P1‐270: A direct comparison between two volumetric measurement techniques using subjects participating in the multi‐centre Alzheimer's disease neuroimaging initiative
Bibliographic record
Abstract
The establishment of biomarkers of disease progression is critical to increase the efficiency of multicentre therapeutic trials in Alzheimer disease (AD) and mild cognitive impairment (MCI). Ventricular enlargement is a candidate surrogate marker of disease progression in subjects with MCI and AD. Objective:This study directly compares a novel ventricular segmentation technique, Brain Ventricle Quantification (BVQ, Cedara Software), to the well-established Boundary Shift Integral (BSI) technique using data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). T1-weighted magnetic resonance images (MRI) and corresponding clinical data were acquired from the ADNI database for a sample of 505 subjects. A subset of 166 subjects (including 49 normal elderly controls (NEC), 81 subjects with MCI, and 36 subjects with AD) had both baseline and six-month BSI ventricular volume data available, and were included in further analysis. For these subjects, images were segmented by the semi-automatic region-growing algorithm BVQ. The operator was blind to all subject data. BSI measures of ventricle volume were contributed to the ADNI database by Fox et.al. from UCL, UK. BSI and BVQ measured ventricular volumes for all subjects at baseline and six-months were compared for absolute agreement by the intraclass correlation coefficient (ICC). For both volumetric tools, an ANOVA was preformed to compare the ventricle enlargement between NEC, MCI, and AD groups. The ICC at baseline was 0.971 (p<0.001) and at six-months was 0.983 (p<0.001). Using BVQ, the AD group had significantly greater absolute ventricular enlargement than the MCI (p<0.05) and NEC group (p<0.01) (Table 1). There were no significant differences between groups for BSI measurements. Both measurement techniques demonstrated high levels of agreement for measurements at both baseline and six-months. However, BVQ was able to distinguish the AD group from both the MCI and NEC groups, while the BSI could not distinguish between the three groups. These preliminary data indicate that BVQ and the BSI provide similar measurements of ventricular change using multi-centre serial MRI. However, in this dataset, there was less variation in the BVQ data, which contributed to BVQs sensitivity to detect differential rates of ventricular enlargement between groups compared to the BSI.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".