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Brain Tumor - Springer Nature

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Last Updated: 10 June 2022

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Body mass index at diagnosis of a childhood brain tumor; a reflection of hypothalamic-pituitary dysfunction or lifestyle?

At follow-up, the Body mass index at diagnosis is similar to BMI at diagnosis. " During follow-up, we investigated whether BMI at childhood brain tumor diagnosis is related to HP dysfunction at diagnosis or its expansion. Methods In a Dutch cohort of 685 CBTS, the association of BMI at diagnosis of a childhood brain tumor to HP dysfunction at diagnosis or during follow-up was investigated, excluding children with craniopharyngioma or a pituitary tumor. Conclusion Overweight or obesity at the time of a childhood brain tumor diagnosis of a childhood brain tumor does not appear to be linked to pituitary deficiencies, as shown by the above. These findings show that genetics and lifestyle can be more significant etiologic variables for elevated BMI in these children than hypothalamic dysfunction. More attention should be paid to lifestyle changes at the time of brain tumor treatment to help with long-term effects of CBTS in terms of overweight and obesity.

Source link: https://doi.org/10.1007/s00520-022-07031-4


Clinicopathological risk factors for a poor prognosis of primary central nervous system lymphoma in elderly patients in the Tohoku and Niigata area: a multicenter, retrospective, cohort study of the Tohoku Brain Tumor Study Group

B-cell lymphoma patients aged 71 years or older who underwent medical treatment in Japan's Tohoku and Niigata areas were retrospectively reviewed. The pathological risk factors, according to a multivariate analysis of median OS, showed that Karnofsky's success was less than 60% three months after treatment, and double expressor lymphoma, an expression of programmed death-ligand 1 in tumor-infiltrating lymphocytes or tumor-associated macrophages, and Epstein's U2013Barr virus infection were the pathological risk factors. ".

Source link: https://doi.org/10.1007/s10014-022-00427-4


Supervoxel-based brain tumor segmentation with multimodal MRI images

"Besides, MRI is often used in brain tumor diagnosis. " People's demand for high-level image segmentation technologies is gradually increasing with the gradual improvement of two-dimensional image segmentation studies. According, three-dimensional image segmentation has played a key role in medical image segmentation. In this paper, a novel 3D supervoxel segmentation system is suggested for the brain tumor in multimodal MRI images.

Source link: https://doi.org/10.1007/s11760-021-02072-4


Polymeric magnetic nanoparticles: a multitargeting approach for brain tumour therapy and imaging

"The use of magnetic nanoparticles can lead to non-invasive drug delivery and a brain tumor bioimaging technique. " A multitherapeutic drug delivery strategy involving the delivery of chemotherapeutic medications with a magnetic targeting strategy, imaging, and hyperthermia can be a promising strategy as a multitherapeutic drug delivery scheme involving the delivery of magnetic iron oxide nanoparticles. "We also discuss the use of polymeric nanoparticles as an effective targeting device for improved drug delivery and imaging for brain cancer. ".

Source link: https://doi.org/10.1007/s13346-021-01063-9


MRI sequences and interslice gap influence leptomeningeal metastasis detection in children with brain tumors

"Purpose Accurate detection of leptomeningeal metastasis is vital for risk stratification and treatment of pediatric brain tumors. " Children with leptomeningeal seeding brain tumors should have accurate MRI findings of leptomeningeal metastasis in children with leptomeningeal seeding brain tumors, according to the researchers. T2 FLAIR + Contrast and sagittal plane for 3DT1WI+ Contrast in brain; and use of alternatives to axial TSE/FSE in spine; and use of alternatives to axial TSE/FSE in spine; lower interslice gap in brain and spine; and use of alternatives to axial TSE/FSE in spine. Conclusion "Using the post-contrast T2 FLAIR and sagittal 3DT1 in brain, small/no interslice gap, and avoiding TSE/FSE axials in spine may help with leptomeningeal metastasis detection in children with brain tumors. ".

Source link: https://doi.org/10.1007/s00234-022-02928-7


Automated brain tumor malignancy detection via 3D MRI using adaptive-3-D U-Net and heuristic-based deep neural network

"The experiment is started with pre-processing using skull stripping and contrast enhancement using the 3D images from public benchmark sites. " Tunicate Swarm Algorithm is another of the Butterfly Optimization Algorithm, which can be used by the Adaptive-3-D U-Net, as well as the hybridized Butterfly Optimization Algorithm. The results of applying the suggested methodology to 3D-MRI images from the Decathlon study show that the proposed procedure is comparable to existing brain tumor segmentation methods.

Source link: https://doi.org/10.1007/s00530-022-00952-4


Beyond standard data collection – the promise and potential of BRAIN (Brain tumour Registry Australia INnovation and translation registry)

"Background Real-world studies are increasingly being used as a valuable source of evidence to address clinical and policy-relevant questions that are unlikely to be answered by clinical trials. " BRAIN, an Australian brain cancer registry that is pursuing these opportunities, is here described here. Methods BRAIN was developed by a committee of physicians in conjunction with BIOGRID to capture comprehensive clinical data on patients with brain tumours diagnosed through diagnosis, or death. Conclusions "We present our first data collection attempt in brain tumours for Australia, which we believe to be unique globally due to the number of sites and patients involved, as well as the extent to which the registry resource is being used to support clinical and translational research. ".

Source link: https://doi.org/10.1186/s12885-022-09700-3


Brain Tumor Detection and Classification Using Cycle Generative Adversarial Networks

"Brain cancer ranks tenth on the list of leading causes of death in both men and women. " Biopsy is one of the most commonly used techniques for diagnosing cancer. Using brain Magnetic Resonance Imaging, this paper proposed an ensemble scheme for detecting and analyzing brain tumors and its stages. For tumor detection from an MRI scan, a modified InceptionResNetV2 pre-trained model is used. According to the experiment findings, the suggested tumor detection and tumor classification algorithms achieve the accuracy of 99% and 98%, respectively. ".

Source link: https://doi.org/10.1007/s12539-022-00502-6


A novel histogram feature for brain tumor detection

"Enhanced and robust extraction as a result of the more accurate result can be obtained by a machine learning scheme. " Every histogram-bin of each quadrant has been divided into two sub-bins, including Then. In the rest of the quadrant, the number of each intensity present in the most connected component is included in one sub-bin, while the other contains the count of each intensity present in the most connected segment. The extracted new feature has been combined with the classic HOG function. U201cExpanded Local Histogramu201d features from all the integrated functions have been used to select 1024 HOG and 128 u201d features from the comprehensive menu.

Source link: https://doi.org/10.1007/s41870-022-00917-w

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions