PROJECT SUMMARY Indeterminate pulmonary nodules (IPNs) are highly prevalent radiologic findings that represent a substantial burden to patients and the national health care system because of the diagnostic challenge they present. There is a dire need to accurately stratify IPNs into low and high malignancy risk subgroups which are associated with clinical management pathways that are standardized and well validated. Clinical prediction models have the potential to do so in a scalable, cost-efficient, automated, and noninvasive manner, but advances in predictive accuracy must be made before they can make a substantial impact in medical practice. An unexplored direction in this area is integrating repeated measures of computed tomography (CT) studies and clinically-collected information within the same prediction model. This joint learning strategy has advantage of potentially modeling how dynamic radiologic changes like nodule growth rate vary with the trajectory of clinical variables such as smoking patterns and laboratory abnormalities. This perspective motivates the hypothesis that integrating information from longitudinal imaging and longitudinal clinical records will improve personalized IPN risk stratification and lung cancer subclassification From a clinician’s lens, this finding would not be surprising given the many time-varying modalities that are involved in diagnosis and decision making. This project leverages artificial intelligence (AI) and radiomic methods to analyze three retrospective cohorts with the possible addition of a large prospective cohort. The proposed work in Aim 1 will extend upon existing deep learning techniques to train a joint learning model on longitudinal images and clinical records to estimate the malignancy probability across time in patients with IPNs in a combined cohort exceeding 2000 subjects. This novel strategy will be evaluated against single-modality models and convention models that are used in practice. The evaluation will compare the models’ performance in stratifying IPNs into the low and high risk subgroups as a measure of clinical utility. Aim 2 asks if longitudinal change in radiomic features can distinguish between indolent and aggressive lung adenocarcinoma, other lung cancer subtypes, and pulmonary metastases. The proposed study will be the first to comprehensively characterize longitudinal radiomics across lung cancer subtypes and has the potential to identify novel longitudinal radiomic features that will aid early IPN evaluation and noninvasive lung cancer subclassification in patients with repeated imaging. In summary, the proposed research asks if clever integration of longitudinal information across different modalities can be leveraged to advance IPN risk stratification and lung cancer subclassification. This fellowship will be conducted at Vanderbilt University in a highly collaborative training environment with mentors in medical imaging AI, pulmonary oncology, biomedical informatics,...