Using Radiomics-based Modeling to Predict Individual Progression From Mild Cognitive Impairment to Alzheimer’s Disease

2021 ◽  
Author(s):  
Jiehui Jiang ◽  
Xiaoming Sun ◽  
Ian Alberts ◽  
Min Wang ◽  
Axel Rominger ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Fdg Pet ◽  
Cox Model ◽  
Standardized Uptake Value ◽  
Predictive Performance ◽  
Clinical Model ◽  
Neuropsychological Assessments

Abstract Background: Predicting the risk of disease progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has important clinical significance. This study aims at providing a personalized MCI-to-AD conversion prediction via radiomics-based predictive modeling (RPM) with multicentre 18F-Fluorodeoxyglucose positron emission tomography (FDG PET) data. Method: Three cohorts of 18F-FDG PET data and neuropsychological assessments were gathered from patients examined at Huashan Hospital (n=22), Xuanwu Hospital (n=80), and from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (n=355). Of these, amyloid images were selected for the ADNI and Xuanwu cohorts. First, 430 radiomic features were extracted from the 80 regions of interest (ROIs) for all PET images. These features were then concatenated for feature selection and an RPM model was constructed on the ADNI dataset. In addition, we used clinical scale data to establish a clinical Cox model, and a combined model for comparison. Afterwards, the images from Huashan Hospital were used to validate the stability and reliability of RPM, and the images from Xuanwu Hospital were used to explore the differences of biomarkers at different cognitive stages. Finally, correlation analysis was conducted between the radiomic biomarkers, neuropsychological assessments, and amyloid burden. Results: Experimental results show that the predictive performance of the PET-modal cox model was better than clinical cox model. In the two test data sets, the C index of PET model is 0.75 and 0.73, respectively; The C index of clinical model is 0.68. Moreover, most crucial image biomarkers had significant differences at different cognitive stages, and were significantly correlated with cognitive ability and the amyloid global level standardized uptake value ratio.Conclusion: The preliminary results demonstrated that the developed RPM approach has the potential to monitor the progress in high-risk populations with AD.

scholarly journals Using a Digital Neuro Signature to measure longitudinal individual-level change in Alzheimer’s disease: the Altoida large cohort study

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Irene B. Meier ◽  
Max Buegler ◽  
Robbert Harms ◽  
Azizi Seixas ◽  
Arzu Çöltekin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Disease Risk ◽  
Intraindividual Variability ◽  
Cognitive Test ◽  
Individual Level ◽  
Level Change ◽  
Neuropsychological Assessments

AbstractConventional neuropsychological assessments for Alzheimer’s disease are burdensome and inaccurate at detecting mild cognitive impairment and predicting Alzheimer’s disease risk. Altoida’s Digital Neuro Signature (DNS), a longitudinal cognitive test consisting of two active digital biomarker metrics, alleviates these limitations. By comparison to conventional neuropsychological assessments, DNS results in faster evaluations (10 min vs 45–120 min), and generates higher test-retest in intraindividual assessment, as well as higher accuracy at detecting abnormal cognition. This study comparatively evaluates the performance of Altoida’s DNS and conventional neuropsychological assessments in intraindividual assessments of cognition and function by means of two semi-naturalistic observational experiments with 525 participants in laboratory and clinical settings. The results show that DNS is consistently more sensitive than conventional neuropsychological assessments at capturing longitudinal individual-level change, both with respect to intraindividual variability and dispersion (intraindividual variability across multiple tests), across three participant groups: healthy controls, mild cognitive impairment, and Alzheimer’s disease. Dispersion differences between DNS and conventional neuropsychological assessments were more pronounced with more advanced disease stages, and DNS-intraindividual variability was able to predict conversion from mild cognitive impairment to Alzheimer’s disease. These findings are instrumental for patient monitoring and management, remote clinical trial assessment, and timely interventions, and will hopefully contribute to a better understanding of Alzheimer’s disease.

scholarly journals Heterogeneity of Amyloid Binding in Cognitively Impaired Patients Consecutively Recruited from a Memory Clinic: Evaluating the Utility of Quantitative 18F-Flutemetamol PET-CT in Discrimination of Mild Cognitive Impairment from Alzheimer’s Disease and Other Dementias

2020 ◽  
pp. 1-14
Author(s):  
Yi-Wen Bao ◽  
Anson C.M. Chau ◽  
Patrick Ka-Chun Chiu ◽  
Yat Fung Shea ◽  
Joseph S.K. Kwan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Amyloid Β ◽  
Standardized Uptake Value ◽  
Subjective Cognitive Decline ◽  
Cognitively Impaired ◽  
Memory Clinic ◽  
Pet Ct

Background: With the more widespread use of 18F-radioligand-based amyloid-β (Aβ) PET-CT imaging, we evaluated Aβ binding and the utility of neocortical 18F-Flutemetamol standardized uptake value ratio (SUVR) as a biomarker. Objective: 18F-Flutemetamol SUVR was used to differentiate 1) mild cognitive impairment (MCI) from Alzheimer’s disease (AD), and 2) MCI from other non-AD dementias (OD). Methods: 109 patients consecutively recruited from a University memory clinic underwent clinical evaluation, neuropsychological test, MRI and 18F-Flutemetamol PET-CT. The diagnosis was made by consensus of a panel consisting of 1 neuroradiologist and 2 geriatricians. The final cohort included 13 subjective cognitive decline (SCD), 22 AD, 39 MCI, and 35 OD. Quantitative analysis of 16 region-of-interests made by Cortex ID software (GE Healthcare). Results: The global mean 18F-Flutemetamol SUVR in SCD, MCI, AD, and OD were 0.50 (SD-0.08), 0.53 (SD-0.16), 0.76 (SD-0.10), and 0.56 (SD-0.16), respectively, with SUVR in SCD and MCI and OD being significantly lower than AD. Aβ binding in SCD, MCI, and OD was heterogeneous, being 23%, 38.5%, and 42.9% respectively, as compared to 100% amyloid positivity in AD. Using global SUVR, ROC analysis showed AUC of 0.868 and 0.588 in differentiating MCI from AD and MCI from OD respectively. Conclusion: 18F-Flutemetamol SUVR differentiated MCI from AD with high efficacy (high negative predictive value), but much lower efficacy from OD. The major benefit of the test was to differentiate cognitively impaired patients (either SCD, MCI, or OD) without AD-related-amyloid-pathology from AD in the clinical setting, which was under-emphasized in the current guidelines proposed by Amyloid Imaging Task Force.

scholarly journals Identification of and a Prediction Model for Cognitive Trajectories in Patients With Mild Cognitive Impairment Due to Alzheimer’s Disease

2020 ◽  
Author(s):  
Sang Won Seo ◽  
Seung Joo Kim ◽  
Sook-Young Woo ◽  
Young Ju Kim ◽  
Yeshin Kim ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Prediction Model ◽  
Multinomial Logistic Regression ◽  
Standardized Uptake Value ◽  
Disease Assessment ◽  
Risk Scores ◽  
Clinical Biomarkers

Abstract Background: Few studies have investigated cognitive trajectories or developed a prediction model for amyloid beta-positive (Aβ+) mild cognitive impairment (MCI) patients. We aimed to identify distinct cognitive trajectories in Aβ+ MCI patients based on longitudinal Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-cog) 13 scores. Furthermore, we aimed to develop and visualize a prediction model to predict trajectory groups using the demographic, genetic, and clinical biomarkers of Aβ+ MCI patients.Methods: We performed a retrospective analysis of the data in 238 Aβ+ MCI patients from the Alzheimer’s Disease Neuroimaging Initiative who underwent at least three rounds of annual neuropsychological testing to identify cognitive trajectories. A group-based trajectory model (GBTM) was used to classify distinct groups based on ADAS-cog 13 scores. The prediction model was estimated using multinomial logistic regression and visualized using a bar-based method for risk prediction. Results: Three distinct classes, namely slow decliners (18.5%), intermediate decliners (42.9%), and fast decliners (38.7%), were suggested. Intermediate decliners were associated with higher age (≥70 years) (odds ratio [OR] 2.72, 95% confidence interval [CI] 1.78-6.28), higher AV45 standardized uptake value ratios (SUVRs)*10 (OR 1.69, 95% CI 1.22-2.34), and lower fluorodeoxyglucose (FDG) SUVR*10 (OR 0.65, 95% CI 0.46-0.93) than slow decliners. Fast decliners were associated with higher age (≥70 years) (OR 3.76, 95% CI 1.40-10.10), greater likelihood of being an apolipoprotein E 4 carrier (OR 4.2, 95% CI 1.53-11.58), higher AV45 positron emission tomography SUVR*10 (OR 2.14, 95% CI 1.50-3.05), and lower FDG SUVR*10 (OR 0.31, 95% CI 0.20-0.48) than slow decliners. The predicted probability of being classified to a trajectory group according to the risk scores of predictors was visualized.Conclusions: Our GBTM analysis yielded novel insights into the heterogeneous cognitive trajectories among Aβ+ MCI patients, which further facilitates the effective stratification of Aβ+ MCI patients in Aβ-targeted clinical trials.

scholarly journals 18F-MK-6240 PET for early and late detection of neurofibrillary tangles

Brain ◽  
2020 ◽  
Vol 143 (9) ◽  
pp. 2818-2830 ◽  
Author(s):  
Tharick A Pascoal ◽  
Joseph Therriault ◽  
Andrea L Benedet ◽  
Melissa Savard ◽  
Firoza Z Lussier ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Amyloid Β ◽  
Standardized Uptake Value ◽  
Braak Staging ◽  
High Affinity ◽  
Tau Pet ◽  
Transentorhinal Cortex

Abstract Braak stages of tau neurofibrillary tangle accumulation have been incorporated in the criteria for the neuropathological diagnosis of Alzheimer’s disease. It is expected that Braak staging using brain imaging can stratify living individuals according to their individual patterns of tau deposition, which may prove crucial for clinical trials and practice. However, previous studies using the first-generation tau PET agents have shown a low sensitivity to detect tau pathology in areas corresponding to early Braak histopathological stages (∼20% of cognitively unimpaired elderly with tau deposition in regions corresponding to Braak I–II), in contrast to ∼80–90% reported in post-mortem cohorts. Here, we tested whether the novel high affinity tau tangles tracer 18F-MK-6240 can better identify individuals in the early stages of tau accumulation. To this end, we studied 301 individuals (30 cognitively unimpaired young, 138 cognitively unimpaired elderly, 67 with mild cognitive impairment, 54 with Alzheimer’s disease dementia, and 12 with frontotemporal dementia) with amyloid-β 18F-NAV4694, tau 18F-MK-6240, MRI, and clinical assessments. 18F-MK-6240 standardized uptake value ratio images were acquired at 90–110 min after the tracer injection. 18F-MK-6240 discriminated Alzheimer’s disease dementia from mild cognitive impairment and frontotemporal dementia with high accuracy (∼85–100%). 18F-MK-6240 recapitulated topographical patterns consistent with the six hierarchical stages proposed by Braak in 98% of our population. Cognition and amyloid-β status explained most of the Braak stages variance (P < 0.0001, R2 = 0.75). No single region of interest standardized uptake value ratio accurately segregated individuals into the six topographic Braak stages. Sixty-eight per cent of the cognitively unimpaired elderly amyloid-β-positive and 37% of the cognitively unimpaired elderly amyloid-β-negative subjects displayed tau deposition, at least in the transentorhinal cortex (Braak I). Tau deposition solely in the transentorhinal cortex was associated with an elevated prevalence of amyloid-β, neurodegeneration, and cognitive impairment (P < 0.0001). 18F-MK-6240 deposition in regions corresponding to Braak IV–VI was associated with the highest prevalence of neurodegeneration, whereas in Braak V–VI regions with the highest prevalence of cognitive impairment. Our results suggest that the hierarchical six-stage Braak model using 18F-MK-6240 imaging provides an index of early and late tau accumulation as well as disease stage in preclinical and symptomatic individuals. Tau PET Braak staging using high affinity tracers has the potential to be incorporated in the diagnosis of living patients with Alzheimer’s disease in the near future.

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