Diffusion-weighted MRI hyperintensity patterns differentiate CJD from other rapid dementias
Bibliographic record
Abstract
BACKGROUND: Diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) MRI have high sensitivity and specificity for Creutzfeldt-Jakob disease (CJD). No studies, however, have demonstrated how MRI can distinguish CJD from nonprion causes of rapidly progressive dementia (npRPD). We sought to determine the diagnostic accuracy of MRI for CJD compared to a cohort of npRPD subjects. METHODS: Two neuroradiologists blinded to diagnosis assessed DWI and FLAIR images in 90 patients with npRPD (n = 29) or prion disease (sporadic CJD [sCJD], n = 48, or genetic prion disease [familial CJD, n = 6, and Gerstmann-Sträussler-Scheinker, n = 7]). Thirty-one gray matter regions per hemisphere were assessed for abnormal hyperintensities. The likelihood of CJD was assessed using our previously published criteria. RESULTS: Gray matter hyperintensities (DWI > FLAIR) were found in all sCJD cases, with certain regions preferentially involved, but never only in limbic regions, and rarely in the precentral gyrus. In all sCJD cases with basal ganglia or thalamic DWI hyperintensities, there was associated restricted diffusion (apparent diffusion coefficient [ADC] map). This restricted diffusion, however, was not seen in any npRPD cases, in whom isolated limbic hyperintensities (FLAIR > DWI) were common. One reader's sensitivity and specificity for sCJD was 94% and 100%, respectively, the other's was 92% and 72%. After consensus review, the readers' combined MRI sensitivity and specificity for sCJD was 96% and 93%, respectively. Familial CJD had overlapping MRI features with sCJD. CONCLUSIONS: The pattern of FLAIR/DWI hyperintensity and restricted diffusion can differentiate sCJD from other RPDs with a high sensitivity and specificity. MRI with DWI and ADC should be included in sCJD diagnostic criteria. New sCJD MRI criteria are proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".