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Lee MH, Zea R, Garrett JW, Summers RM, Pickhardt PJ. AI-based abdominal CT measurements of orthotopic and ectopic fat predict mortality and cardiometabolic disease risk in adults. Eur Radiol 2024:10.1007/s00330-024-10935-w. [PMID: 38995381 DOI: 10.1007/s00330-024-10935-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/27/2024] [Accepted: 05/31/2024] [Indexed: 07/13/2024]
Abstract
OBJECTIVES To evaluate the utility of CT-based abdominal fat measures for predicting the risk of death and cardiometabolic disease in an asymptomatic adult screening population. METHODS Fully automated AI tools quantifying abdominal adipose tissue (L3 level visceral [VAT] and subcutaneous [SAT] fat area, visceral-to-subcutaneous fat ratio [VSR], VAT attenuation), muscle attenuation (L3 level), and liver attenuation were applied to non-contrast CT scans in asymptomatic adults undergoing CT colonography (CTC). Longitudinal follow-up documented subsequent deaths, cardiovascular events, and diabetes. ROC and time-to-event analyses were performed to generate AUCs and hazard ratios (HR) binned by octile. RESULTS A total of 9223 adults (mean age, 57 years; 4071:5152 M:F) underwent screening CTC from April 2004 to December 2016. 549 patients died on follow-up (median, nine years). Fat measures outperformed BMI for predicting mortality risk-5-year AUCs for muscle attenuation, VSR, and BMI were 0.721, 0.661, and 0.499, respectively. Higher visceral, muscle, and liver fat were associated with increased mortality risk-VSR > 1.53, HR = 3.1; muscle attenuation < 15 HU, HR = 5.4; liver attenuation < 45 HU, HR = 2.3. Higher VAT area and VSR were associated with increased cardiovascular event and diabetes risk-VSR > 1.59, HR = 2.6 for cardiovascular event; VAT area > 291 cm2, HR = 6.3 for diabetes (p < 0.001). A U-shaped association was observed for SAT with a higher risk of death for very low and very high SAT. CONCLUSION Fully automated CT-based measures of abdominal fat are predictive of mortality and cardiometabolic disease risk in asymptomatic adults and uncover trends that are not reflected in anthropomorphic measures. CLINICAL RELEVANCE STATEMENT Fully automated CT-based measures of abdominal fat soundly outperform anthropometric measures for mortality and cardiometabolic risk prediction in asymptomatic patients. KEY POINTS Abdominal fat depots associated with metabolic dysregulation and cardiovascular disease can be derived from abdominal CT. Fully automated AI body composition tools can measure factors associated with increased mortality and cardiometabolic risk. CT-based abdominal fat measures uncover trends in mortality and cardiometabolic risk not captured by BMI in asymptomatic outpatients.
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Affiliation(s)
- Matthew H Lee
- Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA.
| | - Ryan Zea
- Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - John W Garrett
- Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Perry J Pickhardt
- Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA
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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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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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Rosenkrantz A. The Yellow Journal: A Look Back at 2023. AJR Am J Roentgenol 2024; 222:e2330657. [PMID: 38090809 DOI: 10.2214/ajr.23.30657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
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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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