| 1. |
- Al Masri, Mohammad, 2001, et al.
(författare)
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The glymphatic system for neurosurgeons: a scoping review
- 2024
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Ingår i: NEUROSURGICAL REVIEW. - 0344-5607 .- 1437-2320. ; 47:1
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Tidskriftsartikel (refereegranskat)abstract
- The discovery of the glymphatic system has revolutionized our understanding of cerebrospinal fluid (CSF) circulation and interstitial waste clearance in the brain. This scoping review aims to synthesize the current literature on the glymphatic system's role in neurosurgical conditions and its potential as a therapeutic target. We conducted a comprehensive search in PubMed and Scopus databases for studies published between January 1, 2012, and October 31, 2023. Studies were selected based on their relevance to neurosurgical conditions and glymphatic function, with both animal and human studies included. Data extraction focused on the methods for quantifying glymphatic function and the main results. A total of 67 articles were included, covering conditions such as idiopathic normal pressure hydrocephalus (iNPH), idiopathic intracranial hypertension (IIH), subarachnoid hemorrhage (SAH), stroke, intracranial tumors, and traumatic brain injury (TBI). Significant glymphatic dysregulation was noted in iNPH and IIH, with evidence of impaired CSF dynamics and delayed clearance. SAH studies indicated glymphatic dysfunction with the potential therapeutic effects of nimodipine and tissue plasminogen activator. In stroke, alterations in glymphatic activity correlated with the extent of edema and neurological recovery. TBI studies highlighted the role of the glymphatic system in post-injury cognitive outcomes. Results indicate that the regulation of aquaporin-4 (AQP4) channels is a critical target for therapeutic intervention. The glymphatic system plays a critical role in the pathophysiology of various neurosurgical conditions, influencing brain edema and CSF dynamics. Targeting the regulation of AQP4 channels presents as a significant therapeutic strategy. Although promising, the translation of these findings into clinical practice requires further human studies. Future research should focus on establishing non-invasive biomarkers for glymphatic function and exploring the long-term effects of glymphatic dysfunction.
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| 2. |
- Alaoui-Ismaili, Adam, et al.
(författare)
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Surgeons' experience of venous risk with CPA surgery.
- 2021
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Ingår i: Neurosurgical review. - : Springer Science and Business Media LLC. - 1437-2320 .- 0344-5607. ; 44:3, s. 1675-1685
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Tidskriftsartikel (refereegranskat)abstract
- The study aims to systematize neurosurgeons' practical knowledge of venous sacrifice as applied to the posterior fossa region and to analyze the collected data to present and preserve relevant experience and expert knowledge for current and future practicing neurosurgeons. The venous structures assessed were the superior petrosal vein (SPV), sigmoid sinus (SS), and the tentorial veins (TV). The survey is constructed to obtain surgeons' idea of assessed risk when sacrificing specific venous structures during posterior fossa surgery. They were asked how they prep for surgery, number of operations conducted, and their basis of knowledge. Collected data were mainly qualitative and analyzed with a mixed-method approach. A mean absolute deviation was calculated measuring rate of disagreement for a given substructure. Consensus existed among the participating surgeons that sacrificing the SPV and the TV was considered safe. Although, the risk of death when occluding major structures like the main trunk of the SPV, one of the SS' and or a total occlusion of all TV yielded high risk of death. The risk of infarction was often too apparent to discredit even with low risk of death among an experienced class of surgeons. Our findings provide an overview of surgical risk associated with venous sacrifice. This will minimize cases where indispensable practical knowledge on safe handling veins in the cerebellopontine angle is either to be lost or taught among few when the neurosurgeons retire. This will lower the disagreement regarding risks and increase the quality of surgical decision-making.
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| 3. |
- Ali, Muhaddisa Barat, 1986, et al.
(författare)
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A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas.
- 2022
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Ingår i: Sensors (Basel, Switzerland). - : MDPI AG. - 1424-8220. ; 22:14
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Tidskriftsartikel (refereegranskat)abstract
- In most deep learning-based brain tumor segmentation methods, training the deep network requires annotated tumor areas. However, accurate tumor annotation puts high demands on medical personnel. The aim of this study is to train a deep network for segmentation by using ellipse box areas surrounding the tumors. In the proposed method, the deep network is trained by using a large number of unannotated tumor images with foreground (FG) and background (BG) ellipse box areas surrounding the tumor and background, and a small number of patients (<20) with annotated tumors. The training is conducted by initial training on two ellipse boxes on unannotated MRIs, followed by refined training on a small number of annotated MRIs. We use a multi-stream U-Net for conducting our experiments, which is an extension of the conventional U-Net. This enables the use of complementary information from multi-modality (e.g., T1, T1ce, T2, and FLAIR) MRIs. To test the feasibility of the proposed approach, experiments and evaluation were conducted on two datasets for glioma segmentation. Segmentation performance on the test sets is then compared with those used on the same network but trained entirely by annotated MRIs. Our experiments show that the proposed method has obtained good tumor segmentation results on the test sets, wherein the dice score on tumor areas is (0.8407, 0.9104), and segmentation accuracy on tumor areas is (83.88%, 88.47%) for the MICCAI BraTS'17 and US datasets, respectively. Comparing the segmented results by using the network trained by all annotated tumors, the drop in the segmentation performance from the proposed approach is (0.0594, 0.0159) in the dice score, and (8.78%, 2.61%) in segmented tumor accuracy for MICCAI and US test sets, which is relatively small. Our case studies have demonstrated that training the network for segmentation by using ellipse box areas in place of all annotated tumors is feasible, and can be considered as an alternative, which is a trade-off between saving medical experts' time annotating tumors and a small drop in segmentation performance.
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| 4. |
- Ali, Muhaddisa Barat, 1986, et al.
(författare)
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A novel federated deep learning scheme for glioma and its subtype classification
- 2023
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Ingår i: Frontiers in Neuroscience. - 1662-4548 .- 1662-453X. ; 17
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Tidskriftsartikel (refereegranskat)abstract
- Background: Deep learning (DL) has shown promising results in molecular-based classification of glioma subtypes from MR images. DL requires a large number of training data for achieving good generalization performance. Since brain tumor datasets are usually small in size, combination of such datasets from different hospitals are needed. Data privacy issue from hospitals often poses a constraint on such a practice. Federated learning (FL) has gained much attention lately as it trains a central DL model without requiring data sharing from different hospitals. Method: We propose a novel 3D FL scheme for glioma and its molecular subtype classification. In the scheme, a slice-based DL classifier, EtFedDyn, is exploited which is an extension of FedDyn, with the key differences on using focal loss cost function to tackle severe class imbalances in the datasets, and on multi-stream network to exploit MRIs in different modalities. By combining EtFedDyn with domain mapping as the pre-processing and 3D scan-based post-processing, the proposed scheme makes 3D brain scan-based classification on datasets from different dataset owners. To examine whether the FL scheme could replace the central learning (CL) one, we then compare the classification performance between the proposed FL and the corresponding CL schemes. Furthermore, detailed empirical-based analysis were also conducted to exam the effect of using domain mapping, 3D scan-based post-processing, different cost functions and different FL schemes. Results: Experiments were done on two case studies: classification of glioma subtypes (IDH mutation and wild-type on TCGA and US datasets in case A) and glioma grades (high/low grade glioma HGG and LGG on MICCAI dataset in case B). The proposed FL scheme has obtained good performance on the test sets (85.46%, 75.56%) for IDH subtypes and (89.28%, 90.72%) for glioma LGG/HGG all averaged on five runs. Comparing with the corresponding CL scheme, the drop in test accuracy from the proposed FL scheme is small (−1.17%, −0.83%), indicating its good potential to replace the CL scheme. Furthermore, the empirically tests have shown that an increased classification test accuracy by applying: domain mapping (0.4%, 1.85%) in case A; focal loss function (1.66%, 3.25%) in case A and (1.19%, 1.85%) in case B; 3D post-processing (2.11%, 2.23%) in case A and (1.81%, 2.39%) in case B and EtFedDyn over FedAvg classifier (1.05%, 1.55%) in case A and (1.23%, 1.81%) in case B with fast convergence, which all contributed to the improvement of overall performance in the proposed FL scheme. Conclusion: The proposed FL scheme is shown to be effective in predicting glioma and its subtypes by using MR images from test sets, with great potential of replacing the conventional CL approaches for training deep networks. This could help hospitals to maintain their data privacy, while using a federated trained classifier with nearly similar performance as that from a centrally trained one. Further detailed experiments have shown that different parts in the proposed 3D FL scheme, such as domain mapping (make datasets more uniform) and post-processing (scan-based classification), are essential.
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| 5. |
- Ali, Muhaddisa Barat, 1986, et al.
(författare)
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Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas
- 2020
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Ingår i: Brain Sciences. - : MDPI AG. - 2076-3425. ; 10:7, s. 1-20
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Tidskriftsartikel (refereegranskat)abstract
- Brain tumors, such as low grade gliomas (LGG), are molecularly classified which require the surgical collection of tissue samples. The pre-surgical or non-operative identification of LGG molecular type could improve patient counseling and treatment decisions. However, radiographic approaches to LGG molecular classification are currently lacking, as clinicians are unable to reliably predict LGG molecular type using magnetic resonance imaging (MRI) studies. Machine learning approaches may improve the prediction of LGG molecular classification through MRI, however, the development of these techniques requires large annotated data sets. Merging clinical data from different hospitals to increase case numbers is needed, but the use of different scanners and settings can affect the results and simply combining them into a large dataset often have a significant negative impact on performance. This calls for efficient domain adaption methods. Despite some previous studies on domain adaptations, mapping MR images from different datasets to a common domain without affecting subtitle molecular-biomarker information has not been reported yet. In this paper, we propose an effective domain adaptation method based on Cycle Generative Adversarial Network (CycleGAN). The dataset is further enlarged by augmenting more MRIs using another GAN approach. Further, to tackle the issue of brain tumor segmentation that requires time and anatomical expertise to put exact boundary around the tumor, we have used a tight bounding box as a strategy. Finally, an efficient deep feature learning method, multi-stream convolutional autoencoder (CAE) and feature fusion, is proposed for the prediction of molecular subtypes (1p/19q-codeletion and IDH mutation). The experiments were conducted on a total of 161 patients consisting of FLAIR and T1 weighted with contrast enhanced (T1ce) MRIs from two different institutions in the USA and France. The proposed scheme is shown to achieve the test accuracy of 74.81% on 1p/19q codeletion and 81.19% on IDH mutation, with marked improvement over the results obtained without domain mapping. This approach is also shown to have comparable performance to several state-of-the-art methods.
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| 6. |
- Ali, Muhaddisa Barat, 1986, et al.
(författare)
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Multi-stream Convolutional Autoencoder and 2D Generative Adversarial Network for Glioma Classification
- 2019
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 11678 LNCS, s. 234-245
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Konferensbidrag (refereegranskat)abstract
- Diagnosis and timely treatment play an important role in preventing brain tumor growth. Deep learning methods have gained much attention lately. Obtaining a large amount of annotated medical data remains a challenging issue. Furthermore, high dimensional features of brain images could lead to over-fitting. In this paper, we address the above issues. Firstly, we propose an architecture for Generative Adversarial Networks to generate good quality synthetic 2D MRIs from multi-modality MRIs (T1 contrast-enhanced, T2, FLAIR). Secondly, we propose a deep learning scheme based on 3-streams of Convolutional Autoencoders (CAEs) followed by sensor information fusion. The rational behind using CAEs is that it may improve glioma classification performance (as comparing with conventional CNNs), since CAEs offer noise robustness and also efficient feature reduction hence possibly reduce the over-fitting. A two-round training strategy is also applied by pre-training on GAN augmented synthetic MRIs followed by refined-training on original MRIs. Experiments on BraTS 2017 dataset have demonstrated the effectiveness of the proposed scheme (test accuracy 92.04%). Comparison with several exiting schemes has provided further support to the proposed scheme.
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| 7. |
- Ali, Muhaddisa Barat, 1986, et al.
(författare)
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Prediction of glioma‑subtypes: comparison of performance on a DL classifier using bounding box areas versus annotated tumors
- 2022
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Ingår i: BioMedical Engineering Online. - : Springer Science and Business Media LLC. - 1475-925X .- 2524-4426. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Background: For brain tumors, identifying the molecular subtypes from magnetic resonance imaging (MRI) isdesirable, but remains a challenging task. Recent machine learning and deep learning (DL) approaches may help theclassification/prediction of tumor subtypes through MRIs. However, most of these methods require annotated datawith ground truth (GT) tumor areas manually drawn by medical experts. The manual annotation is a time consumingprocess with high demand on medical personnel. As an alternative automatic segmentation is often used. However, itdoes not guarantee the quality and could lead to improper or failed segmented boundaries due to differences in MRIacquisition parameters across imaging centers, as segmentation is an ill‑defined problem. Analogous to visual objecttracking and classification, this paper shifts the paradigm by training a classifier using tumor bounding box areas inMR images. The aim of our study is to see whether it is possible to replace GT tumor areas by tumor bounding boxareas (e.g. ellipse shaped boxes) for classification without a significant drop in performance.Method: In patients with diffuse gliomas, training a deep learning classifier for subtype prediction by employ‑ing tumor regions of interest (ROIs) using ellipse bounding box versus manual annotated data. Experiments wereconducted on two datasets (US and TCGA) consisting of multi‑modality MRI scans where the US dataset containedpatients with diffuse low‑grade gliomas (dLGG) exclusively.Results: Prediction rates were obtained on 2 test datasets: 69.86% for 1p/19q codeletion status on US dataset and79.50% for IDH mutation/wild‑type on TCGA dataset. Comparisons with that of using annotated GT tumor data fortraining showed an average of 3.0% degradation (2.92% for 1p/19q codeletion status and 3.23% for IDH genotype).Conclusion: Using tumor ROIs, i.e., ellipse bounding box tumor areas to replace annotated GT tumor areas for train‑ing a deep learning scheme, cause only a modest decline in performance in terms of subtype prediction. With moredata that can be made available, this may be a reasonable trade‑off where decline in performance may be counter‑acted with more data.
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| 8. |
- Antonsson, Malin, 1986, et al.
(författare)
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Post-surgical effects on language in patients with presumed low-grade glioma
- 2018
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Ingår i: Acta Neurologica Scandinavica. - : Hindawi Limited. - 0001-6314. ; 137:5, s. 469-480
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: Low-grade glioma (LGG) is a slow-growing brain tumour often situated in or near areas involved in language and/or cognitive functions. Thus, language impairments due to tumour growth or surgical resection are obvious risks. We aimed to investigate language outcome following surgery in patients with presumed LGG, using a comprehensive and sensitive language assessment. Materials and methods: Thirty-two consecutive patients with presumed LGG were assessed preoperative, early post-operative, and 3 months post-operative using sensitive tests including lexical retrieval, language comprehension and high-level language. The patients’ preoperative language ability was compared with a reference group, but also with performance at post-operative controls. Further, the association between tumour location and language performance pre-and post-operatively was explored. Results: Before surgery, the patients with presumed LGG performed worse on tests of lexical retrieval when compared to a reference group (BNT: LGG-group median 52, Reference-group median 54, P = .002; Animals: LGG-group mean 21.0, Reference-group mean 25, P = 001; Verbs: LGG-group mean 17.3, Reference-group mean 21.4, P = .001). At early post-operative assessment, we observed a decline in all language tests, whereas at 3 months there was only a decline on a single test of lexical retrieval (Animals: preoperative. median 20, post-op median 14, P = .001). The highest proportion of language impairment was found in the group with a tumour in language-eloquent areas at all time-points. Conclusions: Although many patients with a tumour in the left hemisphere deteriorated in their language function directly after surgery, their prognosis for recovery was good.
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| 9. |
- Antonsson, Malin, 1986, et al.
(författare)
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Pre-operative language ability in patients with presumed low-grade glioma
- 2018
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Ingår i: Journal of Neuro-Oncology. - : Springer Science and Business Media LLC. - 0167-594X .- 1573-7373. ; 137:1, s. 93-102
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Tidskriftsartikel (refereegranskat)abstract
- In patients with low-grade glioma (LGG), language deficits are usually only found and investigated after surgery. Deficits may be present before surgery but to date, studies have yielded varying results regarding the extent of this problem and in what language domains deficits may occur. This study therefore aims to explore the language ability of patients who have recently received a presumptive diagnosis of low-grade glioma, and also to see whether they reported any changes in their language ability before receiving treatment. Twenty-three patients were tested using a comprehensive test battery that consisted of standard aphasia tests and tests of lexical retrieval and high-level language functions. The patients were also asked whether they had noticed any change in their use of language or ability to communicate. The test scores were compared to a matched reference group and to clinical norms. The presumed LGG group performed significantly worse than the reference group on two tests of lexical retrieval. Since five patients after surgery were discovered to have a high-grade glioma, a separate analysis excluding them were performed. These analyses revealed comparable results; however one test of word fluency was no longer significant. Individually, the majority exhibited normal or nearly normal language ability and only a few reported subjective changes in language or ability to communicate. This study shows that patients who have been diagnosed with LGG generally show mild or no language deficits on either objective or subjective assessment.
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| 10. |
- Bartek, J., et al.
(författare)
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Clinical Course in Chronic Subdural Hematoma Patients Aged 18-49 Compared to Patients 50 Years and Above: A Multicenter Study and Meta-Analysis
- 2019
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Ingår i: Frontiers in Neurology. - : Frontiers Media SA. - 1664-2295. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Chronic Subdural Hematoma (cSDH) is primarily a disease of elderly, and is rare in patients < 50 years, and this may in part be related to the increased brain atrophy from 50 years of age. This fact may also influence clinical presentation and outcome. The aim of this study was to study the clinical course with emphasis on clinical presentation of cSDH patients in the young (<50 years). Methods: A retrospective review of a population-based cohort of 1,252 patients operated for cSDH from three Scandinavian neurosurgical centers was conducted. The primary end-point was difference in clinical presentation between the patients <50 y/o and the remaining patients (>= 50 y/o group). The secondary end-points were differences in perioperative morbidity, recurrence and mortality between the two groups. In addition, a meta-analysis was performed comparing clinical patterns of cSDH in the two age groups. Results: Fifty-two patients (4.2%) were younger than 50 years. Younger patients were more likely to present with headache (86.5% vs. 37.9%, p < 0.001) and vomiting (25% vs. 5.2%, p < 0.001) than the patients >= 50 y/o, while the >= 50 y/o group more often presented with limb weakness (17.3% vs. 44.8%, p < 0.001), speech impairment (5.8% vs. 26.2%, p = 0.001) and gait disturbance or falls (23.1% vs. 50.7%, p < 0.001). There was no difference between the two groups in recurrence, overall complication rate and mortality within 90 days. Our meta-analysis confirmed that younger patients are more likely to present with headache (p = 0.015) while the hemispheric symptoms are more likely in patients >= 50 y/o (p < 0.001). Conclusion: Younger patients with cSDH present more often with signs of increased intracranial pressure, while those >= 50 y/o more often present with hemispheric symptoms. No difference exists between the two groups in terms of recurrence, morbidity, and short-term mortality. Knowledge of variations in clinical presentation is important for correct and timely diagnosis in younger cSDH patients.
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