Literature DB >> 23408435

An automated algorithm to identify and quantify brown adipose tissue in human 18F-FDG-PET/CT scans.

Megan R Ruth1, Tyler Wellman, Gustavo Mercier, Thomas Szabo, Caroline M Apovian.   

Abstract

OBJECTIVE: To develop an algorithm to identify and quantify BAT from PET/CT scans without radiologist interpretation. DESIGN AND METHODS: Cases (n = 17) were randomly selected from PET/CT scans with documented "brown fat" by the reviewing radiologist. Controls (n = 18) had no documented "brown fat" and were matched with cases for age (49.7 [31.0-63.0] vs. 52.4 [24.0-70.0] yrs), outdoor temperature at scan date (51.8 [38.9-77.0] vs. 54.9 [35.2-74.6] °F), sex (F/M: 15/2 cases; 16/2 controls) and BMI (28.2 [20.0-45.7] vs. 26.8 [21.4-37.1] kg/m(2) ]). PET/CT scans and algorithm-generated images were read by the same radiologist blinded to scan identity. Regions examined included neck, mediastinum, supraclavicular fossae, axilla and paraspinal soft tissues. BAT was scored 0 for no BAT; 1 for faint uptake possibly compatible with BAT or unknown; and 2 for BAT positive.
RESULTS: Agreement between the algorithm and PET/CT scan readings was 85.7% across all regions. The algorithm had a low false negative (1.6%) and higher false positive rate (12.7%). The false positive rate was greater in mediastinum, axilla and neck regions.
CONCLUSION: The algorithm's low false negative rate combined with further refinement will yield a useful tool for efficient BAT identification in a rapidly growing field particularly as it applies to obesity.
Copyright © 2013 The Obesity Society.

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Year:  2013        PMID: 23408435      PMCID: PMC3910095          DOI: 10.1002/oby.20315

Source DB:  PubMed          Journal:  Obesity (Silver Spring)        ISSN: 1930-7381            Impact factor:   5.002


  24 in total

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