Abstract TMP117: Detecting Diseased Tissue in Normal Appearing White Matter of Transient Ischemic Attack and Minor Stroke Patients Using Texture Analysis
Bibliographic record
Abstract
Introduction: Patients with minor strokes and Transient Ischemic Attacks (TIAs) are at risk of recurrent strokes and cognitive decline. Stroke lesions appear as white matter hyperintensities on T2 MR images. We investigated microstructural changes in normal appearing white matter (NAWM) of patients with TIAs and minor stroke. Hypothesis: Longitudinal changes in texture analysis parameters (angular second moment [ASM] and entropy) of NAWM would correspond to a qualitative increase in diseased tissue. Methods: FLAIR MRI data was obtained within 24hrs of injury (baseline [n=86]), and at 3 follow-up times (90D, n=40; 18M, n=46; and 3Y, n=60). 14 regions of interest (ROIs) were manually placed bilaterally on the NAWM of the baseline FLAIR image in 2 locations in the medial temporal lobes (MDTL), genu, splenium, periventricular (PVT), frontal (FWM), parietal (PWM), and posterior cortex WM (COV). ASM and entropy were measured using the GLCM Texture Analysis tool in ImageJ. Friedman’s ANOVA addressed longitudinal changes. Results: Significant changes for 6 ROIs [MDTLR, Splenium, FWMR, PWMR, COVL, COVR (p<0.05)] were observed longitudinally. ASM decreases were seen at 90 days, with a return to baseline by 3 years (Fig1a). An increase in entropy was seen at 90 days; with no return to baseline for 5/6 ROIs (Fig1b). Conclusion: Texture analysis (ASM and entropy) may effectively quantify diseased tissue consistent with qualitative examination of NAWM in FLAIR sequences. Diseased tissue appears maximally present at 90 days with some return to baseline by 3 years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".