Publications de l’Unité

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Many studies are devoted to the design of radiomic models for a prediction task. When no effective model is found, it is often difficult to know whether the radiomic features do not include information relevant to the task or because of insufficient data. We propose a downsampling method to answer that question when considering a classification task into two groups. Using two large patient cohorts, several experimental configurations involving different numbers of patients were created. Univariate or multivariate radiomic models were designed from each configuration. Their performance as reflected by the Youden index (YI) and Area Under the receiver operating characteristic Curve (AUC) was compared to the stable performance obtained with the highest number of patients. A downsampling method is described to predict the YI and AUC achievable with a large number of patients. Using the multivariate models involving machine learning, YI and AUC increased with the number of patients while they decreased for univariate models. The downsampling method better estimated YI and AUC obtained with the largest number of patients than the YI and AUC obtained using the number of available patients and identifies the lack of information relevant to the classification task when no such information exists.

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Diagnosis of large vessel vasculitis (LVV) and evaluation of its inflammatory activity can be challenging. Our aim was to investigate the value of hybrid positron-emission tomography/magnetic resonance imaging (PET/MRI) in LVV. All consecutive patients with LVV from the Department of Internal Medicine who underwent PET/MRI were included. Three PET/MRI patterns were defined: (i) « inflammatory, » with positive PET (>liver uptake) and abnormal MRI (stenosis and/or wall thickening); (ii) « fibrous », negative PET (≤liver uptake) and abnormal MRI; and (iii) « normal ». Thirteen patients (10 female; median age: 67-years [range: 23-87]) underwent 18 PET/MRI scans. PET/MRI was performed at diagnosis (n = 4), at relapse (n = 7), or during remission (n = 7). Among the 18 scans, eight (44%) showed an inflammatory pattern and three (17%) a fibrous pattern; the other seven were normal. The distribution of the three patterns did not differ between patients with Takayasu arteritis (TA, n = 10 scans) and those with giant cell arteritis (GCA, n = 8 scans). PET/MRI findings were normal in 2/10 (20%) TA scans vs. 5/8 (62%) GCA scans (p = 0.3). Median SUV was 4.7 [2.1-8.6] vs. 2 [1.8-2.6] in patients with active disease vs. remission, respectively (p = 0.003). PET/MRI is a new hybrid imaging modality allowing comprehensive and multimodal analysis of vascular wall inflammation and the vascular lumen. This technique offers promising perspectives for the diagnosis and monitoring of LVV.

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Overcoming the efflux mediated by ATP-binding cassette (ABC) transporters at the blood-brain barrier (BBB) remains a challenge for the delivery of small molecule tyrosine kinase inhibitors (TKIs) such as erlotinib to the brain. Inhibition of ABCB1 and ABCG2 at the mouse BBB improved the BBB permeation of erlotinib but could not be achieved in humans. BBB disruption induced by focused ultrasound (FUS) was investigated as a strategy to overcome the efflux transport of erlotinib in vivo. In rats, FUS combined with microbubbles allowed for a large and spatially controlled disruption of the BBB in the left hemisphere. ABCB1/ABCG2 inhibition was performed using elacridar (10 mg/kg i.v). The brain kinetics of erlotinib was studied using C-erlotinib Positron Emission Tomography (PET) imaging in 5 groups (n = 4-5 rats per group) including a baseline group, immediately after sonication (FUS), 48 h after FUS (FUS + 48 h), elacridar (ELA) and their combination (FUS + ELA). BBB integrity was assessed using the Evan’s Blue (EB) extravasation test. Brain exposure to C-erlotinib was measured as the area under the curve (AUC) of the brain kinetics (% injected dose (%ID) versus time (min)) in volumes corresponding to the disrupted (left) and the intact (right) hemispheres, respectively. EB extravasation highlighted BBB disruption in the left hemisphere of animals of the FUS and FUS + ELA groups but not in the control and ELA groups. EB extravasation was not observed 48 h after FUS suggesting recovery of BBB integrity. Compared with the control group (AUC = 1.4 ± 0.5%ID.min), physical BBB disruption did not impact the brain kinetics of C-erlotinib in the left hemisphere (p > .05) either immediately (AUC = 1.2 ± 0.1%ID.min) or 48 h after FUS (AUC = 1.1 ± 0.3%ID.min). Elacridar similarly increased C-erlotinib brain exposure to the left hemisphere in the absence (AUC = 2.2 ± 0.5%ID.min, p < .001) and in the presence of BBB disruption (AUC = 2.1 ± 0.5%ID.min, p < .001). AUC was never significantly different from AUC (p > .05), in any of the tested conditions. BBB integrity is not the rate limiting step for erlotinib delivery to the brain which is mainly governed by ABC-mediated efflux. Efflux transport of erlotinib persisted despite BBB disruption.

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To help interpret measurements in breast tissue and breast tumors from F-FDG PET scans, we studied the influence of age in measurements of PET parameters in normal breast tissue and in a breast cancer (BC) population.

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Radiomics is a recent area of research in precision medicine and is based on the extraction of a large variety of features from medical images. In the field of radiation oncology, comprehensive image analysis is crucial to personalization of treatments. A better characterization of local heterogeneity and the shape of the tumor, depicting individual cancer aggressiveness, could guide dose planning and suggest volumes in which a higher dose is needed for better tumor control. In addition, noninvasive imaging features that could predict treatment outcome from baseline scans could help the radiation oncologist to determine the best treatment strategies and to stratify patients as at low risk or high risk of recurrence. Nuclear medicine molecular imaging reflects information regarding biological processes in the tumor thanks to a wide range of radiotracers. Many studies involving F-fluorodeoxyglucose positron emission tomography suggest an added value of radiomics compared with the use of conventional PET metrics such as standardized uptake value for both tumor diagnosis and prediction of recurrence or treatment outcome. However, these promising results should not hide technical difficulties that still currently prevent the approach from being widely studied or clinically used. These difficulties mostly pertain to the variability of the imaging features as a function of the acquisition device and protocol, the robustness of the models with respect to that variability, and the interpretation of the radiomic models. Addressing the impact of the variability in acquisition and reconstruction protocols is needed, as is harmonizing the radiomic feature calculation methods, to ensure the reproducibility of studies in a multicenter context and their implementation in a clinical workflow. In this review, we explain the potential impact of positron emission tomography radiomics for radiation therapy and underline the various aspects that need to be carefully addressed to make the most of this promising approach.

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Textural and shape analysis is gaining considerable interest in medical imaging, particularly to identify parameters characterizing tumor heterogeneity and to feed radiomic models. Here, we present a free, multiplatform, and easy-to-use freeware called LIFEx, which enables the calculation of conventional, histogram-based, textural, and shape features from PET, SPECT, MR, CT, and US images, or from any combination of imaging modalities. The application does not require any programming skills and was developed for medical imaging professionals. The goal is that independent and multicenter evidence of the usefulness and limitations of radiomic features for characterization of tumor heterogeneity and subsequent patient management can be gathered. Many options are offered for interactive textural index calculation and for increasing the reproducibility among centers. The software already benefits from a large user community (more than 800 registered users), and interactions within that community are part of the development strategy. This study presents a user-friendly, multi-platform freeware to extract radiomic features from PET, SPECT, MR, CT, and US images, or any combination of imaging modalities. .

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PET imaging using radiolabeled avid substrates of the ATP-binding cassette (ABC) transporter P-glycoprotein (ABCB1) has convincingly revealed the role of this major efflux transporter in limiting the influx of its substrates from blood into the brain across the blood-brain barrier (BBB). Many drugs, such as metoclopramide, are weak ABCB1 substrates and distribute into the brain even when ABCB1 is fully functional. In this study, we used kinetic modeling and validated simplified methods to highlight and quantify the impact of ABCB1 on the BBB influx and efflux of C-metoclopramide, as a model of a weak ABCB1 substrate, in nonhuman primates. The regional brain kinetics of a tracer dose of C-metoclopramide (298 ± 44 MBq) were assessed in baboons using PET without ( = 4) or with ( = 4) intravenous coinfusion of the ABCB1 inhibitor tariquidar (4 mg/kg/h). Metabolite-corrected arterial input functions were generated to estimate the regional volume of distribution (), as well as the influx () and efflux () rate constants, using a 1-tissue-compartment model. Modeling outcome parameters were correlated with image-derived parameters, that is, areas under the regional time-activity curves (AUCs) from 0 to 30 min and from 30 to 60 min (SUV⋅min) and the elimination slope (; min) from 30 to 60 min. Tariquidar significantly increased the brain distribution of C-metoclopramide ( = 4.3 ± 0.5 mL/cm and 8.7 ± 0.5 mL/cm for baseline and ABCB1 inhibition conditions, respectively, < 0.001), with a 1.28-fold increase in ( < 0.05) and a 1.64-fold decrease in ( < 0.001). The effect of tariquidar was homogeneous across different brain regions. The parameters most sensitive to ABCB1 inhibition were (2.02-fold increase) and AUC from 30 to 60 min (2.02-fold increase). correlated significantly ( < 0.0001) with AUC from 30 to 60 min ( = 0.95), with AUC from 0 to 30 min ( = 0.87), and with ( = 0.62). C-metoclopramide PET imaging revealed the relative importance of both the influx hindrance and the efflux enhancement components of ABCB1 in a relevant model of the human BBB. The overall impact of ABCB1 on drug delivery to the brain can be noninvasively estimated from image-derived outcome parameters without the need for an arterial input function.

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Mesiotemporal lobe epilepsy is the most common type of drug-resistant partial epilepsy, with a specific history that often begins with status epilepticus due to various neurological insults followed by a silent period. During this period, before the first seizure occurs, a specific lesion develops, described as unilateral hippocampal sclerosis (HS). It is still challenging to determine which drugs, administered at which time point, will be most effective during the formation of this epileptic process. Neuroinflammation plays an important role in pathophysiological mechanisms in epilepsy, and therefore brain inflammation biomarkers such as translocator protein 18 kDa (TSPO) can be potent epilepsy biomarkers. TSPO is associated with reactive astrocytes and microglia. A unilateral intrahippocampal kainate injection mouse model can reproduce the defining features of human temporal lobe epilepsy with unilateral HS and the pattern of chronic pharmacoresistant temporal seizures. We hypothesized that longitudinal imaging using TSPO positron emission tomography (PET) with F-DPA-714 could identify optimal treatment windows in a mouse model during the formation of HS.

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