AdaptAD: Foundation Model Adaptation with Missing Modality Robustness for Alzheimer's Disease Staging Across the Disease Continuum
Staging Alzheimer's disease (AD) across its continuum, from cognitively normal (CN) through mild cognitive impairment (MCI) to dementia from a single structural MRI remains difficult because 3-D networks overfit small cohorts, and FreeSurfer morphometry is often unavailable. We present AdaptAD, which reads a registered T1-weighted scan as 16 anatomically placed 2.5-D slices through the medial temporal lobe and adapts a frozen ImageNet-pretrained ViT-B/16 with low-rank adaptation (LoRA), training 3.5 M of 89 M parameters. Morphometry enters as tokens when present, or as a learned [MISSING] token, and a missing-aware stacker fuses both experts. Under a pre-registered protocol, ten methods were compared for three-class CN/MCI/AD staging on identical 5-fold splits of 597 ADNI participants, 29% without FreeSurfer, with Holm-corrected paired bootstrap tests. The locked model was tested once on 993 OASIS-3 participants (988 scored by every method). On ADNI, AdaptAD achieved a three-class macro-AUC of 0.745 (95% CI 0.709 to 0.781), ranking first and significantly exceeding full fine-tuning, ShaSpec and a 3-D CNN. On OASIS-3 it reached 0.780 (0.748 to 0.814), and on the CDR-based label (0.804) it significantly outperformed every 3-D and image-tabular method. LoRA matched full fine-tuning with 25 times fewer trainable parameters, and removing morphometry changed external macro-AUC by less than 0.003. The disease score tracked MMSE and CDR-SB within diagnostic groups and stratified MCI-to-dementia progression (log-rank p < 0.001), while saliency concentrated in medial temporal and periventricular regions. A parameter-efficient 2-D foundation model can therefore stage the full AD continuum from a routine T1 scan without morphometry, offering a scalable route to memory-clinic deployment.