A model of cerebral aspergillosis in non-immunosuppressed nursing rats

Central nervous system aspergillosis is an often fatal complication of invasive Aspergillus infection. Relevant disease models are needed to study the pathophysiology of cerebral aspergillosis and to develop novel therapeutic approaches. This study presents a model of central nervous system aspergillosis that mimics important aspects of human disease. Eleven-day-old non-immunosuppressed male Wistar rats were infected by an intracisternal injection of 10μl of a conidial suspension of Aspergillus fumigatus. An inoculum of 7.18log10 colony-forming units (CFU) consistently produced cerebral infection and resulted in death of all animals (n=25) within 3-10days. Median survival time was 3days. Histomorphologically, all animals developed intracerebral abscesses (2-26per brain) containing abundant fungal hyphae and neutrophils. Fungal culture of cortical homogenates yielded maximal growth on day 3 after infection (5.4log10CFU/g, n=15) that declined over time. Galactomannan concentrations in cortical homogenates, assessed as an index for hyphal burden, peaked on days 3-5. Fungal infection spread to peripheral organs in 83% of animals. Fungal burden in lung, liver, spleen and kidney was two orders of magnitude lower than in the brain. The successful establishment of a model of cerebral aspergillosis in a non-immunosuppressed host provides the opportunity to investigate mechanisms of disease and to develop novel treatment regimens for this commonly fatal infectio

A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases

Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests.Peer ReviewedPostprint (published version

Evaluating automated longitudinal tumor measurements for glioblastoma response assessment.

Automated tumor segmentation tools for glioblastoma show promising performance. To apply these tools for automated response assessment, longitudinal segmentation, and tumor measurement, consistency is critical. This study aimed to determine whether BraTumIA and HD-GLIO are suited for this task. We evaluated two segmentation tools with respect to automated response assessment on the single-center retrospective LUMIERE dataset with 80 patients and a total of 502 post-operative time points. Volumetry and automated bi-dimensional measurements were compared with expert measurements following the Response Assessment in Neuro-Oncology (RANO) guidelines. The longitudinal trend agreement between the expert and methods was evaluated, and the RANO progression thresholds were tested against the expert-derived time-to-progression (TTP). The TTP and overall survival (OS) correlation was used to check the progression thresholds. We evaluated the automated detection and influence of non-measurable lesions. The tumor volume trend agreement calculated between segmentation volumes and the expert bi-dimensional measurements was high (HD-GLIO: 81.1%, BraTumIA: 79.7%). BraTumIA achieved the closest match to the expert TTP using the recommended RANO progression threshold. HD-GLIO-derived tumor volumes reached the highest correlation between TTP and OS (0.55). Both tools failed at an accurate lesion count across time. Manual false-positive removal and restricting to a maximum number of measurable lesions had no beneficial effect. Expert supervision and manual corrections are still necessary when applying the tested automated segmentation tools for automated response assessment. The longitudinal consistency of current segmentation tools needs further improvement. Validation of volumetric and bi-dimensional progression thresholds with multi-center studies is required to move toward volumetry-based response assessment

The LUMIERE dataset: Longitudinal Glioblastoma MRI with expert RANO evaluation.

Publicly available Glioblastoma (GBM) datasets predominantly include pre-operative Magnetic Resonance Imaging (MRI) or contain few follow-up images for each patient. Access to fully longitudinal datasets is critical to advance the refinement of treatment response assessment. We release a single-center longitudinal GBM MRI dataset with expert ratings of selected follow-up studies according to the response assessment in neuro-oncology criteria (RANO). The expert rating includes details about the rationale of the ratings. For a subset of patients, we provide pathology information regarding methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter status and isocitrate dehydrogenase 1 (IDH1), as well as the overall survival time. The data includes T1-weighted pre- and post-contrast, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) MRI. Segmentations from state-of-the-art automated segmentation tools, as well as radiomic features, complement the data. Possible applications of this dataset are radiomics research, the development and validation of automated segmentation methods, and studies on response assessment. This collection includes MRI data of 91 GBM patients with a total of 638 study dates and 2487 images

A model of cerebral aspergillosis in non-immunosuppressed nursing rats

Central nervous system aspergillosis is an often fatal complication of invasive Aspergillus infection. Relevant disease models are needed to study the pathophysiology of cerebral aspergillosis and to develop novel therapeutic approaches. This study presents a model of central nervous system aspergillosis that mimics important aspects of human disease. Eleven-day-old non-immunosuppressed male Wistar rats were infected by an intracisternal injection of 10 mul of a conidial suspension of Aspergillus fumigatus. An inoculum of 7.18 log(10) colony-forming units (CFU) consistently produced cerebral infection and resulted in death of all animals (n = 25) within 3-10 days. Median survival time was 3 days. Histomorphologically, all animals developed intracerebral abscesses (2-26 per brain) containing abundant fungal hyphae and neutrophils. Fungal culture of cortical homogenates yielded maximal growth on day 3 after infection (5.4 log(10) CFU/g, n = 15) that declined over time. Galactomannan concentrations in cortical homogenates, assessed as an index for hyphal burden, peaked on days 3-5. Fungal infection spread to peripheral organs in 83% of animals. Fungal burden in lung, liver, spleen and kidney was two orders of magnitude lower than in the brain. The successful establishment of a model of cerebral aspergillosis in a non-immunosuppressed host provides the opportunity to investigate mechanisms of disease and to develop novel treatment regimens for this commonly fatal infection