The Impact of Microarray Technology in Brain Cancer

Kounelakis, M.G.; Zervakis, Michalis; Kotsiakis, X.

doi:10.1016/b978-044452855-1/50015-5

Cited by 6 publications

(8 citation statements)

References 54 publications

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“…First, it is based on a robust strategy (i.e. maximum-margin hyper-plane), which is considered to be one of the best to reduce the prediction error in a classification task [ 44 , 45 ]. Second, only few parameters need to be tuned in order to make it fully operational, making SVM relatively easy to set up and use.…”

Section: Discussionmentioning

confidence: 99%

MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study

et al. 2018

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BackgroundWith the shift of research focus towards the pre-dementia stage of Alzheimer’s disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ε4 genotype, can be used to predict amyloid pathology using machine-learning classification.MethodsWe examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n = 337, age 66.5 ± 7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69.1 ± 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 ± 7.7, 48% female, 97% amyloid positive). Structural MRI scans were visually assessed and Freesurfer was used to obtain subcortical volumes, cortical thickness and surface area measures. We first assessed univariate associations between MRI measures and amyloid pathology using mixed models. Next, we developed and tested an automated classifier using demographic, cognitive, MRI and APOE ε4 information to predict amyloid pathology. A support vector machine (SVM) with nested 10-fold cross-validation was applied to identify a set of markers best discriminating between amyloid positive and amyloid negative subjects.ResultsIn univariate associations, amyloid pathology was associated with lower subcortical volumes and thinner cortex in AD-signature regions in CN and MCI. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.81 ± 0.07 in MCI and an AUC of 0.74 ± 0.08 in CN. In CN, selected features for the classifier included APOE ε4, age, memory scores and several MRI measures such as hippocampus, amygdala and accumbens volumes and cortical thickness in temporal and parahippocampal regions. In MCI, the classifier including demographic and APOE ε4 information did not improve after additionally adding imaging measures.ConclusionsAmyloid pathology is associated with changes in structural MRI measures in CN and MCI. An automated classifier based on clinical, imaging and APOE ε4 data can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in clinical trials to pre-screen subjects for anti-amyloid therapies.Electronic supplementary materialThe online version of this article (10.1186/s13195-018-0428-1) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study

et al. 2018

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“…In the field of cancer research, the most commonly used microarray techniques for the molecular profiling of human tumors have been cDNA and oligonucleotide microarrays [Pomeroy S.L. 2002-Kounelakis M.G. et al 2006-Kounelakis M.G.…”

Section: The "Omics" Laboratory Tools For Cancer Managementmentioning

confidence: 99%

“…Brain involvement can occur with tumors of the nasopharyngeal region by direct extension along the cranial nerves or through the foramina at the base of the skull. Dural metastases may constitute up to 9% of total CNS metastases [Kounelakis M.G. et al 2006].…”

Section: Types Of Brain Tumorsmentioning

confidence: 99%

“…The outcomes of these studies are examined and interpreted by radiologists who are specialized on imaging diagnostic techniques. The most current and state-of-the-art imaging diagnostic approaches applied today are analysed below [Wen P. et al 2001-Kounelakis M.G. et al 2006.…”

Section: Non-invasive Imaging Modalitiesmentioning

confidence: 99%

“…Naturally, such forecast issues are by their nature unpredictable. The survival rates in people with brain tumors depend on many different factors [Kounelakis M.G. et al 2006].…”

Section: The Prognosis Stagementioning

confidence: 99%

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