IC‐P‐051: Radiological‐Pathological Correlation in Alzheimer's Disease: Pre‐Mortem Mri Narrative Review
Bibliographic record
Abstract
In order to promote an early diagnosis, we wished to examine the relationship between detected changes on antemortem magnetic resonance imaging (MRI) compared to neuropathological findings in Alzheimer’s disease (AD) by conducting a narrative review of published radiological-pathological correlation studies. We explored the PubMed database in June-July 2015 for published articles with any of the following terms: “Alzheimer's disease”, “antemortem MRI”, “autopsy”, “neuropathology”, “pathology MRI correlation”, “postmortem histopathology”, “quantitative MRI”, “structural neuroimaging”, and “volumetric MRI”. This initial search resulted in 301 articles dated 1988 to 2014. We (C.D.-T.) screened articles for inclusion first by examining the title and abstract, then the full text version. Overall, 286 articles were deemed as not appropriate to fulfill our research aim. A second author (S.D.) reviewed the 15 remaining articles to confirm their relevance, to which were added nine other published work based on their reference list. In fine, we report results based on 24 different manuscripts. As expected, in addition to normal-aging brain atrophy, accumulation of the key neuropathological features of AD, neurofibrillary tangles and senile plaques, are associated with whole-brain atrophy, hippocampal atrophy and particularly with ventricular expansion detected by volumetric MRI. Studies show further that vascular lesions such as cerebral amyloid angiopathy and cortical microinfarcts also add to the loss of brain volume seen in AD. Hippocampal atrophy is also related to hippocampal sclerosis, a lesion occasionally found with AD-related pathology. However, MRI volume loss correlates more with tangles than all other pathological findings. Finally, MRI-detected white matter hyperintensities are strongly associated with vascular pathologies such as arteriosclerosis and micro- or large infarcts, but can also represent other histological changes such as gliosis or demyelination. Anatomic alterations such as cerebral volume loss and white matter changes in the pre-mortem brain have been shown to be reflective of underlying neuropathology as seen at autopsy. It has been proven in different studies that these changes are detectable using antemortem MRI. Adequate pathological staging of AD is therefore possible using this in vivo technique.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".