Improving discrimination of indeterminate pulmonary nodules (IPNs) continues to be a significant challenge in lung cancer management and treatment. While numerous lung cancer risk prediction models have been developed to predict the probability of malignancy of IPNs, many IPNs remain in what is called an indeterminate risk range. IPNs within this range can be potentially misdiagnosed, thus leading to either unnecessary medical procedures or the missed opportunity to begin treatment promptly and increase a patient’s probability of survival. The addition of biomarker information to pre-existing risk prediction models has been proposed as a method to improve prediction of malignancy. In this study, various net reclassification indices (NRIs) were used to measure the incremental value of an additional biomarker including the two-category NRI as well as the bias-corrected clinical NRI. This study also compared the NRIs calculated from logistic regression, Super Learner, and Bayesian stacking approaches. Through these various approaches, we will highlight strategies that will improve the diagnostic accuracy of IPNs and, ultimately, improve the clinical management of IPNs.