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Background <p>Pericoronary adipose tissue (PCAT) radiomics derived from coronary computed tomography angiography (CCTA) for predicting major adverse cardiovascular events (MACE) in patients with acute coronary syndrome (ACS) remains unclear. This study aimed to assess whether PCAT radiomics could further provide complementary predictive value for the risk of MACE during long-term follow-up.</p> Methods <p>A multicenter retrospective study enrolled 777 subjects who underwent pre-intervention CCTA at 3 medical centers. Patients from one institution (n = 664) formed an internal cohort and were randomly split into training and internal test sets (7:3). Multivariable Cox regression models were developed using clinical scores, traditional CCTA, PCAT attenuation (PCATa) and PCAT radiomics, and were tested using the internal test set. Data from two additional institutions (n = 113) were reserved as an external test set to evaluate the applicability and generalizability of models.</p> Results <p>A total of 777 participants (61.0 ± 9.70&#xa0;years; 506 males) were analyzed. During a median follow-up of 5.45&#xa0;years (interquartile range: 4.03, 7.12&#xa0;years), 177 (22.78%) cases experienced a MACE. Adding culprit PCATa or three vessels-based PCATa did not improve predictive ability for the model containing clinical scores and traditional CCTA, whereas further addition of PCAT<sub>culprit</sub> Radscore (C-index: 0.721, 0.652, 0.645) and three vessels-based PCAT Radscore (C-index: 0.725, 0.660, 0.686) improved model predictive performance in the training, internal test and external test sets, without significant differences between datasets or models (all <i>P</i> &gt; 0.05). Adding either the PCAT<sub>culprit</sub> Radscore (training: IDI = 0.031, <i>p</i> &lt; 0.001; NRI = 0.256, <i>p</i> &lt; 0.001; external test: IDI = 0.094, <i>p</i> &lt; 0.001; NRI = 0.339, <i>p</i> = 0.02) or the three vessels-based PCAT Radscore (training: IDI = 0.032, <i>p</i> &lt; 0.001; NRI = 0.224, <i>p</i> = 0.02; external test: IDI = 0.126, <i>p</i> &lt; 0.001; NRI = 0.480, <i>p</i> &lt; 0.001) to a clinical model yielded a significant improvement in discrimination and reclassification ability in the training and external test sets, respectively.</p> Conclusions <p>PCAT radiomics can enhance long-term prediction of MACE in ACS patients beyond current clinical scores, traditional CCTA and PCATa. Addition of PCAT radiomics to a conventional risk assessment improves the identification of high-risk individuals with MACE.</p> Graphical abstract <p>Incremental value of PCAT radiomics for predicting subsequent MACE in ACS patients. This schematic diagram illustrates that incorporating PCAT radiomics into a model comprising clinical scores, traditional CCTA and PCATa improves MACE risk prediction. ACEF, age, creatinine and ejection fraction; ACS, acute coronary syndrome; AUC, area under the ROC curve; CAD-RADS, coronary artery disease-reporting and data system; CCTA, coronary computed tomography angiography; C-index, concordance index; HRP, high-risk plaque; LAD, left anterior descending artery; LCX, left circumflex artery; MACE, major adverse cardiovascular event; PCAT, pericoronary adipose tissue; PCATa, PCAT attenuation; RCA, right coronary artery; ROC, receiver operating characteristic; SYNTAX, the synergy between percutaneous coronary intervention with taxus and cardiac surgery.</p>

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Pericoronary adipose tissue radiomics to improve risk stratification for patients with acute coronary syndrome: a multicenter retrospective cohort study

  • 期刊论文

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  • 发表日期：

    2025年8月31日

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  • Jin Shang,
  • Yanhua Zhen,
  • Zhezhe Zhang,
  • Ziyi Wang,
  • Hang Xu,
  • Yilong Pan,
  • Hongyu Chen,
  • Lu Sun,
  • Xin Pan,
  • Ronghui Ju,
  • Yang Hou

摘要

Background

Pericoronary adipose tissue (PCAT) radiomics derived from coronary computed tomography angiography (CCTA) for predicting major adverse cardiovascular events (MACE) in patients with acute coronary syndrome (ACS) remains unclear. This study aimed to assess whether PCAT radiomics could further provide complementary predictive value for the risk of MACE during long-term follow-up.

Methods

A multicenter retrospective study enrolled 777 subjects who underwent pre-intervention CCTA at 3 medical centers. Patients from one institution (n = 664) formed an internal cohort and were randomly split into training and internal test sets (7:3). Multivariable Cox regression models were developed using clinical scores, traditional CCTA, PCAT attenuation (PCATa) and PCAT radiomics, and were tested using the internal test set. Data from two additional institutions (n = 113) were reserved as an external test set to evaluate the applicability and generalizability of models.

Results

A total of 777 participants (61.0 ± 9.70 years; 506 males) were analyzed. During a median follow-up of 5.45 years (interquartile range: 4.03, 7.12 years), 177 (22.78%) cases experienced a MACE. Adding culprit PCATa or three vessels-based PCATa did not improve predictive ability for the model containing clinical scores and traditional CCTA, whereas further addition of PCATculprit Radscore (C-index: 0.721, 0.652, 0.645) and three vessels-based PCAT Radscore (C-index: 0.725, 0.660, 0.686) improved model predictive performance in the training, internal test and external test sets, without significant differences between datasets or models (all P > 0.05). Adding either the PCATculprit Radscore (training: IDI = 0.031, p < 0.001; NRI = 0.256, p < 0.001; external test: IDI = 0.094, p < 0.001; NRI = 0.339, p = 0.02) or the three vessels-based PCAT Radscore (training: IDI = 0.032, p < 0.001; NRI = 0.224, p = 0.02; external test: IDI = 0.126, p < 0.001; NRI = 0.480, p < 0.001) to a clinical model yielded a significant improvement in discrimination and reclassification ability in the training and external test sets, respectively.

Conclusions

PCAT radiomics can enhance long-term prediction of MACE in ACS patients beyond current clinical scores, traditional CCTA and PCATa. Addition of PCAT radiomics to a conventional risk assessment improves the identification of high-risk individuals with MACE.

Graphical abstract

Incremental value of PCAT radiomics for predicting subsequent MACE in ACS patients. This schematic diagram illustrates that incorporating PCAT radiomics into a model comprising clinical scores, traditional CCTA and PCATa improves MACE risk prediction. ACEF, age, creatinine and ejection fraction; ACS, acute coronary syndrome; AUC, area under the ROC curve; CAD-RADS, coronary artery disease-reporting and data system; CCTA, coronary computed tomography angiography; C-index, concordance index; HRP, high-risk plaque; LAD, left anterior descending artery; LCX, left circumflex artery; MACE, major adverse cardiovascular event; PCAT, pericoronary adipose tissue; PCATa, PCAT attenuation; RCA, right coronary artery; ROC, receiver operating characteristic; SYNTAX, the synergy between percutaneous coronary intervention with taxus and cardiac surgery.