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Background <p>Pericoronary adipose tissue (PCAT) reflects local coronary inflammation and microstructural changes and may predict major adverse cardiac events (MACE). Radiomics extracts high-dimensional features from PCAT on coronary CT angiography, capturing tissue heterogeneity beyond conventional risk factors or plaque metrics. This study evaluated the diagnostic performance of PCAT radiomics for MACE prediction.</p> Materials and methods <p>PubMed, Scopus, and Web of Science were searched from inception to October 2025. Ten retrospective studies were included. Diagnostic metrics were extracted and pooled using random-effects models. Subgroup analyses were performed by classifier, region of interest, and follow-up duration. Methodological quality and certainty of evidence were assessed.</p> Results <p>Radiomics-only models showed moderate performance (sensitivity 0.70, specificity 0.74, area under the curve (AUC) 0.78). Combined models improved discrimination, with radiomics + clinical (AUC 0.80) and radiomics + imaging (sensitivity 0.89; diagnostic odds ratio (LnDOR) 2.93). Triple-combination models achieved the highest performance (AUC 0.87; LnDOR 4.05). Radiomics models showed higher AUC than clinical (ΔAUC = 0.05) and imaging models (ΔAUC = 0.18), with inconsistent sensitivity and specificity differences. Adding clinical variables provided modest improvement, whereas imaging integration yielded greater gains. Triple models showed the largest improvement (ΔAUC = 0.06; ΔLnDOR = 2.21). Mean Radiomics Quality Score was 18/36, and overall evidence certainty was moderate.</p> Conclusion <p>PCAT radiomics derived from CCTA shows moderate predictive performance for MACE in patients with coronary artery disease and may provide incremental value over conventional clinical and imaging models. Standardized radiomics pipelines and multicenter prospective validation are required for clinical translation.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Can quantitative analysis of pericoronary adipose tissue on coronary computed tomography angiography improve the prediction of major adverse cardiac events beyond clinical risk factors?</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>Radiomics models showed higher AUC than clinical and imaging models, while combined models demonstrated the highest predictive performance across included studies.</i></p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>Pericoronary adipose tissue radiomics may provide additional quantitative information on coronary inflammation and may offer potential incremental value for risk prediction in patients undergoing coronary computed tomography angiography. However, further external validation and standardization are required before clinical implementation.</i></p> Graphical Abstract <p></p>

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Pericoronary fat radiomics on coronary CT angiography for predicting major adverse cardiac events: a systematic review and meta-analysis

  • 期刊论文

  • 发表日期：

    2026年7月28日

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  • Seyedeh-Tarlan Mirzohreh,
  • Mahshid Dehghan,
  • Simin Sadeghi,
  • Zohreh Sadeghi,
  • Zahra-Sadat Mirian,
  • Matin Noroozi,
  • Mobina Fathi,
  • Samad Ghaffari,
  • Elnaz Javanshir,
  • Neda Roshanravan,
  • Masood Zangi

摘要

Background

Pericoronary adipose tissue (PCAT) reflects local coronary inflammation and microstructural changes and may predict major adverse cardiac events (MACE). Radiomics extracts high-dimensional features from PCAT on coronary CT angiography, capturing tissue heterogeneity beyond conventional risk factors or plaque metrics. This study evaluated the diagnostic performance of PCAT radiomics for MACE prediction.

Materials and methods

PubMed, Scopus, and Web of Science were searched from inception to October 2025. Ten retrospective studies were included. Diagnostic metrics were extracted and pooled using random-effects models. Subgroup analyses were performed by classifier, region of interest, and follow-up duration. Methodological quality and certainty of evidence were assessed.

Results

Radiomics-only models showed moderate performance (sensitivity 0.70, specificity 0.74, area under the curve (AUC) 0.78). Combined models improved discrimination, with radiomics + clinical (AUC 0.80) and radiomics + imaging (sensitivity 0.89; diagnostic odds ratio (LnDOR) 2.93). Triple-combination models achieved the highest performance (AUC 0.87; LnDOR 4.05). Radiomics models showed higher AUC than clinical (ΔAUC = 0.05) and imaging models (ΔAUC = 0.18), with inconsistent sensitivity and specificity differences. Adding clinical variables provided modest improvement, whereas imaging integration yielded greater gains. Triple models showed the largest improvement (ΔAUC = 0.06; ΔLnDOR = 2.21). Mean Radiomics Quality Score was 18/36, and overall evidence certainty was moderate.

Conclusion

PCAT radiomics derived from CCTA shows moderate predictive performance for MACE in patients with coronary artery disease and may provide incremental value over conventional clinical and imaging models. Standardized radiomics pipelines and multicenter prospective validation are required for clinical translation.

Key Points

Question Can quantitative analysis of pericoronary adipose tissue on coronary computed tomography angiography improve the prediction of major adverse cardiac events beyond clinical risk factors?

Findings Radiomics models showed higher AUC than clinical and imaging models, while combined models demonstrated the highest predictive performance across included studies.

Clinical relevance Pericoronary adipose tissue radiomics may provide additional quantitative information on coronary inflammation and may offer potential incremental value for risk prediction in patients undergoing coronary computed tomography angiography. However, further external validation and standardization are required before clinical implementation.

Graphical Abstract