Validation of <scp>T</scp>1w‐based segmentations of white matter hyperintensity volumes in large‐scale datasets of aging
Bibliographic record
Abstract
INTRODUCTION: Fluid-attenuated Inversion Recovery (FLAIR) and dual T2w and proton density (PD) magnetic resonance images (MRIs) are considered to be the optimum sequences for detecting white matter hyperintensities (WMHs) in aging and Alzheimer's disease populations. However, many existing large multisite studies forgo their acquisition in favor of other MRI sequences due to economic and time constraints. METHODS: In this article, we have investigated whether FLAIR and T2w/PD sequences are necessary to detect WMHs in Alzheimer's and aging studies, compared to using only T1w images. Using a previously validated automated tool based on a Random Forests classifier, WMHs were segmented for the baseline visits of subjects from ADC, ADNI1, and ADNI2/GO studies with and without T2w/PD and FLAIR information. The obtained WMH loads (WMHLs) in different lobes were then correlated with manually segmented WMHLs, each other, age, cognitive, and clinical measures to assess the strength of the correlations with and without using T2w/PD and FLAIR information. RESULTS: The WMHLs obtained from T1w-Only segmentations correlated with the manual WMHLs (ADNI1: r = .743, p < .001, ADNI2/GO: r = .904, p < .001), segmentations obtained from T1w + T2w + PD for ADNI1 (r = .888, p < .001) and T1w + FLAIR for ADNI2/GO (r = .969, p < .001), age (ADNI1: r = .391, p < .001, ADNI2/GO: r = .466, p < .001), and ADAS13 (ADNI1: r = .227, p < .001, ADNI2/GO: r = .190, p < 0.001), and NPI (ADNI1: r = .290, p < .001, ADNI2/GO: r = 0.144, p < .001), controlling for age. CONCLUSION: Our results suggest that while T2w/PD and FLAIR provide more accurate estimates of the true WMHLs, T1w-Only segmentations can still provide estimates that hold strong correlations with the actual WMHLs, age, and performance on various cognitive/clinical scales, giving added value to datasets where T2w/PD or FLAIR are not available.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".