31.12.2022
Automated detection of MCI to Alzheimer's disease conversion before clinical onset by evaluation of atrophy rates, 2022_Dec
Published in
ECR 2022 Book of Abstracts, RPS 1505-9
Autoren
J. Steiglechner, B. Bender, U. Ernemann, K. Scheffler, T. Lindig
Abstract
Purpose: 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 (ADNI1) 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.