Value of 18F-FDG PET/CT in the diagnosis and grading of incidental colorectal adenomas

Purpose: Colorectal adenomas (CRAs) are ata higher risk of progressing to colorectal cancer (CRC) as their histological grade increases. Herein, this study investigated the relationship between the maximum standardized uptake value (SUVmax) on 18F-fluorodeoxyglucose positron emission tomography-computed tomography ([F-18]FDG PET/CT) and the histological grades of CRAs and constructed the optimal regression model for distinguishing between different histological grades. Methods: This study retrospectively analyzed the data of 153 patients with CRAs who had colorectal [18F]FDG uptake incidentally found on PET/CT. The patients were categorized into low-grade intraepithelial neoplasia (LGIN) and high-grade intraepithelial neoplasia (HGIN) groups based on their histological grade. After the analysis of the relationship between SUVmax measured on preoperative [F-18]FDG PET/CT scans and histological grades, receiver-operating characteristic (ROC) curves were analyzed to determine the optimal cut-off values for distinguishing between the two groups. Common clinical and pathological factors were included and subjected to univariate and multivariate logistic regression analyses to identify independent risk factors. A diagnostic model integrating SUVmax and several risk factors was developed with the multivariate logistic regression analysis. Results: SUVmax was significantly different between the two groups (P < .001) and increased with an elevation in the malignancy degree. The area under the ROC curve (AUC) for identifying LGIN and HGIN was 0.796, and the AUC of the combination model was 0.822. Furthermore, SUVmax was an independent risk factor for distinguishing between different histological grades in pairwise comparisons. Conclusion: The regression model involving SUVmax on [F-18]FDG PET/CT can distinguish between histological grades of CRAs, which therefore can be used as a noninvasive tool for the accurate diagnosis of CRAs and assist in developing patient-specific treatment strategies before surgery. (c) 2024 Sociedad Espanola de Medicina Nuclear e Imagen Molecular. Published by Elsevier Espana, S.L.U. All rights are reserved, including those for text and data mining, AI training, and similar technologies.