Using simultaneous PET/MRI to compare the accuracy of diagnosing frontotemporal dementia by arterial spin labelling MRI and FDG-PET
Bibliographic record
Abstract
PURPOSE: The clinical utility of FDG-PET in diagnosing frontotemporal dementia (FTD) has been well demonstrated over the past decades. On the contrary, the diagnostic value of arterial spin labelling (ASL) MRI - a relatively new technique - in clinical diagnosis of FTD has yet to be confirmed. Using simultaneous PET/MRI, we evaluated the diagnostic performance of ASL in identifying pathological abnormalities in FTD (FTD) to determine whether ASL can provide similar diagnostic value as FDG-PET. METHODS: ASL and FDG-PET images were compared in 10 patients with FTD and 10 healthy older adults. Qualitative and quantitative measures of diagnostic equivalency were used to determine the diagnostic utility of ASL compared to FDG-PET. Sensitivity, specificity, and inter-rater reliability were calculated for each modality from scores of subjective visual ratings and from analysis of regional mean values in thirteen a priori regions of interest (ROI). To determine the extent of concordance between modalities in each patient, individual statistical maps generated from comparison of each patient to controls were compared between modalities using the Jaccard similarity index (JI). RESULTS: Visual assessments revealed lower sensitivity, specificity and inter-rater reliability for ASL (66.67%/62.12%/0.2) compared to FDG-PET (88.43%/90.91%/0.61). Across all regions, ASL performed lower than FDG-PET in discriminating patients from controls (areas under the receiver operating curve: ASL = 0.75 and FDG-PET = 0.87). In all patients, ASL identified patterns of reduced perfusion consistent with FTD, but areas of hypometabolism exceeded hypoperfused areas (group-mean JI = 0.30 ± 0.22). CONCLUSION: This pilot study demonstrated that ASL can detect similar spatial patterns of abnormalities in individual FTD patients compared to FDG-PET, but its sensitivity and specificity for discriminant diagnosis of a patient from healthy individuals remained unmatched to FDG-PET. Further studies at the individual level are required to confirm the clinical role of ASL in FTD management.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".