Dataset Information

Data driven diagnostic classification in Alzheimer's disease based on different reference regions for normalization of PiB-PET images and correlation with CSF concentrations of A? species.

ABSTRACT: Positron emission tomography (PET) neuroimaging with the Pittsburgh Compound_B (PiB) is widely used to assess amyloid plaque burden. Standard quantification approaches normalize PiB-PET by mean cerebellar gray matter uptake. Previous studies suggested similar pons and white-matter uptake in Alzheimer's disease (AD) and healthy controls (HC), but lack exhaustive comparison of normalization across the three regions, with data-driven diagnostic classification. We aimed to compare the impact of distinct reference regions in normalization, measured by data-driven statistical analysis, and correlation with cerebrospinal fluid (CSF) amyloid ? (A?) species concentrations. 243 individuals with clinical diagnosis of AD, HC, mild cognitive impairment (MCI) and other dementias, from the Biomarkers for Alzheimer's/Parkinson's Disease (BIOMARKAPD) initiative were included. PiB-PET images and CSF concentrations of A?₃₈, A?₄₀ and A?₄₂ were submitted to classification using support vector machines. Voxel-wise group differences and correlations between normalized PiB-PET images and CSF A? concentrations were calculated. Normalization by cerebellar gray matter and pons yielded identical classification accuracy of AD (accuracy-96%, sensitivity-96%, specificity-95%), and significantly higher than A? concentrations (best accuracy 91%). Normalization by the white-matter showed decreased extent of statistically significant multivoxel patterns and was the only method not outperforming CSF biomarkers, suggesting statistical inferiority. A?₃₈ and A?₄₀ correlated negatively with PiB-PET images normalized by the white-matter, corroborating previous observations of correlations with non-AD-specific subcortical changes in white-matter. In general, when using the pons as reference region, higher voxel-wise group differences and stronger correlation with A?₄₂, the A?₄₂/A?₄₀ or A?₄₂/A?₃₈ ratios were found compared to normalization based on cerebellar gray matter.

SUBMITTER: Oliveira F

PROVIDER: S-EPMC6120605 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Data driven diagnostic classification in Alzheimer's disease based on different reference regions for normalization of PiB-PET images and correlation with CSF concentrations of Aβ species.

Oliveira Francisco F Leuzy Antoine A Castelhano João J Chiotis Konstantinos K Hasselbalch Steen Gregers SG Rinne Juha J Mendonça Alexandre A Otto Markus M Lleó Alberto A Santana Isabel I Johansson Jarkko J Anderl-Straub Sarah S Arnim Christine C Beer Ambros A Blesa Rafael R Fortea Juan J Sanna-Kaisa Herukka H Portelius Erik E Pannee Josef J Zetterberg Henrik H Blennow Kaj K Moreira Ana P AP Abrunhosa Antero A Nordberg Agneta A Castelo-Branco Miguel M

NeuroImage. Clinical 20180819

Positron emission tomography (PET) neuroimaging with the Pittsburgh Compound_B (PiB) is widely used to assess amyloid plaque burden. Standard quantification approaches normalize PiB-PET by mean cerebellar gray matter uptake. Previous studies suggested similar pons and white-matter uptake in Alzheimer's disease (AD) and healthy controls (HC), but lack exhaustive comparison of normalization across the three regions, with data-driven diagnostic classification. We aimed to compare the impact of dist ...[more]

PMID: 30186764

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Project description:ContextColor normalization techniques for histology have not been empirically tested for their utility for computational pathology pipelines.AimsWe compared two contemporary techniques for achieving a common intermediate goal - epithelial-stromal classification.Settings and designExpert-annotated regions of epithelium and stroma were treated as ground truth for comparing classifiers on original and color-normalized images.Materials and methodsEpithelial and stromal regions were annotated on thirty diverse-appearing H and E stained prostate cancer tissue microarray cores. Corresponding sets of thirty images each were generated using the two color normalization techniques. Color metrics were compared for original and color-normalized images. Separate epithelial-stromal classifiers were trained and compared on test images. Main analyses were conducted using a multiresolution segmentation (MRS) approach; comparative analyses using two other classification approaches (convolutional neural network [CNN], Wndchrm) were also performed.Statistical analysisFor the main MRS method, which relied on classification of super-pixels, the number of variables used was reduced using backward elimination without compromising accuracy, and test - area under the curves (AUCs) were compared for original and normalized images. For CNN and Wndchrm, pixel classification test-AUCs were compared.ResultsKhan method reduced color saturation while Vahadane reduced hue variance. Super-pixel-level test-AUC for MRS was 0.010-0.025 (95% confidence interval limits ± 0.004) higher for the two normalized image sets compared to the original in the 10-80 variable range. Improvement in pixel classification accuracy was also observed for CNN and Wndchrm for color-normalized images.ConclusionsColor normalization can give a small incremental benefit when a super-pixel-based classification method is used with features that perform implicit color normalization while the gain is higher for patch-based classification methods for classifying epithelium versus stroma.

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Dataset Information

Data driven diagnostic classification in Alzheimer's disease based on different reference regions for normalization of PiB-PET images and correlation with CSF concentrations of A? species.

Publications

Data driven diagnostic classification in Alzheimer's disease based on different reference regions for normalization of PiB-PET images and correlation with CSF concentrations of Aβ species.

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