Akademik Veri Yönetim Sistemi
CLINICAL NUCLEAR MEDICINE, cilt.50, sa.9, 2025 (SCI-Expanded, Scopus)
Purpose:To evaluate the potential of integrated multiparametric (68)GaDOTATATE PET/MR imaging for assessing liver lesions of well-differentiated neuroendocrine tumors (NETs) and to identify imaging parameters predictive of primary tumor localization. Patients and Methods:This retrospective study involves patients with well-differentiated NETs who underwent (68)GaDOTATATE PET/MRI between September 2018 and November 2024. Inclusion criteria required histopathologically proven NETs with (68)GaDOTATATE-avid liver metastases and complete multiparametric MRI sequences. PET and MRI-derived variables, including SUVmax, ADCmin, T/L ratios, and tumor volume (log-transformed tumor volume: LOGVOL), were analyzed. Linear mixed-effects models and logistic regression analyses were performed to identify relationships between imaging features and tumor characteristics. ROC analyses were conducted to evaluate the accuracy of primary tumor origin predictions. Results:Of 43 imaging sessions, 14 patients (7 male, 7 female; median age 59 y) with 181 lesions met the inclusion criteria. SUVmax was significantly correlated with LOGVOL and contrast enhancement parameters (eg, WOliver). Linear mixed-effects models revealed that LOGVOL and WOliver were independent predictors of SUVmax. In the binomial regression analysis, tumor precontrast T1 intensity, T/Lart, and T/Lven were significant factors in differentiation between pancreatic and gastrointestinal (GIS) NET metastases, with pancreatic tumors demonstrating higher T/Lart and GIS tumors exhibiting higher T/Lven and T1 intensity. Logistic regression achieved an AUC of 0.911, with a sensitivity of 86% and specificity of 76%. Conclusions: (68)GaDOTATATE PET/MRI effectively integrates metabolic and anatomical imaging for characterizing NET liver metastases. Parameters such as LOGVOL and WOliver independently predict SUVmax, while precontrast T1 intensity, T/Lart, and T/Lven assist in differentiating pancreatic from GIS NETs. These findings underscore the potential of (68)GaDOTATATE PET/MRI in personalized NET management and suggest avenues for further research to confirm these results.