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Published on: July 28, 2018
Pericoronary adipose tissue radiomics to improve risk stratification for patients with acute coronary syndrome: a
Jin Shang1, Yanhua Zhen1, Zhezhe Zhang2
1Department of Radiology, Shengjing Hospital of China Medical University, No.36, Sanhao Street, Heping District, Shenyang, 110004, Liaoning Province, China.
Insights
Pericoronary adipose tissue (PCAT) radiomics significantly improves the prediction of major adverse cardiovascular events (MACE) in acute coronary syndrome (ACS) patients. This advanced analysis enhances risk stratification beyond traditional methods.
Area of Science:
- Cardiovascular Imaging
- Radiomics
- Predictive Analytics
Background:
- Predicting major adverse cardiovascular events (MACE) in acute coronary syndrome (ACS) patients using pericoronary adipose tissue (PCAT) radiomics from coronary computed tomography angiography (CCTA) requires further investigation.
- This study aimed to determine if PCAT radiomics offers complementary predictive value for MACE risk during long-term follow-up.
Purpose of the Study:
- To assess the added value of PCAT radiomics in predicting long-term MACE in ACS patients.
- To compare the predictive performance of models incorporating PCAT radiomics against traditional risk assessment methods.
Main Methods:
- A multicenter retrospective study included 777 patients who underwent CCTA.
- Internal (n=664) and external (n=113) cohorts were used to develop and validate multivariable Cox regression models.
- Models integrated clinical scores, CCTA data, PCAT attenuation (PCATa), and PCAT radiomics (PCATculprit Radscore and three vessels-based PCAT Radscore).
Main Results:
- PCAT radiomics (PCATculprit Radscore and three vessels-based PCAT Radscore) significantly improved model predictive performance in training, internal test, and external test sets (C-indices ranging from 0.645 to 0.725).
- Adding PCAT radiomics to clinical models significantly enhanced discrimination and reclassification abilities (IDI and NRI improvements observed).
- PCAT attenuation alone did not improve predictive ability.
Conclusions:
- PCAT radiomics significantly enhances the long-term prediction of MACE in ACS patients.
- Incorporating PCAT radiomics into conventional risk assessment improves the identification of high-risk individuals.
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.
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