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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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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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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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Moeller AR, Garrett JW, Summers RM, Pickhardt PJ. Adjusting for the effect of IV contrast on automated CT body composition measures during the portal venous phase. Abdom Radiol (NY) 2024:10.1007/s00261-024-04376-8. [PMID: 38744704 DOI: 10.1007/s00261-024-04376-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 05/04/2024] [Accepted: 05/06/2024] [Indexed: 05/16/2024]
Abstract
OBJECTIVE Fully-automated CT-based algorithms for quantifying numerous biomarkers have been validated for unenhanced abdominal scans. There is great interest in optimizing the documentation and reporting of biophysical measures present on all CT scans for the purposes of opportunistic screening and risk profiling. The purpose of this study was to determine and adjust the effect of intravenous (IV) contrast on these automated body composition measures at routine portal venous phase post-contrast imaging. METHODS Final study cohort consisted of 1,612 older adults (mean age, 68.0 years; 594 women) all imaged utilizing a uniform CT urothelial protocol consisting of pre-contrast, portal venous, and delayed excretory phases. Fully-automated CT-based algorithms for quantifying numerous biomarkers, including muscle and fat area and density, bone mineral density, and solid organ volume were applied to pre-contrast and portal venous phases. The effect of IV contrast upon these body composition measures was analyzed. Regression analyses, including square of the Pearson correlation coefficient (r2), were performed for each comparison. RESULTS We found that simple, linear relationships can be derived to determine non-contrast equivalent values from the post-contrast CT biomeasures. Excellent positive linear correlation (r2 = 0.91-0.99) between pre- and post-contrast values was observed for all automated soft tissue measures, whereas moderate positive linear correlation was observed for bone attenuation (r2 = 0.58-0.76). In general, the area- and volume-based measurement require less adjustment than attenuation-based measures, as expected. CONCLUSION Fully-automated quantitative CT-biomarker measures at portal venous phase abdominal CT can be adjusted to a non-contrast equivalent using simple, linear relationships.
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Affiliation(s)
- Alexander R Moeller
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center 600 Highland Ave., Madison, WI, 53792-3252, USA
| | - John W Garrett
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center 600 Highland Ave., Madison, WI, 53792-3252, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of 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, E3/311 Clinical Science Center 600 Highland Ave., Madison, WI, 53792-3252, USA.
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Leiby JS, Lee ME, Shivakumar M, Choe EK, Kim D. Deep learning imaging phenotype can classify metabolic syndrome and is predictive of cardiometabolic disorders. J Transl Med 2024; 22:434. [PMID: 38720370 PMCID: PMC11077781 DOI: 10.1186/s12967-024-05163-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 04/04/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Cardiometabolic disorders pose significant health risks globally. Metabolic syndrome, characterized by a cluster of potentially reversible metabolic abnormalities, is a known risk factor for these disorders. Early detection and intervention for individuals with metabolic abnormalities can help mitigate the risk of developing more serious cardiometabolic conditions. This study aimed to develop an image-derived phenotype (IDP) for metabolic abnormality from unenhanced abdominal computed tomography (CT) scans using deep learning. We used this IDP to classify individuals with metabolic syndrome and predict future occurrence of cardiometabolic disorders. METHODS A multi-stage deep learning approach was used to extract the IDP from the liver region of unenhanced abdominal CT scans. In a cohort of over 2,000 individuals the IDP was used to classify individuals with metabolic syndrome. In a subset of over 1,300 individuals, the IDP was used to predict future occurrence of hypertension, type II diabetes, and fatty liver disease. RESULTS For metabolic syndrome (MetS) classification, we compared the performance of the proposed IDP to liver attenuation and visceral adipose tissue area (VAT). The proposed IDP showed the strongest performance (AUC 0.82) compared to attenuation (AUC 0.70) and VAT (AUC 0.80). For disease prediction, we compared the performance of the IDP to baseline MetS diagnosis. The models including the IDP outperformed MetS for type II diabetes (AUCs 0.91 and 0.90) and fatty liver disease (AUCs 0.67 and 0.62) prediction and performed comparably for hypertension prediction (AUCs of 0.77). CONCLUSIONS This study demonstrated the superior performance of a deep learning IDP compared to traditional radiomic features to classify individuals with metabolic syndrome. Additionally, the IDP outperformed the clinical definition of metabolic syndrome in predicting future morbidities. Our findings underscore the utility of data-driven imaging phenotypes as valuable tools in the assessment and management of metabolic syndrome and cardiometabolic disorders.
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Affiliation(s)
- Jacob S Leiby
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 19104, Philadelphia, PA, USA
| | - Matthew E Lee
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 19104, Philadelphia, PA, USA
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 19104, Philadelphia, PA, USA
| | - Eun Kyung Choe
- Department of Surgery, Seoul National University Hospital Healthcare System Gangnam Center, 06236, Seoul, South Korea.
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 19104, Philadelphia, PA, USA.
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 19104, Philadelphia, PA, USA.
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Horbal SR, Belancourt PX, Zhang P, Holcombe SA, Saini S, Wang SC, Sales AE, Su GL. Independent Associations of Aortic Calcification with Cirrhosis and Liver Related Mortality in Veterans with Chronic Liver Disease. Dig Dis Sci 2024:10.1007/s10620-024-08450-5. [PMID: 38653948 DOI: 10.1007/s10620-024-08450-5] [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: 02/14/2024] [Accepted: 04/12/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Abdominal aortic calcifications (AAC) are incidentally found on medical imaging and useful cardiovascular burden approximations. The Morphomic Aortic Calcification Score (MAC) leverages automated deep learning methods to quantify and score AACs. While associations of AAC and non-alcoholic fatty liver disease (NAFLD) have been described, relationships of AAC with other liver diseases and clinical outcome are sparse. This study's purpose was to evaluate AAC and liver-related death in a cohort of Veterans with chronic liver disease (CLD). METHODS We utilized the VISN 10 CLD cohort, a regional cohort of Veterans with the three forms of CLD: NAFLD, hepatitis C (HCV), alcohol-associated (ETOH), seen between 2008 and 2014, with abdominal CT scans (n = 3604). Associations between MAC and cirrhosis development, liver decompensation, liver-related death, and overall death were evaluated with Cox proportional hazard models. RESULTS The full cohort demonstrated strong associations of MAC and cirrhosis after adjustment: HR 2.13 (95% CI 1.63, 2.78), decompensation HR 2.19 (95% CI 1.60, 3.02), liver-related death HR 2.13 (95% CI 1.46, 3.11), and overall death HR 1.47 (95% CI 1.27, 1.71). These associations seemed to be driven by the non-NAFLD groups for decompensation and liver-related death [HR 2.80 (95% CI 1.52, 5.17; HR 2.34 (95% CI 1.14, 4.83), respectively]. DISCUSSION MAC was strongly and independently associated with cirrhosis, liver decompensation, liver-related death, and overall death. Surprisingly, stratification results demonstrated comparable or stronger associations among those with non-NAFLD etiology. These findings suggest abdominal aortic calcification may predict liver disease severity and clinical outcomes in patients with CLD.
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Affiliation(s)
- Steven R Horbal
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA.
- Morphomics Analysis Group, University of Michigan, Ann Arbor, MI, USA.
| | | | - Peng Zhang
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA
- Morphomics Analysis Group, University of Michigan, Ann Arbor, MI, USA
| | - Sven A Holcombe
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA
- Morphomics Analysis Group, University of Michigan, Ann Arbor, MI, USA
| | - Sameer Saini
- Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, MI, USA
| | - Stewart C Wang
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Anne E Sales
- VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
- Sinclair School of Nursing and Department of Family and Community Medicine, University of Missouri, Colombia, MO, USA
| | - Grace L Su
- Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, MI, USA
- Gastroenterology Section, Ann Arbor VA Healthcare System, Ann Arbor, MI, USA
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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. [PMID: 38649795 DOI: 10.1002/jcsm.13487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [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 Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
- Institue for Innovation in Digital Healthcare (IIDH), Yonsei University Health System, Seoul, South Korea
| | - Seungjin Baek
- Department of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Sookyeong Han
- Department of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
- Institue for Innovation in Digital Healthcare (IIDH), Yonsei University Health System, Seoul, South Korea
| | - Chang Oh Kim
- Division of Geriatric Medicine, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Hyeon Chang Kim
- Institue for Innovation in Digital Healthcare (IIDH), Yonsei University Health System, Seoul, South Korea
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Yumie Rhee
- Department of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
- Institue for Innovation in Digital Healthcare (IIDH), Yonsei University Health System, Seoul, South Korea
| | - Namki Hong
- Department of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
- Institue for Innovation in Digital Healthcare (IIDH), Yonsei University Health System, Seoul, South Korea
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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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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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Liu MC, Ho CC, Lin YT, Chai JW, Hung SW, Wu CH, Li JR, Liu YJ. Opportunistic screening with multiphase contrast-enhanced dual-layer spectral CT for osteoblastic lesions in prostate cancer compared with bone scintigraphy. Sci Rep 2024; 14:5310. [PMID: 38438474 PMCID: PMC10912417 DOI: 10.1038/s41598-024-55427-5] [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: 11/23/2023] [Accepted: 02/23/2024] [Indexed: 03/06/2024] Open
Abstract
Our study aimed to compare bone scintigraphy and dual-layer detector spectral CT (DLCT) with multiphase contrast enhancement for the diagnosis of osteoblastic bone lesions in patients with prostate cancer. The patients with prostate cancer and osteoblastic bone lesions detected on DLCT were divided into positive bone scintigraphy group (pBS) and negative bone scintigraphy group (nBS) based on bone scintigraphy. A total of 106 patients (57 nBS and 49 pBS) was included. The parameters of each lesion were measured from DLCT including Hounsfield unit (HU), 40-140 keV monochromatic HU, effective nuclear numbers (Zeff), and Iodine no water (InW) value in non-contrast phase (N), the arterial phase (A), and venous phase (V). The slope of the spectral curve at 40 and 100 keV, the different values of the parameters between A and N phase (A-N), V and N phase (V-N), and hybrid prediction model with multiparameters were used to differentiate pBS from nBS. Receiver operating characteristic analysis was performed to compare the area under the curve (AUC) for differentiating the pBS group from the nBS group. The value of conventional HU values, slope, and InW in A-N and V-N, and hybrid model were significantly higher in the pBS group than in the nBS group. The hybrid model of all significant parameters had the highest AUC of 0.988, with 95.5% sensitivity and 94.6% specificity. DLCT with arterial contrast enhancement phase has the potential to serve as an opportunistic screening tool for detecting positive osteoblastic bone lesions, corresponding to those identified in bone scintigraphy.
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Affiliation(s)
- Ming-Cheng Liu
- Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
- Ph.D. Program of Electrical and Communications Engineering, Feng Chia University, Taichung, Taiwan, ROC
| | - Chi-Chang Ho
- Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
| | - Yen-Ting Lin
- Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
- Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, ROC
| | - Jyh-Wen Chai
- Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
| | - Siu-Wan Hung
- Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
| | - Chen-Hao Wu
- Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
| | - Jian-Ri Li
- Division of Urology, Department of Surgery, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan, ROC
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan, ROC
- Department of Medicine and Nursing, Hungkuang University, Taichung, Taiwan, ROC
| | - Yi-Jui Liu
- Ph.D. Program of Electrical and Communications Engineering, Feng Chia University, Taichung, Taiwan, ROC.
- Department of Automatic Control Engineering, Feng Chia University, No. 100 Wenhwa Rd., Xitun Dist., Taichung, 407102, Taiwan, ROC.
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11
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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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12
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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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13
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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: 0] [Impact Index Per Article: 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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14
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Makimoto H, Kohro T. Adopting artificial intelligence in cardiovascular medicine: a scoping review. Hypertens Res 2024; 47:685-699. [PMID: 37907600 DOI: 10.1038/s41440-023-01469-7] [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] [Received: 04/01/2023] [Revised: 09/03/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.
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Affiliation(s)
- Hisaki Makimoto
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.
| | - Takahide Kohro
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan
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15
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Patil NS, Kielar AZ. Use of Opportunistic CT Biomarkers: Population Health Opportunities in Radiology. Can Assoc Radiol J 2024; 75:22-23. [PMID: 37535874 DOI: 10.1177/08465371231186356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023] Open
Affiliation(s)
- Nikhil S Patil
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | - Ania Z Kielar
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
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16
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Mukherjee P, Lee S, Elton DC, Pickhardt PJ, Summers RM. Longitudinal follow-up of incidental renal calculi on computed tomography. Abdom Radiol (NY) 2024; 49:173-181. [PMID: 37906271 DOI: 10.1007/s00261-023-04075-w] [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: 07/24/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 11/02/2023]
Abstract
RATIONALE AND OBJECTIVES Measuring small kidney stones on CT is a time-consuming task often neglected. Volumetric assessment provides a better measure of size than linear dimensions. Our objective is to analyze the growth rate and prognosis of incidental kidney stones in asymptomatic patients on CT. MATERIALS AND METHODS This retrospective study included 4266 scans from 2030 asymptomatic patients who underwent two or more nonenhanced CT scans for colorectal screening between 2004 and 2016. The DL software identified and measured the volume, location, and attenuation of 883 stones. The corresponding scans were manually evaluated, and patients without follow-up were excluded. At each follow-up, the stones were categorized as new, growing, persistent, or resolved. Stone size (volume and diameter), attenuation, and location were correlated with the outcome and growth rates of the stones. RESULTS The stone cohort comprised 407 scans from 189 (M: 124, F: 65, median age: 55.4 years) patients. The median number of stones per scan was 1 (IQR: [1, 2]). The median stone volume was 17.1 mm3 (IQR: [7.4, 43.6]) and the median peak attenuation was 308 HU (IQR: [204, 532]. The 189 initial scans contained 291stones; 91 (31.3%) resolved, 142 (48.8%) grew, and 58 (19.9) remained persistent at the first follow-up. At the second follow-up (for 27 patients with 2 follow-ups), 14/44 (31.8%) stones had resolved, 19/44 (43.2%) grew and 11/44 (25%) were persistent. The median growth rate of growing stones was 3.3 mm3/year, IQR: [1.4,7.4]. Size and attenuation had a moderate correlation (Spearman rho 0.53, P < .001 for volume, and 0.50 P < .001 for peak attenuation) with the growth rate. Growing and persistent stones had significantly greater maximum axial diameter (2.7 vs 2.3 mm, P =.047) and peak attenuation (300 vs 258 HU, P =.031) CONCLUSION: We report a 12.7% prevalence of incidental kidney stones in asymptomatic adults, of which about half grew during follow-up with a median growth rate of about 3.3 mm3/year.
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Affiliation(s)
- Pritam Mukherjee
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD, 20892-1182, USA
| | - Sungwon Lee
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD, 20892-1182, USA
| | - Daniel C Elton
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD, 20892-1182, USA
| | - Perry J Pickhardt
- Department of Radiology, School of Medicine & Public Health, The University of Wisconsin, Madison, WI, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD, 20892-1182, USA.
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17
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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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Zambrano Chaves JM, Wentland AL, Desai AD, Banerjee I, Kaur G, Correa R, Boutin RD, Maron DJ, Rodriguez F, Sandhu AT, Rubin D, Chaudhari AS, Patel BN. Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach. Sci Rep 2023; 13:21034. [PMID: 38030716 PMCID: PMC10687235 DOI: 10.1038/s41598-023-47895-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 11/20/2023] [Indexed: 12/01/2023] Open
Abstract
Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD) events-the leading cause of global mortality-have known limitations and may be improved by imaging biomarkers. While body composition (BC) imaging biomarkers derived from abdominopelvic computed tomography (CT) correlate with IHD risk, they are impractical to measure manually. Here, in a retrospective cohort of 8139 contrast-enhanced abdominopelvic CT examinations undergoing up to 5 years of follow-up, we developed multimodal opportunistic risk assessment models for IHD by automatically extracting BC features from abdominal CT images and integrating these with features from each patient's electronic medical record (EMR). Our predictive methods match and, in some cases, outperform clinical risk scores currently used in IHD risk assessment. We provide clinical interpretability of our model using a new method of determining tissue-level contributions from CT along with weightings of EMR features contributing to IHD risk. We conclude that such a multimodal approach, which automatically integrates BC biomarkers and EMR data, can enhance IHD risk assessment and aid primary prevention efforts for IHD. To further promote research, we release the Opportunistic L3 Ischemic heart disease (OL3I) dataset, the first public multimodal dataset for opportunistic CT prediction of IHD.
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Affiliation(s)
- Juan M Zambrano Chaves
- Department of Biomedical Data Science, Stanford University, 1265 Welch Road, MSOB West Wing, Third Floor, Stanford, CA, 94305, USA
| | - Andrew L Wentland
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA
| | - Arjun D Desai
- Department of Radiology, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
- Department of Electrical Engineering, Stanford University, 350 Jane Stanford Way, Stanford, CA, 94305, USA
| | - Imon Banerjee
- Department of Radiology, Mayo Clinic, 13400 East Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Gurkiran Kaur
- Department of Radiology, Mayo Clinic, 13400 East Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Ramon Correa
- Department of Radiology, Mayo Clinic, 13400 East Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Robert D Boutin
- Department of Radiology, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - David J Maron
- Division of Cardiovascular Medicine, Department of Medicine, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
- Department of Medicine, Stanford Prevention Research Center, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine, Department of Medicine, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Alexander T Sandhu
- Division of Cardiovascular Medicine, Department of Medicine, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Daniel Rubin
- Department of Biomedical Data Science, Stanford University, 1265 Welch Road, MSOB West Wing, Third Floor, Stanford, CA, 94305, USA
- Department of Radiology, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Akshay S Chaudhari
- Department of Biomedical Data Science, Stanford University, 1265 Welch Road, MSOB West Wing, Third Floor, Stanford, CA, 94305, USA
- Department of Radiology, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
- Cardiovascular Institute, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Bhavik N Patel
- Department of Radiology, Mayo Clinic, 13400 East Shea Blvd, Scottsdale, AZ, 85259, USA.
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19
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Pooler BD, Fleming CJ, Garrett JW, Summers RM, Pickhardt PJ. Artificial intelligence tool detection of intravenous contrast enhancement using spleen attenuation. Abdom Radiol (NY) 2023; 48:3382-3390. [PMID: 37634138 DOI: 10.1007/s00261-023-04020-x] [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: 06/16/2023] [Revised: 08/03/2023] [Accepted: 08/04/2023] [Indexed: 08/29/2023]
Abstract
PURPOSE To assess the ability of an automated AI tool to detect intravenous contrast material (IVCM) in abdominal CT examinations using spleen attenuation. METHODS A previously validated automated AI tool measuring the attenuation of the spleen was deployed on a sample of 32,994 adult (age ≥ 18) patients (mean age, 61.9 ± 14.7 years; 13,869 men, 19,125 women) undergoing 65,449 supine position CT examinations (41,020 with and 24,429 without IVCM by DICOM header) from January 1, 2000 to December 31, 2021. After exclusions, receiver operating characteristic (ROC) curve analysis was performed to determine the optimal threshold for binary classification of IVCM status (non-contrast vs IVCM enhanced), which was then applied to the sample. Discordant examinations (i.e., IVCM status determined by AI tool did not match DICOM header) were manually reviewed to establish ground truth. Repeat ROC curve and contingency table analysis were performed to assess AI tool performance. RESULTS ROC analysis of the initial study sample of 61,783 CT examinations yielded AUC of 0.970 with Youden index suggesting an optimal spleen attenuation threshold of 65 Hounsfield units (HU). Manual review of 2094 discordant CT examinations revealed discordance due to DICOM header error in 1278 (61.0%) and AI tool misclassification in 410 (19.6%), with 406 (9.4%) meeting exclusion criteria. Analysis of 61,377 CT examinations in the final study sample yielded AUC of 0.999 with accuracy of 99.3% at the 65 HU threshold. Error rate for DICOM header information was 2.1% (1278/61,377) versus 0.7% (410/61,377) for the AI tool. CONCLUSION The automated spleen attenuation AI tool was highly accurate for detection of IVCM at a threshold of 65 HU.
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Affiliation(s)
- B Dustin Pooler
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
| | - Cullen J Fleming
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - John W Garrett
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of 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 and Public Health, Madison, WI, USA
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20
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Antonopoulos AS, Papastamos C, Cokkinos DV, Tsioufis K, Tousoulis D. Epicardial Adipose Tissue in Myocardial Disease: From Physiology to Heart Failure Phenotypes. Curr Probl Cardiol 2023; 48:101841. [PMID: 37244513 DOI: 10.1016/j.cpcardiol.2023.101841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 05/22/2023] [Indexed: 05/29/2023]
Abstract
Epicardial adipose tissue (EAT) is increasingly being recognized as a determinant of myocardial biology. The EAT-heart crosstalk suggests causal links between dysfunctional EAT and cardiomyocyte impairment. Obesity promotes EAT dysfunction and shifts in secreted adipokines which adversely affect cardiac metabolism, induce cardiomyocyte inflammation, redox imbalance and myocardial fibrosis. Thus, EAT determines cardiac phenotype via effects on cardiac energetics, contractility, diastolic function, and atrial conduction. Vice-versa the EAT is altered in heart failure (HF), and such phenotypic changes can be detected by noninvasive imaging or incorporated in Artificial Intelligence-enhanced tools to aid the diagnosis, subtyping or risk prognostication of HF. In the present article, we summarize the links between EAT and the heart, explaining how the study of epicardial adiposity can improve the understanding of cardiac disease, serve as a source of diagnostic and prognostic biomarkers, and as a potential therapeutic target in HF to improve clinical outcomes.
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Affiliation(s)
- Alexios S Antonopoulos
- 1st Cardiology Department, National and Kapodistrian University of Athens, Athens, Greece; Clinical, Experimental Surgery and Translational Research Centre, Biomedical Research Foundation Academy of Athens, Athens, Greece.
| | - Charalampos Papastamos
- 1st Cardiology Department, National and Kapodistrian University of Athens, Athens, Greece
| | - Dennis V Cokkinos
- Clinical, Experimental Surgery and Translational Research Centre, Biomedical Research Foundation Academy of Athens, Athens, Greece
| | - Konstantinos Tsioufis
- 1st Cardiology Department, National and Kapodistrian University of Athens, Athens, Greece
| | - Dimitris Tousoulis
- 1st Cardiology Department, National and Kapodistrian University of Athens, Athens, Greece
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21
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Yoo J, Cho H, Lee DH, Cho EJ, Joo I, Jeon SK. Prognostic role of computed tomography analysis using deep learning algorithm in patients with chronic hepatitis B viral infection. Clin Mol Hepatol 2023; 29:1029-1042. [PMID: 37822214 PMCID: PMC10577347 DOI: 10.3350/cmh.2023.0190] [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: 06/01/2023] [Revised: 08/08/2023] [Accepted: 08/27/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND/AIMS The prediction of clinical outcomes in patients with chronic hepatitis B (CHB) is paramount for effective management. This study aimed to evaluate the prognostic value of computed tomography (CT) analysis using deep learning algorithms in patients with CHB. METHODS This retrospective study included 2,169 patients with CHB without hepatic decompensation who underwent contrast-enhanced abdominal CT for hepatocellular carcinoma (HCC) surveillance between January 2005 and June 2016. Liver and spleen volumes and body composition measurements including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle indices were acquired from CT images using deep learning-based fully automated organ segmentation algorithms. We assessed the significant predictors of HCC, hepatic decompensation, diabetes mellitus (DM), and overall survival (OS) using Cox proportional hazard analyses. RESULTS During a median follow-up period of 103.0 months, HCC (n=134, 6.2%), hepatic decompensation (n=103, 4.7%), DM (n=432, 19.9%), and death (n=120, 5.5%) occurred. According to the multivariate analysis, standardized spleen volume significantly predicted HCC development (hazard ratio [HR]=1.01, P=0.025), along with age, sex, albumin and platelet count. Standardized spleen volume (HR=1.01, P<0.001) and VAT index (HR=0.98, P=0.004) were significantly associated with hepatic decompensation along with age and albumin. Furthermore, VAT index (HR=1.01, P=0.001) and standardized spleen volume (HR=1.01, P=0.001) were significant predictors for DM, along with sex, age, and albumin. SAT index (HR=0.99, P=0.004) was significantly associated with OS, along with age, albumin, and MELD. CONCLUSION Deep learning-based automatically measured spleen volume, VAT, and SAT indices may provide various prognostic information in patients with CHB.
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Affiliation(s)
- Jeongin Yoo
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Heejin Cho
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Dong Ho Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Eun Ju Cho
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Sun Kyung Jeon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
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22
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Wiebe N, Lloyd A, Crumley ET, Tonelli M. Associations between body mass index and all-cause mortality: A systematic review and meta-analysis. Obes Rev 2023; 24:e13588. [PMID: 37309266 DOI: 10.1111/obr.13588] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 04/12/2023] [Accepted: 05/22/2023] [Indexed: 06/14/2023]
Abstract
Fasting insulin and c-reactive protein confound the association between mortality and body mass index. An increase in fat mass may mediate the associations between hyperinsulinemia, hyperinflammation, and mortality. The objective of this study was to describe the "average" associations between body mass index and the risk of mortality and to explore how adjusting for fasting insulin and markers of inflammation might modify the association of BMI with mortality. MEDLINE and EMBASE were searched for studies published in 2020. Studies with adult participants where BMI and vital status was assessed were included. BMI was required to be categorized into groups or parametrized as non-first order polynomials or splines. All-cause mortality was regressed against mean BMI squared within seven broad clinical populations. Study was modeled as a random intercept. β coefficients and 95% confidence intervals are reported along with estimates of mortality risk by BMIs of 20, 30, and 40 kg/m2 . Bubble plots with regression lines are drawn, showing the associations between mortality and BMI. Splines results were summarized. There were 154 included studies with 6,685,979 participants. Only five (3.2%) studies adjusted for a marker of inflammation, and no studies adjusted for fasting insulin. There were significant associations between higher BMIs and lower mortality risk in cardiovascular (unadjusted β -0.829 [95% CI -1.313, -0.345] and adjusted β -0.746 [95% CI -1.471, -0.021]), Covid-19 (unadjusted β -0.333 [95% CI -0.650, -0.015]), critically ill (adjusted β -0.550 [95% CI -1.091, -0.010]), and surgical (unadjusted β -0.415 [95% CI -0.824, -0.006]) populations. The associations for general, cancer, and non-communicable disease populations were not significant. Heterogeneity was very large (I2 ≥ 97%). The role of obesity as a driver of excess mortality should be critically re-examined, in parallel with increased efforts to determine the harms of hyperinsulinemia and chronic inflammation.
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Affiliation(s)
- Natasha Wiebe
- Department of Medicine, University of Alberta, Edmonton, Canada
| | - Anita Lloyd
- Department of Medicine, University of Alberta, Edmonton, Canada
| | - Ellen T Crumley
- Rowe School of Business, Dalhousie University, Halifax, Nova Scotia, Canada
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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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Kay FU, Lumby C, Tanabe Y, Abbara S, Rajiah P. Detection of Low Blood Hemoglobin Levels on Pulmonary CT Angiography: A Feasibility Study Combining Dual-Energy CT and Machine Learning. Tomography 2023; 9:1538-1550. [PMID: 37624116 PMCID: PMC10459752 DOI: 10.3390/tomography9040123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/11/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
OBJECTIVES To evaluate if dual-energy CT (DECT) pulmonary angiography (CTPA) can detect anemia with the aid of machine learning. METHODS Inclusion of 100 patients (mean age ± SD, 51.3 ± 14.8 years; male-to-female ratio, 42/58) who underwent DECT CTPA and hemoglobin (Hb) analysis within 24 h, including 50 cases with Hb below and 50 controls with Hb ≥ 12 g/dL. Blood pool attenuation was assessed on virtual noncontrast (VNC) images at eight locations. A classification model using extreme gradient-boosted trees was developed on a training set (n = 76) for differentiating cases from controls. The best model was evaluated in a separate test set (n = 24). RESULTS Blood pool attenuation was significantly lower in cases than controls (p-values < 0.01), except in the right atrium (p = 0.06). The machine learning model had sensitivity, specificity, and accuracy of 83%, 92%, and 88%, respectively. Measurements at the descending aorta had the highest relative importance among all features; a threshold of 43 HU yielded sensitivity, specificity, and accuracy of 68%, 76%, and 72%, respectively. CONCLUSION VNC imaging and machine learning shows good diagnostic performance for detecting anemia on DECT CTPA.
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Affiliation(s)
- Fernando U. Kay
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA;
| | - Cynthia Lumby
- Veterans Affairs North Texas Health Care System, Dallas, TX 75216, USA;
| | - Yuki Tanabe
- Department of Radiology, Ehime University, Matsuyama 790-0825, Japan;
| | - Suhny Abbara
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA;
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25
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Suter B, Anthis AHC, Zehnder A, Mergen V, Rosendorf J, Gerken LRH, Schlegel AA, Korcakova E, Liska V, Herrmann IK. Surgical Sealant with Integrated Shape-Morphing Dual Modality Ultrasound and Computed Tomography Sensors for Gastric Leak Detection. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2301207. [PMID: 37276437 PMCID: PMC10427398 DOI: 10.1002/advs.202301207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/26/2023] [Indexed: 06/07/2023]
Abstract
Postoperative anastomotic leaks are the most feared complications after gastric surgery. For diagnostics clinicians mostly rely on clinical symptoms such as fever and tachycardia, often developing as a result of an already fully developed, i.e., symptomatic, surgical leak. A gastric fluid responsive, dual modality, electronic-free, leak sensor system integrable into surgical adhesive suture support materials is introduced. Leak sensors contain high atomic number carbonates embedded in a polyacrylamide matrix, that upon exposure to gastric fluid convert into gaseous carbon dioxide (CO2 ). CO2 bubbles remain entrapped in the hydrogel matrix, leading to a distinctly increased echogenic contrast detectable by a low-cost and portable ultrasound transducer, while the dissolution of the carbonate species and the resulting diffusion of the cation produces a markedly reduced contrast in computed tomography imaging. The sensing elements can be patterned into a variety of characteristic shapes and can be combined with nonreactive tantalum oxide reference elements, allowing the design of shape-morphing sensing elements visible to the naked eye as well as artificial intelligence-assisted automated detection. In summary, shape-morphing dual modality sensors for the early and robust detection of postoperative complications at deep tissue sites, opening new routes for postoperative patient surveillance using existing hospital infrastructure is reported.
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Affiliation(s)
- Benjamin Suter
- Nanoparticle Systems Engineering LaboratoryInstitute of Energy and Process Engineering (IEPE)Department of Mechanical and Process Engineering (D‐MAVT)ETH ZurichSonneggstrasse 3Zürich8092Switzerland
- Particles‐Biology InteractionsDepartment of Materials Meet LifeSwiss Federal Laboratories for Materials Science and Technology (Empa)Lerchenfeldstrasse 5St. Gallen9014Switzerland
| | - Alexandre H. C. Anthis
- Nanoparticle Systems Engineering LaboratoryInstitute of Energy and Process Engineering (IEPE)Department of Mechanical and Process Engineering (D‐MAVT)ETH ZurichSonneggstrasse 3Zürich8092Switzerland
- Particles‐Biology InteractionsDepartment of Materials Meet LifeSwiss Federal Laboratories for Materials Science and Technology (Empa)Lerchenfeldstrasse 5St. Gallen9014Switzerland
| | - Anna‐Katharina Zehnder
- Nanoparticle Systems Engineering LaboratoryInstitute of Energy and Process Engineering (IEPE)Department of Mechanical and Process Engineering (D‐MAVT)ETH ZurichSonneggstrasse 3Zürich8092Switzerland
| | - Victor Mergen
- Diagnostic and Interventional RadiologyUniversity Hospital ZurichUniversity of ZurichRämistrasse 100Zürich8091Switzerland
| | - Jachym Rosendorf
- Department of SurgeryFaculty of Medicine in PilsenCharles UniversityAlej Svobody 923/80Pilsen32300Czech Republic
- Biomedical CenterFaculty of Medicine in PilsenCharles UniversityAlej Svobody 1655/76Pilsen32300Czech Republic
| | - Lukas R. H. Gerken
- Nanoparticle Systems Engineering LaboratoryInstitute of Energy and Process Engineering (IEPE)Department of Mechanical and Process Engineering (D‐MAVT)ETH ZurichSonneggstrasse 3Zürich8092Switzerland
- Particles‐Biology InteractionsDepartment of Materials Meet LifeSwiss Federal Laboratories for Materials Science and Technology (Empa)Lerchenfeldstrasse 5St. Gallen9014Switzerland
| | - Andrea A. Schlegel
- Department of Surgery and TransplantationSwiss HPB CentreUniversity Hospital ZurichRämistrasse 100Zurich8091Switzerland
- Fondazione IRCCS Ca' GrandaOspedale Maggiore PoliclinicoCentre of Preclinical ResearchMilan20122Italy
- Transplantation Center, Digestive Disease and Surgery Institute and Department of Immunity and Inflammation, Lerner Research InstituteCleveland Clinic9620 Carnegie AveClevelandOH44106United States
| | - Eva Korcakova
- Biomedical CenterFaculty of Medicine in PilsenCharles UniversityAlej Svobody 1655/76Pilsen32300Czech Republic
- Department of Imaging MethodsFaculty of Medicine in Pilsen, Charles UniversityAlej Svobody 80Pilsen30460Czech Republic
| | - Vaclav Liska
- Department of SurgeryFaculty of Medicine in PilsenCharles UniversityAlej Svobody 923/80Pilsen32300Czech Republic
- Biomedical CenterFaculty of Medicine in PilsenCharles UniversityAlej Svobody 1655/76Pilsen32300Czech Republic
| | - Inge K. Herrmann
- Nanoparticle Systems Engineering LaboratoryInstitute of Energy and Process Engineering (IEPE)Department of Mechanical and Process Engineering (D‐MAVT)ETH ZurichSonneggstrasse 3Zürich8092Switzerland
- Particles‐Biology InteractionsDepartment of Materials Meet LifeSwiss Federal Laboratories for Materials Science and Technology (Empa)Lerchenfeldstrasse 5St. Gallen9014Switzerland
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26
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Mascalchi M, Picozzi G, Puliti D, Diciotti S, Deliperi A, Romei C, Falaschi F, Pistelli F, Grazzini M, Vannucchi L, Bisanzi S, Zappa M, Gorini G, Carozzi FM, Carrozzi L, Paci E. Lung Cancer Screening with Low-Dose CT: What We Have Learned in Two Decades of ITALUNG and What Is Yet to Be Addressed. Diagnostics (Basel) 2023; 13:2197. [PMID: 37443590 DOI: 10.3390/diagnostics13132197] [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: 04/26/2023] [Revised: 06/15/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
The ITALUNG trial started in 2004 and compared lung cancer (LC) and other-causes mortality in 55-69 years-aged smokers and ex-smokers who were randomized to four annual chest low-dose CT (LDCT) or usual care. ITALUNG showed a lower LC and cardiovascular mortality in the screened subjects after 13 years of follow-up, especially in women, and produced many ancillary studies. They included recruitment results of a population-based mimicking approach, development of software for computer-aided diagnosis (CAD) and lung nodules volumetry, LDCT assessment of pulmonary emphysema and coronary artery calcifications (CAC) and their relevance to long-term mortality, results of a smoking-cessation intervention, assessment of the radiations dose associated with screening LDCT, and the results of biomarkers assays. Moreover, ITALUNG data indicated that screen-detected LCs are mostly already present at baseline LDCT, can present as lung cancer associated with cystic airspaces, and can be multiple. However, several issues of LC screening are still unaddressed. They include the annual vs. biennial pace of LDCT, choice between opportunistic or population-based recruitment. and between uni or multi-centre screening, implementation of CAD-assisted reading, containment of false positive and negative LDCT results, incorporation of emphysema. and CAC quantification in models of personalized LC and mortality risk, validation of ultra-LDCT acquisitions, optimization of the smoking-cessation intervention. and prospective validation of the biomarkers.
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Affiliation(s)
- Mario Mascalchi
- Department of Clinical and Experimental Biomedical Sciences "Mario Serio", University of Florence, 50121 Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Giulia Picozzi
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Donella Puliti
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 47521 Cesena, Italy
| | - Annalisa Deliperi
- Radiodiagnostic Unit 2, Department of Diagnostic Imaging, Cisanello University Hospital of Pisa, 56124 Pisa, Italy
| | - Chiara Romei
- Radiodiagnostic Unit 2, Department of Diagnostic Imaging, Cisanello University Hospital of Pisa, 56124 Pisa, Italy
| | - Fabio Falaschi
- Radiodiagnostic Unit 2, Department of Diagnostic Imaging, Cisanello University Hospital of Pisa, 56124 Pisa, Italy
| | - Francesco Pistelli
- Pulmonary Unit, Cardiothoracic and Vascular Department, University Hospital of Pisa, 56124 Pisa, Italy
| | - Michela Grazzini
- Division of Pneumonology, San Jacopo Hospital Pistoia, 51100 Pistoia, Italy
| | - Letizia Vannucchi
- Division of Radiology, San Jacopo Hospital Pistoia, 51100 Pistoia, Italy
| | - Simonetta Bisanzi
- Regional Laboratory of Cancer Prevention, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Marco Zappa
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Giuseppe Gorini
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Francesca Maria Carozzi
- Regional Laboratory of Cancer Prevention, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Laura Carrozzi
- Pulmonary Unit, Cardiothoracic and Vascular Department, University Hospital of Pisa, 56124 Pisa, Italy
| | - Eugenio Paci
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
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Nachit M, Horsmans Y, Summers RM, Leclercq IA, Pickhardt PJ. AI-based CT Body Composition Identifies Myosteatosis as Key Mortality Predictor in Asymptomatic Adults. Radiology 2023; 307:e222008. [PMID: 37191484 PMCID: PMC10315523 DOI: 10.1148/radiol.222008] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 03/19/2023] [Accepted: 03/29/2023] [Indexed: 05/17/2023]
Abstract
Background Body composition data have been limited to adults with disease or older age. The prognostic impact in otherwise asymptomatic adults is unclear. Purpose To use artificial intelligence-based body composition metrics from routine abdominal CT scans in asymptomatic adults to clarify the association between obesity, liver steatosis, myopenia, and myosteatosis and the risk of mortality. Materials and Methods In this retrospective single-center study, consecutive adult outpatients undergoing routine colorectal cancer screening from April 2004 to December 2016 were included. Using a U-Net algorithm, the following body composition metrics were extracted from low-dose, noncontrast, supine multidetector abdominal CT scans: total muscle area, muscle density, subcutaneous and visceral fat area, and volumetric liver density. Abnormal body composition was defined by the presence of liver steatosis, obesity, muscle fatty infiltration (myosteatosis), and/or low muscle mass (myopenia). The incidence of death and major adverse cardiovascular events were recorded during a median follow-up of 8.8 years. Multivariable analyses were performed accounting for age, sex, smoking status, myosteatosis, liver steatosis, myopenia, type 2 diabetes, obesity, visceral fat, and history of cardiovascular events. Results Overall, 8982 consecutive outpatients (mean age, 57 years ± 8 [SD]; 5008 female, 3974 male) were included. Abnormal body composition was found in 86% (434 of 507) of patients who died during follow-up. Myosteatosis was found in 278 of 507 patients (55%) who died (15.5% absolute risk at 10 years). Myosteatosis, obesity, liver steatosis, and myopenia were associated with increased mortality risk (hazard ratio [HR]: 4.33 [95% CI: 3.63, 5.16], 1.27 [95% CI: 1.06, 1.53], 1.86 [95% CI: 1.56, 2.21], and 1.75 [95% CI: 1.43, 2.14], respectively). In 8303 patients (excluding 679 patients without complete data), after multivariable adjustment, myosteatosis remained associated with increased mortality risk (HR, 1.89 [95% CI: 1.52, 2.35]; P < .001). Conclusion Artificial intelligence-based profiling of body composition from routine abdominal CT scans identified myosteatosis as a key predictor of mortality risk in asymptomatic adults. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Tong and Magudia in this issue.
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Affiliation(s)
- Maxime Nachit
- From the Laboratory of Hepato-Gastroenterology, Institut de Recherche
Expérimentale et Clinique, UCLouvain, Brussels, Belgium (M.N., I.A.L.);
Service d'Hépato-Gastro-Entérologie, Cliniques
Universitaires Saint-Luc, Brussels, Belgium (Y.H.); Imaging Biomarkers and
Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National
Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of
Radiology, University of Wisconsin School of Medicine & Public Health,
Madison, Wis (P.J.P.)
| | - Yves Horsmans
- From the Laboratory of Hepato-Gastroenterology, Institut de Recherche
Expérimentale et Clinique, UCLouvain, Brussels, Belgium (M.N., I.A.L.);
Service d'Hépato-Gastro-Entérologie, Cliniques
Universitaires Saint-Luc, Brussels, Belgium (Y.H.); Imaging Biomarkers and
Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National
Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of
Radiology, University of Wisconsin School of Medicine & Public Health,
Madison, Wis (P.J.P.)
| | - Ronald M. Summers
- From the Laboratory of Hepato-Gastroenterology, Institut de Recherche
Expérimentale et Clinique, UCLouvain, Brussels, Belgium (M.N., I.A.L.);
Service d'Hépato-Gastro-Entérologie, Cliniques
Universitaires Saint-Luc, Brussels, Belgium (Y.H.); Imaging Biomarkers and
Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National
Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of
Radiology, University of Wisconsin School of Medicine & Public Health,
Madison, Wis (P.J.P.)
| | - Isabelle A. Leclercq
- From the Laboratory of Hepato-Gastroenterology, Institut de Recherche
Expérimentale et Clinique, UCLouvain, Brussels, Belgium (M.N., I.A.L.);
Service d'Hépato-Gastro-Entérologie, Cliniques
Universitaires Saint-Luc, Brussels, Belgium (Y.H.); Imaging Biomarkers and
Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National
Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of
Radiology, University of Wisconsin School of Medicine & Public Health,
Madison, Wis (P.J.P.)
| | - Perry J. Pickhardt
- From the Laboratory of Hepato-Gastroenterology, Institut de Recherche
Expérimentale et Clinique, UCLouvain, Brussels, Belgium (M.N., I.A.L.);
Service d'Hépato-Gastro-Entérologie, Cliniques
Universitaires Saint-Luc, Brussels, Belgium (Y.H.); Imaging Biomarkers and
Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National
Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of
Radiology, University of Wisconsin School of Medicine & Public Health,
Madison, Wis (P.J.P.)
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28
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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: 19] [Impact Index Per Article: 19.0] [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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29
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Weiss J, Raghu VK, Bontempi D, Christiani DC, Mak RH, Lu MT, Aerts HJWL. Deep learning to estimate lung disease mortality from chest radiographs. Nat Commun 2023; 14:2797. [PMID: 37193717 DOI: 10.1038/s41467-023-37758-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 03/29/2023] [Indexed: 05/18/2023] Open
Abstract
Prevention and management of chronic lung diseases (asthma, lung cancer, etc.) are of great importance. While tests are available for reliable diagnosis, accurate identification of those who will develop severe morbidity/mortality is currently limited. Here, we developed a deep learning model, CXR Lung-Risk, to predict the risk of lung disease mortality from a chest x-ray. The model was trained using 147,497 x-ray images of 40,643 individuals and tested in three independent cohorts comprising 15,976 individuals. We found that CXR Lung-Risk showed a graded association with lung disease mortality after adjustment for risk factors, including age, smoking, and radiologic findings (Hazard ratios up to 11.86 [8.64-16.27]; p < 0.001). Adding CXR Lung-Risk to a multivariable model improved estimates of lung disease mortality in all cohorts. Our results demonstrate that deep learning can identify individuals at risk of lung disease mortality on easily obtainable x-rays, which may improve personalized prevention and treatment strategies.
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Affiliation(s)
- Jakob Weiss
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, MA, 02115, USA
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge Street, 02114, Boston, USA
| | - Vineet K Raghu
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge Street, 02114, Boston, USA
| | - Dennis Bontempi
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - David C Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Ave., Boston, MA, 02115, USA
- Pulmonary and Critical Care Division, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Raymond H Mak
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, MA, 02115, USA
| | - Michael T Lu
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge Street, 02114, Boston, USA
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, MA, 02115, USA.
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge Street, 02114, Boston, USA.
- Pulmonary and Critical Care Division, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
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30
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Liu D, Binkley NC, Perez A, Garrett JW, Zea R, Summers RM, Pickhardt PJ. CT image-based biomarkers acquired by AI-based algorithms for the opportunistic prediction of falls. BJR Open 2023; 5:20230014. [PMID: 37953870 PMCID: PMC10636337 DOI: 10.1259/bjro.20230014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/15/2023] [Accepted: 04/11/2023] [Indexed: 11/14/2023] Open
Abstract
Objective Evaluate whether biomarkers measured by automated artificial intelligence (AI)-based algorithms are suggestive of future fall risk. Methods In this retrospective age- and sex-matched case-control study, 9029 total patients underwent initial abdominal CT for a variety of indications over a 20-year interval at one institution. 3535 case patients (mean age at initial CT, 66.5 ± 9.6 years; 63.4% female) who went on to fall (mean interval to fall, 6.5 years) and 5494 controls (mean age at initial CT, 66.7 ± 9.8 years; 63.4% females; mean follow-up interval, 6.6 years) were included. Falls were identified by electronic health record review. Validated and fully automated quantitative CT algorithms for skeletal muscle, adipose tissue, and trabecular bone attenuation at the level of L1 were applied to all scans. Uni- and multivariate assessment included hazard ratios (HRs) and area under the receiver operating characteristic (AUROC) curve. Results Fall HRs (with 95% CI) for low muscle Hounsfield unit, high total adipose area, and low bone Hounsfield unit were 1.82 (1.65-2.00), 1.31 (1.19-1.44) and 1.91 (1.74-2.11), respectively, and the 10-year AUROC values for predicting falls were 0.619, 0.556, and 0.639, respectively. Combining all these CT biomarkers further improved the predictive value, including 10-year AUROC of 0.657. Conclusion Automated abdominal CT-based opportunistic measures of muscle, fat, and bone offer a novel approach to risk stratification for future falls, potentially by identifying patients with osteosarcopenic obesity. Advances in knowledge There are few well-established clinical tools to predict falls. We use novel AI-based body composition algorithms to leverage incidental CT data to help determine a patient's future fall risk.
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Affiliation(s)
- Daniel Liu
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Neil C Binkley
- Osteoporosis Clinical Research Program, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Alberto Perez
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - John W Garrett
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Ryan Zea
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of 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, Madison, WI, USA
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Yamada A, Kamagata K, Hirata K, Ito R, Nakaura T, Ueda D, Fujita S, Fushimi Y, Fujima N, Matsui Y, Tatsugami F, Nozaki T, Fujioka T, Yanagawa M, Tsuboyama T, Kawamura M, Naganawa S. Clinical applications of artificial intelligence in liver imaging. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01638-1. [PMID: 37165151 DOI: 10.1007/s11547-023-01638-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 04/21/2023] [Indexed: 05/12/2023]
Abstract
This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.
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Affiliation(s)
- Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan.
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo, Japan
| | - Kenji Hirata
- Department of Nuclear Medicine, Hokkaido University Hospital, Sapporo, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Chuo-Ku, Kumamoto, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Abeno-Ku, Osaka, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kita-Ku, Okayama, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Minami-Ku, Hiroshima City, Hiroshima, Japan
| | - Taiki Nozaki
- Department of Radiology, St. Luke's International Hospital, Tokyo, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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Tahir I, Cahalane AM, Saenger JA, Leppelmann KS, Abrishami Kashani M, Marquardt JP, Silverman SG, Shyn PB, Mercaldo ND, Fintelmann FJ. Factors Associated with Hospital Length of Stay and Adverse Events following Percutaneous Ablation of Lung Tumors. J Vasc Interv Radiol 2023; 34:759-767.e2. [PMID: 36521793 DOI: 10.1016/j.jvir.2022.12.013] [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: 07/11/2022] [Revised: 10/12/2022] [Accepted: 12/03/2022] [Indexed: 12/14/2022] Open
Abstract
PURPOSE To explore the association between risk factors established in the surgical literature and hospital length of stay (HLOS), adverse events, and hospital readmission within 30 days after percutaneous image-guided thermal ablation of lung tumors. MATERIALS AND METHODS This bi-institutional retrospective cohort study included 131 consecutive adult patients (67 men [51%]; median age, 65 years) with 180 primary or metastatic lung tumors treated in 131 sessions (74 cryoablation and 57 microwave ablation) from 2006 to 2019. Age-adjusted Charlson Comorbidity Index, sex, performance status, smoking status, chronic obstructive pulmonary disease (COPD), primary lung cancer versus pulmonary metastases, number of tumors treated per session, maximum axial tumor diameter, ablation modality, number of pleural punctures, anesthesia type, pulmonary artery-to-aorta ratio, lung densitometry, sarcopenia, and adipopenia were evaluated. Associations between risk factors and outcomes were assessed using univariable and multivariable generalized linear models. RESULTS In univariable analysis, HLOS was associated with current smoking (incidence rate ratio [IRR], 4.54 [1.23-16.8]; P = .02), COPD (IRR, 3.56 [1.40-9.04]; P = .01), cryoablations with ≥3 pleural punctures (IRR, 3.13 [1.07-9.14]; P = .04), general anesthesia (IRR, 10.8 [4.18-27.8]; P < .001), and sarcopenia (IRR, 2.66 [1.10-6.44]; P = .03). After multivariable adjustment, COPD (IRR, 3.56 [1.57-8.11]; P = .003) and general anesthesia (IRR, 12.1 [4.39-33.5]; P < .001) were the only risk factors associated with longer HLOS. No associations were observed between risk factors and adverse events in multivariable analysis. Tumors treated per session were associated with risk of hospital readmission (P = .03). CONCLUSIONS Identified preprocedural risk factors from the surgical literature may aid in risk stratification for HLOS after percutaneous ablation of lung tumors, but were not associated with adverse events.
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Affiliation(s)
- Ismail Tahir
- Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Alexis M Cahalane
- Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Jonathan A Saenger
- Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts; Medical School, Sigmund Freud University, Vienna, Austria
| | - Konstantin S Leppelmann
- Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Maya Abrishami Kashani
- Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - J Peter Marquardt
- Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Stuart G Silverman
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Paul B Shyn
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
| | | | - Florian J Fintelmann
- Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
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Jayaraman P, Crouse A, Nadkarni G, Might M. A Primer in Precision Nephrology: Optimizing Outcomes in Kidney Health and Disease through Data-Driven Medicine. KIDNEY360 2023; 4:e544-e554. [PMID: 36951457 PMCID: PMC10278804 DOI: 10.34067/kid.0000000000000089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 01/04/2023] [Indexed: 03/24/2023]
Abstract
This year marks the 63rd anniversary of the International Society of Nephrology, which signaled nephrology's emergence as a modern medical discipline. In this article, we briefly trace the course of nephrology's history to show a clear arc in its evolution-of increasing resolution in nephrological data-an arc that is converging with computational capabilities to enable precision nephrology. In general, precision medicine refers to tailoring treatment to the individual characteristics of patients. For an operational definition, this tailoring takes the form of an optimization, in which treatments are selected to maximize a patient's expected health with respect to all available data. Because modern health data are large and high resolution, this optimization process requires computational intervention, and it must be tuned to the contours of specific medical disciplines. An advantage of this operational definition for precision medicine is that it allows us to better understand what precision medicine means in the context of a specific medical discipline. The goal of this article was to demonstrate how to instantiate this definition of precision medicine for the field of nephrology. Correspondingly, the goal of precision nephrology was to answer two related questions: ( 1 ) How do we optimize kidney health with respect to all available data? and ( 2 ) How do we optimize general health with respect to kidney data?
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Affiliation(s)
- Pushkala Jayaraman
- The Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai, New York, New York
| | - Andrew Crouse
- Hugh Kaul Precision Medicine Institute, University of Alabama at Birmingham, Birmingham, Alabama
| | - Girish Nadkarni
- The Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai, New York, New York
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Data Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Barbara T Murphy Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Matthew Might
- Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Computer Science, University of Alabama at Birmingham, Birmingham, Alabama
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Flanders D, Lai T, Kutaiba N. Height Estimation from Vertebral Parameters on Routine Computed Tomography in a Contemporary Elderly Australian Population: A Validation of Existing Regression Models. Diagnostics (Basel) 2023; 13:diagnostics13071222. [PMID: 37046440 PMCID: PMC10093189 DOI: 10.3390/diagnostics13071222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/15/2023] [Accepted: 03/21/2023] [Indexed: 04/14/2023] Open
Abstract
The aim of this study is to compare previously published height estimation formulae in a contemporary Australian population using vertebral measurements readily available on abdominal CT. Retrospective analysis of patients undergoing a planning CT prior to transcatheter aortic valve implantation in a 12-month period was conducted; 96 participants were included in the analysis from a total of 137, with 41 excluded due to incomplete data. Seven vertebral measurements were taken from the CT images and height estimates were made for each participant using multiple regression equations from the published literature. Paired sample t-tests were used to compare actual height to estimated height. Many of the models failed to accurately predict patient height in this cohort, with only three equations for each sex resulting in a predicted height that was not statistically significantly different to actual height. The most accurate model in female participants was based on posterior sacral length and resulted in a mean difference between an actual and calculated height of 0.7 cm (±7.4) (p = 0.520). The most accurate model in male participants was based on anterior sacrococcygeal length and resulted in a mean difference of -0.6 ± 6.9 cm (p = 0.544). Height estimation formulae can be used to predict patient height from common vertebral parameters on readily available CT data. This is important for the calculation of anthropometric measures for a variety of uses in clinical medicine. However, more work is needed to generate accurate prediction models for specific populations.
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Affiliation(s)
- Damian Flanders
- Department of Radiology, Austin Health, Melbourne, VIC 3084, Australia
| | - Timothy Lai
- Department of Radiology, Austin Health, Melbourne, VIC 3084, Australia
| | - Numan Kutaiba
- Department of Radiology, Austin Health, Melbourne, VIC 3084, Australia
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Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions. Diagnostics (Basel) 2023; 13:diagnostics13050968. [PMID: 36900112 PMCID: PMC10000509 DOI: 10.3390/diagnostics13050968] [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: 12/23/2022] [Revised: 02/11/2023] [Accepted: 02/18/2023] [Indexed: 03/08/2023] Open
Abstract
CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.
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36
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Pickhardt PJ, Correale L, Hassan C. AI-based opportunistic CT screening of incidental cardiovascular disease, osteoporosis, and sarcopenia: cost-effectiveness analysis. Abdom Radiol (NY) 2023; 48:1181-1198. [PMID: 36670245 DOI: 10.1007/s00261-023-03800-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 01/02/2023] [Accepted: 01/04/2023] [Indexed: 01/22/2023]
Abstract
PURPOSE To assess the cost-effectiveness and clinical efficacy of AI-assisted abdominal CT-based opportunistic screening for atherosclerotic cardiovascular (CV) disease, osteoporosis, and sarcopenia using artificial intelligence (AI) body composition algorithms. METHODS Markov models were constructed and 10-year simulations were performed on hypothetical age- and sex-specific cohorts of 10,000 U.S. adults (base case: 55 year olds) undergoing abdominal CT. Using expected disease prevalence, transition probabilities between health states, associated healthcare costs, and treatment effectiveness related to relevant conditions (CV disease/osteoporosis/sarcopenia) were modified by three mutually exclusive screening models: (1) usual care ("treat none"; no intervention regardless of opportunistic CT findings), (2) universal statin therapy ("treat all" for CV prevention; again, no consideration of CT findings), and (3) AI-assisted abdominal CT-based opportunistic screening for CV disease, osteoporosis, and sarcopenia using automated quantitative algorithms for abdominal aortic calcification, bone mineral density, and skeletal muscle, respectively. Model validity was assessed against published clinical cohorts. RESULTS For the base-case scenarios of 55-year-old men and women modeled over 10 years, AI-assisted CT-based opportunistic screening was a cost-saving and more effective clinical strategy, unlike the "treat none" and "treat all" strategies that ignored incidental CT body composition data. Over a wide range of input assumptions beyond the base case, the CT-based opportunistic strategy was dominant over the other two scenarios, as it was both more clinically efficacious and more cost-effective. Cost savings and clinical improvement for opportunistic CT remained for AI tool costs up to $227/patient in men ($65 in women) from the $10/patient base-case scenario. CONCLUSION AI-assisted CT-based opportunistic screening appears to be a highly cost-effective and clinically efficacious strategy across a broad array of input assumptions, and was cost saving in most scenarios.
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Affiliation(s)
- Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine & Public Heatlh, 600 Highland Ave, Madison, WI, 53792, USA.
| | - Loredana Correale
- Department of Gastroenterology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, Milan, Italy
| | - Cesare Hassan
- Department of Gastroenterology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
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Utility of Fully Automated Body Composition Measures on Pretreatment Abdominal CT for Predicting Survival in Patients With Colorectal Cancer. AJR Am J Roentgenol 2023; 220:371-380. [PMID: 36000663 DOI: 10.2214/ajr.22.28043] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
BACKGROUND. CT examinations contain opportunistic body composition data with potential prognostic utility. Previous studies have primarily used manual or semiautomated tools to evaluate body composition in patients with colorectal cancer (CRC). OBJECTIVE. The purpose of this article is to assess the utility of fully automated body composition measures derived from pretreatment CT examinations in predicting survival in patients with CRC. METHODS. This retrospective study included 1766 patients (mean age, 63.7 ± 14.4 [SD] years; 862 men, 904 women) diagnosed with CRC between January 2001 and September 2020 who underwent pretreatment abdominal CT. A panel of fully automated artificial intelligence-based algorithms was applied to portal venous phase images to quantify skeletal muscle attenuation at the L3 lumbar level, visceral adipose tissue (VAT) area and subcutaneous adipose tissue (SAT) area at L3, and abdominal aorta Agatston score (aortic calcium). The electronic health record was reviewed to identify patients who died of any cause (n = 848). ROC analyses and logistic regression analyses were used to identify predictors of survival, with attention to highest- and lowest-risk quartiles. RESULTS. Patients who died, compared with patients who survived, had lower median muscle attenuation (19.2 vs 26.2 HU, p < .001), SAT area (168.4 cm2 vs 197.6 cm2, p < .001), and aortic calcium (620 vs 182, p < .001). Measures with highest 5-year AUCs for predicting survival in patients without (n = 1303) and with (n = 463) metastatic disease were muscle attenuation (0.666 and 0.701, respectively) and aortic calcium (0.677 and 0.689, respectively). A combination of muscle attenuation, SAT area, and aortic calcium yielded 5-year AUCs of 0.758 and 0.732 in patients without and with metastases, respectively. Risk of death was increased (p < .05) in patients in the lowest quartile for muscle attenuation (hazard ratio [HR] = 1.55) and SAT area (HR = 1.81) and in the highest quartile for aortic calcium (HR = 1.37) and decreased (p < .05) in patients in the highest quartile for VAT area (HR = 0.79) and SAT area (HR = 0.76). In 423 patients with available BMI, BMI did not significantly predict death (p = .75). CONCLUSION. Fully automated CT-based body composition measures including muscle attenuation, SAT area, and aortic calcium predict survival in patients with CRC. CLINICAL IMPACT. Routine pretreatment body composition evaluation could improve initial risk stratification of patients with CRC.
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Technical Adequacy of Fully Automated Artificial Intelligence Body Composition Tools: Assessment in a Heterogeneous Sample of External CT Examinations. AJR Am J Roentgenol 2023:1-9. [PMID: 37095663 DOI: 10.2214/ajr.22.28745] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Please see the Editorial Comment by Robert D. Boutin discussing this article. Chinese (audio/PDF) and Spanish (audio/PDF) translations are available for this article's abstract. Background: Clinically usable artificial intelligence (AI) tools analyzing imaging studies should be robust to expected variations in study parameters. Objective: To assess the technical adequacy of a set of automated AI abdominal CT body composition tools on a heterogeneous sample of external CT examinations performed outside of the authors' hospital system, as well as to explore possible reasons for tool failure. Methods: This retrospective study included 8949 patients (mean age, 55.5±15.9 years; 4256 men, 4693 women) who underwent 11,699 abdominal CT examinations performed at 777 different external institutions using 82 different scanner models from 6 different manufacturers, and subsequently transferred to the local PACS for clinical purposes. Three independent automated AI tools assessing body composition (bone attenuation, muscle amount and attenuation, visceral and subcutaneous fat amounts) were deployed, evaluating one axial series per examination. Technical adequacy was defined as tool output values within empirically derived reference ranges. Failures (i.e., tool output outside of reference range) were reviewed to identify possible causes. Results: All three tools were technically adequate in 11,431/11,699 (97.7%) examinations, with at least one tool failing in 268/11,699 (2.3%). Individual adequacy rates were 97.8%, 99.1%, and 98.0% for bone, muscle, and fat tools, respectively. A single type of image processing error (anisometry error, due to incorrect DICOM header voxel dimension information) accounted for 81/92 (88%) examinations for which all three tools failed, and all three tools failed whenever this error occurred. Anisometry error was the most common specific cause for failure for all tools (31.6% for bone, 81.0% for muscle, and 62.8% for fat). A total of 79/81 (97.5%) anisometry errors occurred on scanners from a single manufacturer; 80/81 (98.8%) occurred on the same scanner model. No cause for failure was identified in 59.4%, 16.0%, and 34.9% of failures for the bone, muscle, and fat tools, respectively. Conclusion: The automated AI body composition tools had high technical adequacy rates in a heterogeneous sample of external CT examinations, supporting the tools' generalizability and potential for broad use. Clinical Impact: Certain reasons for AI tool failure relating to technical factors may be largely preventable through proper acquisition and reconstruction protocols.
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Fully automated CT-based adiposity assessment: comparison of the L1 and L3 vertebral levels for opportunistic prediction. Abdom Radiol (NY) 2023; 48:787-795. [PMID: 36369528 DOI: 10.1007/s00261-022-03728-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 10/23/2022] [Accepted: 10/25/2022] [Indexed: 11/13/2022]
Abstract
PURPOSE The purpose of this study is to compare fully automated CT-based measures of adipose tissue at the L1 level versus the standard L3 level for predicting mortality, which would allow for use at both chest (L1) and abdominal (L3) CT. METHODS This retrospective study of 9066 asymptomatic adults (mean age, 57.1 ± 7.8 [SD] years; 4020 men, 5046 women) undergoing unenhanced low-dose abdominal CT for colorectal cancer screening. A previously validated artificial intelligence (AI) tool was used to assess cross-sectional visceral and subcutaneous adipose tissue areas (SAT and VAT), as well as their ratio (VSR) at the L1 and L3 levels. Post-CT survival prediction was compared using area under the ROC curve (ROC AUC) and hazard ratios (HRs). RESULTS Median clinical follow-up interval after CT was 8.8 years (interquartile range, 5.2-11.6 years), during which 5.9% died (532/9066). No significant difference (p > 0.05) for mortality was observed between L1 and L3 VAT and SAT at 10-year ROC AUC. However, L3 measures were significantly better for VSR at 10-year AUC (p < 0.001). HRs comparing worst-to-best quartiles for mortality at L1 vs. L3 were 2.12 (95% CI, 1.65-2.72) and 2.22 (1.74-2.83) for VAT; 1.20 (0.95-1.52) and 1.16 (0.92-1.46) for SAT; and 2.26 (1.7-2.93) and 3.05 (2.32-4.01) for VSR. In women, the corresponding HRs for VSR were 2.58 (1.80-3.69) (L1) and 4.49 (2.98-6.78) (L3). CONCLUSION Automated CT-based measures of visceral fat (VAT and VSR) at L1 are predictive of survival, although overall measures of adiposity at L1 level are somewhat inferior to the standard L3-level measures. Utilizing predictive L1-level fat measures could expand opportunistic screening to chest CT imaging.
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Pickhardt PJ. Incidentalomas at abdominal imaging. Br J Radiol 2023; 96:20211167. [PMID: 34767479 PMCID: PMC9975518 DOI: 10.1259/bjr.20211167] [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] [Received: 10/18/2021] [Revised: 10/29/2021] [Accepted: 11/03/2021] [Indexed: 01/27/2023] Open
Abstract
As all radiologists are well aware, cross-sectional abdominal imaging tests such as CT, MR, and ultrasound generally include organs and structures that are not directly related to the clinical indication for obtaining the examination. As a result, unsuspected additional findings or "incidentalomas" must be handled in a responsible manner that balances any need for reporting and management against the potential harms that may result from such actions. The majority of abdominal incidentalomas detected at imaging will not cause downstream harm to the patient, unless perhaps the radiologist unleashes an unnecessary work-up cascade that results in patient anxiety, inconvenience, added costs, or complications. Applying the principle of primum non-nocere, an argument can be made for not even reporting incidental imaging findings that have an exceedingly low likelihood of clinical relevance, such as small, simple-appearing sporadic cysts that are commonly seen in many abdominal organs. The situation becomes more challenging, however, when "likely benign" yet indeterminate lesions are encountered. At some threshold, which is difficult to precisely define for all cases, further action may be indicated, be it imaging follow-up to confirm resolution or stability, more definitive imaging characterization, or even tissue sampling. For more concerning or ominous incidentalomas, the need for further work-up will be more clear cut.
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Affiliation(s)
- Perry J. Pickhardt
- The University of Wisconsin School of Medicine & Public Health, Madison, Wisconsin, United States
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Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events. AJR Am J Roentgenol 2023; 220:236-244. [PMID: 36043607 DOI: 10.2214/ajr.22.27977] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND. CT-based body composition (BC) measurements have historically been too resource intensive to analyze for widespread use and have lacked robust comparison with traditional weight metrics for predicting cardiovascular risk. OBJECTIVE. The aim of this study was to determine whether BC measurements obtained from routine CT scans by use of a fully automated deep learning algorithm could predict subsequent cardiovascular events independently from weight, BMI, and additional cardiovascular risk factors. METHODS. This retrospective study included 9752 outpatients (5519 women and 4233 men; mean age, 53.2 years; 890 patients self-reported their race as Black and 8862 self-reported their race as White) who underwent routine abdominal CT at a single health system from January 2012 through December 2012 and who were given no major cardiovascular or oncologic diagnosis within 3 months of undergoing CT. Using publicly available code, fully automated deep learning BC analysis was performed at the L3 vertebral body level to determine three BC areas (skeletal muscle area [SMA], visceral fat area [VFA], and subcutaneous fat area [SFA]). Age-, sex-, and race-normalized reference curves were used to generate z scores for the three BC areas. Subsequent myocardial infarction (MI) or stroke was determined from the electronic medical record. Multivariable-adjusted Cox proportional hazards models were used to determine hazard ratios (HRs) for MI or stroke within 5 years after CT for the three BC area z scores, with adjustment for normalized weight, normalized BMI, and additional cardiovascular risk factors (smoking status, diabetes diagnosis, and systolic blood pressure). RESULTS. In multivariable models, age-, race-, and sex-normalized VFA was associated with subsequent MI risk (HR of highest quartile compared with lowest quartile, 1.31 [95% CI, 1.03-1.67], p = .04 for overall effect) and stroke risk (HR of highest compared with lowest quartile, 1.46 [95% CI, 1.07-2.00], p = .04 for overall effect). In multivariable models, normalized SMA, SFA, weight, and BMI were not associated with subsequent MI or stroke risk. CONCLUSION. VFA derived from fully automated and normalized analysis of abdominal CT examinations predicts subsequent MI or stroke in Black and White patients, independent of traditional weight metrics, and should be considered an adjunct to BMI in risk models. CLINICAL IMPACT. Fully automated and normalized BC analysis of abdominal CT has promise to augment traditional cardiovascular risk prediction models.
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Lee MH, Zea R, Garrett JW, Graffy PM, Summers RM, Pickhardt PJ. Abdominal CT Body Composition Thresholds Using Automated AI Tools for Predicting 10-year Adverse Outcomes. Radiology 2023; 306:e220574. [PMID: 36165792 PMCID: PMC9885340 DOI: 10.1148/radiol.220574] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 07/18/2022] [Accepted: 08/03/2022] [Indexed: 01/26/2023]
Abstract
Background CT-based body composition measures derived from fully automated artificial intelligence tools are promising for opportunistic screening. However, body composition thresholds associated with adverse clinical outcomes are lacking. Purpose To determine population and sex-specific thresholds for muscle, abdominal fat, and abdominal aortic calcium measures at abdominal CT for predicting risk of death, adverse cardiovascular events, and fragility fractures. Materials and Methods In this retrospective single-center study, fully automated algorithms for quantifying skeletal muscle (L3 level), abdominal fat (L3 level), and abdominal aortic calcium were applied to noncontrast abdominal CT scans from asymptomatic adults screened from 2004 to 2016. Longitudinal follow-up documented subsequent death, adverse cardiovascular events (myocardial infarction, cerebrovascular event, and heart failure), and fragility fractures. Receiver operating characteristic (ROC) curve analysis was performed to derive thresholds for body composition measures to achieve optimal ROC curve performance and high specificity (90%) for 10-year risks. Results A total of 9223 asymptomatic adults (mean age, 57 years ± 7 [SD]; 5152 women and 4071 men) were evaluated (median follow-up, 9 years). Muscle attenuation and aortic calcium had the highest diagnostic performance for predicting death, with areas under the ROC curve of 0.76 for men (95% CI: 0.72, 0.79) and 0.72 for women (95% CI: 0.69, 0.76) for muscle attenuation. Sex-specific thresholds were higher in men than women (P < .001 for muscle attenuation for all outcomes). The highest-performing markers for risk of death were muscle attenuation in men (31 HU; 71% sensitivity [164 of 232 patients]; 72% specificity [1114 of 1543 patients]) and aortic calcium in women (Agatston score, 167; 70% sensitivity [152 of 218 patients]; 70% specificity [1427 of 2034 patients]). Ninety-percent specificity thresholds for muscle attenuation for both risk of death and fragility fractures were 23 HU (men) and 13 HU (women). For aortic calcium and risk of death and adverse cardiovascular events, 90% specificity Agatston score thresholds were 1475 (men) and 735 (women). Conclusion Sex-specific thresholds for automated abdominal CT-based body composition measures can be used to predict risk of death, adverse cardiovascular events, and fragility fractures. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Ohliger in this issue.
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Affiliation(s)
- Matthew H. Lee
- From the Departments of Radiology (M.H.L., R.Z., J.W.G., P.M.G.,
P.J.P.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine
and Public Health, 600 Highland Ave, Madison, WI 53792; and Imaging Biomarkers
and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Ryan Zea
- From the Departments of Radiology (M.H.L., R.Z., J.W.G., P.M.G.,
P.J.P.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine
and Public Health, 600 Highland Ave, Madison, WI 53792; and Imaging Biomarkers
and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - John W. Garrett
- From the Departments of Radiology (M.H.L., R.Z., J.W.G., P.M.G.,
P.J.P.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine
and Public Health, 600 Highland Ave, Madison, WI 53792; and Imaging Biomarkers
and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Peter M. Graffy
- From the Departments of Radiology (M.H.L., R.Z., J.W.G., P.M.G.,
P.J.P.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine
and Public Health, 600 Highland Ave, Madison, WI 53792; and Imaging Biomarkers
and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Ronald M. Summers
- From the Departments of Radiology (M.H.L., R.Z., J.W.G., P.M.G.,
P.J.P.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine
and Public Health, 600 Highland Ave, Madison, WI 53792; and Imaging Biomarkers
and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Perry J. Pickhardt
- From the Departments of Radiology (M.H.L., R.Z., J.W.G., P.M.G.,
P.J.P.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine
and Public Health, 600 Highland Ave, Madison, WI 53792; and Imaging Biomarkers
and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
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Gu S, Wang L, Han R, Liu X, Wang Y, Chen T, Zheng Z. Detection of sarcopenia using deep learning-based artificial intelligence body part measure system (AIBMS). Front Physiol 2023; 14:1092352. [PMID: 36776966 PMCID: PMC9909827 DOI: 10.3389/fphys.2023.1092352] [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: 11/10/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023] Open
Abstract
Background: Sarcopenia is an aging syndrome that increases the risks of various adverse outcomes, including falls, fractures, physical disability, and death. Sarcopenia can be diagnosed through medical images-based body part analysis, which requires laborious and time-consuming outlining of irregular contours of abdominal body parts. Therefore, it is critical to develop an efficient computational method for automatically segmenting body parts and predicting diseases. Methods: In this study, we designed an Artificial Intelligence Body Part Measure System (AIBMS) based on deep learning to automate body parts segmentation from abdominal CT scans and quantification of body part areas and volumes. The system was developed using three network models, including SEG-NET, U-NET, and Attention U-NET, and trained on abdominal CT plain scan data. Results: This segmentation model was evaluated using multi-device developmental and independent test datasets and demonstrated a high level of accuracy with over 0.9 DSC score in segment body parts. Based on the characteristics of the three network models, we gave recommendations for the appropriate model selection in various clinical scenarios. We constructed a sarcopenia classification model based on cutoff values (Auto SMI model), which demonstrated high accuracy in predicting sarcopenia with an AUC of 0.874. We used Youden index to optimize the Auto SMI model and found a better threshold of 40.69. Conclusion: We developed an AI system to segment body parts in abdominal CT images and constructed a model based on cutoff value to achieve the prediction of sarcopenia with high accuracy.
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Affiliation(s)
- Shangzhi Gu
- Department of Computer Science and Technology, Institute for Artificial Intelligence, and BNRist, Tsinghua University, Beijing, China,School of Medicine, Tsinghua University, Beijing, China
| | - Lixue Wang
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Rong Han
- Department of Computer Science and Technology, Institute for Artificial Intelligence, and BNRist, Tsinghua University, Beijing, China
| | - Xiaohong Liu
- Department of Computer Science and Technology, Institute for Artificial Intelligence, and BNRist, Tsinghua University, Beijing, China
| | - Yizhe Wang
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Ting Chen
- Department of Computer Science and Technology, Institute for Artificial Intelligence, and BNRist, Tsinghua University, Beijing, China,Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China,*Correspondence: Ting Chen, ; Zhuozhao Zheng,
| | - Zhuozhao Zheng
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China,*Correspondence: Ting Chen, ; Zhuozhao Zheng,
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Anyene I, Caan B, Williams GR, Popuri K, Lenchik L, Giri S, Chow V, Beg MF, Cespedes Feliciano EM. Body composition from single versus multi-slice abdominal computed tomography: Concordance and associations with colorectal cancer survival. J Cachexia Sarcopenia Muscle 2022; 13:2974-2984. [PMID: 36052755 PMCID: PMC9745558 DOI: 10.1002/jcsm.13080] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 07/06/2022] [Accepted: 08/14/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Computed tomography (CT) scans are routinely obtained in oncology and provide measures of muscle and adipose tissue predictive of morbidity and mortality. Automated segmentation of CT has advanced past single slices to multi-slice measurements, but the concordance of these approaches and their associations with mortality after cancer diagnosis have not been compared. METHODS A total of 2871 patients with colorectal cancer diagnosed during 2012-2017 at Kaiser Permanente Northern California underwent abdominal CT scans as part of routine clinical care from which mid-L3 cross-sectional areas and multi-slice T12-L5 volumes of skeletal muscle (SKM), subcutaneous adipose (SAT), visceral adipose (VAT) and intermuscular adipose (IMAT) tissues were assessed using Data Analysis Facilitation Suite, an automated multi-slice segmentation platform. To facilitate comparison between single-slice and multi-slice measurements, sex-specific z-scores were calculated. Pearson correlation coefficients and Bland-Altman analysis were used to quantify agreement. Cox models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for death adjusting for age, sex, race/ethnicity, height, and tumour site and stage. RESULTS Single-slice area and multi-slice abdominal volumes were highly correlated for all tissues (SKM R = 0.92, P < 0.001; SAT R = 0.97, P < 0.001; VAT R = 0.98, P < 0.001; IMAT R = 0.89, P < 0.001). Bland-Altman plots had a bias of 0 (SE: 0.00), indicating high average agreement between measures. The limits of agreement were narrowest for VAT ( ± 0.42 SD) and SAT ( ± 0.44 SD), and widest for SKM ( ± 0.78 SD) and IMAT ( ± 0.92 SD). The HRs had overlapping CIs, and similar magnitudes and direction of effects; for example, a 1-SD increase in SKM area was associated with an 18% decreased risk of death (HR = 0.82; 95% CI: 0.72-0.92), versus 15% for volume from T12 to L5 (HR = 0.85; 95% CI: 0.75-0.96). CONCLUSIONS Single-slice L3 areas and multi-slice T12-L5 abdominal volumes of SKM, VAT, SAT and IMAT are highly correlated. Associations between area and volume measures with all-cause mortality were similar, suggesting that they are equivalent tools for population studies if body composition is assessed at a single timepoint. Future research should examine longitudinal changes in multi-slice tissues to improve individual risk prediction.
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Affiliation(s)
- Ijeamaka Anyene
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Bette Caan
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Grant R Williams
- Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, Birmingham, AL, USA.,Division of Hematology/Oncology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Karteek Popuri
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada
| | - Leon Lenchik
- Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Smith Giri
- Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, Birmingham, AL, USA.,Division of Hematology/Oncology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Vincent Chow
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada
| | - Mirza Faisal Beg
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada
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Variation in aorta attenuation in contrast-enhanced CT and its implications for calcification thresholds. PLoS One 2022; 17:e0277111. [PMID: 36355794 PMCID: PMC9648778 DOI: 10.1371/journal.pone.0277111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 10/20/2022] [Indexed: 11/12/2022] Open
Abstract
Background CT contrast media improves vessel visualization but can also confound calcification measurements. We evaluated variance in aorta attenuation from varied contrast-enhancement scans, and quantified expected plaque detection errors when thresholding for calcification. Methods We measured aorta attenuation (AoHU) in central vessel regions from 10K abdominal CT scans and report AoHU relationships to contrast phase (non-contrast, arterial, venous, delayed), demographic variables (age, sex, weight), body location, and scan slice thickness. We also report expected plaque segmentation false-negative errors (plaque pixels misidentified as non-plaque pixels) and false-positive errors (vessel pixels falsely identified as plaque), comparing a uniform thresholding approach and a dynamic approach based on local mean/SD aorta attenuation. Results Females had higher AoHU than males in contrast-enhanced scans by 65/22/20 HU for arterial/venous/delayed phases (p < 0.001) but not in non-contrast scans (p > 0.05). Weight was negatively correlated with AoHU by 2.3HU/10kg but other predictors explained only small portions of intra-cohort variance (R2 < 0.1 in contrast-enhanced scans). Average AoHU differed by contrast phase, but considerable overlap was seen between distributions. Increasing uniform plaque thresholds from 130HU to 200HU/300HU/400HU produces respective false-negative plaque content losses of 35%/60%/75% from all scans with corresponding false-positive errors in arterial-phase scans of 95%/60%/15%. Dynamic segmentation at 3SD above mean AoHU reduces false-positive errors to 0.13% and false-negative errors to 8%, 25%, and 70% in delayed, venous, and arterial scans, respectively. Conclusion CT contrast produces heterogeneous aortic enhancements not readily determined by demographic or scan protocol factors. Uniform CT thresholds for calcified plaques incur high rates of pixel classification errors in contrast-enhanced scans which can be minimized using dynamic thresholds based on local aorta attenuation. Care should be taken to address these errors and sex-based biases in baseline attenuation when designing automatic calcification detection algorithms intended for broad use in contrast-enhanced CTs.
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Wright DE, Mukherjee S, Patra A, Khasawneh H, Korfiatis P, Suman G, Chari ST, Kudva YC, Kline TL, Goenka AH. Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study. Abdom Radiol (NY) 2022; 47:3806-3816. [PMID: 36085379 DOI: 10.1007/s00261-022-03668-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/26/2022] [Accepted: 08/27/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To determine if pancreas radiomics-based AI model can detect the CT imaging signature of type 2 diabetes (T2D). METHODS Total 107 radiomic features were extracted from volumetrically segmented normal pancreas in 422 T2D patients and 456 age-matched controls. Dataset was randomly split into training (300 T2D, 300 control CTs) and test subsets (122 T2D, 156 control CTs). An XGBoost model trained on 10 features selected through top-K-based selection method and optimized through threefold cross-validation on training subset was evaluated on test subset. RESULTS Model correctly classified 73 (60%) T2D patients and 96 (62%) controls yielding F1-score, sensitivity, specificity, precision, and AUC of 0.57, 0.62, 0.61, 0.55, and 0.65, respectively. Model's performance was equivalent across gender, CT slice thicknesses, and CT vendors (p values > 0.05). There was no difference between correctly classified versus misclassified patients in the mean (range) T2D duration [4.5 (0-15.4) versus 4.8 (0-15.7) years, p = 0.8], antidiabetic treatment [insulin (22% versus 18%), oral antidiabetics (10% versus 18%), both (41% versus 39%) (p > 0.05)], and treatment duration [5.4 (0-15) versus 5 (0-13) years, p = 0.4]. CONCLUSION Pancreas radiomics-based AI model can detect the imaging signature of T2D. Further refinement and validation are needed to evaluate its potential for opportunistic T2D detection on millions of CTs that are performed annually.
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Affiliation(s)
- Darryl E Wright
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Sovanlal Mukherjee
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Anurima Patra
- Department of Radiology, Tata Medical Center, Kolkata, 700160, India
| | - Hala Khasawneh
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Panagiotis Korfiatis
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Garima Suman
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Suresh T Chari
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
- Department of Gastroenterology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Yogish C Kudva
- Department of Endocrinology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Timothy L Kline
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Ajit H Goenka
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA.
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47
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Single CT Appointment for Double Lung and Colorectal Cancer Screening: Is the Time Ripe? Diagnostics (Basel) 2022; 12:diagnostics12102326. [PMID: 36292015 PMCID: PMC9601268 DOI: 10.3390/diagnostics12102326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 09/15/2022] [Accepted: 09/21/2022] [Indexed: 12/24/2022] Open
Abstract
Annual screening of lung cancer (LC) with chest low-dose computed tomography (CT) and screening of colorectal cancer (CRC) with CT colonography every 5 years are recommended by the United States Prevention Service Task Force. We review epidemiological and pathological data on LC and CRC, and the features of screening chest low-dose CT and CT colonography comprising execution, reading, radiation exposure and harm, and the cost effectiveness of the two CT screening interventions. The possibility of combining chest low-dose CT and CT colonography examinations for double LC and CRC screening in a single CT appointment is then addressed. We demonstrate how this approach appears feasible and is already reasonable as an opportunistic screening intervention in 50–75-year-old subjects with smoking history and average CRC risk. In addition to the crucial role Computer Assisted Diagnosis systems play in decreasing the test reading times and the need to educate radiologists in screening chest LDCT and CT colonography, in view of a single CT appointment for double screening, the following uncertainties need to be solved: (1) the schedule of the screening CT; (2) the effectiveness of iterative reconstruction and deep learning algorithms affording an ultra-low-dose CT acquisition technique and (3) management of incidental findings. Resolving these issues will imply new cost-effectiveness analyses for LC screening with chest low dose CT and for CRC screening with CT colonography and, especially, for the double LC and CRC screening with a single-appointment CT.
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48
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AYDIN MM, DAĞISTAN E, COŞGUN Z. Metabolik sendromda visseral ve subkutan yağ miktari ve hepatosteatozun bilgisayarli tomografi ile kantitatif değerlendirilmesi. CUKUROVA MEDICAL JOURNAL 2022. [DOI: 10.17826/cumj.1037220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Purpose: We aimed to evaluate visceral and subcutaneous fat tissue and its association with hepatosteatosis on computed tomography (CT) scans to determine cut-off criteria for metabolic syndrome, measure abdominal obesity directly based on the visceral fat tissue area (VFTA) rather than indirectly based on waist circumference and obtain supportive findings by density measurements in addition to the VFTA measurements.Materials and Methods: The Hounsfield unit (HU) values, visceral, subcutaneous fat areas and HU values of 108 patients diagnosed with metabolic syndrome (MS) were determined according to the National Cholesterol Education Program Adult Treatment Panel III 2001 Criteria by retrospectively analyzing their abdominal CT images taken for various reasons. The relationships of the obtained values with each other and to MS were evaluated.Results: The strongest predictor of MS was VFTA, and 156.47 cm² was the most significant value with 74.1% sensitivity and 58.6% specificity. An HU value of -102.99 for visceral fat tissue density (VFTD) was found as the second most significant finding with 75% sensitivity and 57.6% specificity. The VFTA values of the patients with hepatosteatosis were higher, and increased VFTA values were associated with lower VFTD values.Conclusion: The most important supportive finding was the demonstration of the possibility of measuring abdominal obesity, which has an important place among criteria, directly by measuring VFTA, rather than indirectly based on waist circumference.
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Affiliation(s)
- Mehmet Maruf AYDIN
- SAĞLIK BİLİMLERİ ÜNİVERSİTESİ, SAMSUN SAĞLIK UYGULAMA VE ARAŞTIRMA MERKEZİ
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49
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CT-Derived Body Composition Assessment as a Prognostic Tool in Oncologic Patients: From Opportunistic Research to Artificial Intelligence-Based Clinical Implementation. AJR Am J Roentgenol 2022; 219:671-680. [PMID: 35642760 DOI: 10.2214/ajr.22.27749] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
CT-based body composition measures are well established in research settings as prognostic markers in oncologic patients. Numerous retrospective studies have shown the role of objective measurements extracted from abdominal CT images of skeletal muscle, abdominal fat, and bone mineral density in providing more accurate assessments of frailty and cancer cachexia in comparison with traditional clinical methods. Quantitative CT-based measurements of liver fat and aortic atherosclerotic calcification have received relatively less attention in cancer care but also provide prognostic information. Patients with cancer routinely undergo serial CT scans for staging, treatment response, and surveillance, providing the opportunity for performing quantitative body composition assessment as part of routine clinical care. The emergence of fully automated artificial intelligence-based segmentation and quantification tools to replace earlier time-consuming manual and semi-automated methods for body composition analysis will allow these opportunistic measures to transition from the research realm to clinical practice. With continued investigation, the measurements may ultimately be applied to achieve more precise risk stratification as a component of personalized oncologic care.
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50
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Pickhardt PJ. Value-added Opportunistic CT Screening: State of the Art. Radiology 2022; 303:241-254. [PMID: 35289661 PMCID: PMC9083232 DOI: 10.1148/radiol.211561] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/24/2021] [Accepted: 08/27/2021] [Indexed: 12/13/2022]
Abstract
Opportunistic CT screening leverages robust imaging data embedded within abdominal and thoracic scans that are generally unrelated to the specific clinical indication and have heretofore gone largely unused. This incidental imaging information may prove beneficial to patients in terms of wellness, prevention, risk profiling, and presymptomatic detection of relevant disease. The growing interest in CT-based opportunistic screening relates to a confluence of factors: the objective and generalizable nature of CT-based body composition measures, the emergence of fully automated explainable AI solutions, the sheer volume of body CT scans performed, and the increasing emphasis on precision medicine and value-added initiatives. With a systematic approach to body composition and other useful CT markers, initial evidence suggests that their ability to help radiologists assess biologic age and predict future adverse cardiometabolic events rivals even the best available clinical reference standards. Emerging data suggest that standalone "intended" CT screening over an unorganized opportunistic approach may be justified, especially when combined with established cancer screening. This review will discuss the current status of opportunistic CT screening, including specific body composition markers and the various disease processes that may be impacted. The remaining hurdles to widespread clinical adoption include generalization to more diverse patient populations, disparate technical settings, and reimbursement.
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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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