A Visual Interpretation Algorithm for Assessing Brain Tauopathy with<sup>18</sup>F-MK-6240 PET
Bibliographic record
Abstract
In vivo characterization of pathologic deposition of tau protein in the human brain by PET imaging is a promising tool in drug development trials of Alzheimer disease (AD). 6-(fluoro-<sup>18</sup>F)-3-(1H-pyrrolo[2,3-c]pyridin-1-yl)isoquinolin-5-amine (<sup>18</sup>F-MK-6240) is a radiotracer with high selectivity and subnanomolar affinity for neurofibrillary tangles that shows favorable nonspecific brain penetration and excellent kinetic properties. The purpose of the present investigation was to develop a visual assessment method that provides both an overall assessment of brain tauopathy and regional characterization of abnormal tau deposition. <b>Methods:</b><sup>18</sup>F-MK-6240 scans from 102 participants (including cognitively normal volunteers and patients with AD or other neurodegenerative disorders) were reviewed by an expert nuclear medicine physician masked to each participant’s diagnosis to identify common patterns of brain uptake. This initial visual read method was field-tested in a separate, nonoverlapping cohort of 102 participants, with 2 additional naïve readers trained on the method. Visual read outcomes were compared with semiquantitative assessments using volume-of-interest SUV ratio. <b>Results:</b> For the visual read, the readers assessed 8 gray-matter regions per hemisphere as negative (no abnormal uptake) or positive (1%–25% of the region involved, 25%–75% involvement, or >75% involvement) and then characterized the tau binding pattern as positive or negative for evidence of tau and, if positive, whether brain uptake was in an AD pattern. The readers demonstrated agreement 94% of the time for overall positivity or negativity. Concordance on the determination of regional binary outcomes (negative or positive) showed agreement of 74.3% and a Fleiss κ of 0.912. Using clinical diagnosis as the ground truth, the readers demonstrated a sensitivity of 73%–79% and specificity of 91%–93%, with a combined reader-concordance sensitivity of 80% and specificity of 93%. The average SUV ratio in cortical regions showed a robust correlation with visually derived ratings of regional involvement (<i>r</i> = 0.73, <i>P</i> < 0.0001). <b>Conclusion:</b> We developed a visual read algorithm for <sup>18</sup>F-MK-6240 PET offering determination of both scan positivity and the regional degree of cortical involvement. These cross-sectional results show strong interreader concordance on both binary and regional assessments of tau deposition, as well as good sensitivity and excellent specificity supporting use as a tool for clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".