An automated algorithm to identify and quantify brown adipose tissue in human 18F-FDG-PET/CT scans
Article first published online: 22 AUG 2013
Copyright © 2013 The Obesity Society
Volume 21, Issue 8, pages 1554–1560, August 2013
How to Cite
Ruth, M. R., Wellman, T., Mercier, G., Szabo, T. and Apovian, C. M. (2013), An automated algorithm to identify and quantify brown adipose tissue in human 18F-FDG-PET/CT scans. Obesity, 21: 1554–1560. doi: 10.1002/oby.20315
- Issue published online: 22 AUG 2013
- Article first published online: 22 AUG 2013
- Accepted manuscript online: 14 FEB 2013 07:14AM EST
- Manuscript Accepted: 25 NOV 2012
- Manuscript Received: 9 JUL 2012
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/m2]). 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.
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.
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.