Colonoscopic and Histopathological Evaluation of Incidental Colonic FDG Uptake on PET/CT
Objective: This study aimed to evaluate the colonoscopic and histopathological findings in patients with incidental focal colonic FDG uptake detected on PET/CT and to investigate the correlation between PET/CT findings and colonoscopy results. Methods:We retrospectively analyzed 37 patients (22 males, 15 females; mean age 64.29.5 years) who underwent colonoscopy within 6 weeks after detection of incidental focal colonic FDG uptake on PET/CT between January 2019 and March 2025.Patient demographics, PET/CT indications, localization of uptake, SUVmax values, and colonoscopy findings were recorded.Colonoscopy findings were classified according to histopathological results. Results:The most common PET/CT indications were gastrointestinal malignancies (27.0%), lung cancer (24.3%), and gynecological cancer (18.9%).The mean SUVmax value was 10.24.9.Colonoscopic evaluation revealed malignant lesions in 9 patients (24.3%), adenomas in 16 patients (43.2%), and normal/benign findings in 12 patients (32.4%).Malignant lesions included adenocarcinoma (18.9%), non-Hodgkin's lymphoma (2.7%), and neuroendocrine tumor (2.7%).Among adenomas, tubular (18.9%), tubulovillous (13.5%), villous (5.4%), and serrated (5.4%) types were identified.The mean SUVmax value was significantly higher in patients with malignant lesions (13.55.1)compared to those with adenomas (8.63.5) and normal findings (6.12.3)(p<0.001).The correlation between PET/CT localization and colonoscopic findings was 100% for malignant lesions and 81.2% for adenomas. Conclusion:Incidental focal colonic FDG uptake on PET/CT warrants colonoscopic evaluation as a significant proportion of these patients have malignant or premalignant lesions.Higher SUVmax values are associated with an increased risk of malignancy.
Authors
- Fırat Mülküt (ORCID: https://orcid.org/0000-0003-4049-7595)
Publication Details
- Journal
- Southern Clinics of Istanbul Eurasia
- Published
- 2025-01-01
- DOI
- https://doi.org/10.14744/scie.2025.25665
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00