Quantifying changes on susceptibility weighted images in amyotrophic lateral sclerosis using MRI texture analysis
Bibliographic record
Abstract
Objective: Susceptibility-weighted imaging (SWI) has been used to identify neurodegeneration in amyotrophic lateral sclerosis (ALS) through qualitative gross visual comparison of signal intensity. The aim of this study was to quantitatively identify cerebral degeneration in ALS on SWI using texture analysis. Methods: SW images were acquired from 17 ALS patients (58.4 ± 10.3 years, 13M/4F, ALSFRS-R 41.2 ± 4.1) and 18 healthy controls (56.3 ± 17.6 years, 9M/9F) at 4.7 tesla. Textures were computed within the precentral gyrus and basal ganglia and compared between patients and controls using ANCOVA with age and gender as covariates. Texture features were correlated with clinical measures in patients. Texture features found to be significantly different between patients and controls in the precentral gyrus were then used in a whole-brain 3D texture analysis. Results: The texture feature autocorrelation was significantly higher in ALS patients compared to healthy controls in the precentral gyrus and basal ganglia (p < 0.05). Autocorrelation correlated significantly with clinical measures such as disease progression rate and finger tapping speed (p < 0.05). Whole brain 3D texture analysis using autocorrelation revealed differences between ALS patients and controls within the precentral gyrus on SWI images (p < 0.001). Conclusion: Texture analysis on SWI can quantitatively identify cerebral differences between ALS patients and controls.
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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 | 0.000 | 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) | 0.001 | 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".