Preoperative Image-Based Simulation of Carotid Artery Stenting Considering Agatston Score of Calcified Plaques
Bibliographic record
Abstract
The role of calcification inside fibroatheroma during carotid artery stenting operation is controversial. Cardiologists face a major problem in placing stents: “plaque protrusion” i.e. elastic fibrous caps containing early calcifications that penetrate inside the stent. The aim of this work is to assess the contribution of calcification to plaque vulnerability by using image-based models of carotid artery stenting. Technically, Finite Element Analysis was used to simulate the balloon and stent expansion as a preoperative virtual framework. A nonlinear static structural analysis was performed on 20 image-based models of patients reconstructed from Multidetector Computer Tomography (CT) angiography acquisitions. Subject-specific local Elastic Modulus (EM) of calcified plaques was estimated using the Agatston Calcium (Ca) score, as obtained from CT images. The main findings from the personalized simulations are that maximum values of von Mises stress of 102kPa and 329kPa are obtained on calcified plaques when patient-specific balloon pressure expansion and stent self-expansion, respectively, are modeled. In modeling stent expansion procedure, it is observed a statistically significant positive correlation for EM of calcification with maximum stress (R=0.55; p=0.013) and Plaque Wall Stress (PWS) (R=0.47; p=0.038), while no significant correlation is observed for Ca score with maximum stress (R=0.28; p=0.23) and PWS (R=0.27; p=0.26), this last result suggesting a moderate impact of Ca score in plaque rupture. The approach proposed here could enrich the arsenal of tools available for pre-operative prediction of carotid artery stenting procedure in the presence of calcified plaques. Keywords: Atherosclerotic calcification, calcified plaques, carotid artery stenting, finite element analysis, plaque mechanics, subject-specific model.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".