Search

Background: Brain metastases (BM) are the most frequent malignant brain tumors. The aim of this study was to characterize the tumor microenvironment (TME) of BM and particularly hypoxia and redox state, known to play a role in tumor growth and treatment resistance with multimodal PET and MRI imaging, immunohistochemical and proteomic approaches in a human lung cancer (H2030-BrM3)-derived BM model in rats. Results: First, in vitro studies confirmed that H2030-BrM3 cells respond to hypoxia with increasing expression of HIF-1, HIF-2 and their target genes. Proteomic analyses revealed, among expression changes, proteins associated with metabolism, oxidative stress, metal response and hypoxia signaling in particular in cortical BM. [64Cu][Cu(ATSM)] PET revealed a significant uptake by cortical BM (p < 0.01), while no uptake is observed in striatal BM 23 days after tumor implantation. Pimonidazole, HIF-1α, HIF-2α, CA-IX as well as GFAP, CTR1 and DMT1 immunostainings are positive in both BM. Conclusion: Overall, [64Cu][Cu(ATSM)] imaging and proteomic results showed the presence of hypoxia and protein expression changes linked to hypoxia and oxidative stress in BM, which are more pronounced in cortical BM compared to striatal BM. Moreover, it emphasized the interest of [64Cu][Cu(ATSM)] PET to characterize TME of BM and depict inter-metastasis heterogeneity that could be useful to guide treatments. [ABSTRACT FROM AUTHOR]

Background: The improvement of in vitro assessment of targeted alpha therapy (reproducibility, comparability of experiments...) requires precise evaluation of the dose delivered to the cells. To answer this need, a previous study proposed an innovative dosimetry method based on α‐spectroscopy and a specific deconvolution process to recover the spatial distribution of 212Pb isotopes inside in vitro culture wells. Nevertheless, although promising, the deconvolution method was time consuming and only tested for a simple isotope decay chain. Purpose: The purpose of this work is to propose a new matrix deconvolution method of α spectra based on a constrained‐non‐negative‐maximum‐likelihood decomposition, both faster and offering a greater modelling flexibility, allowing to study independently the kinetics of each of the daughter nuclides of complex decay chains (illustrated here with 223Ra) in in vitro culture wells. Methods: Firstly, the performance of the new method was fully evaluated through Monte Carlo simulations of in vitro irradiations. Different spatial distributions of 212Pb and 223Ra, the corresponding α spectra measured by a silicon detector and the doses delivered to the cells were simulated with Geant4. The deconvolution results were then compared to the simulation results. Secondly, measurements were carried out in culture wells without cells containing 15 kBq of 212Pb or 9.3 kBq of 223Ra, placed above silicon detectors recording α spectra in real time. The matrix deconvolution was then applied to determine the spatial and temporal distribution of all α‐emitting daughters of studied isotopes. Results: The matrix deconvolution was proved to recover the simulated distribution gradients, ensuring simulated doses within 3 % for both tested radionuclides, with errors on dose normally distributed around the reference value (consequently not exhibiting any bias), even in the case of complex decay chains as 223Ra. The experimental study of 212Pb and 223Ra showed highly inhomogeneous distributions and time evolution of the concentration gradients, consistent with the previous study. Furthermore, it highlighted the complex kinetics of 223Ra with different distributions of its α‐emitting daughters (219Rn, 215Po, 215At, 211Bi, 211Po). Conclusions: This study validates a new deconvolution method, fast and flexible, that proved to be accurate and reliable. This method allowed to reveal the complexity of isotopes kinetics in in vitro experiments, especially with complex decay chains. Experimental dosimetry, necessary to improve reliability of in vitro studies in targeted alpha therapy, is demonstrated to be feasible with the proposed method. [ABSTRACT FROM AUTHOR]

Given the constant pressure to increase patient throughput while respecting radiation protection, global body PET image quality (IQ) is not satisfactory in all patients. We first studied the association between IQ and other variables, in particular body habitus, on a digital PET/CT. Second, to improve and homogenize IQ, we evaluated a deep learning PET denoising solution (Subtle PETTM) using convolutional neural networks. We analysed retrospectively in 113 patients visual IQ (by a 5-point Likert score in two readers) and semi-quantitative IQ (by the coefficient of variation in the liver, CVliv) as well as lesion detection and quantification in native and denoised PET. In native PET, visual and semi-quantitative IQ were lower in patients with larger body habitus (p < 0.0001 for both) and in men vs. women (p ≤ 0.03 for CVliv). After PET denoising, visual IQ scores increased and became more homogeneous between patients (4.8 ± 0.3 in denoised vs. 3.6 ± 0.6 in native PET; p < 0.0001). CVliv were lower in denoised PET than in native PET, 6.9 ± 0.9% vs. 12.2 ± 1.6%; p < 0.0001. The slope calculated by linear regression of CVliv according to weight was significantly lower in denoised than in native PET (p = 0.0002), demonstrating more uniform CVliv. Lesion concordance rate between both PET series was 369/371 (99.5%), with two lesions exclusively detected in native PET. SUVmax and SUVpeak of up to the five most intense native PET lesions per patient were lower in denoised PET (p < 0.001), with an average relative bias of −7.7% and −2.8%, respectively. DL-based PET denoising by Subtle PETTM allowed [18F]FDG PET global image quality to be improved and homogenized, while maintaining satisfactory lesion detection and quantification. DL-based denoising may render body habitus adaptive PET protocols unnecessary, and pave the way for the improvement and homogenization of PET modalities. [ABSTRACT FROM AUTHOR]

Acute ischemic stroke results in an ischemic core surrounded by a tissue at risk, named the penumbra, which is potentially salvageable. One way to differentiate the tissues is to measure the hypoxia status. The purpose of the current study is to correlate the abnormal brain tissue volume derived from magnetic resonance‐based imaging of brain oxygen saturation (StO2‐MRI) to the fluorine‐18 fluoromisonidazole ([18F]FMISO) positron emission tomography (PET) volume for hypoxia imaging validation, and to analyze the ability of StO2‐MRI to depict the different hypoxic tissue types in the acute phase of stroke. In a pertinent model of stroke in the rat, the volume of tissue with decreased StO2‐MRI signal and that with increased uptake of [18F]FMISO were equivalent and correlated (r = 0.706; p = 0.015). The values of StO2 in the tissue at risk were significantly greater than those quantified in the core of the lesion, and were less than those for healthy tissue (52.3% ± 2.0%; 43.3% ± 1.9%, and 67.9 ± 1.4%, respectively). A threshold value for StO2 of ≈60% as the cut‐off for the identification of the tissue at risk was calculated. Tissue volumes with reduced StO2‐MRI correlated with the final lesion (r = 0.964, p < 0.0001). The findings show that the StO2‐MRI approach is sensitive for the detection of hypoxia and for the prediction of the final lesion after stroke. Once validated in acute clinical settings, this approach might be used to enhance the stratification of patients for potential therapeutic interventions. [ABSTRACT FROM AUTHOR]

Background: Arc therapy allows for better dose deposition conformation, but the radiotherapy plans (RT plans) are more complex, requiring patient-specific pre-treatment quality assurance (QA). In turn, pre-treatment QA adds to the workload. The objective of this study was to develop a predictive model of Delta4-QA results based on RT-plan complexity indices to reduce QA workload. Methods. Six complexity indices were extracted from 1632 RT VMAT plans. A machine learning (ML) model was developed for classification purpose (two classes: compliance with the QA plan or not). For more complex locations (breast, pelvis and head and neck), innovative deep hybrid learning (DHL) was trained to achieve better performance. Results. For not complex RT plans (with brain and thorax tumor locations), the ML model achieved 100% specificity and 98.9% sensitivity. However, for more complex RT plans, specificity falls to 87%. For these complex RT plans, an innovative QA classification method using DHL was developed and achieved a sensitivity of 100% and a specificity of 97.72%. Conclusions. The ML and DHL models predicted QA results with a high degree of accuracy. Our predictive QA online platform is offering substantial time savings in terms of accelerator occupancy and working time. [ABSTRACT FROM AUTHOR]

Purpose: We investigated whether artificial intelligence (AI)-based denoising halves PET acquisition time in digital PET/CT. Methods: One hundred ninety-five patients referred for [18F]FDG PET/CT were prospectively included. Body PET acquisitions were performed in list mode. Original "PET90" (90 s/bed position) was compared to reconstructed ½-duration PET (45 s/bed position) with and without AI-denoising, "PET45AI and PET45". Denoising was performed by SubtlePET™ using deep convolutional neural networks. Visual global image quality (IQ) 3-point scores and lesion detectability were evaluated. Lesion maximal and peak standardized uptake values using lean body mass (SULmax and SULpeak), metabolic volumes (MV), and liver SULmean were measured, including both standard and EARL1 (European Association of Nuclear Medicine Research Ltd) compliant SUL. Lesion-to-liver SUL ratios (LLR) and liver coefficients of variation (CVliv) were calculated. Results: PET45 showed mediocre IQ (scored poor in 8% and moderate in 68%) and lesion concordance rate with PET90 (88.7%). In PET45AI, IQ scores were similar to PET90 (P = 0.80), good in 92% and moderate in 8% for both. The lesion concordance rate between PET90 and PET45AI was 836/856 (97.7%), with 7 lesions (0.8%) only detected in PET90 and 13 (1.5%) exclusively in PET45AI. Lesion EARL1 SULpeak was not significantly different between both PET (P = 0.09). Lesion standard SULpeak, standard and EARL1 SULmax, LLR and CVliv were lower in PET45AI than in PET90 (P < 0.0001), while lesion MV and liver SULmean were higher (P < 0.0001). Good to excellent intraclass correlation coefficients (ICC) between PET90 and PET45AI were observed for lesion SUL and MV (ICC ≥ 0.97) and for liver SULmean (ICC ≥ 0.87). Conclusion: AI allows [18F]FDG PET duration in digital PET/CT to be halved, while restoring degraded ½-duration PET image quality. Future multicentric studies, including other PET radiopharmaceuticals, are warranted. [ABSTRACT FROM AUTHOR]

Brain metastases (BM) are frequently detected during the follow-up of patients with malignant tumors, particularly in those with advanced disease. Despite a major progress in systemic anti-cancer treatments, the average overall survival of these patients remains limited (6 months from diagnosis). Also, cognitive decline is regularly reported especially in patients treated with whole brain external beam radiotherapy (WBRT), due to the absorbed radiation dose in healthy brain tissue. New targeted therapies, for an earlier and/or more specific treatment of the tumor and its microenvironment, are needed. Radioimmunotherapy (RIT), a combination of a radionuclide to a specific antibody, appears to be a promising tool. Inflammation, which is involved in multiple steps, including the early phase, of BM development is attractive as a relevant target for RIT. This review will focus on the (1) early biomarkers of inflammation in BM pertinent for RIT, (2) state of the art studies on RIT for BM, and (3) the importance of dosimetry to RIT in BM. These two last points will be addressed in comparison to the conventional EBRT treatment, particularly with respect to the balance between tumor control and healthy tissue complications. Finally, because new diagnostic imaging techniques show a potential for the detection of BM at an early stage of the disease, we focus particularly on this therapeutic window. [ABSTRACT FROM AUTHOR]

Background: With a constantly increasing number of diagnostic images performed each year, Artificial Intelligence (AI) denoising methods offer an opportunity to respond to the growing demand. However, it may affect information in the image in an unknown manner. This study quantifies the effect of AI-based denoising on FDG PET textural information in comparison to a convolution with a standard gaussian postfilter (EARL1). Methods: The study was carried out on 113 patients who underwent a digital FDG PET/CT (VEREOS, Philips Healthcare). 101 FDG avid lesions were segmented semi-automatically by a nuclear medicine physician. VOIs in the liver and lung as reference organs were contoured. PET textural features were extracted with pyradiomics. Texture features from AI denoised and EARL1 versus original PET images were compared with a Concordance Correlation Coefficient (CCC). Features with CCC values ≥ 0.85 threshold were considered concordant. Scatter plots of variable pairs with R2 coefficients of the more relevant features were computed. A Wilcoxon signed rank test to compare the absolute values between AI denoised and original images was performed. Results: The ratio of concordant features was 90/104 (86.5%) in AI denoised versus 46/104 (44.2%) with EARL1 denoising. In the reference organs, the concordant ratio for AI and EARL1 denoised images was low, respectively 12/104 (11.5%) and 7/104 (6.7%) in the liver, 26/104 (25%) and 24/104 (23.1%) in the lung. SUVpeak was stable after the application of both algorithms in comparison to SUVmax. Scatter plots of variable pairs showed that AI filtering affected more lower versus high intensity regions unlike EARL1 gaussian post filters, affecting both in a similar way. In lesions, the majority of texture features 79/100 (79%) were significantly (p<0.05) different between AI denoised and original PET images. Conclusions: Applying an AI-based denoising on FDG PET images maintains most of the lesion's texture information in contrast to EARL1-compatible Gaussian filter. Predictive features of a trained model could be thus the same, however with an adapted threshold. Artificial intelligence based denoising in PET is a very promising approach as it adapts the denoising in function of the tissue type, preserving information where it should. [ABSTRACT FROM AUTHOR]

Lung cancer patients frequently develop brain metastases (BM). Despite aggressive treatment including neurosurgery and external-radiotherapy, overall survival remains poor. There is a pressing need to further characterize factors in the microenvironment of BM that may confer resistance to radiotherapy (RT), such as hypoxia. Here, hypoxia was first evaluated in 28 biopsies from patients with non‑small cell lung cancer (NSCLC) BM, using CA-IX immunostaining. Hypoxia characterization (pimonidazole, CA-IX and HIF-1α) was also performed in different preclinical NSCLC BM models induced either by intracerebral injection of tumor cells (H2030-Br3M, H1915) into the cortex and striatum, or intracardial injection of tumor cells (H2030-Br3M). Additionally, [18F]-FMISO-PET and oxygen-saturation-mapping-MRI (SatO2-MRI) were carried out in the intracerebral BM models to further characterize tumor hypoxia and evaluate the potential of Hypoxia-image-guided-RT (HIGRT). The effect of RT on proliferation of BM ([18F]-FLT-PET), tumor volume and overall survival was determined. We showed that hypoxia is a major yet heterogeneous feature of BM from lung cancer both preclinically and clinically. HIGRT, based on hypoxia heterogeneity observed between cortical and striatal metastases in the intracerebrally induced models, showed significant potential for tumor control and animal survival. These results collectively highlight hypoxia as a hallmark of BM from lung cancer and the value of HIGRT in better controlling tumor growth. [ABSTRACT FROM AUTHOR]

Purpose: Targeted alpha therapy (TAT) takes advantage of the short‐range and high‐linear energy transfer of α‐particles and is increasingly used, especially for the treatment of metastatic lesions. Nevertheless, dosimetry of α‐emitters is challenging for the very same reasons, even for in vitro experiments. Assumptions, such as the uniformity of the distribution of radionuclides in the culture medium, are commonly made, which could have a profound impact on dose calculations. In this study we measured the spatial distribution of α‐emitting 212Pb coupled to an anti‐VCAM‐1 antibody (212Pb‐αVCAM‐1) and its evolution over time in the context of in vitro irradiations. Methods: Two experimental setups were implemented without cells to measure α‐particle count rates and energy spectra in culture medium containing 15 kBq of 212Pb‐α‐VCAM‐1. Silicon detectors were placed above and below cell culture dishes for 20 h. One of the dishes had a 2.5‐µm‐thick mylar‐base allowing easy detection of the α‐particles. Monte Carlo simulations were performed to analyze experimental spectra. Experimental setups were modeled and α‐energy spectra were simulated in the silicon detectors for different decay positions in the culture medium. Simulated spectra were then used to deconvolute experimental spectra to determine the spatial distribution of 212Pb‐αVCAM‐1 in the medium. This distribution was finally used to calculate the dose deposition in cell culture experiments. Results: Experimental count rates and energy spectra showed differences in measurements taken at the top and the bottom of dishes and temporal variations that did not follow 212Pb decay. The radionuclide spatial distribution was shown to be composed of a uniform distribution and concentration gradients at the top and the bottom, which were subjected to temporal variations that may be explained by gravity and electrostatic attraction. The absorbed dose in cells calculated from this distribution was compared with the dose expected for a uniform and static distribution and found to be 1.75 times higher, which is highly significant to interpret biological observations. Conclusions: This study demonstrated that accurate dosimetry of α‐emitters requires the experimental determination of radionuclide spatial and temporal distribution and highlighted that in vitro assessment of dose for TAT cannot only rely on a uniform distribution of activity in the culture medium. The reliability and reproducibility of future experiments should benefit from specifically developed dosimetry tools and methods. [ABSTRACT FROM AUTHOR]

The partial pressure in oxygen remains challenging to map in the brain. Two main strategies exist to obtain surrogate measures of tissue oxygenation: the tissue saturation studied by magnetic resonance imaging (StO2-MRI) and the identification of hypoxia by a positron emission tomography (PET) biomarker with 3-[18F]fluoro-1-(2-nitro-1-imidazolyl)-2-propanol ([18F]-FMISO) as the leading radiopharmaceutical. Nonetheless, a formal validation of StO2-MRI against FMISO-PET has not been performed. The objective of our studies was to compare the two approaches in (a) the normal rat brain when the rats were submitted to hypoxemia; (b) animals implanted with four tumour types differentiated by their oxygenation. Rats were submitted to normoxic and hypoxemic conditions. For the brain tumour experiments, U87-MG, U251-MG, 9L and C6 glioma cells were orthotopically inoculated in rats. For both experiments, StO2-MRI and [18F]-FMISO PET were performed sequentially. Under hypoxemia conditions, StO2-MRI revealed a decrease in oxygen saturation in the brain. Nonetheless, [18F]-FMISO PET, pimonidazole immunohistochemistry and molecular biology were insensitive to hypoxia. Within the context of tumours, StO2-MRI was able to detect hypoxia in the hypoxic models, mimicking [18F]-FMISO PET with high sensitivity/specificity. Altogether, our data clearly support that, in brain pathologies, StO2-MRI could be a robust and specific imaging biomarker to assess hypoxia. [ABSTRACT FROM AUTHOR]

The alleviation of hypoxia in glioblastoma with carbogen to improve treatment has met with limited success. Our hypothesis is that the eventual benefits of carbogen depend on the capacity for vasodilation. We examined, with MRI, changes in fractional cerebral blood volume, blood oxygen saturation, and blood oxygenation level dependent signals in response to carbogen. The analyses were performed in two xenograft models of glioma (U87 and U25I) recognized to have different vascular patterns. Carbogen increased fractional cerebral blood volume, blood oxygen saturation, and blood oxygenation level dependent signals in contralateral tissues. In the tumor core and peritumoral regions, changes were dependent on the capacity to vasodilate rather than on resting fractional cerebral blood volume. In the highly vascularised U87 tumor, carbogen induced a greater increase in fractional cerebral blood volume and blood oxygen saturation in comparison to the less vascularized U25I tumor. The blood oxygenation level dependent signal revealed a delayed response in U25I tumors relative to the contralateral tissue. Additionally, we highlight the considerable heterogeneity of fractional cerebral blood volume, blood oxygen saturation, and blood oxygenation level dependent within U25I tumor in which multiple compartments co-exist (tumor core, rim and peritumoral regions). Finally, our study underlines the complexity of the flow/metabolism interactions in different models of glioblastoma. These irregularities should be taken into account in order to palliate intratumoral hypoxia in clinical trials. [ABSTRACT FROM AUTHOR]

Objective: Neuroblastoma has one of the lowest survival rates of all childhood cancers, despite the use of intensive treatment regimens. Preclinical models of neuroblastoma are essential for testing new multimodality protocols, including those that involve radiotherapy (RT). The aim of this study was to develop a robust method for RT planning and tumour response monitoring based on combined MRI and cone-beam CT (CBCT) imaging and to apply it to a widely studied mouse xenograft model of neuroblastoma, SK-N-SH. Methods: As part of a tumour growth inhibition study, SK-N-SH xenografts were generated in BALB/c nu/nu mice. Mice (n = 8) were placed in a printed MR- and CT-compatible plastic cradle, imaged using a 4.7-T MRI scanner and then transferred to a small animal radiation research platform (SARRP) irradiator with on-board CBCT. MRI/CBCT co-registration was performed to enable RT planning using the soft-tissue contrast afforded by MRI prior to delivery of RT (5 Gy). Tumour response was assessed by serial MRI and calliper measurements. Results: SK-N-SH xenografts formed soft, deformable tumours that could not be differentiated from surrounding normal tissues using CBCT. MR images, which allowed clear delineation of tumours, were successfully co-registered with CBCT images, allowing conformal RT to be delivered. MRI measurements of tumour volume 4 days after RT correlated strongly with length of survival time. Conclusion: MRI allowed precision RT of SK-N-SH tumours and provided an accurate means of measuring tumour response. Advances in knowledge: MRI-based RT planning of murine tumours is feasible using an SARRP irradiator. [ABSTRACT FROM AUTHOR]

Purpose: The primary objective of this study was to compare the ability of PET and MRI biomarkers to predict treatment efficacy in a preclinical model of recurrent glioblastoma multiforme. Methods: MRI (anatomical, diffusion, vasculature and oxygenation) and PET ([F]FDG and [F]FLT) parameters were obtained 3 days after the end of treatment and compared with late tumour growth and survival. Results: Early after tumour recurrence, no effect of treatment with temozolomide combined with bevacizumab was observed on tumour volume as assessed by T2-W MRI. At later times, the treatment decreased tumour volume and increased survival. Interestingly, at the earlier time, temozolomide + bevacizumab decreased [F]FLT uptake, cerebral blood volume and oedema. [F]FLT uptake, oedema and cerebral blood volume were correlated with overall survival but [F]FLT uptake had the highest specificity and sensitivity for the early prediction of treatment efficacy. Conclusion: The present investigation in a preclinical model of glioblastoma recurrence underscores the importance of multimodal imaging in the assessment of oedema, tumour vascular status and cell proliferation. Finally, [F]FLT holds the greatest promise for the early assessment of treatment efficacy. These findings may translate clinically in that individualized treatment for recurrent glioma could be prescribed for patients selected after PET/MRI examinations. [ABSTRACT FROM AUTHOR]

Simple Summary: Due to the central role of magnetic resonance Imaging (MRI) in the management of patients with cancer, waiting lists exceed clinically relevant delays. For this reason, many research groups and MRI manufacturers develop algorithms as resampling and denoising models to allow faster acquisition time without deterioration in image quality. Whereas these algorithms are available in all new MRI, it is not clear how they will impact image features as well as the validity of statistical model of radiomics which use deep images characteristics to predict treatment outcome. The aim of this study was to develop resampling and denoising deep learning (DL) models and evaluate their impact on radiomics from post-Gd-T1w-MRI brain images with brain metastases. We show that resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast acquisition loses most of the radiomic-features and invalidates predictive radiomic models, DL models restore these parameters. Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL) solutions to save acquisition time. However, the impact of these algorithms on intensity and texture parameters has been poorly studied. The aim of this study was to evaluate the impact of resampling and denoising DL models on radiomics. Methods: Resampling and denoising DL model was developed on 14,243 T1 brain images from 1.5T-MRI. Radiomics were extracted from 40 brain metastases from 11 patients (2049 images). A total of 104 texture features of DL images were compared to original images with paired t-test, Pearson correlation and concordance-correlation-coefficient (CCC). Results: When two times shorter image acquisition shows strong disparities with the originals concerning the radiomics, with significant differences and loss of correlation of 79.81% and 48.08%, respectively. Interestingly, DL models restore textures with 46.15% of unstable parameters and 25.96% of low CCC and without difference for the first-order intensity parameters. Conclusions: Resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast MRI acquisition loses most of the radiomic features, DL models restore these parameters. [ABSTRACT FROM AUTHOR]

Despite multiple advances in cancer therapies, patients with glioblastoma (GBM) still have a poor prognosis. Numerous glioma models are used not only for the development of innovative therapies but also to optimize conventional ones. Given the significance of hypoxia in drug and radiation resistance and that hypoxia is widely observed among GBM, the establishment of a reliable method to map hypoxia in preclinical human models may contribute to the discovery and translation of future and more targeted therapies. The aim of this study was to compare the hypoxic status of two commonly used human orthotopic glioma models (U87 and U251) developed in rats and studied by noninvasive hypoxia imaging with 3-[18F]fluoro-1-(2-nitro-1-imidazolyl)-2-propanol-micro-positron emission tomography ([18F]-FMISO-μPET). In parallel, because of the relationships between angiogenesis and hypoxia, we used magnetic resonance imaging (MRI), histology, and immunohistochemistry to characterize the tumoral vasculature. Although all tumors were detectable in T2-weighted MRI and 2-deoxy-2-[18F]fluoro- d-glucose-μPET, only the U251 model exhibited [18F]-FMISO uptake. Additionally, the U251 tumors were less densely vascularized than U87 tumors. Our study demonstrates the benefits of noninvasive imaging of hypoxia in preclinical models to define the most reliable one for translation of future therapies to clinic based on the importance of intratumoral oxygen tension for the efficacy of chemotherapy and radiotherapy. [ABSTRACT FROM AUTHOR]

Hypoxia is a common feature of solid tumors, particularly in glioblastoma (GBM), and known to be a poor prognosis factor in GBM patients. The growth of GBM is also associated with a marked inflammation partially characterized by an accumulation of macrophage (MΦ) of the M2 phenotype. However, the transition between M1 MΦ (antitumoral) and M2 MΦ (protumoral) phenotypes is a dynamic process. We made the assumption that oxygen (O2) availability could be a major regulator of this transition and that the intratumoral O2gradient is of importance. We evaluated,in vivo, the impact of hypoxia on MΦ tropism and polarization in two models of human GBM, well differentiated by their degree of hypoxia. MΦ migration in the tumor was more pronounced in the more hypoxic tumor of the two GBM models. In the more hypoxic of the models, we have shown that MΦ migrated at the tumor site only when hypoxia takes place. We also demonstrated that the acquisition of the M2 phenotype was clearly an evolving phenomenon with hypoxia as the major trigger for this transition. In support of thesein vivofinding, M0 but also M1 MΦ cultured in moderate or severe hypoxia displayed a phenotype close to that of M2 MΦ whose phenotype was further reinforced by severe hypoxia. These results highlight the role of hypoxia in the aggressiveness of GBM, in part, by transforming MΦ such that a protumoral activity is expressed. [ABSTRACT FROM PUBLISHER]