MR Imaging Features of Amyloid-Related Imaging Abnormalities
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: AD is one of the few leading causes of death without a disease-modifying drug; however, hopeful agents are in various phases of development. MR imaging abnormalities, collectively referred to as amyloid-related imaging abnormalities, have been reported for several agents that target cerebral Aβ burden. ARIA includes ARIA-E, parenchymal or sulcal hyperintensities on FLAIR indicative of parenchymal edema or sulcal effusions, and ARIA-H, hypointense regions on gradient recalled-echo/T2* indicative of hemosiderin deposition. This report describes imaging characteristics of ARIA-E and ARIA-H identified during studies of bapineuzumab, a humanized monoclonal antibody against Aβ. MATERIALS AND METHODS: Two neuroradiologists with knowledge of imaging changes reflective of ARIA reviewed MR imaging scans from 210 bapineuzumab-treated patients derived from 3 phase 2 studies. Each central reader interpreted the studies independently, and discrepancies were resolved by consensus. The inter-reader κ was 0.76, with 94% agreement between neuroradiologists regarding the presence or absence of ARIA-E in individual patients. RESULTS: Thirty-six patients were identified with incident ARIA-E (17.1%, 36/210) and 26 with incident ARIA-H (12.4%, 26/210); of those with incident ARIA-H, 24 had incident microhemorrhages and 2 had incident large superficial hemosiderin deposits. CONCLUSIONS: In 49% of cases of ARIA-E, there was the associated appearance of ARIA-H. In treated patients without ARIA-E, the risk for incident blood products was 4%. This association between ARIA-E and ARIA-H may suggest a common pathophysiologic mechanism. Familiarity with ARIA should permit radiologists and clinicians to recognize and communicate ARIA findings more reliably for optimal patient management.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".