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  Automated detection of MCI to Alzheimer's disease conversion before clinical onset by evaluation of atrophy rates

Steiglechner, J., Bender, B., Ernemann, U., Scheffler, K., & Lindig, T. (2022). Automated detection of MCI to Alzheimer's disease conversion before clinical onset by evaluation of atrophy rates. In European Congress of Radiology (ECR 2022): Building Bridges (pp. 629-630).

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 Creators:
Steiglechner, J1, Author              
Bender, B, Author              
Ernemann, U, Author
Scheffler, K1, Author              
Lindig, T, Author              
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1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              

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 Abstract: Purpose or Learning Objective: Alzheimer's disease (AD) is the most common type of an irreversible neurodegenerative disorder, affecting millions of people. Especially early stratification of patients with mild cognitive impairment (MCI) into patients who will convert to AD remains a challenging task. We aimed to predict automatically whether MCI patients will develop the disease (MCIc) by following subjects over time and quantifying spatial atrophy rates (AR) in magnetic resonance imaging (MRI). Methods or Background: 3D T1w MRIs at 3T from 276 MCI patients participating in the first period of Alzheimer’s Disease National Initiative (ADNI-1) with at least two MRIs more than 60 days apart without evident artifacts were segmented by a deep-learning-based 3D-UNet into 30 anatomical regions. Z-scores of TIV-adjusted volumes were calculated compared to a normal reference population, and AR of these z-scores were calculated longitudinally per subject (AR=0 normal aging). Rolling AR were calculated as the mean AR over a half-year time window (mRAR). A 80:20 train-test-partition was used to train a logistic regression to discriminate MCIc vs MCInc. Results or Findings: We found accelerated regional mRAR in MCIc. The temporal cortex and hippocampal regions showed the most striking mRAR. On the test set, of 34 MCIc, the classifier predicted 27 as true positive with a median of 1.7 Y (Q1/3=2.0/0.6Y) before conversion (sensitivity=0.79), with 5/22 false positives MCInc (stable specificity=0.77, AUC ROC=0.81). Conclusion: Our method provides reliable results due to a stable specificity that can be obtained well before previous clinical diagnoses for conversions to disease. Therefore, it is suitable for use in subsequent studies. Limitations: Validation in an independent sample is missing.

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 Dates: 2022-06
 Publication Status: Published online
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Title: European Congress of Radiology (ECR 2022): Building Bridges
Place of Event: Wien, Austria
Start-/End Date: 2022-07-13 - 2022-07-17

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Title: European Congress of Radiology (ECR 2022): Building Bridges
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: RPS 1505-9 Start / End Page: 629 - 630 Identifier: -