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Summary metrics to assess Alzheimer disease-related hypometabolic pattern with 18F-FDG PET: head-to-head comparison.

ABSTRACT: In the recently revised diagnostic criteria for Alzheimer disease (AD), the National Institute on Aging and Alzheimer Association suggested that confidence in diagnosing dementia due to AD and mild cognitive impairment (MCI) due to AD could be improved by the use of certain biomarkers, such as (18)F-FDG PET evidence of hypometabolism in AD-affected brain regions. Three groups have developed automated data analysis techniques to characterize the AD-related pattern of hypometabolism in a single measurement. In this study, we sought to directly compare the ability of these three (18)F-FDG PET data analysis techniques--the PMOD Alzheimer discrimination analysis tool, the hypometabolic convergence index, and a set of meta-analytically derived regions of interest reflecting AD hypometabolism pattern (metaROI)--to distinguish moderate or mild AD dementia patients and MCI patients who subsequently converted to AD dementia from cognitively normal older adults.One hundred sixty-six (18)F-FDG PET patients from the AD Neuroimaging Initiative, 308 from the Network for Efficiency and Standardization of Dementia Diagnosis, and 176 from the European Alzheimer Disease Consortium PET study were categorized, with masking of group classification, as AD, MCI, or healthy control. For each AD-related (18)F-FDG PET index, receiver-operating-characteristic curves were used to characterize and compare subject group classifications.The 3 techniques were roughly comparable in their ability to distinguish each of the clinical groups from cognitively normal older adults with high sensitivity and specificity. Accuracy of classification (in terms of area under the curve) in each clinical group varied more as a function of dataset than by technique. All techniques were differentially sensitive to disease severity, with the classification accuracy for MCI due to AD to moderate AD varying from 0.800 to 0.949 (PMOD Alzheimer tool), from 0.774 to 0.967 (metaROI), and from 0.801 to 0.983 (hypometabolic convergence index).The 3 tested techniques have the potential to help detect AD in research and clinical settings. Additional efforts are needed to clarify their ability to address particular scientific and clinical questions. Their incremental diagnostic value over other imaging and biologic markers makes them easier to implement by other groups for these purposes.

SUBMITTER: Caroli A

PROVIDER: S-EPMC3640308 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Summary metrics to assess Alzheimer disease-related hypometabolic pattern with 18F-FDG PET: head-to-head comparison.

Caroli Anna A Prestia Annapaola A Chen Kewei K Ayutyanont Napatkamon N Landau Susan M SM Madison Cindee M CM Haense Cathleen C Herholz Karl K Nobili Flavio F Reiman Eric M EM Jagust William J WJ Frisoni Giovanni B GB Frisoni Giovanni B GB

Journal of nuclear medicine : official publication, Society of Nuclear Medicine 20120217 4

<h4>Unlabelled</h4>In the recently revised diagnostic criteria for Alzheimer disease (AD), the National Institute on Aging and Alzheimer Association suggested that confidence in diagnosing dementia due to AD and mild cognitive impairment (MCI) due to AD could be improved by the use of certain biomarkers, such as (18)F-FDG PET evidence of hypometabolism in AD-affected brain regions. Three groups have developed automated data analysis techniques to characterize the AD-related pattern of hypometabo ...[more]

PMID: 22343502

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Project description:Positron emission tomography/computed tomography with 18F-fluorodeoxy-glucose (18F-FDG-PET/CT) has become the standard staging modality in various tumor entities. Cancer patients frequently receive cardio-toxic therapies. However, routine cardiovascular assessment in oncologic patients is not performed in current clinical practice. Accordingly, this study sought to assess whether myocardial 18F-FDG uptake patterns of patients undergoing oncologic PET/CT can be used for cardiovascular risk stratification. Myocardial 18F-FDG uptake pattern was assessed in 302 patients undergoing both oncologic whole-body 18F-FDG-PET/CT and myocardial perfusion imaging by single-photon emission computed tomography (SPECT-MPI) within a six-month period. Primary outcomes were myocardial 18F-FDG uptake pattern, impaired myocardial perfusion, ongoing ischemia, myocardial scar, and left ventricular ejection fraction. Among all patients, 109 (36.1%) displayed no myocardial 18F-FDG uptake, 77 (25.5%) showed diffuse myocardial 18F-FDG uptake, 24 (7.9%) showed focal 18F-FDG uptake, and 92 (30.5%) had a focal on diffuse myocardial 18F-FDG uptake pattern. In contrast to the other uptake patterns, focal myocardial 18F-FDG uptake was predominantly observed in patients with myocardial abnormalities (i.e., abnormal perfusion, impaired LVEF, myocardial ischemia, or scar). Accordingly, a multivariate logistic regression identified focal myocardial 18F-FDG uptake as a strong predictor of abnormal myocardial function/perfusion (odds ratio (OR) 5.32, 95% confidence interval (CI) 1.73-16.34, p = 0.003). Similarly, focal myocardial 18F-FDG uptake was an independent predictor of ongoing ischemia and myocardial scar (OR 4.17, 95% CI 1.53-11.4, p = 0.005 and OR 3.78, 95% CI 1.47-9.69, p = 0.006, respectively). Focal myocardial 18F-FDG uptake seen on oncologic PET/CT indicates a significantly increased risk for multiple myocardial abnormalities. Obtaining and taking this information into account will help to stratify patients according to risk and will reduce unnecessary cardiovascular complications in cancer patients.

Project description:BACKGROUND:Radiomics is aimed at image-based tumor phenotyping, enabling application within clinical-decision-support-systems to improve diagnostic accuracy and allow for personalized treatment. The purpose was to identify predictive 18-fluor-fluoro-2-deoxyglucose (18F-FDG) positron-emission tomography (PET) radiomic features to predict recurrence, distant metastasis, and overall survival in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy. METHODS:Between 2012 and 2018, 103 retrospectively (training cohort) and 71 consecutively included patients (validation cohort) underwent 18F-FDG-PET/CT imaging. The 434 extracted radiomic features were subjected, after redundancy filtering, to a projection resulting in outcome-independent meta-features (factors). Correlations between clinical, first-order 18F-FDG-PET parameters (e.g., SUVmean), and factors were assessed. Factors were combined with 18F-FDG-PET and clinical parameters in a multivariable survival regression and validated. A clinically applicable risk-stratification was constructed for patients' outcome. RESULTS:Based on 124 retained radiomic features from 103 patients, 8 factors were constructed. Recurrence prediction was significantly most accurate by combining HPV-status, SUVmean, SUVpeak, factor 3 (histogram gradient and long-run-low-grey-level-emphasis), factor 4 (volume-difference, coarseness, and grey-level-non-uniformity), and factor 6 (histogram variation coefficient) (CI = 0.645). Distant metastasis prediction was most accurate assessing metabolic-active tumor volume (MATV)(CI = 0.627). Overall survival prediction was most accurate using HPV-status, SUVmean, SUVmax, factor 1 (least-axis-length, non-uniformity, high-dependence-of-high grey-levels), and factor 5 (aspherity, major-axis-length, inversed-compactness and, inversed-flatness) (CI = 0.764). CONCLUSIONS:Combining HPV-status, first-order 18F-FDG-PET parameters, and complementary radiomic factors was most accurate for time-to-event prediction. Predictive phenotype-specific tumor characteristics and interactions might be captured and retained using radiomic factors, which allows for personalized risk stratification and optimizing personalized cancer care. TRIAL REGISTRATION:Trial NL3946 (NTR4111), local ethics commission reference: Prediction 2013.191 and 2016.498. Registered 7 August 2013, https://www.trialregister.nl/trial/3946.

Project description:BackgroundQuantification of molecular cell processes is important for prognostication and treatment individualization of head and neck cancer (HNC). However, individual tumor comparison can show discord in upregulation similarities when analyzing multiple biological mechanisms. Elaborate tumor characterization, integrating multiple pathways reflecting intrinsic and microenvironmental properties, may be beneficial to group most uniform tumors for treatment modification schemes. The goal of this study was to systematically analyze if immunohistochemical (IHC) assessment of molecular markers, involved in treatment resistance, and 18F-FDG PET parameters could accurately distinguish separate HNC tumors.MethodsSeveral imaging parameters and texture features for 18F-FDG small-animal PET and immunohistochemical markers related to metabolism, hypoxia, proliferation and tumor blood perfusion were assessed within groups of BALB/c nu/nu mice xenografted with 14 human HNC models. Classification methods were used to predict tumor line based on sets of parameters.ResultsWe found that 18F-FDG PET could not differentiate between the tumor lines. On the contrary, combined IHC parameters could accurately allocate individual tumors to the correct model. From 9 analyzed IHC parameters, a cluster of 6 random parameters already classified 70.3% correctly. Combining all PET/IHC characteristics resulted in the highest tumor line classification accuracy (81.0%; cross validation 82.0%), which was just 2.2% higher (p = 5.2×10-32) than the performance of the IHC parameter/feature based model.ConclusionsWith a select set of IHC markers representing cellular processes of metabolism, proliferation, hypoxia and perfusion, one can reliably distinguish between HNC tumor lines. Addition of 18F-FDG PET improves classification accuracy of IHC to a significant yet minor degree. These results may form a basis for development of tumor characterization models for treatment allocation purposes.

Dataset Information

Summary metrics to assess Alzheimer disease-related hypometabolic pattern with 18F-FDG PET: head-to-head comparison.

Publications

Summary metrics to assess Alzheimer disease-related hypometabolic pattern with 18F-FDG PET: head-to-head comparison.

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