Mathematical model observers (MOs) have become popular in task-based CT image quality assessment, since, once proven to be correlated with human observers (HOs), these MOs can be used to estimate HO performance. However, typical MO studies are limited to phantom data which only involve uniform background. In practice, anatomical background variability and tissue non-uniformity affect HO lesion detection performance. Recently, we have proposed a deep-learning-based MO (DL-MO). In this study, we aim to investigate the correlation between this DL-MO and HOs for a lung-nodule localization task in chest CT. Using a patient database that contains 50 lung cancer screening CT patient cases, 12 different experimental conditions were generated, including 4 radiation dose levels, 3 nodule sizes, 2 nodule types and 3 reconstruction types. These conditions were created by using a validated noise and lesion insertion tool. Four subspecialized radiologists performed the HO study for all 12 conditions individually in a randomized fashion. The DL-MO was trained and tested for the same dataset. The performance of DL-MO and HO was compared across all the experimental conditions. DL-MO performance was strongly correlated with HO performance (Pearson's correlation coefficient: 0.988 with a 95% confidence interval of [0.894, 0.999]). These results demonstrate the potential to use the proposed DL-MO to predict HO performance for the task of lung nodule localization in chest CT.