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Published on: June 2, 2023
AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances
Jirong Yi1, Krishna K Patel2, Robert J H Miller1,3
1Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Department of Computational Biomedicine, Biostatistics Shared Resource, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Insights
Artificial intelligence (AI) can now identify hepatic steatosis (HS) from CT scans during myocardial perfusion imaging (MPI). This AI-driven approach improves the prediction of all-cause mortality in patients with coronary artery disease.
Area of Science:
- Cardiology
- Radiology
- Artificial Intelligence
- Hepatology
Background:
- Hepatic steatosis (HS) is a common cardiometabolic risk factor often under-diagnosed in patients with coronary artery disease (CAD).
- Accurate identification of HS is crucial for risk stratification in this patient population.
Purpose of the Study:
- To utilize artificial intelligence (AI) for automated quantification of hepatic tissue measures from CT attenuation correction (CTAC) scans during myocardial perfusion imaging (MPI).
- To evaluate the prognostic value of AI-derived HS detection and a novel AI liver risk index (LIRI) for predicting all-cause mortality in patients undergoing MPI.
Main Methods:
- An AI model was developed to segment liver and spleen on CTAC scans from 27,039 patients, quantifying liver and liver minus spleen (LmS) attenuation.
- HS was defined using thresholds of mean liver attenuation (<40 HU) or LmS attenuation (<-10 HU).
- A liver risk index (LIRI) was developed integrating hepatic measures and validated for prognostic value in predicting all-cause mortality.
Main Results:
- The AI algorithm identified HS in 24% of patients. During follow-up, HS was associated with increased mortality risk (adjusted HR: 1.14).
- The developed AI liver risk index (LIRI) demonstrated superior prognostic value compared to HS alone (adjusted HR: 1.5 vs 1.16).
- AI-based hepatic measures were automatically quantified without additional radiation or physician interaction.
Conclusions:
- AI-based hepatic measures can effectively identify HS from CTAC scans in patients undergoing MPI.
- An integrated AI liver risk assessment provides enhanced risk stratification for all-cause mortality, outperforming traditional HS detection.
Background:
Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary artery disease. We used artificial intelligence (AI) to automatically quantify hepatic tissue measures for identifying HS from CT attenuation correction (CTAC) scans during myocardial perfusion imaging (MPI) and evaluate their added prognostic value for all-cause mortality prediction.
Methods:
This study included 27039 consecutive patients [57% male] with MPI scans from nine sites. We used an AI model to segment liver and spleen on low dose CTAC scans and quantify the liver measures, and the difference of liver minus spleen (LmS) measures. HS was defined as mean liver attenuation < 40 Hounsfield units (HU) or LmS attenuation < -10 HU. Additionally, we used seven sites to develop an AI liver risk index (LIRI) for comprehensive hepatic assessment by integrating the hepatic measures and two external sites to validate its improved prognostic value and generalizability for all-cause mortality prediction over HS.
Findings:
Median (interquartile range [IQR]) age was 67 [58, 75] years and body mass index (BMI) was 29.5 [25.5, 34.7] kg/m2, with diabetes in 8950 (33%) patients. The algorithm identified HS in 6579 (24%) patients. During median [IQR] follow-up of 3.58 [1.86, 5.15] years, 4836 (18%) patients died. HS was associated with increased mortality risk overall (adjusted hazard ratio (HR): 1.14 [1.05, 1.24], p=0.0016) and in subpopulations. LIRI provided higher prognostic value than HS after adjustments overall (adjusted HR 1.5 [1.32, 1.69], p<0.0001 vs HR 1.16 [1.02, 1.31], p=0.0204) and in subpopulations.
Interpretations:
AI-based hepatic measures automatically identify HS from CTAC scans in patients undergoing MPI without additional radiation dose or physician interaction. Integrated liver assessment combining multiple hepatic imaging measures improved risk stratification for all-cause mortality.
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