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Abstract 16796: Machine Learning-Based Hepatic Fat Assessment in Low-Dose Coronary Artery Calcium Scans is Correlated With Human Reader Assessment


 Introduction:
 Hepatic attenuation on computed tomography (CT) is a surrogate marker for cardiometabolic risk, including Type 2 diabetes and its progression. Coronary artery calcium (CAC) screening for assessment of cardiovascular risk often includes portions of the liver and provides an opportunity for concomitant assessment of liver fat. However, the use of lower radiation dose CT and the need for reliable regions of interest (ROI) tracing creates challenges for routine clinical utilization. We present machine learning-based hepatic fat assessment (a) segmentation of liver and spleen and (b) CT attenuation measurement on low dose CT scans performed for CAC screening.
 
 
 Methods:
 Our approach (Figure 1a) comprises two steps. First, a deep learning-based model is utilized to segment the liver and spleen. Second, CT attenuation is measured on imaged organ volume. The segmentation model (U-Net with an EfficientNetB3 backbone) is trained with 5-fold cross-validation on 35 patients enrolled in the CLARIFY registry (clinicaltrials.gov NCT04075162) and validated on an independent set of 21 patients. The ground truth for segmentation is generated using Slicer 3D. Further, mean liver and mean liver-to-spleen CT attenuation is automatically measured on these scans and compared with manual ROI based assessment.
 
 
 Results:
 The segmentation model achieved a dice coefficient of 0.91 on the validation (Figure 1b). The automated volumetric assessment of liver and liver-to-spleen attenuation estimation showed a strong correlation with manual ROI assessment (Pearson correlation coefficient R=0.89 and R=0.88, respectively; Figure 1c).
 
 
 Conclusions:
 The presented machine-learning-based methods enable accurate estimation of liver and liver-to-spleen attenuation ratio on low-dose CAC CT scans. The approach needs to be further validated on larger, multi-institutional datasets to analyze its generalizability for hepatic fat assessment.
 
 
 
