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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]

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]

Approaches able to counteract, at least temporarily, hypoxia, a well-known factor of resistance to treatment in solid tumors are highly desirable. Herein, we report the use of nanosized zeolite crystals as hyperoxic/hypercapnic gas carriers for glioblastoma. First, the non-toxic profile of nanosized zeolite crystals in living animals (mice, rats and non-human primates) and in various cell types is presented. Second, the ability of the nanosized zeolites to act as a vasoactive agent for a targeted re-oxygenation of the tumor after intravenous injection is shown. As attested by an MRI protocol, the zeolites were able to increase oxygenation and blood volume specifically within the brain tumor whilst no changes in the healthy-non tumoral brain-were observed. The first proof of concept for the use of metal-containing nanosized zeolites as a tool for vectorization of hyperoxic/hypercapnic gases in glioblastoma is revealed. Image 1 • Nanosized zeolites show a great affinity to O 2 and CO 2 for in vivo applications. • Nanozeolites are safe in rodents and non-human primates. • Specific accumulation of nanozeolites in the tumor relative to the healthy brain. • Gas-loaded zeolites induce a targeted increase in blood volume in glioblastoma. [ABSTRACT FROM AUTHOR]