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Macošek, Jakub, Bernd Simon, Johanna-Barbara Linse, Pravin Kumar Ankush Jagtap, Sophie L. Winter, Jaelle Foot, Karine Lapouge, et al. "Structure and dynamics of the quaternary hunchback mRNA translation repression complex." Nucleic Acids Research 49, no. 15 (July 30, 2021): 8866–85. http://dx.doi.org/10.1093/nar/gkab635.
Повний текст джерелаАнотація:
Abstract A key regulatory process during Drosophila development is the localized suppression of the hunchback mRNA translation at the posterior, which gives rise to a hunchback gradient governing the formation of the anterior-posterior body axis. This suppression is achieved by a concerted action of Brain Tumour (Brat), Pumilio (Pum) and Nanos. Each protein is necessary for proper Drosophila development. The RNA contacts have been elucidated for the proteins individually in several atomic-resolution structures. However, the interplay of all three proteins during RNA suppression remains a long-standing open question. Here, we characterize the quaternary complex of the RNA-binding domains of Brat, Pum and Nanos with hunchback mRNA by combining NMR spectroscopy, SANS/SAXS, XL/MS with MD simulations and ITC assays. The quaternary hunchback mRNA suppression complex comprising the RNA binding domains is flexible with unoccupied nucleotides functioning as a flexible linker between the Brat and Pum-Nanos moieties of the complex. Moreover, the presence of the Pum-HD/Nanos-ZnF complex has no effect on the equilibrium RNA binding affinity of the Brat RNA binding domain. This is in accordance with previous studies, which showed that Brat can suppress mRNA independently and is distributed uniformly throughout the embryo.
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S, Manimurugan. "Classification of Alzheimer's disease from MRI Images using CNN based Pre-trained VGG-19 Model." Journal of Computational Science and Intelligent Technologies 1, no. 2 (2020): 34–41. http://dx.doi.org/10.53409/mnaa.jcsit20201205.
Повний текст джерелаАнотація:
Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vectormachine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.
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T, Anitha, Charlyn Pushpa Latha G, and Surendra Prasad M. "A Proficient Adaptive K-means based Brain Tumor Segmentation and Detection Using Deep Learning Scheme with PSO." Journal of Computational Science and Intelligent Technologies 1, no. 3 (2020): 9–14. http://dx.doi.org/10.53409/mnaa.jcsit20201302.
Повний текст джерелаАнотація:
Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vector machine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.
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Almajmaie, Layth Kamil Adday, Ahmed Raad Raheem, Wisam Ali Mahmood, and Saad Albawi. "MRI image segmentation using machine learning networks and level set approaches." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (February 1, 2022): 793. http://dx.doi.org/10.11591/ijece.v12i1.pp793-801.
Повний текст джерелаАнотація:
<span>The segmented brain tissues from magnetic resonance images (MRI) always pose substantive challenges to the clinical researcher community, especially while making precise estimation of such tissues. In the recent years, advancements in deep learning techniques, more specifically in fully convolution neural networks (FCN) have yielded path breaking results in segmenting brain tumour tissues with pin-point accuracy and precision, much to the relief of clinical physicians and researchers alike. A new hybrid deep learning architecture combining SegNet and U-Net techniques to segment brain tissue is proposed here. Here, a skip connection of the concerned U-Net network was suitably explored. The results indicated optimal multi-scale information generated from the SegNet, which was further exploited to obtain precise tissue boundaries from the brain images. Further, in order to ensure that the segmentation method performed better in conjunction with precisely delineated contours, the output is incorporated as the level set layer in the deep learning network. The proposed method primarily focused on analysing brain tumor segmentation (BraTS) 2017 and BraTS 2018, dedicated datasets dealing with MRI brain tumour. The results clearly indicate better performance in segmenting brain tumours than existing ones.</span>
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Ravikumar, M., and B. J. Shivaprasad. "Bidirectional ConvLSTMXNet for Brain Tumor Segmentation of MR Images." Tehnički glasnik 15, no. 1 (March 4, 2021): 37–42. http://dx.doi.org/10.31803/tg-20210204162414.
Повний текст джерелаАнотація:
In recent years, deep learning based networks have achieved good performance in brain tumour segmentation of MR Image. Among the existing networks, U-Net has been successfully applied. In this paper, it is propose deep-learning based Bidirectional Convolutional LSTM XNet (BConvLSTMXNet) for segmentation of brain tumor and using GoogLeNet classify tumor & non-tumor. Evaluated on BRATS-2019 data-set and the results are obtained for classification of tumor and non-tumor with Accuracy: 0.91, Precision: 0.95, Recall: 1.00 & F1-Score: 0.92. Similarly for segmentation of brain tumor obtained Accuracy: 0.99, Specificity: 0.98, Sensitivity: 0.91, Precision: 0.91 & F1-Score: 0.88.
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Lokody, Isabel. "BRAF mutation drives rare brain tumour." Nature Reviews Cancer 14, no. 3 (February 24, 2014): 157. http://dx.doi.org/10.1038/nrc3693.
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Berghoff, Anna Sophie, and Matthias Preusser. "BRAF alterations in brain tumours." Current Opinion in Neurology 27, no. 6 (December 2014): 689–96. http://dx.doi.org/10.1097/wco.0000000000000146.
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Lima, Jorge, Jorge Pinheiro, Susana Nunes, Ana Paula Fernandes, Paula Soares, Jose Carlos Machado, Josue Pereira, and Maria Joao Gil da Costa. "TBIO-10. NGS molecular profile of paediatric brain tumours: results from 92 consecutive patients treated at Centro Hospitalar Universitário de São João." Neuro-Oncology 24, Supplement_1 (June 1, 2022): i185. http://dx.doi.org/10.1093/neuonc/noac079.692.
Повний текст джерелаАнотація:
Abstract AIM: Our aim was to progress in bringing molecular medicine to routine clinical practice in the setting of paediatric neuro-oncology. We have implemented a protocol between Ipatimup and Centro Hospitalar Universitário de São João for the rapid and efficient delivery of the molecular portrait of paediatric brain tumours. MATERIAL AND METHODS: We have enrolled 92 patients with the following inclusion criteria: Age 0-18 years; newly diagnosed brain tumour; previously diagnosed brain tumour, whenever it presented as rare, aggressive or refractory disease; availability of tumour material; signed informed consent. Tumour samples were centrally reviewed by expert pathologists and profiled using the Oncomine Childhood Cancer Research Assay. RESULTS: In the 92 tumours that were molecularly profiled, BRAF was the most frequently altered gene, especially in pilocytic astrocytomas, being also detected in other LGG and HGG. Other commonly mutated genes were PIK3CA and FGFR, the former in HGG and the latter in LGG. MYB and RAF1 rearrangements were also found in low grade glial/glioneuronal tumours, while HGG showed a more complex profile, with many cases harbouring multiple alterations in EGFR, PDGFRA, ATRX, H3F3A, HIST1H3B, TP53, among others. A 16-year old patient with CMMR (homozygous mutation in PMS2) developed a glioblastoma that carried nearly 5x the average number of mutations. Among the 8 medulloblastomas, 2 showed mutations in the SHH pathway (1 in PTCH1 and one in SUFU) and 2 in the WNT pathway (1 in CTNNB1 and one in APC). In the remaining cases, one ependymoma presented MYCN amplification, while no alterations were detected in 3 patients. CONCLUSIONS: This study enabled the detailed molecular study of 92 paediatric brain patients, allowing a more robust tumour classification and the identification of actionable alterations. A subset of the patients are already undergoing targeted therapy, mainly using BRAF or MEK inhibitors with generally good improvement.
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Frank, Deborah J., Bruce A. Edgar, and Mark B. Roth. "TheDrosophila melanogastergenebrain tumornegatively regulates cell growth and ribosomal RNA synthesis." Development 129, no. 2 (January 15, 2002): 399–407. http://dx.doi.org/10.1242/dev.129.2.399.
Повний текст джерелаАнотація:
The regulation of ribosome synthesis is likely to play an important role in the regulation of cell growth. Previously, we have shown that the ncl-1 gene in Caenorhabditis elegans functions as an inhibitor of cell growth and ribosome synthesis. We now indicate that the Drosophila melanogaster tumor suppressor brain tumor (brat) is an inhibitor of cell growth and is a functional homolog of the C. elegans gene ncl-1. The brat gene is able to rescue the large nucleolus phenotype of ncl-1 mutants. We also show that brat mutant cells are larger, have larger nucleoli, and have more ribosomal RNA than wild-type cells. Furthermore, brat overexpressing cells contain less ribosomal RNA than control cells. These results suggest that the tumorous phenotype of brat mutants may be due to excess cell growth and ribosome synthesis.
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Sakamoto, Tomohiro, Katsunori Arai, Karen Makishima, and Akira Yamasaki. "BRAF V600E-mutated combined large cell neuroendocrine carcinoma and adenocarcinoma responding to targeted therapy." BMJ Case Reports 14, no. 12 (December 2021): e243295. http://dx.doi.org/10.1136/bcr-2021-243295.
Повний текст джерелаАнотація:
We present a case of combined large cell neuroendocrine carcinoma (LCNEC), harbouring a BRAF V600E mutation, which significantly benefited from BRAF-targeted therapy. A 57-year-old woman was referred to our hospital for headache and vomiting. A head MRI showed a large tumour in her brain, and a whole-body CT revealed a tumour in the hilum of the right lung and mediastinal lymphadenopathies. Both the resected brain tumour and the mediastinal lymph node tissue contained LCNEC. Next-generation sequencing revealed a BRAF V600E mutation, and a combination therapy with dabrafenib and trametinib was initiated. The patient had a good response to treatment. Like non–small cell lung cancer patients, LCNEC patients should undergo multiplex somatic mutation testing.
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Bousselham, Abdelmajid, Omar Bouattane, Mohamed Youssfi, and Abdelhadi Raihani. "Towards Reinforced Brain Tumor Segmentation on MRI Images Based on Temperature Changes on Pathologic Area." International Journal of Biomedical Imaging 2019 (March 3, 2019): 1–18. http://dx.doi.org/10.1155/2019/1758948.
Повний текст джерелаАнотація:
Brain tumor segmentation is the process of separating the tumor from normal brain tissues; in clinical routine, it provides useful information for diagnosis and treatment planning. However, it is still a challenging task due to the irregular form and confusing boundaries of tumors. Tumor cells thermally represent a heat source; their temperature is high compared to normal brain cells. The main aim of the present paper is to demonstrate that thermal information of brain tumors can be used to reduce false positive and false negative results of segmentation performed in MRI images. Pennes bioheat equation was solved numerically using the finite difference method to simulate the temperature distribution in the brain; Gaussian noises of ±2% were added to the simulated temperatures. Canny edge detector was used to detect tumor contours from the calculated thermal map, as the calculated temperature showed a large gradient in tumor contours. The proposed method is compared to Chan–Vese based level set segmentation method applied to T1 contrast-enhanced and Flair MRI images of brains containing tumors with ground truth. The method is tested in four different phantom patients by considering different tumor volumes and locations and 50 synthetic patients taken from BRATS 2012 and BRATS 2013. The obtained results in all patients showed significant improvement using the proposed method compared to segmentation by level set method with an average of 0.8% of the tumor area and 2.48% of healthy tissue was differentiated using thermal images only. We conclude that tumor contours delineation based on tumor temperature changes can be exploited to reinforce and enhance segmentation algorithms in MRI diagnostic.
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Kang, Keiko M., Koushik Muralidharan, Anudeep Yekula, Julia L. Small, Zachary S. Rosh, Pamela S. Jones, Bob S. Carter, and Leonora Balaj. "Blood-Based Detection of BRAF V600E in Gliomas and Brain Tumor Metastasis." Cancers 13, no. 6 (March 11, 2021): 1227. http://dx.doi.org/10.3390/cancers13061227.
Повний текст джерелаАнотація:
Liquid biopsy provides a minimally invasive platform for the detection of tumor-derived information, including hotspot mutations, such as BRAF V600E. In this study, we provide evidence of the technical development of a ddPCR assay for the detection of BRAF V600E mutations in the plasma of patients with glioma or brain metastasis. In a small patient cohort (n = 9, n = 5 BRAF V600E, n = 4 BRAF WT, n = 4 healthy control), we were able to detect the BRAF V600E mutation in the plasma of 4/5 patients with BRAF V600E-tissue confirmed mutant tumors, and none of the BRAF WT tumors. We also provide evidence in two metastatic patients with longitudinal monitoring, where the plasma-based BRAF V600E mutation correlated with clinical disease status. This proof of principle study demonstrates the potential of this assay to serve as an adjunctive tool for the detection, monitoring, and molecular characterization of BRAF mutant gliomas and brain metastasis.
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Schreck, Karisa C., Stuart A. Grossman, and Christine A. Pratilas. "BRAF Mutations and the Utility of RAF and MEK Inhibitors in Primary Brain Tumors." Cancers 11, no. 9 (August 28, 2019): 1262. http://dx.doi.org/10.3390/cancers11091262.
Повний текст джерелаАнотація:
BRAF mutations have been identified as targetable, oncogenic mutations in many cancers. Given the paucity of treatments for primary brain tumors and the poor prognosis associated with high-grade gliomas, BRAF mutations in glioma are of considerable interest. In this review, we present the spectrum of BRAF mutations and fusion alterations present in each class of primary brain tumor based on publicly available databases and publications. We also summarize clinical experience with RAF and MEK inhibitors in patients with primary brain tumors and describe ongoing clinical trials of RAF inhibitors in glioma. Sensitivity to RAF and MEK inhibitors varies among BRAF mutations and between tumor types as only class I BRAF V600 mutations are sensitive to clinically available RAF inhibitors. While class II and III BRAF mutations are found in primary brain tumors, further research is necessary to determine their sensitivity to third-generation RAF inhibitors and/or MEK inhibitors. We recommend that the neuro-oncologist consider using these drugs primarily in the setting of a clinical trial for patients with BRAF-altered glioma in order to advance our knowledge of their efficacy in this patient population.
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Algar, Elizabeth, White Christine, Kathryn Kinross, Molly Buntine, David Jones, Stefan Pfister, Robyn Strong, Nicholas Gottardo, and Jordan Hansford. "PATH-01. MOLECULAR PROFILING OF PAEDIATRIC CENTRAL NERVOUS SYSTEM TUMOURS IN AUSTRALASIA – AN UPDATE ON THE AIM BRAIN AND MNP2.0 PROJECTS." Neuro-Oncology 22, Supplement_3 (December 1, 2020): iii424. http://dx.doi.org/10.1093/neuonc/noaa222.638.
Повний текст джерелаАнотація:
Abstract The Access to Innovative Molecular Profiling for Paediatric Brain Cancers (AIM BRAIN) project is a trial testing the feasibility of clinical implementation of diagnostic methylation and molecular profiling for central nervous system (CNS) tumours in Australia and New Zealand. AIM BRAIN builds on an existing study, MNP2.0, and allows cross-validation of results derived from identical samples in separate laboratories in Melbourne, Australia, and DKFZ, Heidelberg, Germany. Parallel methylation profiling (Illumina 850K EPIC array) from co-enrolled cases has revealed excellent concordance between laboratories with 50/51 cases (98%) yielding identical classification using the DKFZ Molecular Neuropathology 2.0 Classifier v11b4. 77/91 (85%) of AIM BRAIN cases classified concordantly by methylation array when compared to their diagnostic histopathology. Of these 77 cases, 16 had classifications below a threshold of 0.90, however still classified correctly. In 14 discordant cases either the histopathology was not well defined, not represented on the classifier, or a very low classification score was obtained. Molecular profiling through MNP2.0 identified 49/167 (29.3%) tumours with gene fusions including BRAF-KIAA1549 (n=29), RELA-C1lorf95 (n=5) and 15 rare or novel fusions. BRAF-KIAA1549 was almost exclusively associated with pilocytic astrocytoma (28/29) and RELA-C1lorf95 with ependymoma. Six pathogenic germline mutations were identified in TP53 (n=2), BRCA1, NF1, LZTR1 and ATM. The incidence of germline predisposition was low (4%) and sex biased towards females (5F:1M), (p<0.08). Our findings confirm methylation profiling as a robust platform for classifying CNS tumours with potential to reveal new CNS tumour entities when combined with molecular profiling.
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Dhir, Nikita, Sheila Chandrahas, Chibuzo O’Suoji, and Mohamad Al-Rahawan. "EXTH-16. TREATMENT OF THREE DIFFERENT BRAF-V600E POSITIVE BRAIN TUMORS WITH VEMURAFENIB AND DABRAFENIB/TRAMETINIB: A CASE SERIES." Neuro-Oncology 22, Supplement_2 (November 2020): ii90. http://dx.doi.org/10.1093/neuonc/noaa215.370.
Повний текст джерелаАнотація:
Abstract BACKGROUND The BRAF-V600E gene is a protein kinase involved in regulation of the mitogen activated protein kinase pathway (MAPK/MEK) and downstream extracellular receptor kinase (ERK). The BRAF-V600E mutation has a significant role in the progression of pediatric brain tumors. 85% of pediatric CNS tumors express the BRAF mutation. Thus, BRAF targeted therapy in pediatric CNS malignancies has potential to become the standard of care for tumors expressing this mutation. OBJECTIVE Current pediatric CNS brain tumor treatment focuses on chemotherapy and radiation, causing significant toxic side effects for patients. The significance of this case series lies in relaying our experience using targeted therapy in BRAF-V600E positive CNS pediatric brain tumors. METHODS We followed the disease course, progression, and treatment of three pediatric patients with three different CNS tumors. Each of these individuals was treated with surgical resection, chemotherapy, and/or radiation as per standard protocol. When that modality failed to reduce tumor progression, we found that each of their different tumors was BRAF-V600E positive and they were all started on targeted therapy. DISCUSSION Vemurafenib, Dabrafenib, and Trametinib are BRAF-V600E/MEK inhibitors that were initially used to treat melanomas. However, more research has shown that various pediatric CNS tumors are BRAF-V600 positive. Therapy with these BRAF inhibitors has been shown to slow tumor progression, but toxicity can be severe. This case series shows one patient with successful tumor regression, one patient with prolonged disease stabilization, and one patient with initial response but subsequent progression and ultimate death. It has been shown that using BRAF inhibitors in lower grade CNS tumors are more effective than higher grade CNS tumors. CONCLUSION The success of Vemurafenib and Dabrafenib/Trametinib in causing pediatric CNS tumor regression is promising, but further studies are needed to solidify their role in pediatric CNS cancers.
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Ly, David, Hilary P. Bagshaw, Christopher J. Anker, Jonathan D. Tward, Kenneth F. Grossmann, Randy L. Jensen, and Dennis C. Shrieve. "Local control after stereotactic radiosurgery for brain metastases in patients with melanoma with and without BRAF mutation and treatment." Journal of Neurosurgery 123, no. 2 (August 2015): 395–401. http://dx.doi.org/10.3171/2014.9.jns141425.
Повний текст джерелаАнотація:
OBJECT BRAF inhibitors improve progression-free and overall survival in patients with metastatic melanoma. Brain metastases are common, and stereotactic radiosurgery (SRS) has been used, resulting in excellent local control. Because BRAF inhibitors are associated with intracranial responses, the authors hypothesized that BRAF inhibitors would improve local control in patients with melanoma who are receiving SRS for brain metastases. METHODS The authors retrospectively identified patients with metastatic melanoma who had been tested for BRAF mutation and treated with SRS for brain metastases. Patients with previous resection, multiple brain metastases, or multiple courses of SRS were eligible. SRS was delivered in a single fraction to a median dose of 2000 cGy. Patients with a BRAF mutation were treated with a BRAF inhibitor on the basis of physician preference. RESULTS The authors identified 52 patients who were treated in 82 treatment sessions for 185 brain metastases and 13 tumor beds. At a median follow-up of 10.5 months, the 1-year local control rate was 69.2%. At 1 year, the local control rate for brain metastases in patients with BRAF mutation with BRAF treatment was 85.0%, and the local control rate for brain metastases in those without BRAF treatment was 51.5% (p = 0.0077). The rates of distant brain failure, freedom from whole-brain radiation, and overall survival were not different on the basis of BRAF mutation status or inhibitor therapy. The number of new intratumoral hemorrhages after SRS was increased significantly in patients with BRAF treatment. CONCLUSIONS Treatment with BRAF inhibitors was associated with improved local control after SRS in patients with melanoma and brain metastases. An increased number of intratumoral hemorrhages was associated with BRAF inhibitor therapy.
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Ridler, Charlotte. "BRAF mutation promotes epilepsy in paediatric brain tumours." Nature Reviews Neurology 14, no. 11 (September 26, 2018): 632. http://dx.doi.org/10.1038/s41582-018-0087-7.
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Zhao, Liya, and Kebin Jia. "Multiscale CNNs for Brain Tumor Segmentation and Diagnosis." Computational and Mathematical Methods in Medicine 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/8356294.
Повний текст джерелаАнотація:
Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs). Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness.
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Bernstein, Aaron, Oliver D. Mrowczynski, Amrit Greene, Sandra Ryan, Catherine Chung, Brad E. Zacharia, and Michael Glantz. "Dual BRAF/MEK therapy in BRAF V600E-mutated primary brain tumors: a case series showing dramatic clinical and radiographic responses and a reduction in cutaneous toxicity." Journal of Neurosurgery 133, no. 6 (December 2020): 1704–9. http://dx.doi.org/10.3171/2019.8.jns19643.
Повний текст джерелаАнотація:
OBJECTIVEBRAF V600E is a common oncogenic driver in a variety of primary brain tumors. Dual inhibitor therapy using dabrafenib (a selective oral inhibitor of several mutated forms of BRAF kinase) and trametinib (a reversible inhibitor of MEK1 and MEK2) has been used successfully for treatment of metastatic melanoma, anaplastic thyroid cancer, and other tumor types, but has been reported in only a few patients with primary brain tumors and none with pleomorphic xanthoastrocytoma. Here, the authors report on the substantial clinical response and reduction in cutaneous toxicity in a case series of BRAF V600E primary brain cancers treated with dual BRAF/MEK inhibitor therapy.METHODSThe authors treated 4 BRAF V600E patients, each with a different type of primary brain tumor (pilocytic astrocytoma, papillary craniopharyngioma, ganglioglioma, and pleomorphic xanthoastrocytoma) with the combination of dabrafenib and trametinib.RESULTSThe patients with pilocytic astrocytoma, pleomorphic xanthoastrocytoma, and papillary craniopharyngioma experienced near-complete radiographic and complete clinical responses after 8 weeks of therapy. A substantial partial response (by RANO [Response Assessment in Neuro-Oncology] criteria) was observed in the patient with ganglioglioma. The patient with craniopharyngioma developed dramatic, diffuse verrucal keratosis within 2 weeks of starting dabrafenib. This completely resolved within 2 weeks of adding trametinib.CONCLUSIONSDual BRAF/MEK inhibitor therapy represents an exciting treatment option for patients with BRAF V600E primary brain tumors. In addition to greater efficacy than single-agent dabrafenib, this combination has the potential to mitigate cutaneous toxicity, one of the most common and concerning BRAF inhibitor–related adverse events.
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Hajto, Tibor. "Standardized Plant Immunomodulator Increases the Effect of MEK and BRAF Inhibitors with Clinical Benefit. Case Report of a Patient with Carcinoma in Biliary Duct." Biomedical Research and Clinical Reviews 1, no. 5 (December 4, 2020): 01–05. http://dx.doi.org/10.31579/2692-9406/033.
Повний текст джерелаАнотація:
Background: Targeting hyperactive mitogen-activated protein kinase (MAPK) signaling cascade has proven to be an effective treatment for a variety of different cancers. Using an important member of this cascade, namely MEK (mitogen-activated extracellular signal regulation kinase) inhibitors, the clinical responses are often transient and complete remission is rarely observed. It was shown that growth factor receptor signaling pathway inhibitors can increase the immune sensitivity of tumor cells but they can’t activate the down regulated immune effectors. Consequently, the combination of MAPK cascade signaling pathway inhibitors and the immune effectors activating immunomodulators may be a promising new strategy. Material and Methods: In a now 59 years old patient with inoperable (BRAF-mutant) low differentiated adenocarcinoma of biliary ducts after 30GY radiotherapy and two cycles (Gemcitabin+ Cisplatin) chemotherapy a rapid progression of lung, liver and brain metastases were by CT and MR established. A treatment was tarted with BRAF+MEK inhibitors (2x150 mg dabrafenib and 1 x 2 mg trametinib). These inhibitors were combined with daily 45 mg/kg rice bran arabinoxylan concentrate (using Biobran/MGN-3) which was shown to be a pathogenic associated molecular pattern (PAMP)-like molecule and can stimulate the type-1 innate immune cells against tumor cells. Results: After the chemotherapy and before the start of second-line treatment, the patient had a nearly terminal state of her rapidly progressive disease. Eight months after the combination of MEK / BRAF inhibitor and immunomodulator therapy nearly complete remissions of all metastases were established in CT and MR. Conclusion: This case report may support a hypothesis that MEK/BRAF inhibitors and type-1 immune cells activating immunomodulators together may synergistically inhibit the tumor growth. Further clinical investigations are necessary to clarify this question.
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Liu, Dongwei, Ning Sheng, Tao He, Wei Wang, Jianxia Zhang, and Jianxin Zhang. "SGEResU-Net for brain tumor segmentation." Mathematical Biosciences and Engineering 19, no. 6 (2022): 5576–90. http://dx.doi.org/10.3934/mbe.2022261.
Повний текст джерелаАнотація:
<abstract><p>The precise segmentation of tumor regions plays a pivotal role in the diagnosis and treatment of brain tumors. However, due to the variable location, size, and shape of brain tumors, the automatic segmentation of brain tumors is a relatively challenging application. Recently, U-Net related methods, which largely improve the segmentation accuracy of brain tumors, have become the mainstream of this task. Following merits of the 3D U-Net architecture, this work constructs a novel 3D U-Net model called SGEResU-Net to segment brain tumors. SGEResU-Net simultaneously embeds residual blocks and spatial group-wise enhance (SGE) attention blocks into a single 3D U-Net architecture, in which SGE attention blocks are employed to enhance the feature learning of semantic regions and reduce possible noise and interference with almost no extra parameters. Besides, the self-ensemble module is also utilized to improve the segmentation accuracy of brain tumors. Evaluation experiments on the Brain Tumor Segmentation (BraTS) Challenge 2020 and 2021 benchmarks demonstrate the effectiveness of the proposed SGEResU-Net for this medical application. Moreover, it achieves DSC values of 83.31, 91.64 and 86.85%, as well as Hausdorff distances (95%) of 19.278, 5.945 and 7.567 for the enhancing tumor, whole tumor, and tumor core on BraTS 2021 dataset, respectively.</p> </abstract>
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Menze, Bjoern H., Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)." IEEE Transactions on Medical Imaging 34, no. 10 (October 2015): 1993–2024. http://dx.doi.org/10.1109/tmi.2014.2377694.
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Behling, Felix, and Jens Schittenhelm. "Oncogenic BRAF Alterations and Their Role in Brain Tumors." Cancers 11, no. 6 (June 8, 2019): 794. http://dx.doi.org/10.3390/cancers11060794.
Повний текст джерелаАнотація:
Alterations of the v-raf murine sarcoma viral oncogene homolog B (BRAF) have been extensively studied in several tumor entities and are known to drive cell growth in several tumor entities. Effective targeted therapies with mutation-specific small molecule inhibitors have been developed and established for metastasized malignant melanoma. The BRAF V600E mutation and KIAA1549-BRAF fusion are alterations found in several brain tumors and show a distinct prognostic impact in some entities. Besides the diagnostic significance for the classification of central nervous system tumors, these alterations present possible therapy targets that may be exploitable for oncological treatments, as it has been established for malignant melanomas. In this review the different central nervous system tumors harboring BRAF alterations are presented and the diagnostic significance, prognostic role, and therapeutic potential are discussed.
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Behera, Tapas Ranjan, Jung Min Song, Donald Matthew Eicher, Brian Gastman, Daniel H. Farkas, and Pauline Funchain. "Circulating tumor DNA mutation as a prognostic marker in melanoma with brain metastasis." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e21560-e21560. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e21560.
Повний текст джерелаАнотація:
e21560 Background: Prognosis in melanoma with brain metastasis is poor with a median survival of four months and a one-year survival rate of 10–20%. There is an unmet need for surveillance methods that can supplement imaging at regular intervals. Serial analysis of circulating tumor DNA (ctDNA) may aid surveillance and prognostication. A PCR-based, “specimen in/result out” testing device was employed to detect BRAF variants in plasma-derived ctDNA to evaluate the utility of rapid biomarker detection in the management of melanoma with brain metastasis. Methods: Serial blood samples from patients diagnosed with BRAF mutation-positive metastatic melanoma were collected at regular intervals. We employed a real-time PCR-based automated mutation detection system (Idylla; Biocartis, Belgium) to interrogate the plasma samples. The ctDNA mutation detection trend was analyzed relative to disease progression. Results: 39 patients with BRAF mutation positive melanoma were enrolled. 29 patients were treated in the metastatic setting, 10 in the adjuvant setting. 18 of the 29 patients with metastatic disease (62%) had brain metastases. Circulating BRAF mutation was detected in 17 of the 29 (59%) patients with metastatic disease, and was not detected in any patients treated adjuvantly. In the group with metastatic disease, this circulating biomarker changed from undetectable to detectable in eight (28%) and detectable to undetectable in three (10%). No change in circulating mutation status occurred in 18 (62%). In the eight patients who had an initial negative test that later became positive, seven (87%) had brain metastases. In three patients, ctDNA mutation detection occurred before the diagnosis of brain metastases on imaging, with a median lead time of five weeks (range, 3-12 weeks). In one patient with de novo metastatic disease admitted to the ICU, tissue was unavailable for BRAF testing but plasma was found to be positive for ctDNA BRAF detection. BRAF/MEK targeted therapy resulted in a sustained objective response. Five of six (83%) patients that had persistent ctDNA positivity had brain metastases. Among patients with brain metastases, median overall survival (mOS) of patients demonstrating >50% test positivity was numerically longer than those with <50% positivity (mOS 12.3 vs 53.5 months; p = 0.133). Conclusions: Plasma-based, rapid ctDNA testing may be useful as an aid in detecting progression and gauging prognosis in patients with melanoma treated in the metastatic setting. The dynamics of ctDNA test positivity may indicate a need for more urgent imaging, particularly of the brain. Blood-based, semi-automated ctDNA detection may serve as an attractive adjunct to scheduled imaging surveillance in melanoma.
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Shoaib, Muhammad, and Nasir Sayed. "YOLO Object Detector and Inception-V3 Convolutional Neural Network for Improved Brain Tumor Segmentation." Traitement du Signal 39, no. 1 (February 28, 2022): 371–80. http://dx.doi.org/10.18280/ts.390139.
Повний текст джерелаАнотація:
For a range of medical analysis applications, the localization of brain tumors and brain tumor segmentation from magnetic resonance imaging (MRI) are challenging yet critical jobs. Many recent studies have included four modalities: i.e., T1, T1c, T2 & FLAIR, it is because every tumor causing area can be detailed examined by each of these brain imaging modalities. Although the BRATS 2018 datasets give impressive segmentation results, the results are still more complex and need more testing and more training. That’s why this paper recommends operated pre-processing strategies on a small part of an image except for a full image because that’s how an effective and flexible segmented system of brain tumor can be created. In the first phase, an ensemble classification model is developed using different classifiers such as decision tree, SVM, KNN etc. to classify an image into the tumor and non-tumor class by using the strategy of using a small section can completely solve the over-fitting problems and reduces the processing time in a model of YOLO object detector using inceptionv3 CNN features. The second stage is to recommend an efficient and basic Cascade CNN (C-ConvNet/C-CNN), as we deal with a tiny segment of the brain image in each and every slice. In two independent ways, the Cascade-Convolutional Neutral Network model extracts learnable features. On the dataset of BRATS 2018, BRATS 2019 and BRATS 2020, the extensive experimental task has been carried out on the proposed tumor localization framework.: the IoU score achieved of three datasets are 97%, 98% and 100%. Other qualitative evaluations & quantitative evaluations are discussed and presented in the manuscript in detail.
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K. Petritsch, Claudia, Anne Marie Barrette, and Jong-Whi Park. "LGG-05. GENERATION OF NOVEL MOUSE MODELS FOR BRAF V600E MUTANT GLIOMAGENESIS TO GAIN MECHANISTIC INSIGHTS INTO TUMOR FORMATION AND PROGRESSION." Neuro-Oncology 23, Supplement_1 (June 1, 2021): i32. http://dx.doi.org/10.1093/neuonc/noab090.129.
Повний текст джерелаАнотація:
Abstract Background The BRAF V600E mutation occurs in ~ twenty percent of histologically diverse pediatric gliomas and is the second most common mutation in pediatric low-grade gliomas (LGG). BRAF V600E expression in LGG with balanced CDKN2A is associated with a higher rate for progression than for BRAF V600E wildtype tumors, and despite adjuvant therapy, consisting of resection, radiation and chemotherapy. Progression invariably occurs in BRAF V600E mutant CDKN2A deleted gliomas, marking a high-risk group. Here, we aim to overcome the lack BRAF V600E mutant glioma models that allow for studies of stem and progenitor cells and the immune system ability to understand progression. Methods We develop novel immunocompetent, stem and progenitor cell-based mouse models for BRAF mutant gliomas, including genetically engineered mouse models (GEMMs), orthotopic glioma models derived from gliomas in GEMMs as well as in vitro models of those tumors. BRAF mutant mouse brains and cells were analyzed by immunofluorescence staining, flow cytometry, mass cytometry and RNA sequencing. Results Ongoing model development studies indicate that BRAF V600E mutant gliomas in murine brain exhibit very similar neuroanatomical preferences to human gliomas. The BRAF V600E mutation exacerbates the heterogenous cell cycling pattern of normal neural stem and progenitors and expands a symmetrically dividing progenitor population. Cellular plasticity rather than cellular lineage hierarchy drives the generation of a therapy resistant stem cell pool. Transcriptomic analyses of neuroglial stem cells with induced BRAF V600E expression provide insights into mechanisms for neoplastic transformation and progression. Conclusion Analyses of two independent BRAF V600E mutant mouse models provide novel insights into the role for tumor intrinsic factors, such as plasticity and stemness, and the tumor microenvironment in progression.
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Takahashi, Satoshi, Masamichi Takahashi, Manabu Kinoshita, Mototaka Miyake, Risa Kawaguchi, Naoki Shinojima, Akitake Mukasa, et al. "NIMG-29. DEVELOPING AUTOMATIC SEGMENTATION METHOD FOR BRAIN TUMOR MR IMAGES THAT CAN BE USED AT MULTIPLE FACILITIES." Neuro-Oncology 22, Supplement_2 (November 2020): ii153—ii154. http://dx.doi.org/10.1093/neuonc/noaa215.642.
Повний текст джерелаАнотація:
Abstract BACKGROUND Manual segmentation of brain tumor images from a large volume of MR images generated in clinical routines is difficult and time-consuming. Hence, it is imperative to develop a machine learning model for automated segmentation of brain tumor images. PURPOSE Machine learning models for automated MR image segmentation of gliomas may be useful. However, the image differences among facilities cause performance degradation and impede successful automatic segmentation. In this study, we proposed a method to solve this issue. METHODS We used the data from the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) and the Japanese cohort (JC) datasets collected from 10 facilities. Three models for tumor segmentation were developed. The BraTS model was trained on the BraTS dataset, and the JC model was trained on the JC dataset; whereas, the Fine-tuning model was a fine-tuned BraTS model using the JC dataset. RESULTS MR images of 544 patients were obtained for the JC dataset. Half of the JC dataset was used for independent testing. The Dice coefficient score of the JC model for the JC dataset was 0.779± 0.137, whereas that of the BraTS model was remarkably lower (0.717 ± 0.207). The mean of the Fine-tuning models for the JC dataset was 0.769 ± 0.138. There was a significant difference between the BraTS and JC models (P < 0.0001) and the BraTS and Fine-tuning models (P = 0.002); however, no significant difference was observed between the JC and Fine-tuning models (P = 0.673). CONCLUSIONS Application of the BraTS model to heterogeneous datasets can significantly reduce its performance; however, fine-tuning can solve this issue. Since our fine-tuning method only requires less than 20 cases, this methodology is particularly useful for a facility where there are a few glioma cases.
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Saeed, Muhammad Usman, Ghulam Ali, Wang Bin, Sultan H. Almotiri, Mohammed A. AlGhamdi, Arfan Ali Nagra, Khalid Masood, and Riaz ul Amin. "RMU-Net: A Novel Residual Mobile U-Net Model for Brain Tumor Segmentation from MR Images." Electronics 10, no. 16 (August 14, 2021): 1962. http://dx.doi.org/10.3390/electronics10161962.
Повний текст джерелаАнотація:
The most aggressive form of brain tumor is gliomas, which leads to concise life when high grade. The early detection of glioma is important to save the life of patients. MRI is a commonly used approach for brain tumors evaluation. However, the massive amount of data provided by MRI prevents manual segmentation in a reasonable time, restricting the use of accurate quantitative measurements in clinical practice. An automatic and reliable method is required that can segment tumors accurately. To achieve end-to-end brain tumor segmentation, a hybrid deep learning model RMU-Net is proposed. The architecture of MobileNetV2 is modified by adding residual blocks to learn in-depth features. This modified Mobile Net V2 is used as an encoder in the proposed network, and upsampling layers of U-Net are used as the decoder part. The proposed model has been validated on BraTS 2020, BraTS 2019, and BraTS 2018 datasets. The RMU-Net achieved the dice coefficient scores for WT, TC, and ET of 91.35%, 88.13%, and 83.26% on the BraTS 2020 dataset, 91.76%, 91.23%, and 83.19% on the BraTS 2019 dataset, and 90.80%, 86.75%, and 79.36% on the BraTS 2018 dataset, respectively. The performance of the proposed method outperforms with less computational cost and time as compared to previous methods.
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Lind, Katherine T., Hannah V. Chatwin, John DeSisto, Philip Coleman, Bridget Sanford, Andrew M. Donson, Kurtis D. Davies, et al. "Novel RAF Fusions in Pediatric Low-Grade Gliomas Demonstrate MAPK Pathway Activation." Journal of Neuropathology & Experimental Neurology 80, no. 12 (November 28, 2021): 1099–107. http://dx.doi.org/10.1093/jnen/nlab110.
Повний текст джерелаАнотація:
Abstract Brain tumors are the most common solid tumor in children, and low-grade gliomas (LGGs) are the most common childhood brain tumor. Here, we report on 3 patients with LGG harboring previously unreported or rarely reported RAF fusions: FYCO1-RAF1, CTTNBP2-BRAF, and SLC44A1-BRAF. We hypothesized that these tumors would show molecular similarity to the canonical KIAA1549-BRAF fusion that is the most widely seen alteration in pilocytic astrocytoma (PA), the most common pediatric LGG variant, and that this similarity would include mitogen-activated protein kinase (MAPK) pathway activation. To test our hypothesis, we utilized immunofluorescent imaging and RNA-sequencing in normal brain, KIAA1549-BRAF-harboring tumors, and our 3 tumors with novel fusions. We performed immunofluorescent staining of ERK and phosphorylated ERK (p-ERK), identifying increased p-ERK expression in KIAA1549-BRAF fused PA and the novel fusion samples, indicative of MAPK pathway activation. Geneset enrichment analysis further confirmed upregulated downstream MAPK activation. These results suggest that MAPK activation is the oncogenic mechanism in noncanonical RAF fusion-driven LGG. Similarity in the oncogenic mechanism suggests that LGGs with noncanonical RAF fusions are likely to respond to MEK inhibitors.
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Aggarwal, Mukul, Amod Kumar Tiwari, and M. Partha Sarathi. "Comparative Analysis of Deep Learning Models on Brain Tumor Segmentation Datasets: BraTS 2015-2020 Datasets." Revue d'Intelligence Artificielle 36, no. 6 (December 31, 2022): 863–71. http://dx.doi.org/10.18280/ria.360606.
Повний текст джерелаАнотація:
Deep Learning neural networks have shown applicability in segmentation of brain tumor images.This research have been carried for comprehensive review of several deep learning neural networks. The datasets included in this study are standard datasets Multimodal Brain Tumor Segmentation (BraTS). This paper has summarized the performance of various deep learning neural network algorithms on BraTS datasets. Algorithms have been compared and summarized against the baseline models with specific attributes like dice score, PPV and sensitivity. It has been found that out of the different models applied on the BraTS 2015 dataset GAN in the year 2020 algorithm is showing better results on this data set. GAN architecture termed RescueNet gave the best segmentation results in terms of 0.94 dice score and 0.88 Sensitivity. This has been also observed that models used cascaded deep learning models had independent deep learning models at each stage which had no correlation among the stages which can cause class imbalance. Further it have found that the Attention models tried to solve problem of class imbalance in the brain tumor segmentation task. This work also found that existing CNN’s is having overfitting issues. For this ResNet models can add a rapid connect bounce relationship parallel to the layers of CNN to accomplish better outcomes for the brain tumor segmentation task.
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Merishavyan, А. А., L. I. Papusha, E. F. Valiakhmetova, A. E. Druy, L. A. Yasko, V. V. Brilliantova, A. V. Artyomov, and A. I. Karachunsky. "A case report of pilocytic astrocytoma of the brainstem." Pediatric Hematology/Oncology and Immunopathology 19, no. 4 (December 22, 2020): 120–24. http://dx.doi.org/10.24287/1726-1708-2020-19-4-120-124.
Повний текст джерелаАнотація:
Low-grade gliomas (LGGs) of the brainstem are a rare nosological group among brain tumours in children. In most cases, radical resection of the tumours localized in the brainstem is impossible due to the high risk of postoperative neurological complications. Presently, there are no uniform approaches to the management of patients with residual tumours of the brainstem; furthermore, current LGG treatment protocols disregard molecular and genetic features of the tumour. In our article we describe the case of the patient with LGG of the brainstem after the performed biopsy. Despite the large volume of the tumour, we decided to follow the patient over time due to the following factors: verification of the “pilocytic astrocytoma” histological diagnosis, detection of the KIAA1549-BRAF chimeric transcript (a marker of a favourable prognosis), as well as the absence of neurological deficit. According to the neuroimaging data, the child has stable disease for a long period of time. The patients' parents gave their consent to the use of their child's data, including photographs, for research purposes and in publications.
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Ruffle, James, Samia Mohinta, Robert Gray, Harpreet Hyare, and Parashkev Nachev. "Identifying Enhancing Tumour Without Contrast-Enhanced Imaging." Neuro-Oncology 24, Supplement_4 (October 1, 2022): iv3. http://dx.doi.org/10.1093/neuonc/noac200.012.
Повний текст джерелаАнотація:
Abstract AIMS Brain tumours are heterogenous entities comprising multiple broad tissue sub-types when imaged with MRI. Delineating the enhancing tumour component is vital for neuro-oncological therapeutic planning, to-date only demonstrable with contrast-enhanced imaging. But not all patients can undergo this necessary contrast-enhanced acquisition, whether due to allergy, renal impairment, or scanning acquisition parameters. We therefore evaluated how well fully convolutional deep learning models identify a patient’s enhancing tumour when contrast-enhanced imaging was not available. METHOD We constructed a suite of deep-learning models to segment brain tumours when contrast-enhanced imaging was missing. Specifically, we developed all possible combinations of other structural sequences being provided, including T1-weighted, T2-weighted and FLAIR. Models were trained and tested with five-fold cross-validation on the 2021 BraTS-RSNA glioma population of 1251 patients, with additional out-of-sample validation with neuroradiologist hand-labelled lesions from our own centre. RESULTS Models missing post-contrast imaging still achieved a Dice coefficient for the whole tumour of 0.942. Model performances for identifying enhancing-tumour – despite no contrast-enhanced imaging being provided to the model – ranged from Dice coefficients of 0.759 (single sequence model) to 0.790 (three sequence T1 + T2 + FLAIR model). Moreover, models lacking contrast-enhanced imaging still robustly quantified the volume of enhancing tumour (R2 range 0.953-0.976). CONCLUSION Models missing contrast-enhanced imaging still identify both whole lesions and enhancing tumour components, and accurately quantifying the enhancing volumetric burden. These models provide opportunity for lesion detection in patients or clinical situations in which contrast-enhanced imaging cannot be acquired, and challenge the current nosology of defining ‘enhancing tumour’.
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Lee, Rebecca, Dominic G. Rothwell, Shien Chow, Heather May Shaw, Samra Turajlic, Nigel Smith, Alexandra Clipson, et al. "CAcTUS: A parallel arm, biomarker driven, phase II feasibility trial to determine the role of circulating tumor DNA in guiding a switch between targeted therapy and immune therapy in patients with advanced cutaneous melanoma." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): TPS9587. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.tps9587.
Повний текст джерелаАнотація:
TPS9587 Background: Circulating tumor DNA (ctDNA; the tumour derived fraction of circulating free DNA in the blood) has been shown to be a biomarker of tumor burden/progression in many cancers. We recently accurately monitored treatment response and resistance in stage IV melanoma by ctDNA analysis in serial peripheral blood samples. Pre-clinical data has previously revealed that BRAF inhibition provokes a micro-environment with increased T cell infiltration, improved T cell recognition of melanoma associated antigens and reduced production of immunosuppressive cytokines that could enhance immune responses. We aimed to test the hypothesis that ctDNA could be implemented as a personalised, real-time liquid biopsy to identify when tumours are responding to targeted therapy in order optimise a switch to immunotherapy. Methods: We validated the ctDNA assays for BRAF mutation calling as a primary trial endpoint. We designed a phase II multicenter, parallel arm study across 6 UK sites, to assess primary objectives of i). Whether a ctDNA result can be turned around within 7 days and actioned in a clinically relevant timeframe ii). to assess whether a decrease in ctDNA levels of mutant BRAF by ≥80% from baseline on targeted therapy is an appropriate ‘cut off’ to instruct switching to immunotherapy. Secondary endpoints include Overall Response Rate (ORR) to immunotherapy, radiological/clinical and ctDNA determined progression free survival (PFS) on each treatment. Forty patients are planned based on inclusion criteria of stage IV or stage III unresectable cutaneous BRAF mutant melanoma, baseline ctDNA BRAF variant allele frequency (VAF) ≥1.5%, ECOG 0/1/2, no symptomatic brain metastases, no prior adjuvant nivolumab plus ipilimumab (N+I). Prior adjuvant dabrafinib + trametinib (D+T) is allowed as long as recurrence is >6 months from completion. Patients are randomised 1:1 to either standard Arm A; investigator choice of either D+T (150mg BD +2mg OD respectively) or N+I (1 mg/kg N +3 mg/kg I q3 wkly, then N 480mg q4 wkly) first line, then switch on progression to the other treatment. In the experimental Arm B; all patients start on D+T and have BRAF ctDNA monitored q2 wkly for 4 wks then q4 wkly. When ≥80% decrease vs. baseline in ctDNA BRAF VAF occurs, patients switch to N+I. If patients subsequently progress on N+I, they will resume D+T. The study is open with 9 patients enrolled at time of submission. Clinical trial information: NCT03808441.
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Patel, Ankit, Tania Jones, Lewis Woodward, Arran Dokal, Vinothini Rajeeve, Pedro Cutillas, Thomas Stone, Thomas Jacques, and Denise Sheer. "LGG-57. SIGNALLING MECHANISMS IN PAEDIATRIC LOW-GRADE GLIOMA." Neuro-Oncology 22, Supplement_3 (December 1, 2020): iii377. http://dx.doi.org/10.1093/neuonc/noaa222.435.
Повний текст джерелаАнотація:
Abstract Paediatric low-grade gliomas (pLGGs) constitute the largest group of childhood CNS tumours. They often cause significant disability and morbidity, despite their indolent growth and the good survival rate of patients. The most common genetic alterations in these tumours, KIAA1549:BRAF fusion and BRAFV600E mutation, lead to abnormal activation of MAPK signalling. The central role of this pathway in pLGG development is emphasized by the occasional presence of other MAPK-activating alterations such as RTK mutations. It is not known how these different aberrations can induce the variety of clinical phenotypes seen in pLGG. Here, we compared pilocytic astrocytomas (PAs) containing the KIAA1549:BRAF fusion with glioneuronal tumours (GNTs) containing the BRAFV600E mutation, to identify differentially activated downstream targets of the MAPK pathway. Liquid chromatography tandem mass spectrometry (LC-MS/MS) was used as a multi-proteomic approach. Kinase Set Enrichment Analysis (KSEA) using PhosphositePlus and NetworkIN was used to determine relative enrichment of kinase activity in the tumours compared to healthy control brain tissue. Significant similarities and differences were found in the two tumour types. For example, more robust MAPK activation was found in the GNTs than in PAs. However, while PI3K/AKT1/mTOR signalling was active in both PAs and GNTs, there was statistically higher activation in the PAs. In both tumour types, there was significant reduction in casein kinase 2 activity, which likely affects nuclear translocation of ERK and, in turn, alters the range of its phosphorylated substrates. We will present these data together with transcriptomics to further characterise the downstream targets of these genetic alterations.
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Eoli, M., B. Pollo, A. Silvani, R. Paterra, A. Erbetta, E. Anghileri, P. Gaviani, and G. Finocchiaro. "P14.74 Remarkable response to Combined BRAF and MEK Inhibitors in two Adults with leptomeningeal carcinomatosis secondary to Pleomorphic Xantoastrocytoma grade II with BRAFv600E mutation." Neuro-Oncology 21, Supplement_3 (August 2019): iii85. http://dx.doi.org/10.1093/neuonc/noz126.309.
Повний текст джерелаАнотація:
Abstract BACKGROUND Several cancers with the BRAF V600E mutations have been successfully treated with targeted therapy. Pleomorphic xanthoastrocytoma (PXA) is a rare brain tumor, with an incidence of 0.07cases per 100,000. The BRAFV600 mutation is present in 38–60% of PXA. Typical treatment is gross total resection, followed by radiotherapy and cytotoxic chemotherapy at recurrence MATERIAL AND METHODS Two cases are described. RESULTS The first case is a 37 old man with a left temporal lobe lesion who underwent a craniotomy with total tumor resection. Histological diagnosis was PXA WHO grade 2with BRAF V600E mutation.Five months after, MR imaging of his brain and spine showed tumor progression with extensive leptomeningeal disease. The patient received adjuvant brain and spinal radiotherapy Two weeks after, due to rapid clinical worsening he had a new brain and spinal MRI showing hydrocephalus and progression of the pachymeningeal-based masses and received an emergency ventricular -peritoneal shunt. Given the genetic analysis, the extent of disease and rapidity of the progression, BRAF and MEK inhibitors, dabrafenib (150 mg, twice daily) and trametinib (2 mg, daily) were started. Remarkably, within 2 week of initiating dual-targeted therapy, the patient experienced a dramatic improvement in consciousness and overall strength; brainand spinal MRI revealed initial reduction of the leptomenigeal enhacement and no evidence of progression of the intraparenchymal disease. The therapy was well-tolerated. Currently, after sixteen months,the patient remains on treatment with a consistent functional status improvement and no radiological evidence of disease progression. The second case is a 51 old women who developed leptomeningeal carcinomatosis seven year after resection of a frontal left PXA WHO grade 2 with BRAFv600E mutation. The patient had received brain radiotherapy five years after diagnosis and Cyber Knife for tumor progression. Ten months later MR imaging of his brain and spine showed tumor progression with extensive leptomeningeal disease, she was treated with temozolomide for 8 after clinical and radiological worsening she had a second surgery with resection of recurrent frontale left lesion Histopathology PXA WHO grade 2 with BRAF V600E mutation. She developed hydrocephalus, received an emergency ventricular -peritoneal shunt. BRAF and MEK inhibitors, dabrafenib (150 mg, twice daily) and trametinib (2 mg, daily) were started three months ago with initial clinical benefit CONCLUSION All patients with PXA should be tested for the BRAFV600 mutation, since, in these cases, targeted therapy with BRAF and MEK inhibitors seems to be a useful option for salvage treatment.
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Zhang, Jianxin, Xiaogang Lv, Hengbo Zhang, and Bin Liu. "AResU-Net: Attention Residual U-Net for Brain Tumor Segmentation." Symmetry 12, no. 5 (May 2, 2020): 721. http://dx.doi.org/10.3390/sym12050721.
Повний текст джерелаАнотація:
Automatic segmentation of brain tumors from magnetic resonance imaging (MRI) is a challenging task due to the uneven, irregular and unstructured size and shape of tumors. Recently, brain tumor segmentation methods based on the symmetric U-Net architecture have achieved favorable performance. Meanwhile, the effectiveness of enhancing local responses for feature extraction and restoration has also been shown in recent works, which may encourage the better performance of the brain tumor segmentation problem. Inspired by this, we try to introduce the attention mechanism into the existing U-Net architecture to explore the effects of local important responses on this task. More specifically, we propose an end-to-end 2D brain tumor segmentation network, i.e., attention residual U-Net (AResU-Net), which simultaneously embeds attention mechanism and residual units into U-Net for the further performance improvement of brain tumor segmentation. AResU-Net adds a series of attention units among corresponding down-sampling and up-sampling processes, and it adaptively rescales features to effectively enhance local responses of down-sampling residual features utilized for the feature recovery of the following up-sampling process. We extensively evaluate AResU-Net on two MRI brain tumor segmentation benchmarks of BraTS 2017 and BraTS 2018 datasets. Experiment results illustrate that the proposed AResU-Net outperforms its baselines and achieves comparable performance with typical brain tumor segmentation methods.
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Kieran, Mark W. "Targeting BRAF in Pediatric Brain Tumors." American Society of Clinical Oncology Educational Book, no. 34 (May 2014): e436-e440. http://dx.doi.org/10.14694/edbook_am.2014.34.e436.
Повний текст джерелаАнотація:
The role of BRAF in adult malignancy has been well documented over the last decade and recent data have extended these findings to a number of pediatric cancers. In this and the accompanying articles, we will review the importance of the BRAF pathway in signal transduction resulting in cell proliferation, migration, differentiation, and angiogenesis with a focus on three major pediatric diseases: brain tumors, Langerhans cell histiocytosis (LCH), and melanoma. Mutated BRAF proteins are being identified in an increasing number of pediatric cancers and the development of drugs that can target these mutant proteins offers enormous therapeutic opportunity for these diseases. Because of variations in the types of mutations of BRAF observed in different tumors, particularly those of the central nervous system, an understanding of the feedback loops that regulate monomeric and dimeric BRAF signaling will be critical in selecting the optimal targeted inhibitors. The two most commonly observed alterations in BRAF in patients with brain tumor are the BRAF V600E point mutation and the KIAA1549 truncated fusion and targeting of these will need to differ to account for these feedback loops. Many other factors will influence the activity of novel agents in BRAF activated tumors, including their ability to penetrate the blood-brain barrier (for brain tumors and some patients with LCH) as well as the development of drug resistance and toxicity profiles. Well-controlled trials that take these variables into consideration are already underway and highlight the need for molecular classification of pediatric central nervous system tumors.
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Carruthers, Vickyanne, Kathryn Siddle, Gail Halliday, Simon Bailey, and Rebecca Hill. "IMG-08. UNUSUAL IMAGING FINDINGS IN TWO CASES OF PAEDIATRIC LOW GRADE GLIOMA." Neuro-Oncology 22, Supplement_3 (December 1, 2020): iii356. http://dx.doi.org/10.1093/neuonc/noaa222.344.
Повний текст джерелаАнотація:
Abstract Low grade gliomas (LGG), including pilocytic astrocytoma (PCA), are the commonest paediatric brain tumours and their behaviour is well understood, typically following a benign course. BRAF fusion is common, particularly in PCA of the cerebellum and optic pathway. Here we present two patients whose LGG behaved in an unusual fashion. The first patient who was treated 6 years previously on LGG2 with vincristine and carboplatin for a tectal plate lesion was identified on routine imaging to have local tumour progression and underwent completion staging. This showed a new enhancing soft tissue abnormality within the spinal cord at the level of L2. Due to radiological dubiety both lesions were biopsied for histological and molecular analysis, confirming LGG of the tectal plate and finding the spinal lesion to be a myxopapillary ependymoma. The second patient presented with acute hydrocephalus following a 2 year history of neurocognitive impairments. He was found to have a large, complex tumour centred in and expanding the bodies of both lateral ventricles with significant mass effect. Radiologically this was most in keeping with a central neurocytoma but histological analysis confirmed it to be a PCA with KIAA1549-BRAF fusion. The first case demonstrates the utility of molecular analysis in confirming two distinct tumour types in one patient, in a situation where metastasis would not be expected and would significantly alter treatment and prognosis. The second is an example of how imaging can be misleading in a KIAA1549-BRAF fused PCA presenting as an intraventricular mass.
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Li, Shaoqun, Mingyao Lai, and Linbo Cai. "LGG-36. Analysis of BRAF-related mutations in pediatric low-grade glioma." Neuro-Oncology 24, Supplement_1 (June 1, 2022): i96. http://dx.doi.org/10.1093/neuonc/noac079.348.
Повний текст джерелаАнотація:
Abstract BACKGROUND: Most pediatric low-grade gliomas (pLGGs) are driven by a single genetic event resulting in up-regulation of the RAS/MAPK pathway. BRAF-related mutations are the most freauent molecular alteration in the pathway. To explore BRAF-related mutations in pediatric low-grade glioma is helpful for clinical practice. METHODS: In this study, patients with low-grade glioma aged ≤18 years in Guangdong Sanjiu Brain Hospital were enrolled. All patients accepted the tests of BRAF-related mutations with tumor tissue by next-generation sequencing (NGS). Results: A total of 26 patients diagnosed low-grade glioma and underwent NGS detection were included in this study. The male to female ratio was 6:7, and the median age was 9.5 years. 8 patients had tumors located in the cerebral hemisphere, 6 in the third or fourth ventricle, 5 in the cerebellum, 4 in the optic pathway, and 3 in the brain stem. A total of 14 patients took BRAF-related mutations, such as BRAF-KIAA1549 fusion, BRAF p.V600E mutation and other fusion. BRAF-KIAA1549 fusion was detected in 7 patients with pilocytic astrocytoma or pilomyxoid astrocytoma. BRAF p.V600E mutation was detected in 6 patients, two of whom were pleomorphic xanthoastrocytoma. A rare genetic fusion, BCAS1-BRAF fusion, was detected in 1 patient who had brain stem ganglioglioma. Among the 26 patients, 2 patients without BRAF-related mutations had typical multiple cafe-au-lait macules and were diagnosed as NF1-pLGG. These patients were treated with surgery, radiation, chemotherapy and targeted therapy. Only 2 patients received targeted therapy by Trametinib, Vimofinib and Everolimus after progression of the tumor. However, due to the severity of the disease, they eventually died. CONCLUSIONS: More than half of pLGG patients have BRAF-related mutations, which have the opportunity for targeted therapy. However, the optimal timing of targeted therapy still needs further exploration.
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Brandner, Sebastian. "Molecular Diagnostics of Adult Gliomas in Neuropathological Practice." Acta Medica Academica 50, no. 1 (May 26, 2021): 29. http://dx.doi.org/10.5644/ama2006-124.324.
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<p>This review focuses on adult gliomas, highlighting the most relevant biomarkers in the diagnosis of these tumours and the use of DNA methylation arrays to complement conventional molecular diagnostic techniques. The discovery and characterisation of diagnostic and prognostic biomarkers in brain tumours has significantly changed the neuropathological landscape over the last decade. These include mutations in the IDH1 and IDH2 genes in astrocytomas and oligodendrogliomas, histone H3 K27M mutations in midline gliomas, or BRAF mutations in a range of low-grade and high-grade glial and glioneuronal tumours. Other biomarkers of relevance are mutations in the TERT promoter, the ATRX gene, and genomic alterations such as 1p/19q codeletion, EGFR amplification, and chromosome 7 gain and 10 loss. The development of DNA methylation profiling and algorithmic classification of brain tumours has further enhanced the diagnostic abilities of neuropathologists. Methylation profiling is particularly useful for the diagnostic workup of biopsies with an inconclusive molecular test results, small samples, or samples with indistinctive low-grade or high-grade histology. This technology has become indispensable for the risk stratification of ependymal tumours, medulloblastomas and meningiomas.</p><p><strong>Conclusion</strong>. This review highlights the importance of an integrated approach to brain tumour diagnostics and gives a balanced view of the relevance and choice of conventional and molecular techniques in the workup of adult gliomas in diagnostic neuropathology practice.</p><div> </div>
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Colombino, Maria, Mariaelena Capone, Amelia Lissia, Antonio Cossu, Corrado Rubino, Vincenzo De Giorgi, Daniela Massi, et al. "BRAF/NRAS Mutation Frequencies Among Primary Tumors and Metastases in Patients With Melanoma." Journal of Clinical Oncology 30, no. 20 (July 10, 2012): 2522–29. http://dx.doi.org/10.1200/jco.2011.41.2452.
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Purpose The prevalence of BRAF, NRAS, and p16CDKN2A mutations during melanoma progression remains inconclusive. We investigated the prevalence and distribution of mutations in these genes in different melanoma tissues. Patients and Methods In all, 291 tumor tissues from 132 patients with melanoma were screened. Paired samples of primary melanomas (n = 102) and synchronous or asynchronous metastases from the same patients (n = 165) were included. Tissue samples underwent mutation analysis (automated DNA sequencing). Secondary lesions included lymph nodes (n = 84), and skin (n = 36), visceral (n = 25), and brain (n = 44) sites. Results BRAF/NRAS mutations were identified in 58% of primary melanomas (43% BRAF; 15% NRAS); 62% in lymph nodes, 61% subcutaneous, 56% visceral, and 70% in brain sites. Mutations were observed in 63% of metastases (48% BRAF; 15% NRAS), a nonsignificant increase in mutation frequency after progression from primary melanoma. Of the paired samples, lymph nodes (93% consistency) and visceral metastases (96% consistency) presented a highly similar distribution of BRAF/NRAS mutations versus primary melanomas, with a significantly less consistent pattern in brain (80%) and skin metastases (75%). This suggests that independent subclones are generated in some patients. p16CDKN2A mutations were identified in 7% and 14% of primary melanomas and metastases, with a low consistency (31%) between secondary and primary tumor samples. Conclusion In the era of targeted therapies, assessment of the spectrum and distribution of alterations in molecular targets among patients with melanoma is needed. Our findings about the prevalence of BRAF/NRAS/p16CDKN2A mutations in paired tumor lesions from patients with melanoma may be useful in the management of this disease.
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Salman Al-Shaikhli, Saif Dawood, Michael Ying Yang, and Bodo Rosenhahn. "Brain tumor classification and segmentation using sparse coding and dictionary learning." Biomedical Engineering / Biomedizinische Technik 61, no. 4 (August 1, 2016): 413–29. http://dx.doi.org/10.1515/bmt-2015-0071.
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AbstractThis paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.
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Mostafiz, Rafid, Mohammad Shorif Uddin, Iffat Jabin, Muhammad Minoar Hossain, and Mohammad Motiur Rahman. "Automatic Brain Tumor Detection From MRI Using Curvelet Transform and Neural Features." International Journal of Ambient Computing and Intelligence 13, no. 1 (January 2022): 1–18. http://dx.doi.org/10.4018/ijaci.293163.
Повний текст джерелаАнотація:
The brain tumor is one of the most health hazard diseases across the world in recent time. The development of the intelligent system has extended its applications in the automated medical diagnosis domains. However, image-based medical diagnosis result strongly depends on the selection of relevant features. This research focuses on the automatic detection of brain tumors based on the concatenation of curvelet transform and convolutional neural network (CNN) features extracted from the preprocessed MRI sequence of the brain. Relevant features are selected from the feature vector using mutual information based on the minimum redundancy maximum relevance (mRMR) method. The detection is done using the ensemble classifier of the bagging method. The experiment is performed using two standard datasets of BraTS 2018 and BraTS 2019. After five-fold cross-validation, we have obtained an outperforming accuracy of 98.96%.
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Le Flahec, Glen, Manon Briolais, Briac Guibourg, Gilles Lemasson, Jean-Luc Grippari, Francoise Ledé, Pascale Marcorelles, and Arnaud Uguen. "Testing for BRAF fusions in patients with advanced BRAF/NRAS/KIT wild-type melanomas permits to identify patients who could benefit of anti-MEK targeted therapy." Journal of Clinical Pathology 73, no. 2 (September 10, 2019): 116–19. http://dx.doi.org/10.1136/jclinpath-2019-206026.
Повний текст джерелаАнотація:
Beyond targeted therapy for patients with BRAF-mutated melanomas and immunotherapy in patients lacking BRAF mutations, anti-MEK therapy has been proposed in patients with advanced melanomas harbouring BRAF fusions. BRAF fusions diagnosis in patients with advanced melanomas is the subject of the present study. Using BRAF fluorescent in situ hybridisation (FISH), we searched for BRAF fusions in 74 samples of 66 patients with advanced BRAF/NRAS/KIT wild-type melanomas. We identified 2/66 (3%) patients with BRAF fusions in a brain metastasis of one patient and in a lymph node metastasis and in a cutaneous metastasis for the second patient with 90%–95% of tumour nuclei containing isolated 3′-BRAF FISH signals. As a result, we conclude that BRAF FISH in patients with advanced BRAF/NRAS/KIT wild-type melanomas is a valuable and easy-to-perform test to diagnose BRAF fusions and to identify patients who could benefit of anti-MEK targeted therapy.
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Zhang, Ruifeng, Shasha Jia, Mohammed Jajere Adamuand, Weizhi Nie, Qiang Li, and Ting Wu. "HMNet: Hierarchical Multi-Scale Brain Tumor Segmentation Network." Journal of Clinical Medicine 12, no. 2 (January 9, 2023): 538. http://dx.doi.org/10.3390/jcm12020538.
Повний текст джерелаАнотація:
An accurate and efficient automatic brain tumor segmentation algorithm is important for clinical practice. In recent years, there has been much interest in automatic segmentation algorithms that use convolutional neural networks. In this paper, we propose a novel hierarchical multi-scale segmentation network (HMNet), which contains a high-resolution branch and parallel multi-resolution branches. The high-resolution branch can keep track of the brain tumor’s spatial details, and the multi-resolution feature exchange and fusion allow the network’s receptive fields to adapt to brain tumors of different shapes and sizes. In particular, to overcome the large computational overhead caused by expensive 3D convolution, we propose a lightweight conditional channel weighting block to reduce GPU memory and improve the efficiency of HMNet. We also propose a lightweight multi-resolution feature fusion (LMRF) module to further reduce model complexity and reduce the redundancy of the feature maps. We run tests on the BraTS 2020 dataset to determine how well the proposed network would work. The dice similarity coefficients of HMNet for ET, WT, and TC are 0.781, 0.901, and 0.823, respectively. Many comparative experiments on the BraTS 2020 dataset and other two datasets show that our proposed HMNet has achieved satisfactory performance compared with the SOTA approaches.
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Smith-Cohn, Matthew, Christian Davidson, Howard Colman, and Adam L. Cohen. "Challenges of targeting BRAF V600E mutations in adult primary brain tumor patients: a report of two cases." CNS Oncology 8, no. 4 (December 1, 2019): CNS48. http://dx.doi.org/10.2217/cns-2019-0018.
Повний текст джерелаАнотація:
Aim: Therapeutic targeting of BRAF alterations in primary brain tumor patients has demonstrated clinical activity in case reports and early trials; however, there is limited high-level evidence of the efficacy. Patients & results: Targeting BRAF V600E mutations with concurrent dabrafenib and trametinib in anaplastic pleomorphic xanthoastrocytoma resulted in a transient radiographic and clinical response and no therapeutic benefit in a patient with an epithelioid glioblastoma. Conclusion: BRAF/ MEK inhibition did not produce a durable treatment effect in glioblastoma or pleomorphic xanthoastrocytoma with BRAF V600E alterations. Heterogenicity of related cases in the literature makes an evaluation of efficacy BRAF targeting therapies in gliomas difficult and requires additional investigation.
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Harding, James J., Federica Catalanotti, Amin Yaqubie, Gregory C. McDermott, Romona Kersellius, Taha Merghoub, Richard D. Carvajal, et al. "Vemurafenib (VEM) in patients (pts) with BRAF-mutant melanoma and brain metastases (mets)." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): 9060. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.9060.
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9060 Background: Emerging data suggest that RAF inhibitors are an effective therapy for pts with BRAF-mutant melanoma and brain mets. Although reported efficacy is encouraging, these data are derived from case reports or early stage trials enriched with physiologically fit pts. It is therefore of interest to assess the “real world” experience of VEM in this population. Methods: Records of all BRAF-mutant melanoma pts treated with RAF inhibitors at our center from 2007 to 2012 were reviewed retrospectively. We determined the best overall response rate (BORR) and, when applicable, the overrall intracranial response rate (OIRR) by RECIST v1.1, progression-free survival (PFS), and overall survival (OS) to RAF inhibition. Pretreatment formalin-fixed, paraffin-embedded tumor was assessed using an exon capture assay able to sequence coding exons of 279 cancer-associated genes. Results: 21 (18%) of 119 pts with BRAF-mutant melanoma treated with VEM had active brain mets (age range: 25-86, sex: 52% men, median ECOG PS: 1, proportion with extracranial mets: 90%, BRAF mutation: 86% V600E and 14% V600K). 10/21 pts had no prior intracranial (IC) therapy; 11/21 pts received whole brain radiotherapy (WBRT, 7/21), stereotactic radiosurgery (1/21), metastasectomy (2/21) or multimodality therapy (1/21) prior to VEM. 12/21 pts received ipilimumab sometime during their disease course. For radiographically evaluable pts (N=17), the BORR was 65% (95% CI: 43-88) and the OIRR was 40% (95% CI: 15-65). For 4 pts, the BORR and OIRR were discordant−3 pts had IC progression but visceral tumor shrinkage, 1 pt had IC disease control but visceral progression. VEM was effective in pts whether or not they had received prior local brain therapy. The estimated median PFS and OS for all brain mets pts (N=21) were 4 and 8 months, respectively. Pretreatment tumor is available for exon sequencing in approximately half of these patients. This analysis is ongoing. Conclusions: In routine clinical practice, the OIRR to VEM was 40% which is higher than historical response rates to WBRT. VEM may be preferable to WBRT as a first-line therapy for pts with BRAF-mutant melanoma and brain mets. Whether RAF inhibitor treatment improves OS in this population will require further study.
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Bougacha, Aymen, Ines Njeh, Jihene Boughariou, Omar Kammoun, Kheireddine Ben Mahfoudh, Mariem Dammak, Chokri Mhiri, and Ahmed Ben Hamida. "Rank-Two NMF Clustering for Glioblastoma Characterization." Journal of Healthcare Engineering 2018 (October 23, 2018): 1–7. http://dx.doi.org/10.1155/2018/1048164.
Повний текст джерелаАнотація:
This study investigates a novel classification method for 3D multimodal MRI glioblastomas tumor characterization. We formulate our segmentation problem as a linear mixture model (LMM). Thus, we provide a nonnegative matrix M from every MRI slice in every segmentation process’ step. This matrix will be used as an input for the first segmentation process to extract the edema region from T2 and FLAIR modalities. After that, in the rest of segmentation processes, we extract the edema region from T1c modality, generate the matrix M, and segment the necrosis, the enhanced tumor, and the nonenhanced tumor regions. In the segmentation process, we apply a rank-two NMF clustering. We have executed our tumor characterization method on BraTS 2015 challenge dataset. Quantitative and qualitative evaluations over the publicly training and testing dataset from the MICCAI 2015 multimodal brain segmentation challenge (BraTS 2015) attested that the proposed algorithm could yield a competitive performance for brain glioblastomas characterization (necrosis, tumor core, and edema) among several competing methods.
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Rehman, Mobeen Ur, SeungBin Cho, Jeehong Kim, and Kil To Chong. "BrainSeg-Net: Brain Tumor MR Image Segmentation via Enhanced Encoder–Decoder Network." Diagnostics 11, no. 2 (January 25, 2021): 169. http://dx.doi.org/10.3390/diagnostics11020169.
Повний текст джерелаАнотація:
Efficient segmentation of Magnetic Resonance (MR) brain tumor images is of the utmost value for the diagnosis of tumor region. In recent years, advancement in the field of neural networks has been used to refine the segmentation performance of brain tumor sub-regions. The brain tumor segmentation has proven to be a complicated task even for neural networks, due to the small-scale tumor regions. These small-scale tumor regions are unable to be identified, the reason being their tiny size and the huge difference between area occupancy by different tumor classes. In previous state-of-the-art neural network models, the biggest problem was that the location information along with spatial details gets lost in deeper layers. To address these problems, we have proposed an encoder–decoder based model named BrainSeg-Net. The Feature Enhancer (FE) block is incorporated into the BrainSeg-Net architecture which extracts the middle-level features from low-level features from the shallow layers and shares them with the dense layers. This feature aggregation helps to achieve better performance of tumor identification. To address the problem associated with imbalance class, we have used a custom-designed loss function. For evaluation of BrainSeg-Net architecture, three benchmark datasets are utilized: BraTS2017, BraTS 2018, and BraTS 2019. Segmentation of Enhancing Core (EC), Whole Tumor (WT), and Tumor Core (TC) is carried out. The proposed architecture have exhibited good improvement when compared with existing baseline and state-of-the-art techniques. The MR brain tumor segmentation by BrainSeg-Net uses enhanced location and spatial features, which performs better than the existing plethora of brain MR image segmentation approaches.
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Ghate, Sameer, Jackson Tang, Zhiyi Li, and Antonio Reis Nakasato. "Real world treatment patterns of first-line combination therapies among BRAF+ metastatic melanoma patients stratified by tumor burden." Journal of Clinical Oncology 36, no. 5_suppl (February 10, 2018): 198. http://dx.doi.org/10.1200/jco.2018.36.5_suppl.198.
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198 Background: For patients (pts) with metastatic melanoma (MM) and BRAF V600 mutation (BRAF+), options for first-line (1L) systemic combination therapy include immunotherapy (IO) or targeted therapy (TT). This study describes real world treatment patterns among BRAF+ MM pts treated with 1L ipilimumab+nivolumab (I+N) or dabrafenib+trametinib (D+T), stratified by tumor burden. Methods: A retrospective observational analysis used Flatiron Health’s electronic health record-derived database from Oct ’15 - Jul ’16. Pts were aged ≥18 years with a MM diagnosis, tested BRAF+ prior to therapy, and treated with ipilimumab+nivolumab (I+N) or dabrafenib+trametinib (D+T) as 1L therapy. Low tumor burden was defined as low/normal LDH (≤ 333 IU/L) and no brain metastasis. High tumor burden was defined as high LDH ( > 333 IU/L) or brain metastasis. Baseline characteristics and treatment patterns were descriptively assessed. Kaplan-Meier (KM) analysis measured time to discontinuation. Results: Among 76 BRAF+ pts, 38% (29) were treated with I+N as 1L, and 62% (47) were treated with D+T as 1L. Of these, 45% (13/29) of I+N vs. 32% (15/47) of D+T had low tumor burden, while 41% (12/29) of I+N vs. 49% (23/47) of D+T had high tumor burden. The two cohorts did not differ by age or gender. Treatment patterns are summarized below. Conclusions: Among pts with low tumor burden, I+N demonstrated shorter time to discontinuation and higher discontinuation rate relative to D+T. Treatment toxicity and progression was the main reason for discontinuation of I+N and D+T, respectively. Among pts with high tumor burden, I+N demonstrated longer time to discontinuation but higher discontinuation rate relative to D+T. Progression was the main reason for discontinuation of both I+N and D+T. [Table: see text]