Literature DB >> 32780005

Automated Abdominal CT Imaging Biomarkers for Opportunistic Prediction of Future Major Osteoporotic Fractures in Asymptomatic Adults.

Perry J Pickhardt1, Peter M Graffy1, Ryan Zea1, Scott J Lee1, Jiamin Liu1, Veit Sandfort1, Ronald M Summers1.   

Abstract

Background Body composition data from abdominal CT scans have the potential to opportunistically identify those at risk for future fracture. Purpose To apply automated bone, muscle, and fat tools to noncontrast CT to assess performance for predicting major osteoporotic fractures and to compare with the Fracture Risk Assessment Tool (FRAX) reference standard. Materials and Methods Fully automated bone attenuation (L1-level attenuation), muscle attenuation (L3-level attenuation), and fat (L1-level visceral-to-subcutaneous [V/S] ratio) measures were derived from noncontrast low-dose abdominal CT scans in a generally healthy asymptomatic adult outpatient cohort from 2004 to 2016. The FRAX score was calculated from data derived from an algorithmic electronic health record search. The cohort was assessed for subsequent future fragility fractures. Subset analysis was performed for patients evaluated with dual x-ray absorptiometry (n = 2106). Hazard ratios (HRs) and receiver operating characteristic curve analyses were performed. Results A total of 9223 adults were evaluated (mean age, 57 years ± 8 [standard deviation]; 5152 women) at CT and were followed over a median time of 8.8 years (interquartile range, 5.1-11.6 years), with documented subsequent major osteoporotic fractures in 7.4% (n = 686), including hip fractures in 2.4% (n = 219). Comparing the highest-risk quartile with the other three quartiles, HRs for bone attenuation, muscle attenuation, V/S fat ratio, and FRAX were 2.1, 1.9, 0.98, and 2.5 for any fragility fracture and 2.0, 2.5, 1.1, and 2.5 for femoral fractures, respectively (P < .001 for all except V/S ratio, which was P ≥ .51). Area under the receiver operating characteristic curve (AUC) values for fragility fracture were 0.71, 0.65, 0.51, and 0.72 at 2 years and 0.63, 0.62, 0.52, and 0.65 at 10 years, respectively. For hip fractures, 2-year AUC for muscle attenuation alone was 0.75 compared with 0.73 for FRAX (P = .43). Multivariable 2-year AUC combining bone and muscle attenuation was 0.73 for any fragility fracture and 0.76 for hip fractures, respectively (P ≥ .73 compared with FRAX). For the subset with dual x-ray absorptiometry T-scores, 2-year AUC was 0.74 for bone attenuation and 0.65 for FRAX (P = .11). Conclusion Automated bone and muscle imaging biomarkers derived from CT scans provided comparable performance to Fracture Risk Assessment Tool score for presymptomatic prediction of future osteoporotic fractures. Muscle attenuation alone provided effective hip fracture prediction. © RSNA, 2020 See also the editorial by Smith in this issue.

Entities:  

Mesh:

Substances:

Year:  2020        PMID: 32780005      PMCID: PMC7526945          DOI: 10.1148/radiol.2020200466

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  36 in total

1.  Future Osteoporotic Fracture Risk Related to Lumbar Vertebral Trabecular Attenuation Measured at Routine Body CT.

Authors:  Scott J Lee; Peter M Graffy; Ryan D Zea; Timothy J Ziemlewicz; Perry J Pickhardt
Journal:  J Bone Miner Res       Date:  2018-02-05       Impact factor: 6.741

2.  Underdiagnosis and Undertreatment of Osteoporosis: The Battle to Be Won.

Authors:  Paul D Miller
Journal:  J Clin Endocrinol Metab       Date:  2016-02-24       Impact factor: 5.958

Review 3.  Machine Learning in Medicine.

Authors:  Alvin Rajkomar; Jeffrey Dean; Isaac Kohane
Journal:  N Engl J Med       Date:  2019-04-04       Impact factor: 91.245

Review 4.  Osteoporosis treatment: recent developments and ongoing challenges.

Authors:  Sundeep Khosla; Lorenz C Hofbauer
Journal:  Lancet Diabetes Endocrinol       Date:  2017-07-07       Impact factor: 32.069

Review 5.  Current Applications and Future Impact of Machine Learning in Radiology.

Authors:  Garry Choy; Omid Khalilzadeh; Mark Michalski; Synho Do; Anthony E Samir; Oleg S Pianykh; J Raymond Geis; Pari V Pandharipande; James A Brink; Keith J Dreyer
Journal:  Radiology       Date:  2018-06-26       Impact factor: 11.105

6.  A Crisis in the Treatment of Osteoporosis.

Authors:  Sundeep Khosla; Elizabeth Shane
Journal:  J Bone Miner Res       Date:  2016-06-28       Impact factor: 6.741

7.  Fully automated segmentation and quantification of visceral and subcutaneous fat at abdominal CT: application to a longitudinal adult screening cohort.

Authors:  Scott J Lee; Jiamin Liu; Jianhua Yao; Andrew Kanarek; Ronald M Summers; Perry J Pickhardt
Journal:  Br J Radiol       Date:  2018-03-28       Impact factor: 3.039

8.  Feasibility of simultaneous computed tomographic colonography and fully automated bone mineral densitometry in a single examination.

Authors:  Ronald M Summers; Nicolai Baecher; Jianhua Yao; Jiamin Liu; Perry J Pickhardt; J Richard Choi; Suvimol Hill
Journal:  J Comput Assist Tomogr       Date:  2011 Mar-Apr       Impact factor: 1.826

9.  Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images.

Authors:  Joseph E Burns; Jianhua Yao; Ronald M Summers
Journal:  Radiology       Date:  2017-03-16       Impact factor: 11.105

10.  Automated Liver Fat Quantification at Nonenhanced Abdominal CT for Population-based Steatosis Assessment.

Authors:  Peter M Graffy; Veit Sandfort; Ronald M Summers; Perry J Pickhardt
Journal:  Radiology       Date:  2019-09-17       Impact factor: 11.105

View more
  19 in total

Review 1.  Glenoid bony morphology of osteoarthritis prior to shoulder arthroplasty: what the surgeon wants to know and why.

Authors:  Lawrence Lo; Scott Koenig; Natalie L Leong; Brian B Shiu; S Ashfaq Hasan; Mohit N Gilotra; Kenneth C Wang
Journal:  Skeletal Radiol       Date:  2020-10-23       Impact factor: 2.199

2.  Deep Radiomics-based Approach to the Diagnosis of Osteoporosis Using Hip Radiographs.

Authors:  Sangwook Kim; Bo Ram Kim; Hee-Dong Chae; Jimin Lee; Sung-Joon Ye; Dong Hyun Kim; Sung Hwan Hong; Ja-Young Choi; Hye Jin Yoo
Journal:  Radiol Artif Intell       Date:  2022-05-25

3.  Fully Automated Deep Learning Tool for Sarcopenia Assessment on CT: L1 Versus L3 Vertebral Level Muscle Measurements for Opportunistic Prediction of Adverse Clinical Outcomes.

Authors:  Perry J Pickhardt; Alberto A Perez; John W Garrett; Peter M Graffy; Ryan Zea; Ronald M Summers
Journal:  AJR Am J Roentgenol       Date:  2021-08-18       Impact factor: 6.582

4.  Improved CT-based Osteoporosis Assessment with a Fully Automated Deep Learning Tool.

Authors:  Perry J Pickhardt; Thang Nguyen; Alberto A Perez; Peter M Graffy; Samuel Jang; Ronald M Summers; John W Garrett
Journal:  Radiol Artif Intell       Date:  2022-08-31

5.  Opportunistic screening for osteoporosis and osteopenia from CT scans of the abdomen and pelvis using machine learning.

Authors:  Ronnie Sebro; Cynthia De la Garza-Ramos
Journal:  Eur Radiol       Date:  2022-09-27       Impact factor: 7.034

6.  Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study.

Authors:  Darryl E Wright; Sovanlal Mukherjee; Anurima Patra; Hala Khasawneh; Panagiotis Korfiatis; Garima Suman; Suresh T Chari; Yogish C Kudva; Timothy L Kline; Ajit H Goenka
Journal:  Abdom Radiol (NY)       Date:  2022-09-10

7.  Diagnostic Performance of Multitarget Stool DNA and CT Colonography for Noninvasive Colorectal Cancer Screening.

Authors:  Perry J Pickhardt; Peter M Graffy; Benjamin Weigman; Nimrod Deiss-Yehiely; Cesare Hassan; Jennifer M Weiss
Journal:  Radiology       Date:  2020-08-11       Impact factor: 11.105

8.  Nomograms for Automated Body Composition Analysis: A Crucial Step for Routine Clinical Implementation.

Authors:  Ronald M Summers
Journal:  Radiology       Date:  2020-11-24       Impact factor: 11.105

Review 9.  Opportunistic Screening at Abdominal CT: Use of Automated Body Composition Biomarkers for Added Cardiometabolic Value.

Authors:  Perry J Pickhardt; Peter M Graffy; Alberto A Perez; Meghan G Lubner; Daniel C Elton; Ronald M Summers
Journal:  Radiographics       Date:  2021 Mar-Apr       Impact factor: 5.333

10.  Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly.

Authors:  Alberto A Perez; Victoria Noe-Kim; Meghan G Lubner; Peter M Graffy; John W Garrett; Daniel C Elton; Ronald M Summers; Perry J Pickhardt
Journal:  Radiology       Date:  2021-10-26       Impact factor: 11.105

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.