Computer-Assisted Identification And Volumetric Quantification Of Dynamic Contrast Enhancement In Brain Mri: An Interactive System

We present a dedicated segmentation system for tumor identification and volumetric quantification in dynamic contrast brain magnetic resonance (MR) scans. Our goal is to offer a practically useful tool at the end of clinicians in order to boost volumetric tumor assessment. The system is designed to work in an interactive mode such that maximizes the integration of computing capacity and clinical intelligence. We demonstrate the main functions of the system in terms of its functional flow and conduct preliminary validation using a representative pilot dataset. The system is inexpensive, user-friendly, easy to deploy and integrate with picture archiving and communication systems (PACS), and possible to be open-source, which enable it to potentially serve as a useful assistant for radiologists and oncologists. It is anticipated that in the future the system can be integrated into clinical workflow so that become routine available to help clinicians make more objective interpretations of treatment interventions and natural history of disease to best advocate patient needs. © 2013 SPIE

New treatment strategies for malignant gliomas

Although survival in patients with malignant gliomas remains limited, there is renewed optimism with the emergence of novel treatment strategies. Cytotoxic agents such as temozolomide and CPT-11 have shown promising clinical activity. Biological treatments for brain tumors, including antisense oligonucleotides, gene therapy, and angiogenesis inhibitors, are also being evaluated in clinical trials. Delivery strategies have been developed to overcome challenges presented by the blood-brain barrier. These noteworthy treatments, alone or in combination, may ultimately prolong survival and enhance quality of life in this group of patients. The Oncologist 1999;4:209-22

Confidence Guided Enhancing Brain Tumor Segmentation In Multi-Parametric Mri

Enhancing brain tumor segmentation for accurate tumor volume measurement is a challenging task due to the large variation of tumor appearance and shape, which makes it difficult to incorporate prior knowledge commonly used by other medical image segmentation tasks. In this paper, a novel idea of confidence surface is proposed to guide the segmentation of enhancing brain tumor using information across multi-parametric magnetic resonance imaging (MRI). Texture information along with the typical intensity information from pre-contrast T1 weighted (T1 pre), post-contrast T1 weighted (T1 post), T2 weighted (T2), and fluid attenuated inversion recovery (FLAIR) MRI images are used to train a discriminative classifier at pixel level. The classifier is used to generate a confidence surface, which gives a likelihood of each pixel being a tumor or non-tumor. The obtained confidence surface is then incorporated into two classical methods for segmentation guidance. The proposed approach was evaluated on 19 groups of MRI images with tumor and promising results have been demonstrated. © 2012 IEEE

CONFIDENCE GUIDED ENHANCING BRAIN TUMOR SEGMENTATION IN MULTI-PARAMETRIC MRI

Enhancing brain tumor segmentation for accurate tumor volume measurement is a challenging task due to the large variation of tumor appearance and shape, which makes it difficult to incorporate prior knowledge commonly used by other medical image segmentation tasks. In this paper, a novel idea of confidence surface is proposed to guide the segmentation of enhancing brain tumor using information across multi-parametric magnetic resonance imaging (MRI). Texture information along with the typical intensity information from pre-contrast T1 weighted (T1pre), post-contrast T1 weighted (T1post), T2 weighted (T2), and fluid attenuated inversion recovery (FLAIR) MRI images are used to train a discriminative classifier at pixel level. The classifier is used to generate a confidence surface, which gives a likelihood of each pixel being a tumor or non-tumor. The obtained confidence surface is then incorporated into two classical methods for segmentation guidance. The proposed approach was evaluated on 19 groups of MRI images with tumor and promising results have been demonstrated

Levetiracetam enhances p53-mediated MGMT inhibition and sensitizes glioblastoma cells to temozolomide

Antiepileptic drugs (AEDs) are frequently used to treat seizures in glioma patients. AEDs may have an unrecognized impact in modulating O6-methylguanine-DNA methyltransferase (MGMT), a DNA repair protein that has an important role in tumor cell resistance to alkylating agents. We report that levetiracetam (LEV) is the most potent MGMT inhibitor among several AEDs with diverse MGMT regulatory actions. In vitro, when used at concentrations within the human therapeutic range for seizure prophylaxis, LEV decreases MGMT protein and mRNA expression levels. Chromatin immunoprecipitation analysis reveals that LEV enhances p53 binding on the MGMT promoter by recruiting the mSin3A/histone deacetylase 1 (HDAC1) corepressor complex. However, LEV does not exert any MGMT inhibitory activity when the expression of either p53, mSin3A, or HDAC1 is abrogated. LEV inhibits malignant glioma cell proliferation and increases glioma cell sensitivity to the monofunctional alkylating agent temozolomide. In 4 newly diagnosed patients who had 2 craniotomies 7–14 days apart, prior to the initiation of any tumor-specific treatment, samples obtained before and after LEV treatment showed the inhibition of MGMT expression. Our results suggest that the choice of AED in patients with malignant gliomas may have an unrecognized impact in clinical practice and research trial design