Early termination of ISRCTN45828668, a phase 1/2 prospective, randomized study of Sulfasalazine for the treatment of progressing malignant gliomas in adults

BACKGROUND: Sulfasalazine, a NF-kappaB and x(c)-cystine/glutamate antiport inhibitor, has demonstrated a strong antitumoral potential in preclinical models of malignant gliomas. As it presents an excellent safety profile, we initiated a phase 1/2 clinical study of this anti-inflammatory drug for the treatment of recurrent WHO grade 3 and 4 astrocytic gliomas in adults. METHODS: 10 patients with advanced recurrent anaplastic astrocytoma (n = 2) or glioblastoma (n = 8) aged 32-62 years were recruited prior to the planned interim analysis of the study. Subjects were randomly assigned to daily doses of 1.5, 3, 4.5, or 6 grams of oral sulfasalazine, and treated until clinical or radiological evidence of disease progression or the development of serious or unbearable side effects. Primary endpoints were the evaluation of toxicities according to the CTCAE v.3.0, and the observation of radiological tumor responses based on MacDonald criteria. RESULTS: No clinical response was observed. One tumor remained stable for 2 months with sulfasalazine treatment, at the lowest daily dose of the drug. The median progression-free survival was 32 days. Side effects were common, as all patients developed grade 1-3 adverse events (mean: 7.2/patient), four patients developed grade 4 toxicity. Two patients died while on treatment or shortly after its discontinuation. CONCLUSION: Although the proper influence of sulfasalazine treatment on patient outcome was difficult to ascertain in these debilitated patients with a large tumor burden (median KPS = 50), ISRCTN45828668 was terminated after its interim analysis. This study urges to exert cautiousness in future trials of Sulfasalazine for the treatment of malignant gliomas. TRIAL REGISTRATION: Current Controlled Trials ISRCTN45828668

Dexamethasone inhibits the HSV-tk/ ganciclovir bystander effect in malignant glioma cells

BACKGROUND: HSV-tk/ ganciclovir (GCV) gene therapy has been extensively studied in the setting of brain tumors and largely relies on the bystander effect. Large studies have however failed to demonstrate any significant benefit of this strategy in the treatment of human brain tumors. Since dexamethasone is a frequently used symptomatic treatment for malignant gliomas, its interaction with the bystander effect and the overall efficacy of HSV-TK gene therapy ought to be assessed. METHODS: Stable clones of TK-expressing U87, C6 and LN18 cells were generated and their bystander effect on wild type cells was assessed. The effects of dexamethasone on cell proliferation and sensitivity to ganciclovir were assessed with a thymidine incorporation assay and a MTT test. Gap junction mediated intercellular communication was assessed with microinjections and FACS analysis of calcein transfer. The effect of dexamethasone treatment on the sensitivity of TK-expressing to FAS-dependent apoptosis in the presence or absence of ganciclovir was assessed with an MTT test. Western blot was used to evidence the effect of dexamethasone on the expression of Cx43, CD95, CIAP2 and Bcl(XL). RESULTS: Dexamethasone significantly reduced the bystander effect in TK-expressing C6, LN18 and U87 cells. This inhibition results from a reduction of the gap junction mediated intercellular communication of these cells (GJIC), from an inhibition of their growth and thymidine incorporation and from a modulation of the apoptotic cascade. CONCLUSION: The overall efficacy of HSV-TK gene therapy is adversely affected by dexamethasone co-treatment in vitro. Future HSV-tk/ GCV gene therapy clinical protocols for gliomas should address this interference of corticosteroid treatment

Glioblastoma surgery imaging—reporting and data system: Standardized reporting of tumor volume, location, and resectability based on automated segmentations

Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated or manually delineated. This process is time consuming and subjective. Hence, comparison across cohorts, trials, or registries are subject to assessment bias. In this study, we propose a standardized Glioblastoma Surgery Imaging Reporting and Data System (GSI-RADS) based on an automated method of tumor segmentation that provides standard reports on tumor features that are potentially relevant for glioblastoma surgery. As clinical validation, we determine the agreement in extracted tumor features between the automated method and the current standard of manual segmentations from routine clinical MR scans before treatment. In an observational consecutive cohort of 1596 adult patients with a first time surgery of a glioblastoma from 13 institutions, we segmented gadolinium-enhanced tumor parts both by a human rater and by an automated algorithm. Tumor features were extracted from segmentations of both methods and compared to assess differences, concordance, and equivalence. The laterality, contralateral infiltration, and the laterality indices were in excellent agreement. The native and normalized tumor volumes had excellent agreement, consistency, and equivalence. Multifocality, but not the number of foci, had good agreement and equivalence. The location profiles of cortical and subcortical structures were in excellent agreement. The expected residual tumor volumes and resectability indices had excellent agreement, consistency, and equivalence. Tumor probability maps were in good agreement. In conclusion, automated segmentations are in excellent agreement with manual segmentations and practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard GSI-RADS reports can be generated by open access software

Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task

For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection

Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms

This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVIDIA-net (nv-Net) were trained and tested using manual segmentations from preoperative MRI of glioblastoma (GBM) and low-grade gliomas (LGG) from the BraTS 2021 dataset (1251 in total), in addition to 275 GBM and 205 LGG acquired clinically across 12 hospitals worldwide. Data was split into 80% training, 5% validation, and 15% internal test data. An additional external test-set of 158 GBM and 69 LGG was used to assess generalisability to other hospitals’ data. All models’ median Dice similarity coefficient (DSC) for both test sets were within, or higher than, previously reported human inter-rater agreement (range of 0.74–0.85). For both test sets, nn-Unet achieved the highest DSC (internal = 0.86, external = 0.93) and the lowest Hausdorff distances (10.07, 13.87 mm, respectively) for all tumor classes (p < 0.001). By applying Sparsified training, missing MRI sequences did not statistically affect the performance. nn-Unet achieves accurate segmentations in clinical settings even in the presence of incomplete MRI datasets. This facilitates future clinical adoption of automated glioma segmentation, which could help inform treatment planning and glioma monitoring

Asteroseismology and Interferometry

Asteroseismology provides us with a unique opportunity to improve our understanding of stellar structure and evolution. Recent developments, including the first systematic studies of solar-like pulsators, have boosted the impact of this field of research within Astrophysics and have led to a significant increase in the size of the research community. In the present paper we start by reviewing the basic observational and theoretical properties of classical and solar-like pulsators and present results from some of the most recent and outstanding studies of these stars. We centre our review on those classes of pulsators for which interferometric studies are expected to provide a significant input. We discuss current limitations to asteroseismic studies, including difficulties in mode identification and in the accurate determination of global parameters of pulsating stars, and, after a brief review of those aspects of interferometry that are most relevant in this context, anticipate how interferometric observations may contribute to overcome these limitations. Moreover, we present results of recent pilot studies of pulsating stars involving both asteroseismic and interferometric constraints and look into the future, summarizing ongoing efforts concerning the development of future instruments and satellite missions which are expected to have an impact in this field of research.Comment: Version as published in The Astronomy and Astrophysics Review, Volume 14, Issue 3-4, pp. 217-36

Plants in aquatic ecosystems: current trends and future directions

Aquatic plants fulfil a wide range of ecological roles, and make a substantial contribution to the structure, function and service provision of aquatic ecosystems. Given their well-documented importance in aquatic ecosystems, research into aquatic plants continues to blossom. The 14th International Symposium on Aquatic Plants, held in Edinburgh in September 2015, brought together 120 delegates from 28 countries and six continents. This special issue of Hydrobiologia includes a select number of papers on aspects of aquatic plants, covering a wide range of species, systems and issues. In this paper we present an overview of current trends and future directions in aquatic plant research in the early 21st century. Our understanding of aquatic plant biology, the range of scientific issues being addressed and the range of techniques available to researchers have all arguably never been greater; however, substantial challenges exist to the conservation and management of both aquatic plants and the ecosystems in which they are found. The range of countries and continents represented by conference delegates and authors of papers in the special issue illustrate the global relevance of aquatic plant research in the early 21st century but also the many challenges that this burgeoning scientific discipline must address