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

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.