Literature DB >> 33646902

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

Perry J Pickhardt1, Peter M Graffy1, Alberto A Perez1, Meghan G Lubner1, Daniel C Elton1, Ronald M Summers1.   

Abstract

Abdominal CT is a frequently performed imaging examination for a wide variety of clinical indications. In addition to the immediate reason for scanning, each CT examination contains robust additional data on body composition that generally go unused in routine clinical practice. There is now growing interest in harnessing this additional information. Prime examples of cardiometabolic information include measurement of bone mineral density for osteoporosis screening, quantification of aortic calcium for assessment of cardiovascular risk, quantification of visceral fat for evaluation of metabolic syndrome, assessment of muscle bulk and density for diagnosis of sarcopenia, and quantification of liver fat for assessment of hepatic steatosis. All of these relevant biometric measures can now be fully automated through the use of artificial intelligence algorithms, which provide rapid and objective assessment and allow large-scale population-based screening. Initial investigations into these measures of body composition have demonstrated promising performance for prediction of future adverse events that matches or exceeds the best available clinical prediction models, particularly when these CT-based measures are used in combination. In this review, the concept of CT-based opportunistic screening is discussed, and an overview of the various automated biomarkers that can be derived from essentially all abdominal CT examinations is provided, drawing heavily on the authors' experience. As radiology transitions from a volume-based to a value-based practice, opportunistic screening represents a promising example of adding value to services that are already provided. If the potentially high added value of these objective CT-based automated measures is ultimately confirmed in subsequent investigations, this opportunistic screening approach could be considered for intentional CT-based screening. ©RSNA, 2021.

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Year:  2021        PMID: 33646902      PMCID: PMC7924410          DOI: 10.1148/rg.2021200056

Source DB:  PubMed          Journal:  Radiographics        ISSN: 0271-5333            Impact factor:   5.333


  82 in total

1.  Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning.

Authors:  Alexander D Weston; Panagiotis Korfiatis; Timothy L Kline; Kenneth A Philbrick; Petro Kostandy; Tomas Sakinis; Motokazu Sugimoto; Naoki Takahashi; Bradley J Erickson
Journal:  Radiology       Date:  2018-12-11       Impact factor: 11.105

2.  Fibrosis stage is the strongest predictor for disease-specific mortality in NAFLD after up to 33 years of follow-up.

Authors:  Mattias Ekstedt; Hannes Hagström; Patrik Nasr; Mats Fredrikson; Per Stål; Stergios Kechagias; Rolf Hultcrantz
Journal:  Hepatology       Date:  2015-03-23       Impact factor: 17.425

3.  Opportunistic Quantitative CT Bone Mineral Density Measurement at the Proximal Femur Using Routine Contrast-Enhanced Scans: Direct Comparison With DXA in 355 Adults.

Authors:  Timothy J Ziemlewicz; Alyssa Maciejewski; Neil Binkley; Alan D Brett; J Keenan Brown; Perry J Pickhardt
Journal:  J Bone Miner Res       Date:  2016-05-06       Impact factor: 6.741

4.  The Liver Segmental Volume Ratio for Noninvasive Detection of Cirrhosis: Comparison With Established Linear and Volumetric Measures.

Authors:  Oliver M Furusato Hunt; Meghan G Lubner; Timothy J Ziemlewicz; Alejandro Muñoz Del Rio; Perry J Pickhardt
Journal:  J Comput Assist Tomogr       Date:  2016 May-Jun       Impact factor: 1.826

5.  Multiparametric CT for Noninvasive Staging of Hepatitis C Virus-Related Liver Fibrosis: Correlation With the Histopathologic Fibrosis Score.

Authors:  Perry J Pickhardt; Peter M Graffy; Adnan Said; Daniel Jones; Brandon Welsh; Ryan Zea; Meghan G Lubner
Journal:  AJR Am J Roentgenol       Date:  2019-01-15       Impact factor: 3.959

6.  General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

Authors:  Ralph B D'Agostino; Ramachandran S Vasan; Michael J Pencina; Philip A Wolf; Mark Cobain; Joseph M Massaro; William B Kannel
Journal:  Circulation       Date:  2008-01-22       Impact factor: 29.690

7.  Opportunistic screening for osteoporosis using the sagittal reconstruction from routine abdominal CT for combined assessment of vertebral fractures and density.

Authors:  S J Lee; N Binkley; M G Lubner; R J Bruce; T J Ziemlewicz; P J Pickhardt
Journal:  Osteoporos Int       Date:  2015-09-29       Impact factor: 4.507

Review 8.  Progression of NAFLD to diabetes mellitus, cardiovascular disease or cirrhosis.

Authors:  Quentin M Anstee; Giovanni Targher; Christopher P Day
Journal:  Nat Rev Gastroenterol Hepatol       Date:  2013-03-19       Impact factor: 46.802

9.  Natural history of hepatic steatosis: observed outcomes for subsequent liver and cardiovascular complications.

Authors:  Perry J Pickhardt; Luke Hahn; Alejandro Muñoz del Rio; Seong Ho Park; Scott B Reeder; Adnan Said
Journal:  AJR Am J Roentgenol       Date:  2014-04       Impact factor: 3.959

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  9 in total

1.  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

2.  The Cases for and against Artificial Intelligence in the Medical School Curriculum.

Authors:  Brandon Ngo; Diep Nguyen; Eric vanSonnenberg
Journal:  Radiol Artif Intell       Date:  2022-08-17

3.  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

4.  The Usefulness of Radiomics Methodology for Developing Descriptive and Prognostic Image-Based Phenotyping in the Aging Population: Results From a Small Feasibility Study.

Authors:  Rebeca Mirón Mombiela; Consuelo Borrás
Journal:  Front Aging       Date:  2022-04-28

5.  Fully Automated Abdominal CT Biomarkers for Type 2 Diabetes Using Deep Learning.

Authors:  Perry J Pickhardt; Ronald M Summers; Hima Tallam; Daniel C Elton; Sungwon Lee; Paul Wakim
Journal:  Radiology       Date:  2022-04-05       Impact factor: 29.146

Review 6.  Value-added Opportunistic CT Screening: State of the Art.

Authors:  Perry J Pickhardt
Journal:  Radiology       Date:  2022-03-15       Impact factor: 29.146

7.  CT Colonography: The Role of Radiologist Training.

Authors:  Perry J Pickhardt
Journal:  Radiology       Date:  2022-02-15       Impact factor: 29.146

8.  Body composition assessment: comparison of quantitative values between magnetic resonance imaging and computed tomography.

Authors:  Chiara Zaffina; Rolf Wyttenbach; Alberto Pagnamenta; Rosario Francesco Grasso; Matteo Biroli; Filippo Del Grande; Stefania Rizzo
Journal:  Quant Imaging Med Surg       Date:  2022-02

9.  Automated CT-Based Body Composition Analysis: A Golden Opportunity.

Authors:  Perry J Pickhardt; Ronald M Summers; John W Garrett
Journal:  Korean J Radiol       Date:  2021-10-26       Impact factor: 3.500

  9 in total

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