OBJECTIVES/GOALS: Our overall objective is to investigate the relationship between radiologic features of meningioma with recently identified histopathological and molecular biomarkers, and to apply a machine learning (ML) approach to further demonstrate their utility in predicting clinical outcomes. METHODS/STUDY POPULATION: We have enrolled a cohort of 84 patients with meningioma diagnosed on the basis of conventional gadolinium-enhanced MRI imaging features since September 2019. Each patient has demographic and clinical data, Ga-68-DOTATATE MRI/PET SUV and dynamic metrics, DCE-MRI perfusion parameters, and histopathologic data. Various tumor subregions will be segmented semi-automatically and later confirmed by experienced neuroradiologist. Histopathologic data will include histologic grade, mitotic rate, Ki67 proliferative index, and presence of WHO established atypical histologic features, immunohistochemical parameters, and established high-grade molecular features. We will use supervised learning techniques to develop algorithms for predicting molecular features from imaging phenotypes. RESULTS/ANTICIPATED RESULTS: Anticipated results - advancements in understanding the molecular biomarkers of meningiomas has uncovered genetic alterations and epigenetic changes that more accurately determine tumor behavior. Currently, the imaging correlates of these molecular biomarkers are unknown, and utilizing radiographic data to predict prognosis and imaging-based classifications of meningiomas have not yet been investigated. Validated imaging correlates of molecular biomarkers not only provide an in-vivo assessment of tumor biology, but can also be integrated with histopathologic features ( radiopathomics models’) for more accurate disease prognostication. We anticipate that our results will identify surrogate imaging features for some of the recently emerged molecular biomarkers of meningioma. DISCUSSION/SIGNIFICANCE: There is a paucity of data on the importance of imaging phenotypes in determining tumor biology. This work has the potential of significant clinical impact by enabling a priori molecular characterization of meningiomas at the time of new diagnosis or recurrence, thereby allowing a personalized medicine approach to treatment planning.