NIMG-34. QUANTIFICATION OF GLYCOLYTIC FLUX REDUCTION IN RECURRENT GLIOBLASTOMA AFTER EGFR INHIBITION USING MOLECULAR MR-PET
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PROVIDER: S-EPMC9660931 | biostudies-literature | 2022 Nov
REPOSITORIES: biostudies-literature
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| S-EPMC9660816 | biostudies-literature
Project description:BackgroundGlioblastoma Multiforme (GBM) is a malignant primary brain tumor in which the standard treatment, ionizing radiation (IR), achieves a median survival of about 15 months. GBM harbors glioblastoma stem-like cells (GSCs), which play a crucial role in therapeutic resistance and recurrence.MethodsPatient-derived GSCs, GBM cell lines, intracranial GBM xenografts, and GBM sections were used to measure mRNA and protein expression and determine the related molecular mechanisms by qRT-PCR, immunoblot, immunoprecipitation, immunofluorescence, OCR, ECAR, live-cell imaging, and immunohistochemistry. Orthotopic GBM xenograft models were applied to investigate tumor inhibitory effects of glimepiride combined with radiotherapy.ResultsWe report that GSCs that survive standard treatment radiation upregulate Speedy/RINGO cell cycle regulator family member A (Spy1) and downregulate CAP-Gly domain containing linker protein 3 (CLIP3, also known as CLIPR-59). We discovered that Spy1 activation and CLIP3 inhibition coordinately shift GBM cell glucose metabolism to favor glycolysis via two cellular processes: transcriptional regulation of CLIP3 and facilitating Glucose transporter 3 (GLUT3) trafficking to cellular membranes in GBM cells. Importantly, in combination with IR, glimepiride, an FDA-approved medication used to treat type 2 diabetes mellitus, disrupts GSCs maintenance and suppresses glycolytic activity by restoring CLIP3 function. In addition, combining radiotherapy and glimepiride significantly reduced GBM growth and improved survival in a GBM orthotopic xenograft mouse model.ConclusionsOur data suggest that radioresistant GBM cells exhibit enhanced stemness and glycolytic activity mediated by the Spy1-CLIP3 axis. Thus, glimepiride could be an attractive strategy for overcoming radioresistance and recurrence by rescuing CLIP3 expression.
| S-EPMC8420000 | biostudies-literature
Project description:Structural MR images concomitantly acquired with PET images can provide crucial anatomic information for precise quantitative analysis. However, in the clinical setting, not all the subjects have corresponding MR images. Here, we developed a model to generate structural MR images from amyloid PET using deep generative networks. We applied our model to quantification of cortical amyloid load without structural MR. Methods: We used florbetapir PET and structural MR data from the Alzheimer Disease Neuroimaging Initiative database. The generative network was trained to generate realistic structural MR images from florbetapir PET images. After the training, the model was applied to the quantification of cortical amyloid load. PET images were spatially normalized to the template space using the generated MR, and then SUV ratio (SUVR) of the target regions was measured by predefined regions of interest. A real MR-based quantification was used as the gold standard to measure the accuracy of our approach. Other MR-less methods-a normal PET template-based, a multiatlas PET template-based, and a PET segmentation-based normalization/quantification-were also tested. We compared the performance of quantification methods using generated MR with that of MR-based and MR-less quantification methods. Results: Generated MR images from florbetapir PET showed signal patterns that were visually similar to the real MR. The structural similarity index between real and generated MR was 0.91 ± 0.04. The mean absolute error of SUVR of cortical composite regions estimated by the generated MR-based method was 0.04 ± 0.03, which was significantly smaller than other MR-less methods (0.29 ± 0.12 for the normal PET template, 0.12 ± 0.07 for the multiatlas PET template, and 0.08 ± 0.06 for the PET segmentation-based methods). Bland-Altman plots revealed that the generated MR-based SUVR quantification was the closest to the SUVRs estimated by the real MR-based method. Conclusion: Structural MR images were successfully generated from amyloid PET images using deep generative networks. Generated MR images could be used as templates for accurate and precise amyloid quantification. This generative method might be used to generate multimodal images of various organs for further quantitative analyses.
| S-EPMC6910644 | biostudies-literature
Project description:The goal of this work was to investigate the effects of MRI surface coils on attenuation-corrected PET emission data. The authors studied the cases where either an MRI or a CT scan would be used to provide PET attenuation correction (AC). Combined MR/PET scanners that use the MRI for PET AC (MR-AC) face the challenge of absent surface coils in MR images and thus cannot directly account for attenuation in the coils. Combining MR and PET images could be achieved by transporting the subject on a stereotactically registered table between independent MRI and PET scanners. In this case, conventional PET CT-AC methods could be used. A challenge here is that high atomic number materials within MR coils cause artifacts in CT images and CT based AC is typically not validated for coil materials.The authors evaluated PET artifacts when MR coils were absent from AC data (MR-AC), or when coil attenuation was measured by CT scanning (CT-AC). They scanned PET phantoms with MR surface coils on a clinical PET/CT system and used CT-AC to reconstruct PET data. The authors then omitted the coil from the CT-AC image to mimic the MR-AC scenario. Images were acquired using cylinder and anthropomorphic phantoms. They evaluated and compared the following five scenarios: (1) A uniform cylinder phantom and head coil scanned and reconstructed using CT-AC; (2) similar emission data (with head coil present) were reconstructed without the head coil in the AC data; (3) the same cylinder scanned without the head coil present (reference scan); (4) a PET torso phantom with a full MR torso coil present in both PET and CT; (5) only half of the separable torso coil present in the PET/CT acquisition. The authors also performed analytic simulations of the first three scenarios.Streak artifacts were present in CT images containing MR surface coils due to metal components. These artifacts persisted after the CT images were converted for PET AC. The artifacts were significantly reduced when half of the separable coil was removed during the scan. CT scans tended to over-estimate the linear attenuation coefficient (micro) of the metal components when using conventional methods for converting from CT number to micro(511 keV). Artifacts were visible outside the phantom in some of the PET emission images, corresponding to the MRI coil geometry. However, only subtle artifacts were apparent in the emission images inside the phantoms. On the other hand, the PET emission image quantitative accuracy was significantly affected: the activity was underestimated by 19% when AC did not include the head coil, and overestimated by 28% when the CT-AC included the head coil.The presence of MR coils during PET or PET/CT scanning can cause subtle artifacts and potentially important quantification errors. Alternative CT techniques that mitigate artifacts should be used to improve AC accuracy. When possible, removing segments of an MR coil prior to the PET/CT exam is recommended. Further, MR coils could be redesigned to reduce artifacts by rearranging placement of the most attenuating materials.
| S-EPMC3117895 | biostudies-other
Project description:Combined MR∕PET is a relatively new, hybrid imaging modality. A human MR∕PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR∕PET for brain imaging.The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR∕PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [(11)C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods.For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%.MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR∕PET.
| S-EPMC3477199 | biostudies-other
Project description:Quantification of amyloid load with positron emission tomography can be useful to assess Alzheimer's Disease in-vivo. However, quantification can be affected by the image processing methodology applied. This study's goal was to address how amyloid quantification is influenced by different semi-automatic image processing pipelines. Images were analysed in their Native Space and Standard Space; non-rigid spatial transformation methods based on maximum a posteriori approaches and tissue probability maps (TPM) for regularisation were explored. Furthermore, grey matter tissue segmentations were defined before and after spatial normalisation, and also using a population-based template. Five quantification metrics were analysed: two intensity-based, two volumetric-based, and one multi-parametric feature. Intensity-related metrics were not substantially affected by spatial normalisation and did not significantly depend on the grey matter segmentation method, with an impact similar to that expected from test-retest studies (≤10%). Yet, volumetric and multi-parametric features were sensitive to the image processing methodology, with an overall variability up to 45%. Therefore, the analysis should be carried out in Native Space avoiding non-rigid spatial transformations. For analyses in Standard Space, spatial normalisation regularised by TPM is preferred. Volumetric-based measurements should be done in Native Space, while intensity-based metrics are more robust against differences in image processing pipelines.
| S-EPMC7935288 | biostudies-literature
Project description: Not available
| S-EPMC9660819 | biostudies-literature
Project description:AimThe aim of this study was to evaluate and correct for partial-volume-effects (PVE) on [68Ga]Ga-Pentixafor uptake in atherosclerotic plaques of the carotid arteries, and the impact of ignoring bone in MR-based attenuation correction (MR-AC).MethodsTwenty [68Ga]Ga-pentixafor PET/MR examinations including a high-resolution T2-TSE MR of the neck were included in this study. Carotid plaques located at the carotid bifurcation were delineated and the anatomical information was used for partial-volume-correction (PVC). Mean and max tissue-to-background ratios (TBR) of the [68Ga]Ga-Pentixafor uptake were compared for standard and PVC-PET images. A potential influence of ignoring bone in MR-AC was assessed in a subset of the data reconstructed after incorporating bone into MR-AC and a subsequent comparison of standardized-uptake values (SUV).ResultsIn total, 34 atherosclerotic plaques were identified. Following PVC, mean and max TBR increased by 77 and 95%, respectively, when averaged across lesions. When accounting for bone in the MR-AC, SUV of plaque changed by 0.5%.ConclusionQuantitative readings of [68Ga]Ga-pentixafor uptake in plaques are strongly affected by PVE, which can be reduced by PVC. Including bone information into the MR-AC yielded no clinically relevant effect on tracer quantification.
| S-EPMC8993720 | biostudies-literature
Project description:Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions. We developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each individual tumor-a key factor for future personalized therapeutic paradigms. The model used probability distributions for each sample prediction to quantify uncertainty, and used transductive learning to reduce the overall uncertainty. We compared predictive accuracy and uncertainty of the transductive learning GP model against a standard GP model using leave-one-patient-out cross validation. Additionally, we used a separate dataset containing 24 image-localized biopsies from 7 high-grade glioma patients to validate the model. Predictive uncertainty informed the likelihood of achieving an accurate sample prediction. When stratifying predictions based on uncertainty, we observed substantially higher performance in the group cohort (75% accuracy, n = 95) and amongst sample predictions with the lowest uncertainty (83% accuracy, n = 72) compared to predictions with higher uncertainty (48% accuracy, n = 23), due largely to data interpolation (rather than extrapolation). On the separate validation set, our model achieved 78% accuracy amongst the sample predictions with lowest uncertainty. We present a novel approach to quantify radiogenomics uncertainty to enhance model performance and clinical interpretability. This should help integrate more reliable radiogenomics models for improved medical decision-making.
| S-EPMC7886858 | biostudies-literature
Project description:Despite the high frequency of EGFR genetic alterations in glioblastoma (GBM), EGFR-targeted therapies have not had success in this disease. To improve the likelihood of efficacy, we targeted adult patients with recurrent GBM enriched for EGFR gene amplification, which occurs in approximately half of GBM, with dacomitinib, a second-generation, irreversible epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that penetrates the blood-brain barrier, in a multicenter phase II trial. We retrospectively explored whether previously described EGFR extracellular domain (ECD)-sensitizing mutations in the context of EGFR gene amplification could predict response to dacomitinib, and in a predefined subset of patients, we measured post-treatment intratumoral dacomitinib levels to verify tumor penetration. We found that dacomitinib effectively penetrates contrast-enhancing GBM tumors. Among all 56 treated patients, 8 (14.3%) had a clinical benefit as defined by a duration of treatment of at least 6 months, of whom 5 (8.9%) remained progression free for at least 1 year. Presence of EGFRvIII or EGFR ECD missense mutation was not associated with clinical benefit. We evaluated the pretreatment transcriptome in circulating extracellular vesicles (EVs) by RNA sequencing in a subset of patients and identified a signature that distinguished patients who had durable benefit versus those with rapid progression. While dacomitinib was not effective in most patients with EGFR-amplified GBM, a subset experienced a durable, clinically meaningful benefit. Moreover, EGFRvIII and EGFR ECD mutation status in archival tumors did not predict clinical benefit. RNA signatures in circulating EVs may warrant investigation as biomarkers of dacomitinib efficacy in GBM.
| S-EPMC7446412 | biostudies-literature