| 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. |
- Nibali, L, et al.
(författare)
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Periodontal Furcation Lesions : A Survey of Diagnosis and Management by General Dental Practitioners.
- 2021
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Ingår i: Journal of Clinical Periodontology. - : John Wiley & Sons. - 0303-6979 .- 1600-051X. ; 48:11, s. 1441-1448
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Tidskriftsartikel (refereegranskat)abstract
- AIMS: The aim of this study was to explore general dental practitioners' (GDP) attitude to periodontal furcation involvement (FI).MATERIALS AND METHODS: An online survey focused on diagnosis and management of periodontal FI was circulated to general dental practitioners in 7 different countries.RESULTS: A total of 400 responses were collected. Nearly a fifth of participants reported rarely or never taking 6-point pocket charts. 65.8% of participants had access to a Nabers probe in their practice. When shown clinical pictures and radiographs of FI-involved molars, the majority of participants correctly diagnosed it. Although 47.1% of participants were very/extremely confident in detecting FI, only 8.9% felt very-extremely confident at treating it. Differences in responses were detected according to country and year of qualification, with a trend towards less interest in periodontal diagnosis and treatment in younger generations. Lack of knowledge of management/referral pathways (reported by 22.8%) and lack of correct equipment were considered the biggest barriers to FI management. Most participants (80.9%) were interested in learning more about FI, ideally face-to-face, followed by online tutorials.CONCLUSIONS: Plans should be put in place to improve general dentists' knowledge and ability to manage FI, as this can have a significant public health impact.
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| 4. |
- Zanello, Marc, et al.
(författare)
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Predictors of Epileptic Seizures and Ability to Work in Supratentorial Cavernous Angioma Located Within Eloquent Brain Areas
- 2019
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Ingår i: Neurosurgery. - : Ovid Technologies (Wolters Kluwer Health). - 0148-396X .- 1524-4040. ; 85:4, s. E702-E713
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: The postoperative outcomes and the predictors of seizure control are poorly studied for supratentorial cavernous angiomas (CA) within or close to the eloquent brain area.OBJECTIVE: To assess the predictors of preoperative seizure control, postoperative seizure control, and postoperative ability to work, and the safety of the surgery.METHODS: Multicenter international retrospective cohort analysis of adult patients benefitting from a functional-based surgical resection with intraoperative functional brain mapping for a supratentorial CA within or close to eloquent brain areas.RESULTS: A total of 109 patients (66.1% women; mean age 38.4 ± 12.5 yr), were studied. Age >38 yr (odds ratio [OR], 7.33; 95% confidence interval [CI], 1.53-35.19; P = .013) and time to surgery > 12 mo (OR, 18.21; 95% CI, 1.11-296.55; P = .042) are independent predictors of uncontrolled seizures at the time of surgery. Focal deficit (OR, 10.25; 95% CI, 3.16-33.28; P < .001) is an independent predictor of inability to work at the time of surgery. History of epileptic seizures at the time of surgery (OR, 7.61; 95% CI, 1.67-85.42; P = .003) and partial resection of the CA and/or of the hemosiderin rim (OR, 12.02; 95% CI, 3.01-48.13; P < .001) are independent predictors of uncontrolled seizures postoperatively. Inability to work at the time of surgery (OR, 19.54; 95% CI, 1.90-425.48; P = .050), Karnofsky Performance Status ≤ 70 (OR, 51.20; 95% CI, 1.20-2175.37; P = .039), uncontrolled seizures postoperatively (OR, 105.33; 95% CI, 4.32-2566.27; P = .004), and worsening of cognitive functions postoperatively (OR, 13.71; 95% CI, 1.06-176.66; P = .045) are independent predictors of inability to work postoperatively.CONCLUSION: The functional-based resection using intraoperative functional brain mapping allows safe resection of CA and the peripheral hemosiderin rim located within or close to eloquent brain areas.
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