1
|
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.
Collapse
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
| |
Collapse
|
2
|
Kong SH, Cho W, Park SB, Choo J, Kim JH, Kim SW, Shin CS. A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study. J Med Internet Res 2024; 26:e48535. [PMID: 38995678 DOI: 10.2196/48535] [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: 04/27/2023] [Revised: 01/27/2024] [Accepted: 05/30/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations. OBJECTIVE The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles. METHODS The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles. RESULTS The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001). CONCLUSIONS The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.
Collapse
Affiliation(s)
- Sung Hye Kong
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Wonwoo Cho
- Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Sung Bae Park
- Department of Neurosurgery, Seoul National University Boramae Hospital, Seoul, Republic of Korea
| | - Jaegul Choo
- Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jung Hee Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sang Wan Kim
- Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul, Republic of Korea
| | - Chan Soo Shin
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| |
Collapse
|
3
|
Oh J, Kim B, Oh G, Hwangbo Y, Ye JC. End-to-End Semi-Supervised Opportunistic Osteoporosis Screening Using Computed Tomography. Endocrinol Metab (Seoul) 2024; 39:500-510. [PMID: 38721637 PMCID: PMC11220219 DOI: 10.3803/enm.2023.1860] [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: 10/20/2023] [Revised: 02/19/2024] [Accepted: 03/05/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGRUOUND Osteoporosis is the most common metabolic bone disease and can cause fragility fractures. Despite this, screening utilization rates for osteoporosis remain low among populations at risk. Automated bone mineral density (BMD) estimation using computed tomography (CT) can help bridge this gap and serve as an alternative screening method to dual-energy X-ray absorptiometry (DXA). METHODS The feasibility of an opportunistic and population agnostic screening method for osteoporosis using abdominal CT scans without bone densitometry phantom-based calibration was investigated in this retrospective study. A total of 268 abdominal CT-DXA pairs and 99 abdominal CT studies without DXA scores were obtained from an oncology specialty clinic in the Republic of Korea. The center axial CT slices from the L1, L2, L3, and L4 lumbar vertebrae were annotated with the CT slice level and spine segmentation labels for each subject. Deep learning models were trained to localize the center axial slice from the CT scan of the torso, segment the vertebral bone, and estimate BMD for the top four lumbar vertebrae. RESULTS Automated vertebra-level DXA measurements showed a mean absolute error (MAE) of 0.079, Pearson's r of 0.852 (P<0.001), and R2 of 0.714. Subject-level predictions on the held-out test set had a MAE of 0.066, Pearson's r of 0.907 (P<0.001), and R2 of 0.781. CONCLUSION CT scans collected during routine examinations without bone densitometry calibration can be used to generate DXA BMD predictions.
Collapse
Affiliation(s)
- Jieun Oh
- Healthcare AI Team, National Cancer Center, Goyang, Korea
| | - Boah Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
| | - Gyutaek Oh
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
| | - Yul Hwangbo
- Healthcare AI Team, National Cancer Center, Goyang, Korea
| | - Jong Chul Ye
- Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
| |
Collapse
|
4
|
Praveen AD, Sollmann N, Baum T, Ferguson SJ, Benedikt H. CT image-based biomarkers for opportunistic screening of osteoporotic fractures: a systematic review and meta-analysis. Osteoporos Int 2024; 35:971-996. [PMID: 38353706 PMCID: PMC11136833 DOI: 10.1007/s00198-024-07029-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 01/19/2024] [Indexed: 05/30/2024]
Abstract
The use of opportunistic computed tomography (CT) image-based biomarkers may be a low-cost strategy for screening older individuals at high risk for osteoporotic fractures and populations that are not sufficiently targeted. This review aimed to assess the discriminative ability of image-based biomarkers derived from existing clinical routine CT scans for hip, vertebral, and major osteoporotic fracture prediction. A systematic search in PubMed MEDLINE, Embase, Cochrane, and Web of Science was conducted from the earliest indexing date until July 2023. The evaluation of study quality was carried out using a modified Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2) checklist. The primary outcome of interest was the area under the curve (AUC) and its corresponding 95% confidence intervals (CIs) obtained for four main categories of biomarkers: areal bone mineral density (BMD), image attenuation, volumetric BMD, and finite element (FE)-derived biomarkers. The meta-analyses were performed using random effects models. Sixty-one studies were included in this review, among which 35 were synthesized in a meta-analysis and the remaining articles were qualitatively synthesized. In comparison to the pooled AUC of areal BMD (0.73 [95% CI 0.71-0.75]), the pooled AUC values for predicting osteoporotic fractures for FE-derived parameters (0.77 [95% CI 0.72-0.81]; p < 0.01) and volumetric BMD (0.76 [95% CI 0.71-0.81]; p < 0.01) were significantly higher, but there was no significant difference with the pooled AUC for image attenuation (0.73 [95% CI 0.66-0.79]; p = 0.93). Compared to areal BMD, volumetric BMD and FE-derived parameters may provide a significant improvement in the discrimination of osteoporotic fractures using opportunistic CT assessments.
Collapse
Affiliation(s)
- Anitha D Praveen
- Early Detection of Health Risks and Prevention, Future Health Technologies, Singapore-ETH Centre (SEC), Campus for Research Excellence and Technological Enterprise (CREATE), 1 Create Way, CREATE Tower, #06-01, Singapore, 138602, Singapore.
| | - Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stephen J Ferguson
- Early Detection of Health Risks and Prevention, Future Health Technologies, Singapore-ETH Centre (SEC), Campus for Research Excellence and Technological Enterprise (CREATE), 1 Create Way, CREATE Tower, #06-01, Singapore, 138602, Singapore
- Institute for Biomechanics, ETH-Zurich, Zurich, Switzerland
| | - Helgason Benedikt
- Early Detection of Health Risks and Prevention, Future Health Technologies, Singapore-ETH Centre (SEC), Campus for Research Excellence and Technological Enterprise (CREATE), 1 Create Way, CREATE Tower, #06-01, Singapore, 138602, Singapore
- Institute for Biomechanics, ETH-Zurich, Zurich, Switzerland
| |
Collapse
|
5
|
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.
| |
Collapse
|
6
|
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.
Collapse
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.
| |
Collapse
|
7
|
Roux C. Opportunistic screening for osteoporosis. Joint Bone Spine 2024; 91:105726. [PMID: 38582362 DOI: 10.1016/j.jbspin.2024.105726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/14/2024] [Accepted: 03/18/2024] [Indexed: 04/08/2024]
Affiliation(s)
- Christian Roux
- Department of Rheumatology, Epidemiology and Biostatistics, Sorbonne Paris Cité Research Center, Cochin Hospital, Assistance publique-Hôpitaux de Paris, Inserm U1153, Paris-Cité University, 75014 Paris, France.
| |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
Li YX, Liang XL, Liu J, Ma YJ. Assessment of Osteoporosis at the Lumbar Spine Using Ultrashort Echo Time Magnetization Transfer (UTE-MT) MRI. J Magn Reson Imaging 2024; 59:1285-1298. [PMID: 37470693 PMCID: PMC10799192 DOI: 10.1002/jmri.28910] [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: 03/10/2023] [Revised: 07/06/2023] [Accepted: 07/07/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND Bone collagen-matrix contributes to the mechanical properties of bone by imparting tensile strength and elasticity, which can be indirectly quantified by ultrashort echo time magnetization transfer ratio (UTE-MTR) to assess osteoporosis. PURPOSE To evaluate osteoporosis at the human lumbar spine using UTE-MTR. STUDY TYPE Prospective. POPULATION One hundred forty-eight-volunteers (age-range, 50-85; females, N = 90), including 81-normal bone density, 35-osteopenic, and 32-osteoporotic subjects. Ten additional healthy volunteers were recruited to study the intrasession reproducibility of the UTE-MT. FIELD STRENGTH/SEQUENCE 3T/UTE-MT, short repetition-time adiabatic inversion recovery prepared UTE (STAIR-UTE), and iterative decomposition of water-and-fat with echo-asymmetry and least-squares estimation (IDEAL-IQ). ASSESSMENT Fracture risk was calculated using Fracture-Risk-Assessment-Tool (FRAX). Region-of-interests (ROIs) were delineated on the trabecular area in the maps of bone-mineral-density, UTE-MTR, collagen-bound water proton-fraction (CBWPF), and bone-marrow fat fraction (BMFF). STATISTICAL TESTS Linear-regression and Bland-Altman analysis were performed to assess the reproducibility of UTE-MTR measurements in the different scans. UTE-MTR and BMFF were correlated with bone-mineral-density using Pearson's regression and with FRAX scores using nonlinear regression. The abilities of UTE-MTR, CBWPF, and BMFF to discriminate between the three patient subgroups were evaluated using receiver-operator-characteristic (ROC) analysis and area-under-the-curve (AUC). Decision-curve-analysis (DCA) and clinical-impact curves were used to evaluate the value of UTE-MTR in clinical diagnosis. The DeLong test was used to compare the ROC curves. P-value <0.05 was considered statistically significant. RESULTS Excellent reproducibility was obtained for the UTE-MT measurements. UTE-MTR strongly correlated with bone-mineral-density (r = 0.76) and FRAX scores (r = -0.77). UTE-MTR exhibited higher AUCs (≥0.723) than BMFF, indicating its superior ability to distinguish between the three patient subgroups. The DCA and clinical-impact curves confirmed the diagnostic value of UTE-MTR. UTE-MTR and CBWPF showed similar performance in correlation with bone-mineral-density and cohort classification. DATA CONCLUSION UTE-MTR strongly correlates with bone-mineral-density and FRAX and shows great potential in distinguishing between normal, osteopenic, and osteoporotic subjects. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Yu-Xuan Li
- Shanxi Medical University, Taiyuan, China
| | - Xiao-Ling Liang
- Department of Radiology, University of California San Diego, 9452 Medical Center Drive, La Jolla, CA 92037, USA
| | - Jin Liu
- Department of Radiology, University of California San Diego, 9452 Medical Center Drive, La Jolla, CA 92037, USA
| | - Ya-Jun Ma
- Department of Radiology, University of California San Diego, 9452 Medical Center Drive, La Jolla, CA 92037, USA
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
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.
Collapse
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
| |
Collapse
|
12
|
Gruenewald LD, Booz C, Gotta J, Reschke P, Martin SS, Mahmoudi S, Bernatz S, Eichler K, D'Angelo T, Chernyak V, Sommer CM, Vogl TJ, Koch V. Incident fractures of the distal radius: Dual-energy CT-derived metrics for opportunistic risk stratification. Eur J Radiol 2024; 171:111283. [PMID: 38183896 DOI: 10.1016/j.ejrad.2023.111283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/13/2023] [Accepted: 12/28/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Dual-energy CT (DECT)-derived bone mineral density (BMD) of the distal radius and other CT-derived metrics related to bone health have been suggested for opportunistic osteoporosis screening and risk evaluation for sustaining distal radius fractures (DRFs). METHODS The distal radius of patients who underwent DECT between 01/2016 and 08/2021 was retrospectively analyzed. Cortical Hounsfield Unit (HU), trabecular HU, cortical thickness, and DECT-based BMD were acquired from a non-fractured, metaphyseal area in all examinations. Receiver-operating characteristic (ROC) analysis was conducted to determine the area under the curve (AUC) values for predicting DRFs based on DECT-derived BMD, HU values, and cortical thickness. Logistic regression models were then employed to assess the associations of these parameters with the occurrence of DRFs. RESULTS In this study, 263 patients (median age: 52 years; interquartile range: 36-64; 132 women; 192 fractures) were included. ROC curve analysis revealed a higher area under the curve (AUC) value for DECT-derived BMD compared to cortical HU, trabecular HU, and cortical thickness (0.91 vs. 0.61, 0.64, and 0.69, respectively; p <.001). Logistic regression models confirmed the association between lower DECT-derived BMD and the occurrence of DRFs (Odds Ratio, 0.83; p <.001); however, no influence was observed for cortical HU, trabecular HU, or cortical thickness. CONCLUSIONS DECT can be used to assess the BMD of the distal radius without dedicated equipment such as calibration phantoms to increase the detection rates of osteoporosis and stratify the individual risk to sustain DRFs. In contrast, assessing HU-based values and cortical thickness does not provide clinical benefit.
Collapse
Affiliation(s)
- Leon D Gruenewald
- Goethe University Frankfurt, University Hospital, Department of Diagnostic and Interventional Radiology, Frankfurt am Main, Germany
| | - Christian Booz
- Goethe University Frankfurt, University Hospital, Department of Diagnostic and Interventional Radiology, Frankfurt am Main, Germany
| | - Jennifer Gotta
- Goethe University Frankfurt, University Hospital, Department of Diagnostic and Interventional Radiology, Frankfurt am Main, Germany
| | - Philipp Reschke
- Goethe University Frankfurt, University Hospital, Department of Diagnostic and Interventional Radiology, Frankfurt am Main, Germany
| | - Simon S Martin
- Goethe University Frankfurt, University Hospital, Department of Diagnostic and Interventional Radiology, Frankfurt am Main, Germany
| | - Scherwin Mahmoudi
- Goethe University Frankfurt, University Hospital, Department of Diagnostic and Interventional Radiology, Frankfurt am Main, Germany
| | - Simon Bernatz
- Goethe University Frankfurt, University Hospital, Department of Diagnostic and Interventional Radiology, Frankfurt am Main, Germany
| | - Katrin Eichler
- Goethe University Frankfurt, University Hospital, Department of Diagnostic and Interventional Radiology, Frankfurt am Main, Germany
| | - Tommaso D'Angelo
- Department of Biomedical Sciences and Morphological and Functional Imaging, University Hospital Messina, Messina, Italy
| | - Victoria Chernyak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Christof M Sommer
- Clinic of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany.
| | - Thomas J Vogl
- Goethe University Frankfurt, University Hospital, Department of Diagnostic and Interventional Radiology, Frankfurt am Main, Germany
| | - Vitali Koch
- Goethe University Frankfurt, University Hospital, Department of Diagnostic and Interventional Radiology, Frankfurt am Main, Germany
| |
Collapse
|
13
|
Wang M, Tang H, Chen X, Liu J, Hu N, Cui W, Zhang C, Xie C, Chen X. Opportunistic Muscle Evaluation During Chest CT Is Associated With Vertebral Compression Fractures in Old Adults: A Longitudinal Study. J Gerontol A Biol Sci Med Sci 2024; 79:glad162. [PMID: 37422853 DOI: 10.1093/gerona/glad162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Indexed: 07/11/2023] Open
Abstract
BACKGROUND Sarcopenia and bone loss are both common in older individuals. However, the association between sarcopenia and bone fractures has not been evaluated longitudinally. In this study, we evaluated the association between computed tomography (CT)-based erector spinae muscle area and attenuation and vertebral compression fracture (VCF) in elderly individuals in a longitudinal study. METHODS This study recruited individuals 50 years of age and older, who did not have VCF and underwent CT imaging for lung cancer screening during January 2016 to December 2019. Participants were followed up annually until January 2021. Muscle CT value and muscle area of the erector spinae were determined for muscle assessment. Genant score was used to define new-onset VCF. Cox proportional hazards models were used to assess the association between muscle area/attenuation and VCF. RESULTS Of the 7 906 included participants, 72 developed new VCF over a median follow-up of 2 years. Large area of the erector spinae (adjusted hazard ratio [HR] = 0.2, 95% confidence interval [CI]: 0.1-0.7) and high bone attenuation (adjusted HR = 0.2, 95% CI: 0.1-0.5) were independently associated with VCF. High muscle attenuation was associated with severe VCF (adjusted HR = 0.46, 95% CI: 0.24-0.86). The addition of muscle area improved the area under the curve of bone attenuation from 0.79 (95% CI: 0.74-0.86) to 0.86 (95% CI: 0.82-0.91; p = .001). CONCLUSIONS CT-based muscle area/attenuation of the erector spinae was associated with VCF in elderly individuals, independently of bone attenuation. The addition of muscle area improved the performance of bone attenuation in predicting VCF.
Collapse
Affiliation(s)
- Miaomiao Wang
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Hongye Tang
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Xin Chen
- Department of Radiology, Longhua Hospital Shanghai University of Chinese Traditional Medicine, Shanghai, China
| | - Jingjing Liu
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Nandong Hu
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Wenjing Cui
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Chao Zhang
- Department of Orthopedics, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Chao Xie
- Center for Musculoskeletal Research, School of Medicine and Dentistry, University of Rochester, Rochester, New York, USA
| | - Xiao Chen
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| |
Collapse
|
14
|
Lu F, Fan J, Li F, Liu L, Chen Z, Tian Z, Zuo L, Yu D. Abdominal adipose tissue and type 2 diabetic kidney disease: adipose radiology assessment, impact, and mechanisms. Abdom Radiol (NY) 2024; 49:560-574. [PMID: 37847262 DOI: 10.1007/s00261-023-04062-1] [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: 09/09/2023] [Accepted: 09/11/2023] [Indexed: 10/18/2023]
Abstract
Diabetic kidney disease (DKD) is a significant healthcare burden worldwide that substantially increases the risk of kidney failure and cardiovascular events. To reduce the prevalence of DKD, extensive research is being conducted to determine the risk factors and consequently implement early interventions. Patients with type 2 diabetes mellitus (T2DM) are more likely to be obese. Abdominal adiposity is associated with a greater risk of kidney damage than general obesity. Abdominal adipose tissue can be divided into different fat depots according to the location and function, including visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), perirenal adipose tissue (PAT), and renal sinus adipose tissue (RSAT), which can be accurately measured by radiology techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI). Abdominal fat depots may affect the development of DKD through different mechanisms, and radiologic abdominal adipose characteristics may serve as imaging indicators of DKD risk. This review will first describe the CT/MRI-based assessment of abdominal adipose depots and subsequently describe the current studies on abdominal adipose tissue and DKD development, as well as the underlying mechanisms in patients of T2DM with DKD.
Collapse
Affiliation(s)
- Fei Lu
- School of Medical Imaging, Weifang Medical University, Weifang, 261053, Shandong, China
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Jinlei Fan
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Fangxuan Li
- Department of Ultrasound, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Lijing Liu
- Department of Imaging, Yantaishan Hospital, Yantai, 264001, Shandong, China
| | - Zhiyu Chen
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Ziyu Tian
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Liping Zuo
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Dexin Yu
- School of Medical Imaging, Weifang Medical University, Weifang, 261053, Shandong, China.
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China.
| |
Collapse
|
15
|
Nachef C, Bousson V, Belmatoug N, Cohen-Solal M, Vilgrain V, Roux O, Francoz C, Durand F, Funck-Brentano T. Osteoporosis and Fragility Fractures in Patients With Cirrhosis Evaluated for Liver Transplantation: Identification of High-Risk Patients Based on Computed Tomography at Evaluation. Am J Gastroenterol 2024; 119:367-370. [PMID: 37734343 DOI: 10.14309/ajg.0000000000002507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 08/04/2023] [Indexed: 09/23/2023]
Abstract
INTRODUCTION Osteoporosis in candidates for liver transplantation (LT) is often underdiagnosed despite the important consequences of morbidity. METHODS We included 376 patients with cirrhosis evaluated for LT with available computed tomography (CT) scans. Prevalent vertebral fractures (VFs) were identified on CT reconstructions, and bone density was assessed by measuring CT attenuation of the L1 vertebra (L1-CT). RESULTS We identified 139 VFs in 55 patients (14.6%). Logistic regression models showed that low L1-CT was the only independent determinant of VF. DISCUSSION In patients with cirrhosis evaluated for LT, CT scans identified persons with severe osteoporosis without additional costs.
Collapse
Affiliation(s)
- Clément Nachef
- Department of Rheumatology, Lariboisière Hospital, APHP.Nord, Université Paris Cité, Paris, France
- Bioscar INSERM U1132, Université de Paris, Paris, France
| | - Valérie Bousson
- Department of Radiology, Lariboisière Hospital, APHP.Nord, Université Paris Cité, Paris, France
| | - Nadia Belmatoug
- Department of Internal Medicine, Beaujon Hospital, APHP.Nord, Université de Paris, Paris, France
| | - Martine Cohen-Solal
- Department of Rheumatology, Lariboisière Hospital, APHP.Nord, Université Paris Cité, Paris, France
- Bioscar INSERM U1132, Université de Paris, Paris, France
| | - Valérie Vilgrain
- Department of Radiology, Beaujon Hospital, APHP.Nord, Université Paris Cité, Paris, France
| | - Olivier Roux
- Department of Hepatology & Liver Intensive Care, Beaujon Hospital, APHP.Nord, Université Paris Cité, Paris, France
| | - Claire Francoz
- Department of Hepatology & Liver Intensive Care, Beaujon Hospital, APHP.Nord, Université Paris Cité, Paris, France
| | - François Durand
- Department of Hepatology & Liver Intensive Care, Beaujon Hospital, APHP.Nord, Université Paris Cité, Paris, France
| | - Thomas Funck-Brentano
- Department of Rheumatology, Lariboisière Hospital, APHP.Nord, Université Paris Cité, Paris, France
- Bioscar INSERM U1132, Université de Paris, Paris, France
| |
Collapse
|
16
|
Li MD, Jaremko JL. Personalizing Short-term Fracture Prevention After Hip Fracture: CT-based AI Risk Stratification. Radiology 2024; 310:e233396. [PMID: 38289218 DOI: 10.1148/radiol.233396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Affiliation(s)
- Matthew D Li
- From the Department of Radiology and Diagnostic Imaging, Faculty of Medicine and Dentistry, University of Alberta Hospital, 8440 112 St NW, 2A2.41 WMC, Edmonton, AB, Canada T6G 2B7
| | - Jacob L Jaremko
- From the Department of Radiology and Diagnostic Imaging, Faculty of Medicine and Dentistry, University of Alberta Hospital, 8440 112 St NW, 2A2.41 WMC, Edmonton, AB, Canada T6G 2B7
| |
Collapse
|
17
|
Ong W, Liu RW, Makmur A, Low XZ, Sng WJ, Tan JH, Kumar N, Hallinan JTPD. Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography. Bioengineering (Basel) 2023; 10:1364. [PMID: 38135954 PMCID: PMC10741220 DOI: 10.3390/bioengineering10121364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.
Collapse
Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
| | - Ren Wei Liu
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Weizhong Jonathan Sng
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore; (J.H.T.); (N.K.)
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore; (J.H.T.); (N.K.)
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| |
Collapse
|
18
|
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.
Collapse
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
| |
Collapse
|
19
|
Salyapongse AM, Rose SD, Pickhardt PJ, Lubner MG, Toia GV, Bujila R, Yin Z, Slavic S, Szczykutowicz TP. CT Number Accuracy and Association With Object Size: A Phantom Study Comparing Energy-Integrating Detector CT and Deep Silicon Photon-Counting Detector CT. AJR Am J Roentgenol 2023; 221:539-547. [PMID: 37255042 DOI: 10.2214/ajr.23.29463] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
BACKGROUND. Variable beam hardening based on patient size causes variation in CT numbers for energy-integrating detector (EID) CT. Photon-counting detector (PCD) CT more accurately determines effective beam energy, potentially improving CT number reliability. OBJECTIVE. The purpose of the present study was to compare EID CT and deep silicon PCD CT in terms of both the effect of changes in object size on CT number and the overall accuracy of CT numbers. METHODS. A phantom with polyethylene rings of varying sizes (mimicking patient sizes) as well as inserts of different materials was scanned on an EID CT scanner in single-energy (SE) mode (120-kV images) and in rapid-kilovoltage-switching dual-energy (DE) mode (70-keV images) and on a prototype deep silicon PCD CT scanner (70-keV images). ROIs were placed to measure the CT numbers of the materials. Slopes of CT number as a function of object size were computed. Materials' ideal CT number at 70 keV was computed using the National Institute of Standards and Technology XCOM Photon Cross Sections Database. The root mean square error (RMSE) between measured and ideal numbers was calculated across object sizes. RESULTS. Slope (expressed as Hounsfield units per centimeter) was significantly closer to zero (i.e., less variation in CT number as a function of size) for PCD CT than for SE EID CT for air (1.2 vs 2.4 HU/cm), water (-0.3 vs -1.0 HU/cm), iodine (-1.1 vs -4.5 HU/cm), and bone (-2.5 vs -10.1 HU/cm) and for PCD CT than for DE EID CT for air (1.2 vs 2.8 HU/cm), water (-0.3 vs -1.0 HU/cm), polystyrene (-0.2 vs -0.9 HU/cm), iodine (-1.1 vs -1.9 HU/cm), and bone (-2.5 vs -6.2 HU/cm) (p < .05). For all tested materials, PCD CT had the smallest RMSE, indicating CT numbers closest to ideal numbers; specifically, RMSE (expressed as Hounsfield units) for SE EID CT, DE EID CT, and PCD CT was 32, 44, and 17 HU for air; 7, 8, and 3 HU for water; 9, 10, and 4 HU for polystyrene; 31, 37, and 13 HU for iodine; and 69, 81, and 20 HU for bone, respectively. CONCLUSION. For numerous materials, deep silicon PCD CT, in comparison with SE EID CT and DE EID CT, showed lower CT number variability as a function of size and CT numbers closer to ideal numbers. CLINICAL IMPACT. Greater reliability of CT numbers for PCD CT is important given the dependence of diagnostic pathways on CT numbers.
Collapse
Affiliation(s)
- Aria M Salyapongse
- Department of Radiology, University of Wisconsin Madison, 1005 Wisconsin Institute for Medical Research, 1111 Highland Ave, Madison, WI 53705
| | - Sean D Rose
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston, Houston, TX
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin Madison, 1005 Wisconsin Institute for Medical Research, 1111 Highland Ave, Madison, WI 53705
- University of Wisconsin Carbone Cancer Center, University of Wisconsin Madison, Madison, WI
| | - Meghan G Lubner
- Department of Radiology, University of Wisconsin Madison, 1005 Wisconsin Institute for Medical Research, 1111 Highland Ave, Madison, WI 53705
| | - Giuseppe V Toia
- Department of Radiology, University of Wisconsin Madison, 1005 Wisconsin Institute for Medical Research, 1111 Highland Ave, Madison, WI 53705
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI
| | | | | | | | - Timothy P Szczykutowicz
- Department of Radiology, University of Wisconsin Madison, 1005 Wisconsin Institute for Medical Research, 1111 Highland Ave, Madison, WI 53705
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI
- Department of Biomedical Engineering, University of Wisconsin Madison, Madison, WI
| |
Collapse
|
20
|
Gruenewald LD, Koch V, Yel I, Eichler K, Gruber-Rouh T, Alizadeh LS, Mahmoudi S, D'Angelo T, Wichmann JL, Wesarg S, Vogl TJ, Booz C. Association of Phantomless Dual-Energy CT-based Volumetric Bone Mineral Density with the Prevalence of Acute Insufficiency Fractures of the Spine. Acad Radiol 2023; 30:2110-2117. [PMID: 36577605 DOI: 10.1016/j.acra.2022.11.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/01/2022] [Accepted: 11/15/2022] [Indexed: 12/27/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate the bone mineral density (BMD) of the lumbar spine derived from dual-energy CT (DECT)-based volumetric material decomposition and its association with acute insufficiency fractures of the thoracolumbar spine. MATERIALS AND METHODS L1 of 160 patients (77 men, 83 women; mean age 64.3 years, range, 22-94 years) who underwent third-generation dual-source DECT between January 2016 and December 2021 due to suspected insufficiency fractures was retrospectively analyzed. All depicted vertebrae were examined for signs of recent fractures. A dedicated DECT postprocessing software using material decomposition was applied for phantomless BMD assessment. Receiver-operating characteristic (ROC) analysis identified optimal BMD thresholds. Associations of BMD, sex, and age with the occurrence of insufficiency fractures were examined with logistic regression models. RESULTS A DECT-derived BMD threshold of 120.40 mg/cm³ yielded 90.1% specificity and 59.32% sensitivity to differentiate patients with at least one insufficiency fracture from patients without fracture. No patient without fracture had a DECT-derived BMD below 85 mg/cm3. Lower DECT-derived bone mineral density was associated with an increased risk of insufficiency fractures (Odds ratio of 0.93, 95% CI, 0.91-0.96, p < 0.001). Overall ROC-derived AUC was 0.82 (p < 0.0001) for the differentiation of patients that sustained an insufficiency fracture from the control group. CONCLUSION Dual-Energy CT-based BMD assessment can accurately differentiate patients with acute insufficiency fractures of the thoracolumbar spine from patients without fracture. This algorithm can be used for phantomless risk stratification of patients undergoing routine CT to sustain insufficiency fractures of the thoracolumbar spine The identified cut-off value of 120.4 mg/cm³ is in line with current American College of Radiology (ACR) recommendations to differentiate healthy individuals from those with reduced bone mineral density.
Collapse
Affiliation(s)
- Leon D Gruenewald
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, HE, Germany
| | - Vitali Koch
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, HE, Germany
| | - Ibrahim Yel
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, HE, Germany
| | - Katrin Eichler
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, HE, Germany
| | - Tatjana Gruber-Rouh
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, HE, Germany
| | - Leona S Alizadeh
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, HE, Germany
| | - Scherwin Mahmoudi
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, HE, Germany
| | - Tommaso D'Angelo
- Department of Biomedical Sciences and Morphological and Functional Imaging, University Hospital Messina, Messina, ME, Italy
| | - Julian L Wichmann
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, HE, Germany
| | | | - Thomas J Vogl
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, HE, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, HE, Germany
| | - Christian Booz
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, HE, Germany.
| |
Collapse
|
21
|
Pickhardt PJ, Summers RM, Garrett JW, Krishnaraj A, Agarwal S, Dreyer KJ, Nicola GN. Opportunistic Screening: Radiology Scientific Expert Panel. Radiology 2023; 307:e222044. [PMID: 37219444 PMCID: PMC10315516 DOI: 10.1148/radiol.222044] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/03/2022] [Accepted: 12/01/2022] [Indexed: 05/24/2023]
Abstract
Radiologic tests often contain rich imaging data not relevant to the clinical indication. Opportunistic screening refers to the practice of systematically leveraging these incidental imaging findings. Although opportunistic screening can apply to imaging modalities such as conventional radiography, US, and MRI, most attention to date has focused on body CT by using artificial intelligence (AI)-assisted methods. Body CT represents an ideal high-volume modality whereby a quantitative assessment of tissue composition (eg, bone, muscle, fat, and vascular calcium) can provide valuable risk stratification and help detect unsuspected presymptomatic disease. The emergence of "explainable" AI algorithms that fully automate these measurements could eventually lead to their routine clinical use. Potential barriers to widespread implementation of opportunistic CT screening include the need for buy-in from radiologists, referring providers, and patients. Standardization of acquiring and reporting measures is needed, in addition to expanded normative data according to age, sex, and race and ethnicity. Regulatory and reimbursement hurdles are not insurmountable but pose substantial challenges to commercialization and clinical use. Through demonstration of improved population health outcomes and cost-effectiveness, these opportunistic CT-based measures should be attractive to both payers and health care systems as value-based reimbursement models mature. If highly successful, opportunistic screening could eventually justify a practice of standalone "intended" CT screening.
Collapse
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.)
| |
Collapse
|
22
|
Gates M, Pillay J, Nuspl M, Wingert A, Vandermeer B, Hartling L. Screening for the primary prevention of fragility fractures among adults aged 40 years and older in primary care: systematic reviews of the effects and acceptability of screening and treatment, and the accuracy of risk prediction tools. Syst Rev 2023; 12:51. [PMID: 36945065 PMCID: PMC10029308 DOI: 10.1186/s13643-023-02181-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 02/02/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND To inform recommendations by the Canadian Task Force on Preventive Health Care, we reviewed evidence on the benefits, harms, and acceptability of screening and treatment, and on the accuracy of risk prediction tools for the primary prevention of fragility fractures among adults aged 40 years and older in primary care. METHODS For screening effectiveness, accuracy of risk prediction tools, and treatment benefits, our search methods involved integrating studies published up to 2016 from an existing systematic review. Then, to locate more recent studies and any evidence relating to acceptability and treatment harms, we searched online databases (2016 to April 4, 2022 [screening] or to June 1, 2021 [predictive accuracy]; 1995 to June 1, 2021, for acceptability; 2016 to March 2, 2020, for treatment benefits; 2015 to June 24, 2020, for treatment harms), trial registries and gray literature, and hand-searched reviews, guidelines, and the included studies. Two reviewers selected studies, extracted results, and appraised risk of bias, with disagreements resolved by consensus or a third reviewer. The overview of reviews on treatment harms relied on one reviewer, with verification of data by another reviewer to correct errors and omissions. When appropriate, study results were pooled using random effects meta-analysis; otherwise, findings were described narratively. Evidence certainty was rated according to the GRADE approach. RESULTS We included 4 randomized controlled trials (RCTs) and 1 controlled clinical trial (CCT) for the benefits and harms of screening, 1 RCT for comparative benefits and harms of different screening strategies, 32 validation cohort studies for the calibration of risk prediction tools (26 of these reporting on the Fracture Risk Assessment Tool without [i.e., clinical FRAX], or with the inclusion of bone mineral density (BMD) results [i.e., FRAX + BMD]), 27 RCTs for the benefits of treatment, 10 systematic reviews for the harms of treatment, and 12 studies for the acceptability of screening or initiating treatment. In females aged 65 years and older who are willing to independently complete a mailed fracture risk questionnaire (referred to as "selected population"), 2-step screening using a risk assessment tool with or without measurement of BMD probably (moderate certainty) reduces the risk of hip fractures (3 RCTs and 1 CCT, n = 43,736, absolute risk reduction [ARD] = 6.2 fewer in 1000, 95% CI 9.0-2.8 fewer, number needed to screen [NNS] = 161) and clinical fragility fractures (3 RCTs, n = 42,009, ARD = 5.9 fewer in 1000, 95% CI 10.9-0.8 fewer, NNS = 169). It probably does not reduce all-cause mortality (2 RCTs and 1 CCT, n = 26,511, ARD = no difference in 1000, 95% CI 7.1 fewer to 5.3 more) and may (low certainty) not affect health-related quality of life. Benefits for fracture outcomes were not replicated in an offer-to-screen population where the rate of response to mailed screening questionnaires was low. For females aged 68-80 years, population screening may not reduce the risk of hip fractures (1 RCT, n = 34,229, ARD = 0.3 fewer in 1000, 95% CI 4.2 fewer to 3.9 more) or clinical fragility fractures (1 RCT, n = 34,229, ARD = 1.0 fewer in 1000, 95% CI 8.0 fewer to 6.0 more) over 5 years of follow-up. The evidence for serious adverse events among all patients and for all outcomes among males and younger females (<65 years) is very uncertain. We defined overdiagnosis as the identification of high risk in individuals who, if not screened, would never have known that they were at risk and would never have experienced a fragility fracture. This was not directly reported in any of the trials. Estimates using data available in the trials suggest that among "selected" females offered screening, 12% of those meeting age-specific treatment thresholds based on clinical FRAX 10-year hip fracture risk, and 19% of those meeting thresholds based on clinical FRAX 10-year major osteoporotic fracture risk, may be overdiagnosed as being at high risk of fracture. Of those identified as being at high clinical FRAX 10-year hip fracture risk and who were referred for BMD assessment, 24% may be overdiagnosed. One RCT (n = 9268) provided evidence comparing 1-step to 2-step screening among postmenopausal females, but the evidence from this trial was very uncertain. For the calibration of risk prediction tools, evidence from three Canadian studies (n = 67,611) without serious risk of bias concerns indicates that clinical FRAX-Canada may be well calibrated for the 10-year prediction of hip fractures (observed-to-expected fracture ratio [O:E] = 1.13, 95% CI 0.74-1.72, I2 = 89.2%), and is probably well calibrated for the 10-year prediction of clinical fragility fractures (O:E = 1.10, 95% CI 1.01-1.20, I2 = 50.4%), both leading to some underestimation of the observed risk. Data from these same studies (n = 61,156) showed that FRAX-Canada with BMD may perform poorly to estimate 10-year hip fracture risk (O:E = 1.31, 95% CI 0.91-2.13, I2 = 92.7%), but is probably well calibrated for the 10-year prediction of clinical fragility fractures, with some underestimation of the observed risk (O:E 1.16, 95% CI 1.12-1.20, I2 = 0%). The Canadian Association of Radiologists and Osteoporosis Canada Risk Assessment (CAROC) tool may be well calibrated to predict a category of risk for 10-year clinical fractures (low, moderate, or high risk; 1 study, n = 34,060). The evidence for most other tools was limited, or in the case of FRAX tools calibrated for countries other than Canada, very uncertain due to serious risk of bias concerns and large inconsistency in findings across studies. Postmenopausal females in a primary prevention population defined as <50% prevalence of prior fragility fracture (median 16.9%, range 0 to 48% when reported in the trials) and at risk of fragility fracture, treatment with bisphosphonates as a class (median 2 years, range 1-6 years) probably reduces the risk of clinical fragility fractures (19 RCTs, n = 22,482, ARD = 11.1 fewer in 1000, 95% CI 15.0-6.6 fewer, [number needed to treat for an additional beneficial outcome] NNT = 90), and may reduce the risk of hip fractures (14 RCTs, n = 21,038, ARD = 2.9 fewer in 1000, 95% CI 4.6-0.9 fewer, NNT = 345) and clinical vertebral fractures (11 RCTs, n = 8921, ARD = 10.0 fewer in 1000, 95% CI 14.0-3.9 fewer, NNT = 100); it may not reduce all-cause mortality. There is low certainty evidence of little-to-no reduction in hip fractures with any individual bisphosphonate, but all provided evidence of decreased risk of clinical fragility fractures (moderate certainty for alendronate [NNT=68] and zoledronic acid [NNT=50], low certainty for risedronate [NNT=128]) among postmenopausal females. Evidence for an impact on risk of clinical vertebral fractures is very uncertain for alendronate and risedronate; zoledronic acid may reduce the risk of this outcome (4 RCTs, n = 2367, ARD = 18.7 fewer in 1000, 95% CI 25.6-6.6 fewer, NNT = 54) for postmenopausal females. Denosumab probably reduces the risk of clinical fragility fractures (6 RCTs, n = 9473, ARD = 9.1 fewer in 1000, 95% CI 12.1-5.6 fewer, NNT = 110) and clinical vertebral fractures (4 RCTs, n = 8639, ARD = 16.0 fewer in 1000, 95% CI 18.6-12.1 fewer, NNT=62), but may make little-to-no difference in the risk of hip fractures among postmenopausal females. Denosumab probably makes little-to-no difference in the risk of all-cause mortality or health-related quality of life among postmenopausal females. Evidence in males is limited to two trials (1 zoledronic acid, 1 denosumab); in this population, zoledronic acid may make little-to-no difference in the risk of hip or clinical fragility fractures, and evidence for all-cause mortality is very uncertain. The evidence for treatment with denosumab in males is very uncertain for all fracture outcomes (hip, clinical fragility, clinical vertebral) and all-cause mortality. There is moderate certainty evidence that treatment causes a small number of patients to experience a non-serious adverse event, notably non-serious gastrointestinal events (e.g., abdominal pain, reflux) with alendronate (50 RCTs, n = 22,549, ARD = 16.3 more in 1000, 95% CI 2.4-31.3 more, [number needed to treat for an additional harmful outcome] NNH = 61) but not with risedronate; influenza-like symptoms with zoledronic acid (5 RCTs, n = 10,695, ARD = 142.5 more in 1000, 95% CI 105.5-188.5 more, NNH = 7); and non-serious gastrointestinal adverse events (3 RCTs, n = 8454, ARD = 64.5 more in 1000, 95% CI 26.4-13.3 more, NNH = 16), dermatologic adverse events (3 RCTs, n = 8454, ARD = 15.6 more in 1000, 95% CI 7.6-27.0 more, NNH = 64), and infections (any severity; 4 RCTs, n = 8691, ARD = 1.8 more in 1000, 95% CI 0.1-4.0 more, NNH = 556) with denosumab. For serious adverse events overall and specific to stroke and myocardial infarction, treatment with bisphosphonates probably makes little-to-no difference; evidence for other specific serious harms was less certain or not available. There was low certainty evidence for an increased risk for the rare occurrence of atypical femoral fractures (0.06 to 0.08 more in 1000) and osteonecrosis of the jaw (0.22 more in 1000) with bisphosphonates (most evidence for alendronate). The evidence for these rare outcomes and for rebound fractures with denosumab was very uncertain. Younger (lower risk) females have high willingness to be screened. A minority of postmenopausal females at increased risk for fracture may accept treatment. Further, there is large heterogeneity in the level of risk at which patients may be accepting of initiating treatment, and treatment effects appear to be overestimated. CONCLUSION An offer of 2-step screening with risk assessment and BMD measurement to selected postmenopausal females with low prevalence of prior fracture probably results in a small reduction in the risk of clinical fragility fracture and hip fracture compared to no screening. These findings were most applicable to the use of clinical FRAX for risk assessment and were not replicated in the offer-to-screen population where the rate of response to mailed screening questionnaires was low. Limited direct evidence on harms of screening were available; using study data to provide estimates, there may be a moderate degree of overdiagnosis of high risk for fracture to consider. The evidence for younger females and males is very limited. The benefits of screening and treatment need to be weighed against the potential for harm; patient views on the acceptability of treatment are highly variable. SYSTEMATIC REVIEW REGISTRATION International Prospective Register of Systematic Reviews (PROSPERO): CRD42019123767.
Collapse
Affiliation(s)
- Michelle Gates
- Department of Pediatrics, Alberta Research Centre for Health Evidence, University of Alberta, Edmonton Clinic Health Academy, 11405-87 Avenue NW, Edmonton, Alberta, T6G 1C9, Canada
| | - Jennifer Pillay
- Department of Pediatrics, Alberta Research Centre for Health Evidence, University of Alberta, Edmonton Clinic Health Academy, 11405-87 Avenue NW, Edmonton, Alberta, T6G 1C9, Canada.
| | - Megan Nuspl
- Department of Pediatrics, Alberta Research Centre for Health Evidence, University of Alberta, Edmonton Clinic Health Academy, 11405-87 Avenue NW, Edmonton, Alberta, T6G 1C9, Canada
| | - Aireen Wingert
- Department of Pediatrics, Alberta Research Centre for Health Evidence, University of Alberta, Edmonton Clinic Health Academy, 11405-87 Avenue NW, Edmonton, Alberta, T6G 1C9, Canada
| | - Ben Vandermeer
- Department of Pediatrics, Alberta Research Centre for Health Evidence, University of Alberta, Edmonton Clinic Health Academy, 11405-87 Avenue NW, Edmonton, Alberta, T6G 1C9, Canada
| | - Lisa Hartling
- Department of Pediatrics, Alberta Research Centre for Health Evidence, University of Alberta, Edmonton Clinic Health Academy, 11405-87 Avenue NW, Edmonton, Alberta, T6G 1C9, Canada
| |
Collapse
|
23
|
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.
Collapse
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
| |
Collapse
|
24
|
Sebro R, De la Garza-Ramos C. Opportunistic screening for osteoporosis and osteopenia from CT scans of the abdomen and pelvis using machine learning. Eur Radiol 2023; 33:1812-1823. [PMID: 36166085 DOI: 10.1007/s00330-022-09136-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/15/2022] [Accepted: 08/30/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To use multivariable machine learning using the computed tomography (CT) attenuation of each of the bones in the lumbar spine, pelvis, and sacrum, to predict osteoporosis/osteopenia. METHODS This was a retrospective study of 394 patients aged 50 years or older with CT scans of the abdomen and pelvis and dual-energy x-ray absorptiometry (DXA) scans obtained within 6 months of each other. Volumetric segmentations were performed for each of the bones from L1-L4 vertebrae, pelvis, and sacrum to obtain the mean CT attenuation of each bone. The data was randomly split into training/validation (n = 274, 70%) and test (n = 120, 30%) datasets. The CT attenuation of the L1 vertebrae, univariate logistic regression, least absolute shrinkage and selection operator (LASSO), and support vector machines (SVM) with radial basis function (RBF) were used to predict osteoporosis/osteopenia. The performance of using the CT attenuation at L1 to the univariate logistic regression, LASSO, and SVM models were compared using DeLong's test in the test dataset. RESULTS All CT attenuation measurements were predictive of osteoporosis/osteopenia (p < 0.001 for all). The SVM model (accuracy = 0.892, AUC = 0.886) outperformed the models using the CT attenuation of threshold of 173.9 Hounsfield units (HU) at L1 (accuracy = 0.725, AUC = 0.739, p = 0.010), the univariate logistic regression model (accuracy = 0.767, AUC = 0.533, p < 0.001) and the LASSO model (accuracy = 0.817, AUC = 0.711, p = 0.007) to predict osteoporosis/osteopenia. CONCLUSION A SVM model using the CT attenuations of multiple bones within the lumbar spine and pelvis and clinical data has a better ability to predict osteoporosis/osteopenia than using the CT attenuation of L1 or a LASSO model. KEY POINTS • Multivariable SVM model using the CT attenuation of multiple bones and clinical/demographic data was more predictive than using the CT attenuation at L1 only.
Collapse
Affiliation(s)
- Ronnie Sebro
- Department of Radiology, Mayo Clinic, Jacksonville, FL, 32224, USA. .,Center for Augmented Intelligence, Mayo Clinic, Jacksonville, FL, 32224, USA.
| | | |
Collapse
|
25
|
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.
Collapse
|
26
|
Santhanam P, Nath T, Peng C, Bai H, Zhang H, Ahima RS, Chellappa R. Artificial intelligence and body composition. Diabetes Metab Syndr 2023; 17:102732. [PMID: 36867973 DOI: 10.1016/j.dsx.2023.102732] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 02/27/2023]
Abstract
AIMS Although obesity is associated with chronic disease, a large section of the population with high BMI does not have an increased risk of metabolic disease. Increased visceral adiposity and sarcopenia are also risk factors for metabolic disease in people with normal BMI. Artificial Intelligence (AI) techniques can help assess and analyze body composition parameters for predicting cardiometabolic health. The purpose of the study was to systematically explore literature involving AI techniques for body composition assessment and observe general trends. METHODS We searched the following databases: Embase, Web of Science, and PubMed. There was a total of 354 search results. After removing duplicates, irrelevant studies, and reviews(a total of 303), 51 studies were included in the systematic review. RESULTS AI techniques have been studied for body composition analysis in the context of diabetes mellitus, hypertension, cancer and many specialized diseases. Imaging techniques employed for AI methods include CT (Computerized Tomography), MRI (Magnetic Resonance Imaging), ultrasonography, plethysmography, and EKG(Electrocardiogram). Automatic segmentation of body composition by deep learning with convolutional networks has helped determine and quantify muscle mass. Limitations include heterogeneity of study populations, inherent bias in sampling, and lack of generalizability. Different bias mitigation strategies should be evaluated to address these problems and improve the applicability of AI to body composition analysis. CONCLUSIONS AI assisted measurement of body composition might assist in improved cardiovascular risk stratification when applied in the appropriate clinical context.
Collapse
Affiliation(s)
- Prasanna Santhanam
- Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
| | - Tanmay Nath
- Department Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Cheng Peng
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Harrison Bai
- Department of Radiology and Radiology Sciences, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Helen Zhang
- The Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Rexford S Ahima
- Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Rama Chellappa
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| |
Collapse
|
27
|
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.
Collapse
|
28
|
Tariq A, Patel BN, Sensakovic WF, Fahrenholtz SJ, Banerjee I. Opportunistic screening for low bone density using abdominopelvic computed tomography scans. Med Phys 2023. [PMID: 36748265 DOI: 10.1002/mp.16230] [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: 10/12/2022] [Revised: 12/08/2022] [Accepted: 12/21/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND While low bone density is a major burden on US health system, current osteoporosis screening guidelines by the US Preventive Services Task Force are limited to women aged ≥65 and all postmenopausal women with certain risk factors. Even within recommended screening groups, actual screening rates are low (<26%) and vary across socioeconomic groups. The proposed model can opportunistically screen patients using abdominal CT studies for low bone density who may otherwise go undiagnosed. PURPOSE To develop an artificial intelligence (AI) model for opportunistic screening of low bone density using both contrast and non-contrast abdominopelvic computed tomography (CT) exams, for the purpose of referral to traditional bone health management, which typically begins with dual energy X-ray absorptiometry (DXA). METHODS We collected 6083 contrast-enhanced CT imaging exams paired with DXA exams within ±6 months documented between May 2015 and August 2021 in a single institution with four major healthcare practice regions. Our fusion AI pipeline receives the coronal and axial plane images of a contrast enhanced abdominopelvic CT exam and basic patient demographics (age, gender, body cross section lengths) to predict risk of low bone mass. The models were trained on lumbar spine T-scores from DXA exams and tested on multi-site imaging exams. The model was again tested in a prospective group (N = 344) contrast-enhanced and non-contrast-enhanced studies. RESULTS The models were evaluated on the same test set (1208 exams)-(1) Baseline model using demographic factors from electronic medical records (EMR) - 0.7 area under the curve of receiver operator characteristic (AUROC); Imaging based models: (2) axial view - 0.83 AUROC; (3) coronal view- 0.83 AUROC; (4) Fusion model-Imaging + demographic factors - 0.86 AUROC. The prospective test yielded one missed positive DXA case with a hip prosthesis among 23 positive contrast-enhanced CT exams and 0% false positive rate for non-contrast studies. Both positive cases among non-contrast enhanced CT exams were successfully detected. While only about 8% patients from prospective study received a DXA exam within 2 years, about 30% were detected with low bone mass by the fusion model, highlighting the need for opportunistic screening. CONCLUSIONS The fusion model, which combines two planes of CT images and EMRs data, outperformed individual models and provided a high, robust diagnostic performance for opportunistic screening of low bone density using contrast and non-contrast CT exams. This model could potentially improve bone health risk assessment with no additional cost. The model's handling of metal implants is an ongoing effort.
Collapse
Affiliation(s)
- Amara Tariq
- Department of Administration, Mayo Clinic, Phoenix, Arizona, USA
| | - Bhavik N Patel
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA.,Department of Computer Engineering, Ira A. Fulton School of Engineering, Arizona State University, Phoenix, Arizona, USA
| | | | | | - Imon Banerjee
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA.,Department of Computer Engineering, Ira A. Fulton School of Engineering, Arizona State University, Phoenix, Arizona, USA
| |
Collapse
|
29
|
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.
Collapse
Affiliation(s)
- Perry J. Pickhardt
- The University of Wisconsin School of Medicine & Public Health, Madison, Wisconsin, United States
| |
Collapse
|
30
|
Psoas attenuation and cross-sectional area improve performance of traditional sarcopenia measurements in predicting one-year mortality among elderly patients undergoing emergency abdominal surgery: a pilot study of five computed tomography techniques. Abdom Radiol (NY) 2023; 48:796-805. [PMID: 36383241 DOI: 10.1007/s00261-022-03652-9] [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: 05/31/2022] [Revised: 08/06/2022] [Accepted: 08/09/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND Risk stratification is challenging in the growing population of geriatric patients requiring emergency surgery. Sarcopenia, which assesses muscle bulk, is a surrogate for frailty and predicts 1-year mortality, but does not incorporate potentially valuable additional information about muscle quality. OBJECTIVE To describe five different CT methods of measuring sarcopenia and muscle quality and to determine which method has the greatest sensitivity for predicting 1-year mortality following emergency abdominal surgery in elderly patients. METHODS This retrospective study includes 297 patients 70 years and older who underwent "urgent" or "emergent" laparotomy or laparoscopy for acute abdominal disease between 2006 and 2011 at a single quaternary academic medical center. All patients received a CT abdomen and pelvis with intravenous contrast within 1 month of surgery. Five different methods were applied to the psoas muscles on CT: method 1 (total psoas index TPI, which is total psoas area TPA normalized by height), method 2 ("pseudoarea" = anterior-posterior × transverse dimensions), method 3 (average HU), method 4 (TPA × HU), and method 5 ("pseudoarea" × HU). RESULTS For all five CT measures, mortality was greatest for the lowest quartile by univariate and adjusted Cox proportional hazard analyses at all time points up to 1-year. The C-statistic was highest for Method 4, using a composite index of TPA and Hounsfield Units, indicating the greatest predictive ability to estimate mortality at all time points. CONCLUSION Muscle quality and muscle size can be used in tandem to refine risk assessment of older patients undergoing emergency abdominal surgery. Routine calculation of the composite score of psoas cross-sectional area and HU in the emergency room setting may provide surgeons and patients valuable insight on the risk of 1-year mortality to guide preoperative decision-making and counseling. CLINICAL IMPACT Muscle quality and size, both strong independent predictors of surgical outcomes in older patients undergoing emergency abdominal surgery, may be used in tandem to refine risk assessment. A composite score of psoas muscle cross-sectional area and Hounsfield units on CT may provide insight on 1-year mortality in this patient population.
Collapse
|
31
|
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.
Collapse
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.)
| |
Collapse
|
32
|
Wang F, Zheng K, Lu L, Xiao J, Wu M, Kuo CF, Miao S. Lumbar Bone Mineral Density Estimation From Chest X-Ray Images: Anatomy-Aware Attentive Multi-ROI Modeling. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:257-267. [PMID: 36155432 DOI: 10.1109/tmi.2022.3209648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g., via Dual-energy X-ray Absorptiometry (DXA). This paper proposes a method to predict BMD from Chest X-ray (CXR), one of the most commonly accessible and low-cost medical imaging examinations. The proposed method first automatically detects Regions of Interest (ROIs) of local CXR bone structures. Then a multi-ROI deep model with transformer encoder is developed to exploit both local and global information in the chest X-ray image for accurate BMD estimation. The proposed method is evaluated on 13719 CXR patient cases with ground truth BMD measured by the gold standard DXA. The model predicted BMD has a strong correlation with the ground truth (Pearson correlation coefficient 0.894 on lumbar 1). When applied in osteoporosis screening, it achieves a high classification performance (average AUC of 0.968). As the first effort of using CXR scans to predict the BMD, the proposed algorithm holds strong potential to promote early osteoporosis screening and public health.
Collapse
|
33
|
Wang M, Chen X, Cui W, Wang X, Hu N, Tang H, Zhang C, Shen J, Xie C, Chen X. A computed tomography-based radiomics nomogram for predicting osteoporotic vertebral fractures: A longitudinal study. J Clin Endocrinol Metab 2022; 108:e283-e294. [PMID: 36494103 DOI: 10.1210/clinem/dgac722] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 11/09/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022]
Abstract
CONTEXT Fractures are serious consequence of osteoporosis in old adults. However, few longitudinal studies showed the role of computed tomography (CT)-based radiomics in predicting osteoporotic fractures. OBJECTIVE We evaluated the performance of CT radiomics-based model for osteoporotic vertebral fractures (OVF) in a longitudinal study. METHODS 7906 subjects without OVF who were aged over 50 years, and underwent CT scans between 2016 and 2019 were enrolled and followed up until 2021. Seventy-two cases of new OVF were identified. One hundred and forty-four people without OVF during follow-up were selected as control. Radiomics features were extracted from baseline CT images. CT values of trabecular bone, and area and density of erector spinae were determined. Cox regression analysis was used to identify the independent associated factors. The predictive performance of the nomogram was assessed using the receiver operating characteristic (ROC) curve, calibration curve and decision curve. RESULTS CT value of vertebra (adjusted hazard ratio (aHR) = 2.04, 95% confidence interval (CI): 1.07, 3.89), radiomics score (aHR = 6.56, 95%CI:3.47, 12.38) and area of erector spinae (aHR = 1.68, 95%CI: 1.02, 2.78) were independently associated with OVF. Radscore was associated with severe OVF (aHR = 6.00, 95% CI:2.78-12.93). The nomogram showed good discrimination with a C-index of 0.82 (95%CI: 0.77, 0.87). The area under the curve of nomogram and radscore were both higher than osteoporosis + muscle area for 3-year and 4-year risk of fractures (p < 0.05). Decision curve also demonstrated that the radiomics nomogram was useful. CONCLUSIONS Bone radiomics is associated with OVF and the nomogram based on radiomics signature and muscle provides a tool for the prediction of OVF.
Collapse
Affiliation(s)
- Miaomiao Wang
- Department of Radiology, the Second Affiliated Hospital of Soochow University, 1055 Sanxiang road, Suzhou 215008, China
- Department of Radiology, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Xin Chen
- Department of Radiology, Shanghai Sixth People's Hospital, Shanghai 200233, China
| | - Wenjing Cui
- Department of Radiology, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Xinru Wang
- Department of Radiology, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Nandong Hu
- Department of Radiology, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Hongye Tang
- Department of Radiology, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Chao Zhang
- Department of Orthopaedics, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Jirong Shen
- Department of Orthopaedics, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Chao Xie
- Department of Orthopaedics, University of Rochester School of Medicine, NY 14642, USA
| | - Xiao Chen
- Department of Radiology, the Second Affiliated Hospital of Soochow University, 1055 Sanxiang road, Suzhou 215008, China
| |
Collapse
|
34
|
Yang J, Liao M, Wang Y, Chen L, He L, Ji Y, Xiao Y, Lu Y, Fan W, Nie Z, Wang R, Qi B, Yang F. Opportunistic osteoporosis screening using chest CT with artificial intelligence. Osteoporos Int 2022; 33:2547-2561. [PMID: 35931902 DOI: 10.1007/s00198-022-06491-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 07/04/2022] [Indexed: 11/25/2022]
Abstract
UNLABELLED Osteoporosis has a high incidence and a low detection rate. If it is not detected in time, it will cause osteoporotic fracture and other serious consequences. This study showed that the attenuation values of vertebrae on chest CT could be used for opportunistic screening of osteoporosis. This will be beneficial to improve the detection rate of osteoporosis and reduce the incidence of adverse events caused by osteoporosis. INTRODUCTION To explore the value of the attenuation values of all thoracic vertebrae and the first lumbar vertebra measured by artificial intelligence on non-enhanced chest CT to do osteoporosis screening. METHODS On base of images of chest CT, using artificial intelligence (AI) to measure the attenuation values (HU) of all thoracic and the first vertebrae of patients who underwent CT examination for lung cancer screening and dual-energy X-ray absorptiometry (DXA) examination during the same period. The patients were divided into three groups: normal group, osteopenia group, and osteoporosis group according to the results of DXA. Clinical baseline data and attenuation values were compared among the three groups. The correlation between attenuation values and BMD values was analyzed, and the predictive ability and diagnostic efficacy of attenuation values of thoracic and first lumbar vertebrae on osteopenia or osteoporosis risk were further evaluated. RESULTS CT values of each thoracic vertebrae and the first lumbar vertebrae decreased with age, especially in menopausal women and presented high predictive ability and diagnostic efficacy for osteopenia or osteoporosis. After clinical data correction, with every 10 HU increase of CT values, the risk of osteopenia or osteoporosis decreased by 32 ~ 44% and 61 ~ 80%, respectively. And the combined diagnostic efficacy of all thoracic vertebrae was higher than that of a single vertebra. The AUC of recognizing osteopenia or osteoporosis from normal group was 0.831and 0.972, respectively. CONCLUSIONS The routine chest CT with AI is of great value in opportunistic screening for osteopenia or osteoporosis, which can quickly screen the population at high risk of osteoporosis without increasing radiation dose, thus reducing the incidence of osteoporotic fracture.
Collapse
Affiliation(s)
- Jinrong Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277, Jiefang Avenue, Wuhan, Hubei Province, China
| | - Man Liao
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277, Jiefang Avenue, Wuhan, Hubei Province, China
| | - Yaoling Wang
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277, Jiefang Avenue, Wuhan, Hubei Province, China
| | - Leqing Chen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277, Jiefang Avenue, Wuhan, Hubei Province, China
| | - Linfeng He
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277, Jiefang Avenue, Wuhan, Hubei Province, China
| | - Yingying Ji
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277, Jiefang Avenue, Wuhan, Hubei Province, China
| | - Yao Xiao
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277, Jiefang Avenue, Wuhan, Hubei Province, China
| | - Yichen Lu
- Siemens Healthineers Digital Technology (Shanghai) Co., Ltd, No. 278, Zhouzhu Road, Nanhui, Shanghai, China
| | - Wenliang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277, Jiefang Avenue, Wuhan, Hubei Province, China
| | - Zhuang Nie
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277, Jiefang Avenue, Wuhan, Hubei Province, China
| | - Ruiyun Wang
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277, Jiefang Avenue, Wuhan, Hubei Province, China
| | - Benling Qi
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277, Jiefang Avenue, Wuhan, Hubei Province, China.
| | - Fan Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277, Jiefang Avenue, Wuhan, Hubei Province, China.
| |
Collapse
|
35
|
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.
Collapse
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.
| |
Collapse
|
36
|
Fleps I, Morgan EF. A Review of CT-Based Fracture Risk Assessment with Finite Element Modeling and Machine Learning. Curr Osteoporos Rep 2022; 20:309-319. [PMID: 36048316 PMCID: PMC10941185 DOI: 10.1007/s11914-022-00743-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/06/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE OF REVIEW We reviewed advances over the past 3 years in assessment of fracture risk based on CT scans, considering methods that use finite element models, machine learning, or a combination of both. RECENT FINDINGS Several studies have demonstrated that CT-based assessment of fracture risk, using finite element modeling or biomarkers derived from machine learning, is equivalent to currently used clinical tools. Phantomless calibration of CT scans for bone mineral density enables accurate measurements from routinely taken scans. This opportunistic use of CT scans for fracture risk assessment is facilitated by high-quality automated segmentation with deep learning, enabling workflows that do not require user intervention. Modeling of more realistic and diverse loading conditions, as well as improved modeling of fracture mechanisms, has shown promise to enhance our understanding of fracture processes and improve the assessment of fracture risk beyond the performance of current clinical tools. CT-based screening for fracture risk is effective and, by analyzing scans that were taken for other indications, could be used to expand the pool of people screened, therefore improving fracture prevention. Finite element modeling and machine learning both provide valuable tools for fracture risk assessment. Future approaches should focus on including more loading-related aspects of fracture risk.
Collapse
Affiliation(s)
- Ingmar Fleps
- College of Mechanical Engineering, Boston University, Boston, USA.
| | - Elise F Morgan
- College of Mechanical Engineering, Boston University, Boston, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| |
Collapse
|
37
|
Lewiecki EM, Bilezikian JP, Binkley N, Bouxsein ML, Bukata SV, Dempster DW, Drake MT, McClung MR, Miller PD, Rosenthal E, Tosi LL. Proceedings of the 2022 Santa Fe Bone Symposium: Current Concepts in the Care of Patients with Osteoporosis and Metabolic Bone Diseases. J Clin Densitom 2022; 25:649-667. [PMID: 36280582 DOI: 10.1016/j.jocd.2022.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 10/12/2022] [Indexed: 11/11/2022]
Abstract
The 22nd Annual Santa Fe Bone Symposium (SFBS) was a hybrid meeting held August 5-6, 2022, with in-person and virtual attendees. Altogether, over 400 individuals registered, a majority of whom attended in-person, representing many states in the USA plus 7 other countries. The SFBS included 10 plenary presentations, 2 faculty panel discussions, satellite symposia, Bone Health & Osteoporosis Foundation Fracture Liaison Service Boot Camp, and a Project ECHO workshop, with lively interactive discussions for all events. Topics of interest included fracture prevention at different stages of life; how to treat and when to change therapy; skeletal health in cancer patients; advanced imaging to assess bone strength; the state of healthcare in the USA; osteosarcopenia; vitamin D update; perioperative bone health care; new guidelines for managing primary hyperparathyroidism; new concepts on bone modeling and remodeling; and an overview on the care of rare bone diseases, including hypophosphatasia, X-linked hypophosphatemia, tumor induced osteomalacia, osteogenesis imperfecta, fibrodysplasia ossificans progressiva, and osteopetrosis. The SFBS was preceded by the Santa Fe Fellows Workshop on Osteoporosis and Metabolic Bone Diseases, a collaboration of the Endocrine Fellows Foundation and the Osteoporosis Foundation of New Mexico. From the Workshop, 4 participating fellows were selected to give oral presentations at the bone symposium. These proceedings represent the clinical highlights of 2022 SFBS presentations and the discussions that followed, all with the aim of optimizing skeletal health and minimizing the consequences of fragile bones.
Collapse
Affiliation(s)
- E Michael Lewiecki
- New Mexico Clinical Research & Osteoporosis Center, Albuquerque, NM, USA.
| | - John P Bilezikian
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Neil Binkley
- University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | | | | | - David W Dempster
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | | | - Michael R McClung
- Oregon Osteoporosis Center, Portland, OR, USA; Mary MacKillop Center for Health Research, Australian Catholic University, Melbourne, VIC, Australia
| | - Paul D Miller
- University of Colorado Health Sciences Center, Denver, CO, USA
| | | | | |
Collapse
|
38
|
Pickhardt PJ, Nguyen T, Perez AA, Graffy PM, Jang S, Summers RM, Garrett JW. Improved CT-based Osteoporosis Assessment with a Fully Automated Deep Learning Tool. Radiol Artif Intell 2022; 4:e220042. [PMID: 36204542 PMCID: PMC9530763 DOI: 10.1148/ryai.220042] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/12/2022] [Accepted: 08/17/2022] [Indexed: 11/11/2022]
Abstract
Purpose To develop, test, and validate a deep learning (DL) tool that improves upon a previous feature-based CT image processing bone mineral density (BMD) algorithm and compare it against the manual reference standard. Materials and Methods This single-center, retrospective, Health Insurance Portability and Accountability Act-compliant study included manual L1 trabecular Hounsfield unit measurements from abdominal CT scans in 11 035 patients (mean age, 58 years ± 12 [SD]; 6311 women) as the reference standard. Automated level selection and L1 trabecular region of interest (ROI) placement were then performed in this CT cohort with both a previously validated feature-based image processing tool and a new DL tool. Overall technical success rates and agreement with the manual reference standard were assessed. Results The overall success rate of the DL tool in this heterogeneous patient cohort was significantly higher than that of the older image processing BMD algorithm (99.3% vs 89.4%, P < .001). Using this DL tool, the closest median Hounsfield unit values for single-, three-, and seven-slice vertebral ROIs were within 5% of the manual reference standard Hounsfield unit values in 35.1%, 56.9%, and 85.8% of scans; within 10% in 56.6%, 75.6%, and 92.9% of scans; and within 25% in 76.5%, 89.3%, and 97.1% of scans, respectively. Trade-offs in sensitivity and specificity for osteoporosis assessment were observed from the single-slice approach (sensitivity, 39.4%; specificity, 98.3%) to the minimum value of the multislice approach (for seven contiguous slices; sensitivity, 71.3% and specificity, 94.6%). Conclusion The new DL BMD tool demonstrated a higher success rate than the older feature-based image processing tool, and its outputs can be targeted for higher specificity or sensitivity for osteoporosis assessment.Keywords: CT, CT-Quantitative, Abdomen/GI, Skeletal-Axial, Spine, Deep Learning, Machine Learning Supplemental material is available for this article. © RSNA, 2022.
Collapse
Affiliation(s)
- Perry J. Pickhardt
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., T.N., A.A.P., P.M.G., S.J., J.W.G.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Thang Nguyen
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., T.N., A.A.P., P.M.G., S.J., J.W.G.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Alberto A. Perez
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., T.N., A.A.P., P.M.G., S.J., J.W.G.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Peter M. Graffy
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., T.N., A.A.P., P.M.G., S.J., J.W.G.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Samuel Jang
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., T.N., A.A.P., P.M.G., S.J., J.W.G.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Ronald M. Summers
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., T.N., A.A.P., P.M.G., S.J., J.W.G.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - John W. Garrett
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., T.N., A.A.P., P.M.G., S.J., J.W.G.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| |
Collapse
|
39
|
Klintström B, Henriksson L, Moreno R, Malusek A, Smedby Ö, Woisetschläger M, Klintström E. Photon-counting detector CT and energy-integrating detector CT for trabecular bone microstructure analysis of cubic specimens from human radius. Eur Radiol Exp 2022; 6:31. [PMID: 35882679 PMCID: PMC9325937 DOI: 10.1186/s41747-022-00286-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 05/23/2022] [Indexed: 12/03/2022] Open
Abstract
Background As bone microstructure is known to impact bone strength, the aim of this in vitro study was to evaluate if the emerging photon-counting detector computed tomography (PCD-CT) technique may be used for measurements of trabecular bone structures like thickness, separation, nodes, spacing and bone volume fraction. Methods Fourteen cubic sections of human radius were scanned with two multislice CT devices, one PCD-CT and one energy-integrating detector CT (EID-CT), using micro-CT as a reference standard. The protocols for PCD-CT and EID-CT were those recommended for inner- and middle-ear structures, although at higher mAs values: PCD-CT at 450 mAs and EID-CT at 600 (dose equivalent to PCD-CT) and 1000 mAs. Average measurements of the five bone parameters as well as dispersion measurements of thickness, separation and spacing were calculated using a three-dimensional automated region growing (ARG) algorithm. Spearman correlations with micro-CT were computed. Results Correlations with micro-CT, for PCD-CT and EID-CT, ranged from 0.64 to 0.98 for all parameters except for dispersion of thickness, which did not show a significant correlation (p = 0.078 to 0.892). PCD-CT had seven of the eight parameters with correlations ρ > 0.7 and three ρ > 0.9. The dose-equivalent EID-CT instead had four parameters with correlations ρ > 0.7 and only one ρ > 0.9. Conclusions In this in vitro study of radius specimens, strong correlations were found between trabecular bone structure parameters computed from PCD-CT data when compared to micro-CT. This suggests that PCD-CT might be useful for analysing bone microstructure in the peripheral human skeleton.
Collapse
Affiliation(s)
- Benjamin Klintström
- Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Hälsovägen 11C, SE-14157, Huddinge, Sweden.
| | - Lilian Henriksson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, SE-58185, Linköping, Sweden.,Department of Radiology and Department of Health, Medicine and Caring Sciences, Linköping University, SE-58185, Linköping, Sweden
| | - Rodrigo Moreno
- Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Hälsovägen 11C, SE-14157, Huddinge, Sweden
| | - Alexandr Malusek
- Center for Medical Image Science and Visualization (CMIV), Linköping University, SE-58185, Linköping, Sweden.,Radiation Physics, Department of Health, Medicine and Caring Sciences, Linköping University, SE-58183, Linköping, Sweden
| | - Örjan Smedby
- Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Hälsovägen 11C, SE-14157, Huddinge, Sweden
| | - Mischa Woisetschläger
- Center for Medical Image Science and Visualization (CMIV), Linköping University, SE-58185, Linköping, Sweden.,Department of Radiology and Department of Health, Medicine and Caring Sciences, Linköping University, SE-58185, Linköping, Sweden
| | - Eva Klintström
- Center for Medical Image Science and Visualization (CMIV), Linköping University, SE-58185, Linköping, Sweden.,Department of Radiology and Department of Health, Medicine and Caring Sciences, Linköping University, SE-58185, Linköping, Sweden
| |
Collapse
|
40
|
Kim S, Kim BR, Chae HD, Lee J, Ye SJ, Kim DH, Hong SH, Choi JY, Yoo HJ. Deep Radiomics-based Approach to the Diagnosis of Osteoporosis Using Hip Radiographs. Radiol Artif Intell 2022; 4:e210212. [PMID: 35923378 PMCID: PMC9344212 DOI: 10.1148/ryai.210212] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 04/28/2022] [Accepted: 05/10/2022] [Indexed: 06/15/2023]
Abstract
PURPOSE To develop and validate deep radiomics models for the diagnosis of osteoporosis using hip radiographs. MATERIALS AND METHODS A deep radiomics model was developed using 4924 hip radiographs from 4308 patients (3632 women; mean age, 62 years ± 13 [SD]) obtained between September 2009 and April 2020. Ten deep features, 16 texture features, and three clinical features were used to train the model. T score measured with dual-energy x-ray absorptiometry was used as a reference standard for osteoporosis. Seven deep radiomics models that combined different types of features were developed: clinical (model C); texture (model T); deep (model D); texture and clinical (model TC); deep and clinical (model DC); deep and texture (model DT); and deep, texture, and clinical features (model DTC). A total of 444 hip radiographs obtained between January 2019 and April 2020 from another institution were used for the external test. Six radiologists performed an observer performance test. The area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic performance. RESULTS For the external test set, model D (AUC, 0.92; 95% CI: 0.89, 0.95) demonstrated higher diagnostic performance than model T (AUC, 0.77; 95% CI: 0.70, 0.83; adjusted P < .001). Model DC (AUC, 0.95; 95% CI: 0.92, 0.97; adjusted P = .03) and model DTC (AUC, 0.95; 95% CI: 0.92, 0.97; adjusted P = .048) showed improved diagnostic performance compared with model D. When observer performance without and with the assistance of the model DTC prediction was compared, performance improved from a mean AUC of 0.77 to 0.87 (P = .002). CONCLUSION Deep radiomics models using hip radiographs could be used to diagnose osteoporosis with high performance.Keywords: Skeletal-Appendicular, Hip, Absorptiometry/Bone Densitometry© RSNA, 2022.
Collapse
|
41
|
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.
Collapse
|
42
|
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.
Collapse
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
| |
Collapse
|
43
|
Computed Tomography Assessment of Sarcopenic Myosteatosis for Predicting Overall Survival in Colorectal Carcinoma: Systematic Review. J Comput Assist Tomogr 2022; 46:157-162. [PMID: 35297571 DOI: 10.1097/rct.0000000000001281] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND As the US population ages, cancer incidence and prevalence are projected to increase. In the last decade, there has been an increased interest in the opportunistic use of computed tomography (CT) scan data to predict cancer prognosis and inform treatment based on body composition measures, especially muscle measures for sarcopenia. OBJECTIVE This article aimed to perform a systematic review of current literature related to CT assessment of muscle attenuation values for myosteatosis in colorectal cancer (CRC) survival prediction. RESULTS Initial broad search of CT and CRC yielded 4234 results. A more focused search strategy narrowed this to 129 research papers, and 13 articles met the final inclusion criteria. Twelve of 13 studies found a statistically significant decrease in overall survival according to Hounsfield unit (HU)-based sarcopenia, with hazard ratios ranging from 1.36 to 2.94 (mean, 1.78). However, the specific criteria used to define myosteatosis by CT varied widely, with attenuation thresholds ranging from 22.5 to 47.3 HU, often further subdivided by sex and/or body mass index. CONCLUSIONS Current evidence suggests that a strong association between CT-based muscle attenuation values for myosteatosis assessment correlates with overall survival in CRC. However, more research is needed to verify these findings and determine appropriate threshold values for more diverse patient populations. Because CRC patients are staged and followed by CT, the opportunity exists for routine objective myosteatosis assessment in the clinical setting.
Collapse
|
44
|
The Impact of Diabetes on Osteoporosis Management and Secondary Fracture Risk After Primary Fragility Fractures: A Propensity Score-Matched Cohort Study. J Am Acad Orthop Surg 2022; 30:e204-e212. [PMID: 34543247 DOI: 10.5435/jaaos-d-21-00185] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 08/22/2021] [Indexed: 02/01/2023] Open
Abstract
INTRODUCTION Rates of osteoporosis evaluation and management after primary fragility fractures have remained low in recent years. The extent to which this treatment gap affects patients with diabetes is unclear. This study aimed to compare the risk of secondary fractures and rates of osteoporosis diagnosis and management after sentinel fractures in patients with and without diabetes. METHODS A propensity score-matched cohort study was conducted using the PearlDiver database. Patients aged 50 years and older with primary fragility fractures of the hip, wrist, spine, pelvis, humerus, and other locations were identified. Rates of secondary fractures, dual radiograph absorptiometry (DXA) scans, charted osteoporosis diagnoses (International Classification of Diseases, Ninth and Tenth Revisions), and osteoporosis pharmacotherapy within 2 years were compared for patients with and without diabetes using multivariable logistic regression. RESULTS Matching yielded 27,052 patients in each cohort. Index humerus fractures were more common in the diabetic cohort (15.0% versus 11.6%, P < 0.001), whereas wrist fractures were more prevalent among the nondiabetic cohort (15.2% versus 19.3%, P < 0.001). Incidence of secondary fractures at 2 years was higher for diabetic patients than nondiabetic patients (5.2% versus 4.7%; odds ratio [OR] 1.08; 95% confidence interval [CI], 0.99 to 1.17). Diabetic patients were significantly less likely to receive a DXA scan (13.2% versus 13.5%; OR 0.93; 95% CI, 0.88 to 0.98), be diagnosed with osteoporosis (9.3% versus 11.9%; OR 0.77; 95% CI, 0.73 to 0.82), or start pharmacotherapy (8.1% versus 8.7; OR 0.93; 95% CI, 0.87 to 0.99). CONCLUSION Despite diabetes being a well-established risk factor for fragility fractures, diabetic patients were significantly less likely to receive DXA scan evaluation, be formally diagnosed with osteoporosis, or be treated with osteoporosis pharmacotherapy after a sentinel fragility fracture. Incidence of secondary fractures within 2 years was also higher among diabetic patients.
Collapse
|
45
|
Liu Y, Yu A, Li K, Wang L, Huang P, Geng J, Zhang Y, Duanmu YY, Blake GM, Cheng X. Differences in spine volumetric bone mineral density between grade 1 vertebral fracture and non-fractured participants in the China action on spine and hip status study. Front Endocrinol (Lausanne) 2022; 13:1013597. [PMID: 36387886 PMCID: PMC9647629 DOI: 10.3389/fendo.2022.1013597] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 10/14/2022] [Indexed: 12/03/2022] Open
Abstract
PURPOSE This study evaluated the prevalence of vertebral fractures (VF) in middle-aged and elderly Chinese men and women and explored the differences in lumbar spine volumetric bone mineral density (vBMD) derived from quantitative CT (QCT) between those with a grade 1 vertebral fracture and non-fractured individuals. MATERIALS AND METHODS 3,457 participants were enrolled in the China Action on Spine and Hip Status (CASH) study and had upper abdominal CT examinations. Vertebral fractures were identified by Genant's semi-quantitative method from lateral CT scout views or CT sagittal views. L1-3 vBMD was measured by Mindways QCT Pro v5.0 software. The characteristics of different fracture severity groups were compared using one-way ANOVA, independent-samples t-tests, and Kruskal-Wallis H-tests. RESULTS 1267 males (aged 62.77 ± 9.20 years) and 2170 females (aged 61.41 ± 9.01 years) were included in the analysis. In men, the prevalence of VF increased from 14.7% at age<50 years to 23.2% at age ≥70 years, and in women from 5.1% at age<50 years to 33.0% at age ≥70 years. Differences in mean age and vBMD were found between the different fracture grade groups. After age stratification, vBMD differences in men aged < 50 years old disappeared (p = 0.162) but remained in the older age bands. There was no significant difference in mean vBMD between those with multiple mild fractures and those with a single mild fracture. CONCLUSION In women, the prevalence of VF increased rapidly after age 50, while it grew more slowly in men. In general, with the exception of men <50 years old, participants with a grade 1 VF had lower vBMD than non-fractured individuals. The majority of women younger than 50 with a grade 1 VF had normal bone mass. We recommend that a vertebral height reduction ratio of <25% be diagnosed as a deformity rather than a fracture in people under the age of 50. The presence of multiple mild fractured vertebrae does not imply lower BMD.
Collapse
Affiliation(s)
- Yandong Liu
- Radiology Department, Peking University Fourth School of Clinical Medicine, Beijing, China
| | - Aihong Yu
- Radiology Department, Beijing Anding Hospital Capital Medical University, Beijing, China
| | - Kai Li
- Radiology Department, Peking University Fourth School of Clinical Medicine, Beijing, China
| | - Ling Wang
- Radiology Department, Peking University Fourth School of Clinical Medicine, Beijing, China
| | - Pengju Huang
- Radiology Department, Peking University Fourth School of Clinical Medicine, Beijing, China
| | - Jian Geng
- Radiology Department, Peking University Fourth School of Clinical Medicine, Beijing, China
| | - Yong Zhang
- Intervention Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Yang-yang Duanmu
- South Medical Image Center, The First Affiliated Hospital of University of Science and Technology of China (USTC), Anhui, China
| | - Glen M. Blake
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
| | - Xiaoguang Cheng
- Radiology Department, Peking University Fourth School of Clinical Medicine, Beijing, China
- *Correspondence: Xiaoguang Cheng,
| |
Collapse
|
46
|
Eche T, Schwartz LH, Mokrane FZ, Dercle L. Toward Generalizability in the Deployment of Artificial Intelligence in Radiology: Role of Computation Stress Testing to Overcome Underspecification. Radiol Artif Intell 2021; 3:e210097. [PMID: 34870222 DOI: 10.1148/ryai.2021210097] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 09/20/2021] [Accepted: 10/12/2021] [Indexed: 12/20/2022]
Abstract
The clinical deployment of artificial intelligence (AI) applications in medical imaging is perhaps the greatest challenge facing radiology in the next decade. One of the main obstacles to the incorporation of automated AI-based decision-making tools in medicine is the failure of models to generalize when deployed across institutions with heterogeneous populations and imaging protocols. The most well-understood pitfall in developing these AI models is overfitting, which has, in part, been overcome by optimizing training protocols. However, overfitting is not the only obstacle to the success and generalizability of AI. Underspecification is also a serious impediment that requires conceptual understanding and correction. It is well known that a single AI pipeline, with prescribed training and testing sets, can produce several models with various levels of generalizability. Underspecification defines the inability of the pipeline to identify whether these models have embedded the structure of the underlying system by using a test set independent of, but distributed identically, to the training set. An underspecified pipeline is unable to assess the degree to which the models will be generalizable. Stress testing is a known tool in AI that can limit underspecification and, importantly, assure broad generalizability of AI models. However, the application of stress tests is new in radiologic applications. This report describes the concept of underspecification from a radiologist perspective, discusses stress testing as a specific strategy to overcome underspecification, and explains how stress tests could be designed in radiology-by modifying medical images or stratifying testing datasets. In the upcoming years, stress tests should become in radiology the standard that crash tests have become in the automotive industry. Keywords: Computer Applications-General, Informatics, Computer-aided Diagnosis © RSNA, 2021.
Collapse
Affiliation(s)
- Thomas Eche
- Department of Radiology, Toulouse Rangueil Hospital, Toulouse, France (T.E., F.Z.M.); and Department of Radiology, NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, 622 West 168th St, New York, NY 10032 (T.E., L.H.S., L.D.)
| | - Lawrence H Schwartz
- Department of Radiology, Toulouse Rangueil Hospital, Toulouse, France (T.E., F.Z.M.); and Department of Radiology, NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, 622 West 168th St, New York, NY 10032 (T.E., L.H.S., L.D.)
| | - Fatima-Zohra Mokrane
- Department of Radiology, Toulouse Rangueil Hospital, Toulouse, France (T.E., F.Z.M.); and Department of Radiology, NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, 622 West 168th St, New York, NY 10032 (T.E., L.H.S., L.D.)
| | - Laurent Dercle
- Department of Radiology, Toulouse Rangueil Hospital, Toulouse, France (T.E., F.Z.M.); and Department of Radiology, NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, 622 West 168th St, New York, NY 10032 (T.E., L.H.S., L.D.)
| |
Collapse
|
47
|
Tandon P, Abrams ND, Carrick DM, Chander P, Dwyer J, Fuldner R, Gannot G, Laughlin M, McKie G, PrabhuDas M, Singh A, Tsai SYA, Vedamony MM, Wang C, Liu CH. Metabolic Regulation of Inflammation and Its Resolution: Current Status, Clinical Needs, Challenges, and Opportunities. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2021; 207:2625-2630. [PMID: 34810268 PMCID: PMC9996538 DOI: 10.4049/jimmunol.2100829] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 09/29/2021] [Indexed: 02/05/2023]
Abstract
Metabolism and inflammation have been viewed as two separate processes with distinct but critical functions for our survival: metabolism regulates the utilization of nutrients, and inflammation is responsible for defense and repair. Both respond to an organism's stressors to restore homeostasis. The interplay between metabolic status and immune response (immunometabolism) plays an important role in maintaining health or promoting disease development. Understanding these interactions is critical in developing tools for facilitating novel preventative and therapeutic approaches for diseases, including cancer. This trans-National Institutes of Health workshop brought together basic scientists, technology developers, and clinicians to discuss state-of-the-art, innovative approaches, challenges, and opportunities to understand and harness immunometabolism in modulating inflammation and its resolution.
Collapse
Affiliation(s)
- Pushpa Tandon
- National Cancer Institute, National Institutes of Health, Rockville, MD;
| | - Natalie D Abrams
- National Cancer Institute, National Institutes of Health, Rockville, MD
| | | | - Preethi Chander
- National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD
| | - Johanna Dwyer
- Office of Dietary Supplements, National Institutes of Health, Bethesda, MD
| | - Rebecca Fuldner
- National Institute of Aging, National Institutes of Health, Bethesda, MD
| | - Gallya Gannot
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD
| | - Maren Laughlin
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD
| | - George McKie
- National Eye Institute, National Institutes of Health, Bethesda, MD
| | - Mercy PrabhuDas
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD
| | - Anju Singh
- National Cancer Institute, National Institutes of Health, Rockville, MD
| | - Shang-Yi Anne Tsai
- National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD
| | - Merriline M Vedamony
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD; and
| | - Chiayeng Wang
- National Cancer Institute, National Institutes of Health, Rockville, MD
| | - Christina H Liu
- National Institute of General Medical Sciences, National Institutes of Health, Bethesda, MD
| |
Collapse
|
48
|
Roux C, Rozes A, Reizine D, Hajage D, Daniel C, Maire A, Bréant S, Taright N, Gordon R, Fechtenbaum J, Kolta S, Feydy A, Briot K, Tubach F. Fully automated opportunistic screening of vertebral fractures and osteoporosis on more than 150,000 routine computed tomography scans. Rheumatology (Oxford) 2021; 61:3269-3278. [PMID: 34850864 DOI: 10.1093/rheumatology/keab878] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 11/12/2021] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE Osteoporosis is underdiagnosed and undertreated, although severe complications of osteoporotic fractures, including vertebral fractures, are well known. This study sought to assess the feasibility and results of an opportunistic screening of vertebral fractures and osteoporosis in a large database of lumbar or abdominal CT scans. MATERIAL AND METHODS Data were analyzed from CT scans obtained in 35 hospitals from patients aged 60 years and more and stored in a Picture Archiving and Communication System in Assistance-Publique-Hôpitaux de Paris, from 2007 to 2013. Dedicated software analyzed the presence of at least 1 vertebral fracture (VF), and measured Hounsfield Units (HU) in lumbar vertebrae. A simulated T-score was calculated. RESULTS Data were analyzed from 152 268 patients (73.2 ± 9.07 years). Success rates for VF assessment and HU measurements were 82 and 87% respectively. Prevalence of VF was 24.5% and increased with age. Areas under the receiver operating characteristic curves for the detection of VF were 0.61 and 0.62 for mean HU of lumbar vertebrae and L1 HU, respectively. In patients without VF, HU decreased with age, similarly in males and females. The prevalence of osteoporosis (sT-score ≤ - 2.5) was 23.8% and 36.5% in patients without and with VFs respectively. CONCLUSION Opportunistic screening in patients 60 years and older having lumbar or abdominal CT scans is feasible at large scale to screen vertebral fractures and osteoporosis.
Collapse
Affiliation(s)
- Christian Roux
- Department of Rheumatology, INSERM UMR 1153, APHP. Centre-Université de Paris, Institut de Recherche des Maladies Ostéo-Articulaires de l'Université de Paris, Hôpital Cochin
| | - Antoine Rozes
- AP-HP, Sorbonne Université, Hôpital Pitié Salpêtrière, Centre de Pharmacoépidémiologie (Cephepi), Unité de Recherche Clinique PSL-CFX, CIC-1901
| | | | - David Hajage
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, AP-HP, Sorbonne Université, Hôpital Pitié Salpêtrière, Département de Santé Publique, Centre de Pharmacoépidémiologie (Cephepi), Unité de Recherche Clinique PSL-CFX, CIC-1901
| | - Christel Daniel
- AP-HP, Direction des Systèmes d'Information, Pôle Innovation et Données
- INSERM UMRS 1142
| | - Aurélien Maire
- AP-HP, Direction des Systèmes d'Information, Pôle Innovation et Données
| | - Stéphane Bréant
- AP-HP, Direction des Systèmes d'Information, Pôle Innovation et Données
| | - Namik Taright
- AP-HP, Direction de la Stratégie et de la Transformation, Pôle Sciences des données et Information médicale, Paris, France
| | | | - Jacques Fechtenbaum
- Department of Rheumatology, APHP, Centre-Université de Paris, Hôpital Cochin
| | - Sami Kolta
- Department of Rheumatology, APHP, Centre-Université de Paris, Hôpital Cochin
| | - Antoine Feydy
- Department of Rheumatology, INSERM UMR 1153, APHP. Centre-Université de Paris, Institut de Recherche des Maladies Ostéo-Articulaires de l'Université de Paris, Hôpital Cochin
- Service de Radiologie Ostéo-Articulaire, Hôpital Cochin, Collégiale de Radiologie, AP-HP, Paris, France
| | - Karine Briot
- Department of Rheumatology, INSERM UMR 1153, APHP. Centre-Université de Paris, Institut de Recherche des Maladies Ostéo-Articulaires de l'Université de Paris, Hôpital Cochin
| | - Florence Tubach
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, AP-HP, Sorbonne Université, Hôpital Pitié Salpêtrière, Département de Santé Publique, Centre de Pharmacoépidémiologie (Cephepi), Unité de Recherche Clinique PSL-CFX, CIC-1901
| |
Collapse
|
49
|
Pickhardt PJ, Summers RM, Garrett JW. Automated CT-Based Body Composition Analysis: A Golden Opportunity. Korean J Radiol 2021; 22:1934-1937. [PMID: 34719894 PMCID: PMC8628162 DOI: 10.3348/kjr.2021.0775] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 10/07/2021] [Indexed: 11/15/2022] Open
Affiliation(s)
- Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA.
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - John W Garrett
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| |
Collapse
|
50
|
Diagnostic accuracy of quantitative dual-energy CT-based volumetric bone mineral density assessment for the prediction of osteoporosis-associated fractures. Eur Radiol 2021; 32:3076-3084. [PMID: 34713330 PMCID: PMC9038932 DOI: 10.1007/s00330-021-08323-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 08/09/2021] [Accepted: 09/09/2021] [Indexed: 10/27/2022]
Abstract
OBJECTIVES To evaluate the predictive value of volumetric bone mineral density (BMD) assessment of the lumbar spine derived from phantomless dual-energy CT (DECT)-based volumetric material decomposition as an indicator for the 2-year occurrence risk of osteoporosis-associated fractures. METHODS L1 of 92 patients (46 men, 46 women; mean age, 64 years, range, 19-103 years) who had undergone third-generation dual-source DECT between 01/2016 and 12/2018 was retrospectively analyzed. For phantomless BMD assessment, dedicated DECT postprocessing software using material decomposition was applied. Digital files of all patients were sighted for 2 years following DECT to obtain the incidence of osteoporotic fractures. Receiver operating characteristic (ROC) analysis was used to calculate cut-off values and logistic regression models were used to determine associations of BMD, sex, and age with the occurrence of osteoporotic fractures. RESULTS A DECT-derived BMD cut-off of 93.70 mg/cm3 yielded 85.45% sensitivity and 89.19% specificity for the prediction to sustain one or more osteoporosis-associated fractures within 2 years after BMD measurement. DECT-derived BMD was significantly associated with the occurrence of new fractures (odds ratio of 0.8710, 95% CI, 0.091-0.9375, p < .001), indicating a protective effect of increased DECT-derived BMD values. Overall AUC was 0.9373 (CI, 0.867-0.977, p < .001) for the differentiation of patients who sustained osteoporosis-associated fractures within 2 years of BMD assessment. CONCLUSIONS Retrospective DECT-based volumetric BMD assessment can accurately predict the 2-year risk to sustain an osteoporosis-associated fracture in at-risk patients without requiring a calibration phantom. Lower DECT-based BMD values are strongly associated with an increased risk to sustain fragility fractures. KEY POINTS •Dual-energy CT-derived assessment of bone mineral density can identify patients at risk to sustain osteoporosis-associated fractures with a sensitivity of 85.45% and a specificity of 89.19%. •The DECT-derived BMD threshold for identification of at-risk patients lies above the American College of Radiology (ACR) QCT guidelines for the identification of osteoporosis (93.70 mg/cm3 vs 80 mg/cm3).
Collapse
|