Scholarlink

Purpose <p>Detecting vulnerable coronary plaques through coronary computed tomography angiography (CCTA) is a crucial, yet challenging task. To date, most of the proposed vulnerability markers have been studied in isolation. This study introduces the first integrated analysis combining radiomic, mechanical, and hemodynamic factors to explore their synergistic contribution to plaque vulnerability.</p> Methods <p>The study analyzed 161 plaques in 46 coronary arteries from 39 patients, with 7 arteries (28 plaques) from 7 individuals, labeled as vulnerable from intravascular imaging. First, CCTA radiomic features were extracted. Second, mechanical markers were computed through finite element simulations conducted with varying material characteristics, accounting for the arterial wall mechanical properties’ uncertainties. Third, hemodynamic markers were derived from transient computational fluid dynamics simulations. Finally, a machine learning pipeline was developed to classify coronary arteries and patients based on radiomic, mechanical, and hemodynamic features, both individually and in combination.</p> Results <p>Radiomics achieved the highest sensitivity (1.00), with all vulnerable patients identified, but lower specificity (0.69). Differently, mechanics and hemodynamics achieved higher specificities (0.94 and 0.97, respectively) but lower sensitivities (both 0.86). By integrating at least two out of the three models, the predictive performance improved, up to sensitivity = 1.00 and specificity = 0.97, with only one misclassified case.</p> Conclusion <p>Although based on only 39 patients, the results highlight the power of a multi-level integrative approach for coronary plaque assessment. The study revealed that (i) hemodynamics outperformed mechanics and radiomics; (ii) while radiomics maximized sensitivity, mechanics and hemodynamics prioritized specificity, and (iii) integrating at least two variable types added value.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Advancing Coronary Risk Assessment Through Combined Radiomic, Mechanical, and Hemodynamic Analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月23日

查看全文
  • Anna Corti,
  • Marco Stefanati,
  • Vittorio Lissoni,
  • Matteo Leccardi,
  • Francesco Bruno,
  • Alessandro Depaoli,
  • Pietro Cerveri,
  • Francesco Migliavacca,
  • Valentina D. A. Corino,
  • José F. Rodriguez Matas,
  • Luca Mainardi,
  • Gabriele Dubini

摘要

Purpose

Detecting vulnerable coronary plaques through coronary computed tomography angiography (CCTA) is a crucial, yet challenging task. To date, most of the proposed vulnerability markers have been studied in isolation. This study introduces the first integrated analysis combining radiomic, mechanical, and hemodynamic factors to explore their synergistic contribution to plaque vulnerability.

Methods

The study analyzed 161 plaques in 46 coronary arteries from 39 patients, with 7 arteries (28 plaques) from 7 individuals, labeled as vulnerable from intravascular imaging. First, CCTA radiomic features were extracted. Second, mechanical markers were computed through finite element simulations conducted with varying material characteristics, accounting for the arterial wall mechanical properties’ uncertainties. Third, hemodynamic markers were derived from transient computational fluid dynamics simulations. Finally, a machine learning pipeline was developed to classify coronary arteries and patients based on radiomic, mechanical, and hemodynamic features, both individually and in combination.

Results

Radiomics achieved the highest sensitivity (1.00), with all vulnerable patients identified, but lower specificity (0.69). Differently, mechanics and hemodynamics achieved higher specificities (0.94 and 0.97, respectively) but lower sensitivities (both 0.86). By integrating at least two out of the three models, the predictive performance improved, up to sensitivity = 1.00 and specificity = 0.97, with only one misclassified case.

Conclusion

Although based on only 39 patients, the results highlight the power of a multi-level integrative approach for coronary plaque assessment. The study revealed that (i) hemodynamics outperformed mechanics and radiomics; (ii) while radiomics maximized sensitivity, mechanics and hemodynamics prioritized specificity, and (iii) integrating at least two variable types added value.