Regional assessment of aortic valve calcification using topographic maps in contrast-enhanced CT: in-vivo sex and severity-based differences in calcific presentation
Bibliographic record
Abstract
Background: Procedural planning for transcatheter aortic valve replacement (TAVR) is routinely performed using contrast computed tomography (CT) in patients with severe aortic stenosis (AS). Despite its potential, little investigation has been done into the possibility of aortic valve calcification (AVC) scoring in contrast-enhanced CT. Contrast CT has superior spatial and contrast resolution compared to the non-contrast Agatston score protocol, which would allow for development of better pattern and distribution descriptors of calcific lesions in the aortic valve (AV). Methods: We developed a new false positive rate (FPR) based method that can quantify leaflet calcification based on shape overlap metrics. We also introduce a novel regional scheme for quantifying the shape and structure of calcification using topographic maps. The study was designed to: (I) determine the feasibility of using a novel method based on FPR to detect AVC using contrast-enhanced CT images by assessing the volume scores measured using FPR versus non-contrast methods and alternative contrast methods for volume scoring based on fixed or dynamic HU thresholds. (II) Develop a new scheme for assessing calcific geometry and structure and evaluate patterns of calcification in the varied presentation of AS. Results: Our results show a very strong correlation with non-contrast volume (r=0.919, P<0.001; n=178) and Agatston scores (r=0.913, P<0.001; n=178) that were evaluated using a standard calcium scoring technique. Finally, we analyzed the differences and similarities in the patterns of calcific deposition with respect to sex and degree of severity. Conclusions: The FPR method demonstrates the best overall agreement with non-contrast scores across both low and high ends of calcific density compared to luminal attenuation methods. In addition, we showed that leaflet calcific deposition follows distinctive patterns across the belly of the leaflet, with the rate of calcific progression peaking at the non-coronary cusp (NCC) leaflet and lowest for the right-coronary cusp. Females experience significantly lower calcific deposition compared to males despite showing similar patterns and symptoms. Our findings suggest that precise regional assessment of calcific progression could be an important tool for monitoring AS development as well as predicting peri-procedural complications in TAVR.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".