Incorporation of quantitative imaging data using artificial intelligence improves risk prediction in veterans with liver disease

Abstract

Background and Aims: Utilization of electronic health records data to derive predictive indexes such as the electronic Child Turcotte Pugh Score can have significant utility in health care delivery. Within the records, CT scans contain phenotypic data which have significant prognostic value. However, data extractions have not traditionally been applied to imaging data. In this study, we utilized artificial intelligence to automate biomarker extraction from CT scans and examined the value of these features in improving risk prediction in patients with liver disease. Approach: Using a regional liver disease cohort from the Veterans Health System, we retrieved administrative, laboratory and clinical data for Veterans who had CT scans performed for any clinical indication between 2008 to 2014. Imaging biomarkers were automatically derived using the analytic morphomics platform. Results: 4614 patients were included. We found that the electronic Child Turcotte Pugh Score had a Concordance-index of 0.64 for the prediction of overall mortality while the imaging based model alone or with electronic Child Turcotte Pugh Score performed significantly better, Concordance-index of 0.72 and 0.73 (p<0.001). For the subset of patients without hepatic decompensation at baseline (n=4452), the Concordance-index for predicting future decompensation was 0.67, 0.79 and 0.80 for electronic Child Turcotte Pugh Score , imaging alone or combined respectively. Conclusion: This proof of concept demonstrates the potential of utilizing automated extraction of imaging features within CT scans either alone or in conjunction with classic health data can improve risk prediction in patients with chronic liver disease.