| 1. |
- Bouget, D., et al.
(författare)
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Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting
- 2022
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Ingår i: Frontiers in Neurology. - : Frontiers Media SA. - 1664-2295. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16-54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5-15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.
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| 2. |
- Gomez-Cabrero, D, et al.
(författare)
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STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse
- 2019
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Ingår i: Scientific data. - : Springer Science and Business Media LLC. - 2052-4463. ; 6:1, s. 256-
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Tidskriftsartikel (refereegranskat)abstract
- Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.
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| 3. |
- Schumann, G, et al.
(författare)
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Stratified medicine for mental disorders
- 2014
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Ingår i: European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology. - : Elsevier BV. - 1873-7862. ; 24:1, s. 5-50
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Tidskriftsartikel (refereegranskat)
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| 4. |
- Planell, N, et al.
(författare)
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STATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline
- 2021
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Ingår i: Frontiers in genetics. - : Frontiers Media SA. - 1664-8021. ; 12, s. 620453-
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Tidskriftsartikel (refereegranskat)abstract
- Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package.1
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| 5. |
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| 6. |
- Porcheret, Kate, et al.
(författare)
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Investigation of the impact of total sleep deprivation at home on the number of intrusive memories to an analogue trauma
- 2019
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Ingår i: Translational Psychiatry. - : Nature Publishing Group. - 2158-3188. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Sleep enhances the consolidation of memory; however, this property of sleep may be detrimental in situations where memories of an event can lead to psychopathology, such as following a traumatic event. Intrusive memories of trauma are emotional memories that spring to mind involuntarily and are a core feature of post-traumatic stress disorder. Total sleep deprivation in a hospital setting on the first night after an analogue trauma (a trauma film) led to fewer intrusive memories compared to sleep as usual in one study. The current study aimed to test an extension of these findings: sleep deprivation under more naturalistic conditions-at home. Polysomnographic recordings show inconsistent sleep deprivation was achieved at home. Fewer intrusive memories were reported on day 1 after the trauma film in the sleep-deprived condition. On day 2 the opposite was found: more intrusive memories in the sleep-deprived condition. However, no significant differences were found with the removal of two participants with extreme values and no difference was found in the total number of intrusive memories reported in the week following the trauma film. Voluntary memory of the trauma film was found to be slightly impaired in the sleep deprivation condition. In conclusion, compared to our eariler findings using total sleep deprivation in a hospital setting, in the current study the use of inconsistent sleep deprivation at home does not replicate the pattern of results on reducing the number of intrusive memories. Considering the conditions under which sleep deprivation (naturalistic versus hospital) was achieved requires further examination.
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