Texture analysis differentiates persistent and transient T1 black holes at acute onset in multiple sclerosis: A preliminary study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The persistence of new enhancing T1 hypointense lesions (acute black holes, ABHs) in multiple sclerosis (MS) cannot be predicted visually at lesion onset. Texture analysis using the polar Stockwell transform (PST) applied to conventional MR images however shows promise in quantifying tissue injury early. The objective of this study was to explore whether ABHs that persist (pABHs) differ from those that are transient (tABHs) using PST texture analysis. METHODS: Fifteen ABHs (8 pABHs; 7 tABHs) from 9 patients were analyzed on 3T images obtained during a clinical trial. Persistence was defined as remaining T1 hypointense 5-8 months later. NAWM regions were examined to control for changes unrelated to ABHs. RESULTS: At first appearance, there was higher coarse texture indicating greater tissue damage in the pABHs than in the tABHs (p<0.01). Both had greater coarse texture than the contralateral and general NAWM (p≤0.01). No difference was identified in normalized signal intensity between pABHs and tABHs and neither demonstrated location preference. While tABHs tended to be smaller than pABHs there was no correlation between lesion size and texture (r=0.44, p>0.05). Furthermore, coarse texture content appeared to predict persistence of individual lesions. CONCLUSIONS: This preliminary study suggests that PST texture could predict persistence of tissue injury based on the severity of structural disorganization within acute lesions. While confirmation of this data is required texture analysis may prove to be a valuable tool to quantify tissue damage and predict recovery in proof-of-concept neuroprotection and repair trials.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".