Multi-echo chemical shift-encoded (CSE)-MRI techniques enable liver PDFF and R2* quantification, which enable staging and treatment monitoring of liver fat and iron content, respectively. However, the common requirement of breath-holding in CSE-MRI acquisitions is challenging for many patients. Furthermore, the required specialized multi-echo acquisition and reconstruction are not available in all scanners. In this work, we assessed the accuracy of deep learning (DL)-based PDFF and R2* quantification using reduced numbers of echoes. Preliminary results demonstrate the potential of this approach and suggest that at least four echoes are needed for quantifying PDFF and R2* at 1.5T and 3.0T.