Multiple and Diffuse Gliomas by 18F-Fluorocholine PET/CT: Two Sides of the Same Coin.

ABSTRACT: Gliomas are characterized by an inherent diffuse and irregular morphology that prevents defining a boundary between tumor and healthy tissue, both in imaging assessment and surgical field. The effective identification of the extent of the disease in diffuse and multiple gliomas is crucial for their management but doing so by radiological means can be challenging. We present a broad spectrum of diffuse and multiple gliomas using 18F-fluorocholine PET/CT, demonstrating the potential of metabolic imaging in the evaluation of these gliomas, with implications in patient clinical management and outcome.

18F-Fluorocholine PET/CT in the Prediction of Molecular Subtypes and Prognosis for Gliomas.

AIM: To study the association of metabolic features of F-fluorocholine in gliomas with histopathological and molecular parameters, progression-free survival (PFS) and overall survival (OS). METHODS: Prospective multicenter and nonrandomized study (Functional and Metabolic Glioma Analysis). Patients underwent a basal F-fluorocholine PET/CT and were included after histological confirmation of glioma. Histological and molecular profile was assessed: grade, Ki-67, isocitrate dehydrogenase status and 1p/19q codeletion. Patients underwent standard treatment after surgery or biopsy, depending on their clinical situation. Overall survival and PFS were obtained after follow-up. After tumor segmentation of PET images, SUV and volume-based variables, sphericity, surface, coefficient of variation, and multilesionality were obtained. Relations of metabolic variables with histological, molecular profile and prognosis were evaluated using Pearson χ and t test. Receiver operator caracteristic curves were used to obtain the cutoff of PET variables. Survival analysis was performed using Kaplan-Meier and Cox regression analysis. RESULTS: Forty-five patients were assessed; 38 were diagnosed as having high-grade gliomas. Significant differences of SUV-based variables with isocitrate dehydrogenase status, tumor grade, and Ki-67 were found. Tumor grade, Ki-67, SUVmax, and SUVmean were related to progression. Kaplan-Meier analysis revealed significant associations of SUVmax, SUVmean, and multilesionaly with OS and PFS. SUVmean, sphericity, and multilesionality were independent predictors of OS and PFS in Cox regression analysis. CONCLUSIONS: Metabolic information obtained from F-fluorocholine PET of patients with glioma may be useful in the prediction of tumor biology and patient prognosis.

Metabolic Tumor Burden Assessed by Dual Time Point [18F]FDG PET/CT in Locally Advanced Breast Cancer: Relation with Tumor Biology.

PURPOSE: The aim of the study was to investigate the influence of dual time point 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/x-ray computed tomography (PET/CT) on the standard uptake value (SUV) and volume-based metabolic variables of breast lesions and their relation with biological characteristics and molecular phenotypes. PROCEDURES: Retrospective analysis including 67 patients with locally advanced breast cancer (LABC). All patients underwent a dual time point [18F]FDG PET/CT, 1 h (PET-1) and 3 h (PET-2) after [18F]FDG administration. Tumors were segmented following a three-dimensional methodology. Semiquantitative metabolic variables (SUVmax, SUVmean, and SUVpeak) and volume-based variables (metabolic tumor volume, MTV, and total lesion glycolysis, TLG) were obtained. Biologic prognostic parameters, such as the hormone receptors status, p53, HER2 expression, proliferation rate (Ki-67), and grading were obtained. Molecular phenotypes and risk-classification [low: luminal A, intermediate: luminal B HER2 (-) or luminal B HER2 (+), and high: HER2 pure or triple negative] were established. Relations between clinical and biological variables with the metabolic parameters were studied. The relevance of each metabolic variable in the prediction of phenotype risk was assessed using a multivariate analysis. RESULTS: SUV-based variables and TLG obtained in the PET-1 and PET-2 showed high and significant correlations between them. MTV and SUV variables (SUVmax, SUVmean, and SUVpeak) where only marginally correlated. Significant differences were found between mean SUV variables and TLG obtained in PET-1 and PET-2. High and significant associations were found between metabolic variables obtained in PET-1 and their homonymous in PET-2. Based on that, only relations of PET-1 variables with biological tumor characteristics were explored. SUV variables showed associations with hormone receptors status (p < 0.001 and p = 0.001 for estrogen and progesterone receptor, respectively) and risk-classification according to phenotype (SUVmax, p = 0.003; SUVmean, p = 0.004; SUVpeak, p = 0.003). As to volume-based variables, only TLG showed association with hormone receptors status (estrogen, p < 0.001; progesterone, p = 0.031), risk-classification (p = 0.007), and grade (p = 0.036). Hormone receptor negative tumors, high-grade tumors, and high-risk phenotypes showed higher TLG values. No association was found between the metabolic variables and Ki-67, HER2, or p53 expression. CONCLUSION: Statistical differences were found between mean SUV-based variables and TLG obtained in the dual time point PET/CT. Most of PET-derived parameters showed high association with molecular factors of breast cancer. However, dual time point PET/CT did not offer any added value to the single PET acquisition with respect to the relations with biological variables, based on PET-1 SUV, and volume-based variables were predictors of those obtained in PET-2.