Visualization of fibroblast activation using 68Ga-FAPI PET/CT after pulmonary vein isolation with pulsed field compared with cryoballoon ablation
Bibliographic record
Abstract
Pulsed-field ablation (PFA) is a novel ablation modality for atrial fibrillation (AF) ablating myocardium by electroporation without tissue-heating. With its different mechanism of tissue ablation, it is assumed that lesion creation is divergent to thermal energy sources. 68 Ga-fibroblast-activation protein inhibitor (FAPI) PET/CT targets FAP-alpha expressed by activated fibroblasts. We aimed to assess 68 Ga-FAPI uptake in pulmonary veins as surrogate for ablation damage after PFA and cryoballoon ablation (CBA). 26 patients (15 PFA, 11 CBA) underwent 68 Ga-FAPI-PET/CT after ablation. Standardized uptake values (SUV) and fibroblast-activation volumes of localized tracer uptake were assessed. Patient characteristics were comparable between groups. In PFA, focal FAPI uptake was only observed in 3/15 (20%) patients, whereas in the CBA cohort, 10/11 (90.9%) patients showed atrial visual uptake. We observed lower values of SUV max (2.85 ± 0.56 vs 4.71 ± 2.06, P = 0.025) and FAV (1.13 ± 0.84 cm 3 vs 3.91 ± 2.74 cm 3 , P = 0.014) along with a trend towards lower SUV peak and SUV mean in PFA vs CBA patients, respectively. Tissue response with respect to fibroblast activation seems to be less pronounced in PFA compared to established thermal ablation systems. This functional assessment might contribute to a better understanding of lesion formation in thermal and PFA ablation potentially contributing to better safety outcomes.
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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 | 0.000 | 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 | 0.000 |
| 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".