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Sökning: WFRF:(Robe Pierre A)

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1.
  • Freyschlag, Christian F, et al. (författare)
  • Imaging practice in low-grade gliomas among European specialized centers and proposal for a minimum core of imaging.
  • 2018
  • Ingår i: Journal of Neuro-Oncology. - : Springer Science and Business Media LLC. - 0167-594X .- 1573-7373. ; 139:3, s. 699-711
  • Tidskriftsartikel (refereegranskat)abstract
    • OBJECTIVE: Imaging studies in diffuse low-grade gliomas (DLGG) vary across centers. In order to establish a minimal core of imaging necessary for further investigations and clinical trials in the field of DLGG, we aimed to establish the status quo within specialized European centers.METHODS: An online survey composed of 46 items was sent out to members of the European Low-Grade Glioma Network, the European Association of Neurosurgical Societies, the German Society of Neurosurgery and the Austrian Society of Neurosurgery.RESULTS: A total of 128 fully completed surveys were received and analyzed. Most centers (n = 96, 75%) were academic and half of the centers (n = 64, 50%) adhered to a dedicated treatment program for DLGG. There were national differences regarding the sequences enclosed in MRI imaging and use of PET, however most included T1 (without and with contrast, 100%), T2 (100%) and TIRM or FLAIR (20, 98%). DWI is performed by 80% of centers and 61% of centers regularly performed PWI.CONCLUSION: A minimal core of imaging composed of T1 (w/wo contrast), T2, TIRM/FLAIR, PWI and DWI could be identified. All morphologic images should be obtained in a slice thickness of ≤ 3 mm. No common standard could be obtained regarding advanced MRI protocols and PET.IMPORTANCE OF THE STUDY: We believe that our study makes a significant contribution to the literature because we were able to determine similarities in numerous aspects of LGG imaging. Using the proposed "minimal core of imaging" in clinical routine will facilitate future cooperative studies.
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2.
  • Helland, Ragnhild Holden, et al. (författare)
  • Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.
  • 2023
  • Ingår i: Scientific reports. - 2045-2322. ; 13:1
  • Tidskriftsartikel (refereegranskat)abstract
    • 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.
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