Abstract 15620: Diagnostic Accuracy of Virtual Non-Contrast CT for Aortic Valve Stenosis Severity Evaluation
Bibliographic record
Abstract
Introduction: Computed tomography (CT) aortic valve calcium (AVC) score has important prognostic and diagnostic value in patients with aortic stenosis, especially in those evaluated for TAVR, but at the expense of an additional non-contrast scan. Dual-energy CT (DECT) allows virtual non-contrast reconstructions (VNC) from conventional contrast acquisitions. Aims: Compare the diagnostic performance of VNC-AVC score as compared to TNC-AVC score for identifying severe AS. Methods: We prospectively included patients undergoing pre-TAVR CT with a DECT system (IQon, Philips). TNC-AVC score was acquired using a prospective acquisition and VNC-AVC score derived from a contrast-enhanced retrospective scan. Both scores were calculated using the Agatston method. A correction proportionality constant was applied. Concordant severe AS was defined as AVA<1cm2 and either Vmax >4cm/s or mean gradient ≥40mmHg. Spearman, Cohen’s Kappa, and Bland-Altman were used to assess concordance. Results: In total, 109 patients were included: mean age of 79 ± 10 yrs, 55% female, 43% with concordant severe, 43% LF-LG, and 14% moderate AS. TNC scan median radiation was 116 mGy*cm (IQR 53-366). The median TNC-AVC was 2753 AU (1652-4135), while the median VNC-AVC was 1900 AU (946-2752) after applying the constant (1.43). A strong correlation was demonstrated between methods (r=0.89; p<0.001; Figure). Using accepted thresholds (>1300 AU for women and >2000 AU for men), 68% (n=74) of patients had severe AS by TNC. After estimating thresholds for VNC (>660 for women and >1519 for men), 60% (n=65) had severe AS, demonstrating substantial agreement with TNC-AVC (K;=0.71). Among individuals diagnosed with LF-LG, 38% and 34% exhibited severe AS by TNC/VNC, respectively, and only 12% and 11% in those with moderate AS. Conclusions: DECT-derived VNC-AVC demonstrates substantial agreement with TNC AVC, without requiring an additional scan and reducing both radiation exposure and acquisition time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".