Julie Bolcaen

Structural and metabolic features of two different variants of multiple sclerosis: a PET/MRI study.

Multimodality imaging such as proton magnetic resonance spectroscopy (MRS) and positron emission tomography (PET) have provided information specific to the underlying mechanisms of many brain diseases, including multiple sclerosis (MS).

The optimal timing for imaging brain tumours and other brain lesions with 18F-labelled fluoromethylcholine: a dynamic positron emission tomography study.

PurposeTo determine the optimal timing for imaging brain tumours and other brain lesions with 18F-labelled fluoromethylcholine (18F-FCho) PET. Materials and methodsDynamic PET imaging with 18F-FCho (acquisition time of 28 min) was performed in 24 patients with space-occupying lesions in the brain. On the coregistered PET and MRI, lesion-to-normal tissue uptake ratios (LNRs) were calculated. Time–activity curves (TACs) were generated on the basis of the LNRs. Changes in LNR over time were calculated on the basis of the linear part of the TAC (last 22 min of the acquisition). ResultsTACs for 18F-FCho in gliomas of different grading showed that, after a rapid uptake phase, the mean increase in LNR was 1.07±0.93 for glioblastomas, −0.52±1.56 for anaplastic astrocytomas, 0.04±0.13 for grade 2 oligoastrocytomas and 0.37 in a case of a pilocytic astrocytoma. The average increase in LNR was 0.46 for a brain metastasis, 0.41±0.69 for radiation-induced mass lesions and 1.07 for a tumefactive demyelinating lesion. In contrast, TACs for 18F-FCho in meningiomas showed that, after a rapid uptake phase, the average change in LNR was −5.25±4.19 for typical meningiomas and −3.04 in a case of a mixed angiomatous and clear cell meningioma. ConclusionOn the basis of the TACs, PET imaging with 18F-FCho starting within minutes after the administration of the tracer is preferred for the detection of brain tumours and other brain lesions. If discrimination between meningioma and other brain tumours is of concern, both ‘early’ and ‘late’ PET imaging could be helpful.

18F-fluoromethylcholine (FCho), 18F-fluoroethyltyrosine (FET), and 18F-fluorodeoxyglucose (FDG) for the discrimination between high-grade glioma and radiation necrosis in rats: A PET study

INTRODUCTION Discrimination between (high-grade) brain tumor recurrence and radiation necrosis (RN) remains a diagnostic challenge because both entities have similar imaging characteristics on conventional magnetic resonance imaging (MRI). Metabolic imaging, such as positron emission tomography (PET) could overcome this diagnostic dilemma. In this study, we investigated the potential of 2-[(18)F]-fluoro-2-deoxy-D-glucose ((18)F-FDG), O-(2-[(18)F]-fluoroethyl)-L-tyrosine ((18)F-FET), and [(18)F]-Fluoromethyl-dimethyl-2-hydroxyethylammonium ((18)F-fluoromethylcholine, (18)F-FCho) PET in discriminating high-grade tumor from RN. METHODS We developed a glioblastoma (GB) rat model by inoculating F98 GB cells into the right frontal region. Induction of RN was achieved by irradiating the right frontal region with 60 Gy using three arcs with a beam aperture of 3×3 mm (n=3). Dynamic PET imaging with (18)F-FDG, (18)F-FET, and (18)F-FCho, as well as (18)F-FDG PET at a delayed time interval (240 min postinjection), was acquired. RESULTS MRI revealed contrast-enhancing tumors at 15 days after inoculation (n=4) and contrast-enhancing RN lesions 5-6 months postirradiation (n=3). On (18)F-FDG PET, the mean lesion-to-normal ratio (LNRmean) was significantly higher in GB than in RN (p=0.034). The difference in the LNRmean between tumors and RN was higher on the late (18)F-FDG PET images than on the PET images reconstructed from the last time frame of the dynamic acquisition (this is at a conventional time interval). LNRs obtained from (18)F-FCho PET were not significantly different between GB and RN (p=1.000). On (18)F-FET PET, the LNRmean was significantly higher in GB compared to RN (p=0.034). CONCLUSIONS Unlike (18)F-FCho, (18)F-FDG and (18)F-FET PET were effective in discriminating GB from RN. Interestingly, in the case of (18)F-FDG, delayed PET seems particularly useful. ADVANCES IN KNOWLEDGE AND IMPLICATIONS FOR PATIENT CARE Our results suggest that (delayed) (18)F-FDG and (18)F-FET PET can be used to discriminate GB (recurrence) from RN. Confirmation of these results in clinical studies is needed.

Correction: Kinetic Modeling and Graphical Analysis of 18F-Fluoromethylcholine (FCho), 18F-Fluoroethyltyrosine (FET) and 18F-Fluorodeoxyglucose (FDG) PET for the Fiscrimination between High-Grade Glioma and Radiation Necrosis in Rats

Background Discrimination between glioblastoma (GB) and radiation necrosis (RN) post-irradiation remains challenging but has a large impact on further treatment and prognosis. In this study, the uptake mechanisms of 18F-fluorodeoxyglucose (18F-FDG), 18F-fluoroethyltyrosine (18F-FET) and 18F-fluoromethylcholine (18F-FCho) positron emission tomography (PET) tracers were investigated in a F98 GB and RN rat model applying kinetic modeling (KM) and graphical analysis (GA) to clarify our previous results. Methods Dynamic 18F-FDG (GB n = 6 and RN n = 5), 18F-FET (GB n = 5 and RN n = 5) and 18F-FCho PET (GB n = 5 and RN n = 5) were acquired with continuous arterial blood sampling. Arterial input function (AIF) corrections, KM and GA were performed. Results The influx rate (Ki) of 18F-FDG uptake described by a 2-compartmental model (CM) or using Patlak GA, showed more trapping (k3) in GB (0.07 min-1) compared to RN (0.04 min-1) (p = 0.017). K1 of 18F-FET was significantly higher in GB (0.06 ml/ccm/min) compared to RN (0.02 ml/ccm/min), quantified using a 1-CM and Logan GA (p = 0.036). 18F-FCho was rapidly oxidized complicating data interpretation. Using a 1-CM and Logan GA no clear differences were found to discriminate GB from RN. Conclusions Based on our results we concluded that using KM and GA both 18F-FDG and 18F-FET were able to discriminate GB from RN. Using a 2-CM model more trapping of 18F-FDG was found in GB compared to RN. Secondly, the influx of 18F-FET was higher in GB compared to RN using a 1-CM model. Important correlations were found between SUV and kinetic or graphical measures for 18F-FDG and 18F-FET. 18F-FCho PET did not allow discrimination between GB and RN.

18F-FCho PET and MRI for the prediction of response in glioblastoma patients according to the RANO criteria

Purpose In this study, we investigated fluorine-18 fluoromethylcholine (18F-FCho) PET and contrast-enhanced MRI for predicting therapy response in glioblastoma (GB) patients according to the Response Assessment in Neuro-Oncology criteria. Our second aim was to investigate which imaging modality enabled prediction of treatment response first. Materials and methods Eleven GB patients who underwent no surgery or debulking only and received concomitant radiation therapy (RT) and temozolomide were included. The gold standard Response Assessment in Neuro-Oncology criteria were applied 6 months after RT to define responders and nonresponders. 18F-FCho PET and MRI were performed before RT, during RT (week 2, 4, and 6), and 1 month after RT. The contrast-enhancing tumor volume on T1-weighted MRI (GdTV) and the metabolic tumor volume (MTV) were calculated. GdTV, standardized uptake value (SUV)mean, SUVmax, MTV, MTV×SUVmean, and percentage change of these variables between all time-points were assessed to differentiate responders from nonresponders. Results Absolute SUV values did not predict response. MTV must be taken into account. 18F-FCho PET could predict response with a 100% sensitivity and specificity using MTV×SUVmean 1 month after RT. A decrease in GdTV between week 2 and 6, week 4 and 6 during RT and week 2 during RT, and 1 month after RT of at least 31%, at least 18%, and at least 53% predicted response with a sensitivity and specificity of 100%. As such, the parameter that predicts therapy response first is MR derived, namely, GdTV. Conclusion Our data indicate that both 18F-FCho PET and contrast-enhanced T1-weighted MRI can predict response early in GB patients treated with RT and temozolomide.

PET and MRI guided irradiation of a glioblastoma rat model using a micro-irradiator

For decades, small animal radiation research was mostly performed using fairly crude experimental setups applying simple single-beam techniques without the ability to target a specific or well-delineated tumor volume. The delivery of radiation was achieved using fixed radiation sources or linear accelerators producing megavoltage (MV) X-rays. These devices are unable to achieve sub-millimeter precision required for small animals. Furthermore, the high doses delivered to healthy surrounding tissue hamper response assessment. To increase the translation between small animal studies and humans, our goal was to mimic the treatment of human glioblastoma in a rat model. To enable a more accurate irradiation in a preclinical setting, recently, precision image-guided small animal radiation research platforms were developed. Similar to human planning systems, treatment planning on these micro-irradiators is based on computed tomography (CT). However, low soft-tissue contrast on CT makes it very challenging to localize targets in certain tissues, such as the brain. Therefore, incorporating magnetic resonance imaging (MRI), which has excellent soft-tissue contrast compared to CT, would enable a more precise delineation of the target for irradiation. In the last decade also biological imaging techniques, such as positron emission tomography (PET) gained interest for radiation therapy treatment guidance. PET enables the visualization of e.g., glucose consumption, amino-acid transport, or hypoxia, present in the tumor. Targeting those highly proliferative or radio-resistant parts of the tumor with a higher dose could give a survival benefit. This hypothesis led to the introduction of the biological tumor volume (BTV), besides the conventional gross target volume (GTV), clinical target volume (CTV), and planned target volume (PTV). At the preclinical imaging lab of Ghent University, a micro-irradiator, a small animal PET, and a 7 T small animal MRI are available. The goal was to incorporate MRI-guided irradiation and PET-guided sub-volume boosting in a glioblastoma rat model.