| 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. |
- Helland, Ragnhild Holden, et al.
(författare)
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Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.
- 2023
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Ingår i: Scientific reports. - 2045-2322. ; 13:1
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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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| 3. |
- Hoang-Xuan, Khê, et al.
(författare)
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European Association of Neuro-Oncology (EANO) guidelines for treatment of primary central nervous system lymphoma (PCNSL)
- 2023
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Ingår i: Neuro-Oncology. - : Oxford University Press. - 1522-8517 .- 1523-5866. ; 25:1, s. 37-53
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Tidskriftsartikel (refereegranskat)abstract
- The management of primary central nervous system (PCNSL) is one of the most controversial topics in neuro-oncology because of the complexity of the disease and the limited number of controlled studies available. In 2021, given recent advances and the publication of practice-changing randomized trials, the European Association of Neuro-Oncology (EANO) created a multidisciplinary task force to update the previously published evidence-based guidelines for immunocompetent adult patients with PCNSL and added a section on immunosuppressed patients. The guideline provides consensus considerations and recommendations for the treatment of PCNSL, including intraocular manifestations and specific management of the elderly. The main changes from the previous guideline include strengthened evidence for the consolidation with ASCT in first-line treatment, prospectively assessed chemotherapy combinations for both young and elderly patients, clarification of the role of rituximab even though the data remain inconclusive, of the role of new agents, and the incorporation of immunosuppressed patients and primary ocular lymphoma. The guideline should aid the clinicians in everyday practice and decision making and serve as a basis for future research in the field.
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