Ganna Blazhenets1, Yilong Ma2, Arnd Sörensen3, Gerta Rücker4, Florian Schiller3, David Eidelberg2, Lars Frings3,5, Philipp T Meyer. 1. Department of Nuclear Medicine, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany ganna.blazhenets@uniklinik-freiburg.de. 2. Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York. 3. Department of Nuclear Medicine, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany. 4. Institute of Medical Biometry and Statistics, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; and. 5. Center for Geriatrics and Gerontology Freiburg, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Abstract
The value of 18F-FDG PET for predicting conversion from mild cognitive impairment (MCI) to Alzheimer dementia (AD) is currently under debate. We used a principal components analysis (PCA) to identify a metabolic AD conversion-related pattern (ADCRP) and investigated the prognostic value of the resulting pattern expression score (PES). Methods: 18F-FDG PET scans of 544 MCI patients were obtained from the Alzheimer Disease Neuroimaging Initiative database and analyzed. We implemented voxel-based PCA and standard Statistical Parametric Mapping analysis (as a reference) to disclose cerebral metabolic patterns associated with conversion from MCI to AD. By Cox proportional hazards regression, we examined the prognostic value of candidate predictors. Also, we constructed prognostic models with clinical, imaging, and clinical and imaging variables in combination. Results: PCA revealed an ADCRP that involved regions with relative decreases in metabolism (temporoparietal, frontal, posterior cingulate, and precuneus cortices) and relative increases in metabolism (sensorimotor and occipital cortices, cerebellum, and left putamen). Among the predictor variables age, sex, Functional Activities Questionnaire, Mini-Mental State Examination, apolipoprotein E, PES, and normalized 18F-FDG uptake (regions with significant hypo- and hypermetabolism in patients with conversion vs. those without conversion), PES was the best independent predictor of conversion (hazard ratio, 1.77, per z score increase; 95% CI, 1.24-2.52; P < 0.001). Moreover, adding PES to the model including the clinical variables significantly increased its prognostic value. Conclusion: The ADCRP expression score was a valid predictor of conversion. A combination of clinical variables and PES yielded a higher accuracy than each single tool in predicting conversion from MCI to AD, underlining the incremental utility of 18F-FDG PET.
The value of 18F-FDG PET for predicting conversion from mild cognitive impairment (MCI) to Alzheimer dementia (AD) is currently under debate. We used a principal components analysis (PCA) to identify a metabolic AD conversion-related pattern (ADCRP) and investigated the prognostic value of the resulting pattern expression score (PES). Methods:18F-FDG PET scans of 544 MCI patients were obtained from the Alzheimer Disease Neuroimaging Initiative database and analyzed. We implemented voxel-based PCA and standard Statistical Parametric Mapping analysis (as a reference) to disclose cerebral metabolic patterns associated with conversion from MCI to AD. By Cox proportional hazards regression, we examined the prognostic value of candidate predictors. Also, we constructed prognostic models with clinical, imaging, and clinical and imaging variables in combination. Results: PCA revealed an ADCRP that involved regions with relative decreases in metabolism (temporoparietal, frontal, posterior cingulate, and precuneus cortices) and relative increases in metabolism (sensorimotor and occipital cortices, cerebellum, and left putamen). Among the predictor variables age, sex, Functional Activities Questionnaire, Mini-Mental State Examination, apolipoprotein E, PES, and normalized 18F-FDG uptake (regions with significant hypo- and hypermetabolism in patients with conversion vs. those without conversion), PES was the best independent predictor of conversion (hazard ratio, 1.77, per z score increase; 95% CI, 1.24-2.52; P < 0.001). Moreover, adding PES to the model including the clinical variables significantly increased its prognostic value. Conclusion: The ADCRP expression score was a valid predictor of conversion. A combination of clinical variables and PES yielded a higher accuracy than each single tool in predicting conversion from MCI to AD, underlining the incremental utility of 18F-FDG PET.
Authors: Jonas A Hosp; Andrea Dressing; Ganna Blazhenets; Tobias Bormann; Alexander Rau; Marius Schwabenland; Johannes Thurow; Dirk Wagner; Cornelius Waller; Wolf D Niesen; Lars Frings; Horst Urbach; Marco Prinz; Cornelius Weiller; Nils Schroeter; Philipp T Meyer Journal: Brain Date: 2021-04-03 Impact factor: 13.501
Authors: Luana Ibiapina Cordeiro Calíope Pinheiro; Maria Lúcia Duarte Pereira; Marcial Porto Fernandez; Francisco Mardônio Vieira Filho; Wilson Jorge Correia Pinto de Abreu; Pedro Gabriel Calíope Dantas Pinheiro Journal: Comput Math Methods Med Date: 2021-07-09 Impact factor: 2.238