Significance of Subtle Diffusion Weighted Imaging Lesion Dynamics: A Comparative Analysis of Methods for Detecting Diffusion Weighted Imaging Lesion Reversal in Endovascular Stroke Treatment
Bibliographic record
Abstract
BACKGROUND: Restrictive diffusion on magnetic resonance imaging is recognized as an early marker of ischemic brain damage, even though diffusion-weighted imaging lesion reversal (DWI-R) is well known. This study aimed to compare methodologies for detecting DWI-R, including voxel-based analysis, which captures subtle lesion dynamics, and to test their correlation with clinical outcomes. METHODS: We retrospectively analyzed magnetic resonance imaging data from 216 consecutive patients with acute ischemic stroke obtained before and after endovascular therapy. DWI-R was defined either as an increase in DWI-Alberta Stroke Program Early Computed Tomography Score, a decrease of total DWI signal volume or as partial reversal of the initial DWI lesion, irrespective of the final DWI load. Associations between 3-month poststroke modified Rankin scale score and DWI-R was assessed according to the different definitions of DWI-R using logistic binary regression. RESULTS: In patients undergoing endovascular therapy, 25% had increased DWI-Alberta Stroke Program Early Computed Tomography Score and 32% showed reduced DWI volume. Both measures were strongly associated with favorable outcomes (modified Rankin Scale score ≤2) with odds ratios of 4.90 and 5.60, respectively (95% CIs: 1.66-14.46 and 2.09-14.98). Voxel-based analysis revealed DWI-R of ≥20% of the initial lesion in 64.5% of cases. Even with an overall increase of lesion volume due to progression elsewhere, ≥20% reversal of initial lesion was associated with a significantly improved outcome compared with <20% reversal, odds ratio 2.22 (95% CIs: 1.05-4.70). CONCLUSION: DWI-R was common in patients treated with endovascular therapy and linked to favorable outcomes. Subtle lesion dynamics detected only by the voxel-based analysis also conferred significant clinical benefits, supporting DWI-R as a continuum rather than a binary measure as "present" or "absent."
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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 | 0.027 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".