The effects of metoclopramide on the central nervous system (CNS) in patients suggest substantial brain distribution. Previous data suggest that metoclopramide brain kinetics may nonetheless be ...controlled by ATP-binding cassette (ABC) transporters expressed at the blood-brain barrier. We used (11)C-metoclopramide PET imaging to elucidate the kinetic impact of transporter function on metoclopramide exposure to the brain.
(11)C-metoclopramide transport by P-glycoprotein (P-gp; ABCB1) and the breast cancer resistance protein (BCRP; ABCG2) was tested using uptake assays in cells overexpressing P-gp and BCRP. (11)C-metoclopramide brain kinetics were compared using PET in rats (n = 4-5) in the absence and presence of a pharmacologic dose of metoclopramide (3 mg/kg), with or without P-gp inhibition using intravenous tariquidar (8 mg/kg). The (11)C-metoclopramide brain distribution (VT based on Logan plot analysis) and brain kinetics (2-tissue-compartment model) were characterized with either a measured or an imaged-derived input function. Plasma and brain radiometabolites were studied using radio-high-performance liquid chromatography analysis.
(11)C-metoclopramide transport was selective for P-gp over BCRP. Pharmacologic dose did not affect baseline (11)C-metoclopramide brain kinetics (VT = 2.28 ± 0.32 and 2.04 ± 0.19 mL⋅cm(-3) using microdose and pharmacologic dose, respectively). Tariquidar significantly enhanced microdose (11)C-metoclopramide VT (7.80 ± 1.43 mL⋅cm(-3)) with a 4.4-fold increase in K1 (influx rate constant) and a 2.3-fold increase in binding potential (k3/k4) in the 2-tissue-compartment model. In the pharmacologic situation, P-gp inhibition significantly increased metoclopramide brain distribution (VT = 6.28 ± 0.48 mL⋅cm(-3)) with a 2.0-fold increase in K1 and a 2.2-fold decrease in k2 (efflux rate), with no significant impact on binding potential. In this situation, only parent (11)C-metoclopramide could be detected in the brains of P-gp-inhibited rats.
(11)C-metoclopramide benefits from favorable pharmacokinetic properties that offer reliable quantification of P-gp function at the blood-brain barrier in a pharmacologic situation. Using metoclopramide as a model of CNS drug, we demonstrated that P-gp function not only reduces influx but also mediates the efflux from the brain back to the blood compartment, with additional impact on brain distribution. This PET-based strategy of P-gp function investigation may provide new insight on the contribution of P-gp to the variability of response to CNS drugs between patients.
Purpose
To evaluate whether radiomics from 18F-FDG PET and/or MRI before re-irradiation (reRT) of recurrent head and neck cancer (HNC) could predict the occurrence and the location “in-field” or ...“outside” of a second locoregional recurrence (LR).
Methods
Among the 55 patients re-irradiated at curative intend for HNC from 2012 to 2019, 48 had an MRI and/or PET before the start of the reRT. Thirty-nine radiomic features (RF) were extracted from the re-irradiated GTV (rGTV) using LIFEx software. Student
t
tests and Spearman correlation coefficient were used to select the RF that best separate patients who recurred from those who did not, and “in-field” from “outside” recurrences. Principal component analysis involving these features only was used to create a prediction model. Leave-one-out cross-validation was performed to evaluate the models.
Results
After a median follow-up of 17 months, 40/55 patients had developed a second LR, including 18 “in-field” and 22 “outside” recurrences. From pre-reRT MRI, a model based on three RF (GLSZM_SZHGLE, GLSZM_LGLZE, and skewness) predicted whether patients would recur with a balanced accuracy (BA) of 83.5%. Another model from pre-reRT MRI based on three other RF (GLSZM_ LZHGE, NGLDM_Busyness, and GLZLM_SZE) predicted whether patients would recur “in-field” or “outside” with a BA of 78.5%. From pre-reRT PET, a model based on four RF (Kurtosis, SUVbwmin, GLCM_Correlation, and GLCM_Contrast) predicted the LR location with a BA of 84.5%.
Conclusion
RF characterizing tumor heterogeneity extracted from pre-reRT PET and MRI predicted whether patients would recur, and whether they would recur “in-field” or “outside”.
Transporters of the solute carrier O (SLCO) family, former organic anion-transporting polypeptides, are now recognized as key players in pharmacokinetics. Imaging is increasingly regarded as a ...relevant method to elucidate and decipher the intrinsic role of SLCO in controlling drug disposition in plasma and tissues. Current research in this representative field of translational research is based on different imaging modalities including nuclear imaging, such as single-photon emission computed tomography or positron emission tomography, and magnetic resonance imaging. Imaging modalities can be compared in terms of sensitivity, quantitative properties, spatial resolution, variety of ligands, and radiation exposure. All these approaches rely on the use of SLCO substrates that are detected using corresponding modalities. The present review aims at reporting and comparing the imaging probes that have been proposed to study SLCO-transport function, in terms of in vitro specificity, in vivo behavior, and clinical validation.
Objective
Quantitative analysis in MRI is challenging due to variabilities in intensity distributions across patients, acquisitions and scanners and suffers from bias field inhomogeneity. Radiomic ...studies are impacted by these effects that affect radiomic feature values. This paper describes a dedicated pipeline to increase reproducibility in breast MRI radiomic studies.
Materials and methods
T1, T2, and T1-DCE MR images of two breast phantoms were acquired using two scanners and three dual breast coils. Images were retrospectively corrected for bias field inhomogeneity and further normalised using
Z
score or histogram matching. Extracted radiomic features were harmonised between coils by the ComBat method. The whole pipeline was assessed qualitatively and quantitatively using statistical comparisons on two series of radiomic feature values computed in the gel mimicking the normal breast tissue or in dense lesions.
Results
Intra and inter-acquisition variabilities were strongly reduced by the standardisation pipeline. Harmonisation by ComBat lowered the percentage of radiomic features significantly different between the three coils from 87% after bias field correction and MR normalisation to 3% in the gel, while preserving or improving performance of lesion classification in the phantoms.
Discussion
A dedicated standardisation pipeline was developed to reduce variabilities in breast MRI, which paves the way for robust multi-scanner radiomic studies but needs to be assessed on patient data.
Purpose
To improve reproducibility and predictive performance of PET radiomic features in multicentric studies by cycle-consistent generative adversarial network (GAN) harmonization approaches.
...Methods
GAN-harmonization was developed to harmonize whole-body PET scans to perform image style and texture translation between different centers and scanners. GAN-harmonization was evaluated by application to two retrospectively collected open datasets and different tasks. First, GAN-harmonization was performed on a dual-center lung cancer cohort (127 female, 138 male) where the reproducibility of radiomic features in healthy liver tissue was evaluated. Second, GAN-harmonization was applied to a head and neck cancer cohort (43 female, 154 male) acquired from three centers. Here, the clinical impact of GAN-harmonization was analyzed by predicting the development of distant metastases using a logistic regression model incorporating first-order statistics and texture features from baseline
18
F-FDG PET before and after harmonization.
Results
Image quality remained high (structural similarity: left kidney
≥
0.800, right kidney
≥
0.806, liver
≥
0.780, lung
≥
0.838, spleen
≥
0.793, whole-body
≥
0.832) after image harmonization across all utilized datasets. Using GAN-harmonization, inter-site reproducibility of radiomic features in healthy liver tissue increased at least by
≥
5 ± 14% (first-order),
≥
16 ± 7% (GLCM),
≥
19 ± 5% (GLRLM),
≥
16 ± 8% (GLSZM),
≥
17 ± 6% (GLDM), and
≥
23 ± 14% (NGTDM). In the head and neck cancer cohort, the outcome prediction improved from AUC 0.68 (95% CI 0.66–0.71) to AUC 0.73 (0.71–0.75) by application of GAN-harmonization.
Conclusions
GANs are capable of performing image harmonization and increase reproducibility and predictive performance of radiomic features derived from different centers and scanners.
Monte Carlo simulations in emission tomography and GATE: An overview Buvat, Irène; Lazaro, Delphine
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
12/2006, Letnik:
569, Številka:
2
Journal Article
Recenzirano
Monte Carlo simulations are nowadays an essential tool in emission tomography (Single-Photon Emission Computed Tomography—SPECT and Positron Emission Tomography—PET), for assisting system design and ...optimizing imaging and processing protocols. Several Monte Carlo simulation software are currently available for modeling SPECT and PET configurations.
This paper presents an overview of current trends concerning Monte Carlo simulations in SPECT and PET. The evolution of the place of Monte Carlo simulations in SPECT and PET since 1995 is studied, together with the evolution of the codes used for Monte Carlo simulations. New features present in current codes are described, and new applications of Monte Carlo simulations in SPECT and PET are reviewed. Finally, upcoming developments in the field of Monte Carlo simulations in SPECT or PET are discussed. In this paper, a particular emphasis is given to the GATE code, as it is the most recent and publicly available code for Monte Carlo simulations appropriate for both SPECT and PET applications.
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.
In clinical positron emission tomography (PET) imaging, quantification of radiotracer uptake in tumours is often performed using semi-quantitative measurements such as the standardised uptake value ...(SUV). For small objects, the accuracy of SUV estimates is limited by the noise properties of PET images and the partial volume effect. There is need for methods that provide more accurate and reproducible quantification of radiotracer uptake.
In this work, we present a deep learning approach with the aim of improving quantification of lung tumour radiotracer uptake and tumour shape definition. A set of simulated tumours, assigned with 'ground truth' radiotracer distributions, are used to generate realistic PET raw data which are then reconstructed into PET images. In this work, the ground truth images are generated by placing simulated tumours characterised by different sizes and activity distributions in the left lung of an anthropomorphic phantom. These images are then used as input to an analytical simulator to simulate realistic raw PET data. The PET images reconstructed from the simulated raw data and the corresponding ground truth images are used to train a 3D convolutional neural network.
When tested on an unseen set of reconstructed PET phantom images, the network yields improved estimates of the corresponding ground truth. The same network is then applied to reconstructed PET data generated with different point spread functions. Overall the network is able to recover better defined tumour shapes and improved estimates of tumour maximum and median activities.
Our results suggest that the proposed approach, trained on data simulated with one scanner geometry, has the potential to restore PET data acquired with different scanners.
Dissemination, expressed recently by the largest Euclidian distance between lymphoma sites (SDmax), appeared a promising risk factor in DLBCL patients. We investigated alternative distance metrics to ...characterize the robustness of the dissemination information. In 290 patients from the REMARC trial (NCT01122472), the Euclidean (Euc), Manhattan (Man), and Tchebychev (Tch) distances between the furthest lesions, firstly based on the centroid of each lesion and then directly from the two most distant tumor voxels and the Travelling Salesman Problem distance (TSP) were calculated. For PFS, the areas under the ROC curves were between 0.63 and 0.64, and between 0.62 and 0.65 for OS. Patients with high SDmax whatever the method of calculation or high SD_TSP had a significantly poorer outcome than patients with low SDmax or SD_TSP (p < 0.001 for both PFS and OS), with significance maintained in Ann Arbor advanced-stage patients. In multivariate analysis with total metabolic tumor volume and ECOG, each distance feature had an independent prognostic value for PFS. For OS, only SDmax_Tch, SDmax_Euc _Vox, and SDmax_Man _Vox reached significance. The spread of DLBCL lesions measured by the largest distance between lymphoma sites is a strong independent prognostic factor and could be measured directly from tumor voxels, allowing its development in the area of the deep learning segmentation methods.
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