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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]

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]

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]

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]