| 1. |
- 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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| 2. |
- Sabelström, H., et al.
(författare)
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Driving Neuronal Differentiation through Reversal of an ERK1/2-miR-124-SOX9 Axis Abrogates Glioblastoma Aggressiveness
- 2019
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Ingår i: Cell Reports. - : Elsevier BV. - 2211-1247. ; 28:8, s. 11-2079
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Tidskriftsartikel (refereegranskat)abstract
- Identifying cellular programs that drive cancers to be stem-like and treatment resistant is critical to improving outcomes in patients. Here, we demonstrate that constitutive extracellular signal-regulated kinase 1/2 (ERK1/2) activation sustains a stem-like state in glioblastoma (GBM), the most common primary malignant brain tumor. Pharmacological inhibition of ERK1/2 activation restores neurogenesis during murine astrocytoma formation, inducing neuronal differentiation in tumorspheres. Constitutive ERK1/2 activation globally regulates miRNA expression in murine and human GBMs, while neuronal differentiation of GBM tumorspheres following the inhibition of ERK1/2 activation requires the functional expression of miR-124 and the depletion of its target gene SOX9. Overexpression of miR124 depletes SOX9 in vivo and promotes a stem-like-to-neuronal transition, with reduced tumorigenicity and increased radiation sensitivity. Providing a rationale for reports demonstrating miR-124-induced abrogation of GBM aggressiveness, we conclude that reversal of an ERK1/2-miR-124-SOX9 axis induces a neuronal phenotype and that enforcing neuronal differentiation represents a therapeutic strategy to improve outcomes in GBM. Sabelström et al. show that the loss of neurogenesis is reversible during neural stem cell-derived glioma formation. Pharmacological inhibition of ERK1/2 globally regulates miRNAs and induces neuronal differentiation, a process that is dependent on the modulation of an miR-124-SOX9 axis in glioblastoma (GBM) cells. The overexpression of miR-124 induces neuronal differentiation that abrogates GBM aggressiveness.
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| 3. |
- Hervey-Jumper, Shawn L, et al.
(författare)
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Interactive Effects of Molecular, Therapeutic, and Patient Factors on Outcome of Diffuse Low-Grade Glioma.
- 2023
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Ingår i: Journal of clinical oncology : official journal of the American Society of Clinical Oncology. - 1527-7755. ; 41:11, s. 2029-2042
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Tidskriftsartikel (refereegranskat)abstract
- In patients with diffuse low-grade glioma (LGG), the extent of surgical tumor resection (EOR) has a controversial role, in part because a randomized clinical trial with different levels of EOR is not feasible.In a 20-year retrospective cohort of 392 patients with IDH-mutant grade 2 glioma, we analyzed the combined effects of volumetric EOR and molecular and clinical factors on overall survival (OS) and progression-free survival by recursive partitioning analysis. The OS results were validated in two external cohorts (n = 365). Propensity score analysis of the combined cohorts (n = 757) was used to mimic a randomized clinical trial with varying levels of EOR.Recursive partitioning analysis identified three survival risk groups. Median OS was shortest in two subsets of patients with astrocytoma: those with postoperative tumor volume (TV) > 4.6 mL and those with preoperative TV > 43.1 mL and postoperative TV ≤ 4.6 mL. Intermediate OS was seen in patients with astrocytoma who had chemotherapy with preoperative TV ≤ 43.1 mL and postoperative TV ≤ 4.6 mL in addition to oligodendroglioma patients with either preoperative TV > 43.1 mL and residual TV ≤ 4.6 mL or postoperative residual volume > 4.6 mL. Longest OS was seen in astrocytoma patients with preoperative TV ≤ 43.1 mL and postoperative TV ≤ 4.6 mL who received no chemotherapy and oligodendroglioma patients with preoperative TV ≤ 43.1 mL and postoperative TV ≤ 4.6 mL. EOR ≥ 75% improved survival outcomes, as shown by propensity score analysis.Across both subtypes of LGG, EOR beginning at 75% improves OS while beginning at 80% improves progression-free survival. Nonetheless, maximal resection with preservation of neurological function remains the treatment goal. Our findings have implications for surgical strategies for LGGs, particularly oligodendroglioma.
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| 4. |
- 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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| 5. |
- 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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| 6. |
- 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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| 7. |
- Persson, Anders I, et al.
(författare)
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Non-stem cell origin for oligodendroglioma
- 2010
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Ingår i: Cancer Cell. - : Elsevier BV. - 1535-6108 .- 1878-3686. ; 18:6, s. 669-682
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Tidskriftsartikel (refereegranskat)abstract
- Malignant astrocytic brain tumors are among the most lethal cancers. Quiescent and therapy-resistant neural stem cell (NSC)-like cells in astrocytomas are likely to contribute to poor outcome. Malignant oligodendroglial brain tumors, in contrast, are therapy sensitive. Using magnetic resonance imaging (MRI) and detailed developmental analyses, we demonstrated that murine oligodendroglioma cells show characteristics of oligodendrocyte progenitor cells (OPCs) and are therapy sensitive, and that OPC rather than NSC markers enriched for tumor formation. MRI of human oligodendroglioma also suggested a white matter (WM) origin, with markers for OPCs rather than NSCs similarly enriching for tumor formation. Our results suggest that oligodendroglioma cells show hallmarks of OPCs, and that a progenitor rather than a NSC origin underlies improved prognosis in patients with this tumor.
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