
- From the Department of Computer Science (K.X., Y.H., T.A.L., B.A.L.),
Department of Biomedical Engineering (T.Z.L., B.A.L.), School of Medicine
(T.Z.L.), and Department of Electrical and Computer Engineering (Y.H., B.A.L.),
Vanderbilt University, 2301 Vanderbilt Pl, Nashville, TN 37235; University of
Missouri–Kansas City, Kansas City, Mo (M.S.K.); Saint Luke’s Mid
America Heart Institute, Kansas City, Mo (M.S.K.); Siemens Healthineers,
Princeton, NJ (R.G.); Department of Radiology (J.G.T., J.J.C., B.A.L., K.L.S.),
Department of Biomedical Informatics (T.A.L., J.J.C., B.A.L.), Division of
Cardiovascular Medicine (J.J.C.), Division of Allergy, Pulmonary and Critical
Care Medicine (F.M.), Vanderbilt University Institute of Imaging Science
(B.A.L.), Vanderbilt Brain Institute (B.A.L.), Department of Psychiatry and
Behavioral Sciences (B.A.L.), Department of Neurology (B.A.L.), and Vanderbilt
Memory & Alzheimer’s Center (B.A.L.), Vanderbilt University
Medical Center, Nashville, Tenn
| | - Riqiang Gao
- From the Department of Computer Science (K.X., Y.H., T.A.L., B.A.L.),
Department of Biomedical Engineering (T.Z.L., B.A.L.), School of Medicine
(T.Z.L.), and Department of Electrical and Computer Engineering (Y.H., B.A.L.),
Vanderbilt University, 2301 Vanderbilt Pl, Nashville, TN 37235; University of
Missouri–Kansas City, Kansas City, Mo (M.S.K.); Saint Luke’s Mid
America Heart Institute, Kansas City, Mo (M.S.K.); Siemens Healthineers,
Princeton, NJ (R.G.); Department of Radiology (J.G.T., J.J.C., B.A.L., K.L.S.),
Department of Biomedical Informatics (T.A.L., J.J.C., B.A.L.), Division of
Cardiovascular Medicine (J.J.C.), Division of Allergy, Pulmonary and Critical
Care Medicine (F.M.), Vanderbilt University Institute of Imaging Science
(B.A.L.), Vanderbilt Brain Institute (B.A.L.), Department of Psychiatry and
Behavioral Sciences (B.A.L.), Department of Neurology (B.A.L.), and Vanderbilt
Memory & Alzheimer’s Center (B.A.L.), Vanderbilt University
Medical Center, Nashville, Tenn
| | - James G. Terry
- From the Department of Computer Science (K.X., Y.H., T.A.L., B.A.L.),
Department of Biomedical Engineering (T.Z.L., B.A.L.), School of Medicine
(T.Z.L.), and Department of Electrical and Computer Engineering (Y.H., B.A.L.),
Vanderbilt University, 2301 Vanderbilt Pl, Nashville, TN 37235; University of
Missouri–Kansas City, Kansas City, Mo (M.S.K.); Saint Luke’s Mid
America Heart Institute, Kansas City, Mo (M.S.K.); Siemens Healthineers,
Princeton, NJ (R.G.); Department of Radiology (J.G.T., J.J.C., B.A.L., K.L.S.),
Department of Biomedical Informatics (T.A.L., J.J.C., B.A.L.), Division of
Cardiovascular Medicine (J.J.C.), Division of Allergy, Pulmonary and Critical
Care Medicine (F.M.), Vanderbilt University Institute of Imaging Science
(B.A.L.), Vanderbilt Brain Institute (B.A.L.), Department of Psychiatry and
Behavioral Sciences (B.A.L.), Department of Neurology (B.A.L.), and Vanderbilt
Memory & Alzheimer’s Center (B.A.L.), Vanderbilt University
Medical Center, Nashville, Tenn
| | - Yuankai Huo
- From the Department of Computer Science (K.X., Y.H., T.A.L., B.A.L.),
Department of Biomedical Engineering (T.Z.L., B.A.L.), School of Medicine
(T.Z.L.), and Department of Electrical and Computer Engineering (Y.H., B.A.L.),
Vanderbilt University, 2301 Vanderbilt Pl, Nashville, TN 37235; University of
Missouri–Kansas City, Kansas City, Mo (M.S.K.); Saint Luke’s Mid
America Heart Institute, Kansas City, Mo (M.S.K.); Siemens Healthineers,
Princeton, NJ (R.G.); Department of Radiology (J.G.T., J.J.C., B.A.L., K.L.S.),
Department of Biomedical Informatics (T.A.L., J.J.C., B.A.L.), Division of
Cardiovascular Medicine (J.J.C.), Division of Allergy, Pulmonary and Critical
Care Medicine (F.M.), Vanderbilt University Institute of Imaging Science
(B.A.L.), Vanderbilt Brain Institute (B.A.L.), Department of Psychiatry and
Behavioral Sciences (B.A.L.), Department of Neurology (B.A.L.), and Vanderbilt
Memory & Alzheimer’s Center (B.A.L.), Vanderbilt University
Medical Center, Nashville, Tenn
| | - Thomas A. Lasko
- From the Department of Computer Science (K.X., Y.H., T.A.L., B.A.L.),
Department of Biomedical Engineering (T.Z.L., B.A.L.), School of Medicine
(T.Z.L.), and Department of Electrical and Computer Engineering (Y.H., B.A.L.),
Vanderbilt University, 2301 Vanderbilt Pl, Nashville, TN 37235; University of
Missouri–Kansas City, Kansas City, Mo (M.S.K.); Saint Luke’s Mid
America Heart Institute, Kansas City, Mo (M.S.K.); Siemens Healthineers,
Princeton, NJ (R.G.); Department of Radiology (J.G.T., J.J.C., B.A.L., K.L.S.),
Department of Biomedical Informatics (T.A.L., J.J.C., B.A.L.), Division of
Cardiovascular Medicine (J.J.C.), Division of Allergy, Pulmonary and Critical
Care Medicine (F.M.), Vanderbilt University Institute of Imaging Science
(B.A.L.), Vanderbilt Brain Institute (B.A.L.), Department of Psychiatry and
Behavioral Sciences (B.A.L.), Department of Neurology (B.A.L.), and Vanderbilt
Memory & Alzheimer’s Center (B.A.L.), Vanderbilt University
Medical Center, Nashville, Tenn
| | - John Jeffrey Carr
- From the Department of Computer Science (K.X., Y.H., T.A.L., B.A.L.),
Department of Biomedical Engineering (T.Z.L., B.A.L.), School of Medicine
(T.Z.L.), and Department of Electrical and Computer Engineering (Y.H., B.A.L.),
Vanderbilt University, 2301 Vanderbilt Pl, Nashville, TN 37235; University of
Missouri–Kansas City, Kansas City, Mo (M.S.K.); Saint Luke’s Mid
America Heart Institute, Kansas City, Mo (M.S.K.); Siemens Healthineers,
Princeton, NJ (R.G.); Department of Radiology (J.G.T., J.J.C., B.A.L., K.L.S.),
Department of Biomedical Informatics (T.A.L., J.J.C., B.A.L.), Division of
Cardiovascular Medicine (J.J.C.), Division of Allergy, Pulmonary and Critical
Care Medicine (F.M.), Vanderbilt University Institute of Imaging Science
(B.A.L.), Vanderbilt Brain Institute (B.A.L.), Department of Psychiatry and
Behavioral Sciences (B.A.L.), Department of Neurology (B.A.L.), and Vanderbilt
Memory & Alzheimer’s Center (B.A.L.), Vanderbilt University
Medical Center, Nashville, Tenn
| | - Fabien Maldonado
- From the Department of Computer Science (K.X., Y.H., T.A.L., B.A.L.),
Department of Biomedical Engineering (T.Z.L., B.A.L.), School of Medicine
(T.Z.L.), and Department of Electrical and Computer Engineering (Y.H., B.A.L.),
Vanderbilt University, 2301 Vanderbilt Pl, Nashville, TN 37235; University of
Missouri–Kansas City, Kansas City, Mo (M.S.K.); Saint Luke’s Mid
America Heart Institute, Kansas City, Mo (M.S.K.); Siemens Healthineers,
Princeton, NJ (R.G.); Department of Radiology (J.G.T., J.J.C., B.A.L., K.L.S.),
Department of Biomedical Informatics (T.A.L., J.J.C., B.A.L.), Division of
Cardiovascular Medicine (J.J.C.), Division of Allergy, Pulmonary and Critical
Care Medicine (F.M.), Vanderbilt University Institute of Imaging Science
(B.A.L.), Vanderbilt Brain Institute (B.A.L.), Department of Psychiatry and
Behavioral Sciences (B.A.L.), Department of Neurology (B.A.L.), and Vanderbilt
Memory & Alzheimer’s Center (B.A.L.), Vanderbilt University
Medical Center, Nashville, Tenn
| | - Bennett A. Landman
- From the Department of Computer Science (K.X., Y.H., T.A.L., B.A.L.),
Department of Biomedical Engineering (T.Z.L., B.A.L.), School of Medicine
(T.Z.L.), and Department of Electrical and Computer Engineering (Y.H., B.A.L.),
Vanderbilt University, 2301 Vanderbilt Pl, Nashville, TN 37235; University of
Missouri–Kansas City, Kansas City, Mo (M.S.K.); Saint Luke’s Mid
America Heart Institute, Kansas City, Mo (M.S.K.); Siemens Healthineers,
Princeton, NJ (R.G.); Department of Radiology (J.G.T., J.J.C., B.A.L., K.L.S.),
Department of Biomedical Informatics (T.A.L., J.J.C., B.A.L.), Division of
Cardiovascular Medicine (J.J.C.), Division of Allergy, Pulmonary and Critical
Care Medicine (F.M.), Vanderbilt University Institute of Imaging Science
(B.A.L.), Vanderbilt Brain Institute (B.A.L.), Department of Psychiatry and
Behavioral Sciences (B.A.L.), Department of Neurology (B.A.L.), and Vanderbilt
Memory & Alzheimer’s Center (B.A.L.), Vanderbilt University
Medical Center, Nashville, Tenn
| | - Kim L. Sandler
- From the Department of Computer Science (K.X., Y.H., T.A.L., B.A.L.),
Department of Biomedical Engineering (T.Z.L., B.A.L.), School of Medicine
(T.Z.L.), and Department of Electrical and Computer Engineering (Y.H., B.A.L.),
Vanderbilt University, 2301 Vanderbilt Pl, Nashville, TN 37235; University of
Missouri–Kansas City, Kansas City, Mo (M.S.K.); Saint Luke’s Mid
America Heart Institute, Kansas City, Mo (M.S.K.); Siemens Healthineers,
Princeton, NJ (R.G.); Department of Radiology (J.G.T., J.J.C., B.A.L., K.L.S.),
Department of Biomedical Informatics (T.A.L., J.J.C., B.A.L.), Division of
Cardiovascular Medicine (J.J.C.), Division of Allergy, Pulmonary and Critical
Care Medicine (F.M.), Vanderbilt University Institute of Imaging Science
(B.A.L.), Vanderbilt Brain Institute (B.A.L.), Department of Psychiatry and
Behavioral Sciences (B.A.L.), Department of Neurology (B.A.L.), and Vanderbilt
Memory & Alzheimer’s Center (B.A.L.), Vanderbilt University
Medical Center, Nashville, Tenn
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Pickhardt PJ, Summers RM, Garrett JW, Krishnaraj A, Agarwal S, Dreyer KJ, Nicola GN. Opportunistic Screening: Radiology Scientific Expert Panel. Radiology 2023; 307:e222044. [PMID: 37219444 PMCID: PMC10315516 DOI: 10.1148/radiol.222044] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/03/2022] [Accepted: 12/01/2022] [Indexed: 05/24/2023]
Abstract
Radiologic tests often contain rich imaging data not relevant to the clinical indication. Opportunistic screening refers to the practice of systematically leveraging these incidental imaging findings. Although opportunistic screening can apply to imaging modalities such as conventional radiography, US, and MRI, most attention to date has focused on body CT by using artificial intelligence (AI)-assisted methods. Body CT represents an ideal high-volume modality whereby a quantitative assessment of tissue composition (eg, bone, muscle, fat, and vascular calcium) can provide valuable risk stratification and help detect unsuspected presymptomatic disease. The emergence of "explainable" AI algorithms that fully automate these measurements could eventually lead to their routine clinical use. Potential barriers to widespread implementation of opportunistic CT screening include the need for buy-in from radiologists, referring providers, and patients. Standardization of acquiring and reporting measures is needed, in addition to expanded normative data according to age, sex, and race and ethnicity. Regulatory and reimbursement hurdles are not insurmountable but pose substantial challenges to commercialization and clinical use. Through demonstration of improved population health outcomes and cost-effectiveness, these opportunistic CT-based measures should be attractive to both payers and health care systems as value-based reimbursement models mature. If highly successful, opportunistic screening could eventually justify a practice of standalone "intended" CT screening.
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Affiliation(s)
- Perry J. Pickhardt
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - Ronald M. Summers
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - John W. Garrett
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - Arun Krishnaraj
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - Sheela Agarwal
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - Keith J. Dreyer
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - Gregory N. Nicola
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
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