PET/CT analysis of 21 patients with breast cancer: physiological distribution of 18F-choline and diagnostic pitfalls.
Fathinul Fikri Ahmad SaadMohd Hazeman ZakariaBahunu AppannaPublished in: The Journal of international medical research (2018)
Objectives 18F-choline is a useful tracer for detecting tumours with high lipogenesis. Knowledge of its biodistribution pattern is essential to recognise physiological variants. The aim of this study was to describe the physiologic distribution of 18F-choline and pitfalls in patients with breast cancer. Methods Twenty-one consecutive patients with breast cancer (10 premenopausal and 11 postmenopausal women; mean age, 52.82 ± 10.71 years) underwent 18F-choline positron emission tomography (PET)/computed tomography (CT) for staging. Whole-body PET/CT was acquired after 40 minutes of 18F-choline uptake. Acquired PET images were measured semiquantitatively. Results All patients showed pitfalls unrelated to breast cancer. These findings were predominantly caused by physiological glandular uptake in the liver, spleen, pancreas, bowels, axial skeleton (85%-100%), inflammation and benign changes (4.76%), appendicular skeleton (4.76%-19.049%), and site contamination (61.9%). In <1%, a concomitant metastatic neoplasm was found. The breast showed higher physiological uptake in premenopausal compared with postmenopausal woman (18F-choline maximum standardised uptake values [g/dL] of the right breast = 2.04 ± 0.404 vs 1.59 ± 0.97 and left breast = 2.00 ± 0.56 vs 1.93 ± 1.28, respectively). Conclusion 18F-choline uptake was higher in premenopausal women. Physiological 18F-choline uptake was observed in many sites, representing possible pathologies.
Keyphrases
- positron emission tomography
- pet ct
- computed tomography
- postmenopausal women
- bone mineral density
- pet imaging
- breast cancer risk
- small cell lung cancer
- squamous cell carcinoma
- end stage renal disease
- ejection fraction
- magnetic resonance imaging
- prognostic factors
- newly diagnosed
- deep learning
- lymph node
- adipose tissue
- risk assessment
- oxidative stress
- machine learning
- image quality
- young adults
- type diabetes
- chronic kidney disease
- copy number
- body composition
- pregnant women
- human health
- drinking water