Raúl San José Estépar, PhD
Chronic Obstructive Pulmonary Disease (COPD) is a chronic, inflammatory lung disease that arises from exposure to cigarette smoke and other inhaled toxins. It is the 3rd leading cause of death worldwide, affecting about 10% of the general population, but its prevalence among heavy smokers can reach 50%. The main clinical feature of COPD is a limitation of airflow that is not fully reversible. Despite the simplicity of the clinical definition, the heterogeneity of this disease makes the clinical assessment and treatment complex. In-vivo Quantitative Chest Computed Tomography provides high-resolution structural information of the lung that enables better characterization of patients suffering from COPD. In this talk, I will give an overview of the image-based biomarkers that are being developed to phenotype the different pathophysiological components of COPD: airway disease, emphysema, and pulmonary vascular disease. I will present the current paradigm to develop image-based biomarkers in lung diseases, and I will introduce some of the new deep learning approaches that are allowing us to perform end-to-end automatic imaging phenotyping in large populations. Finally, I will show some of the applications of these new approaches for clinical and genetic discovery.
Raúl San José Estépar is co-director of the Applied Chest Imaging Laboratory at Brigham and Women’s Hospital and Associate Professor of Radiology at Harvard Medical School. His laboratory focuses on computational imaging and quantitative biomarkers to enable epidemiological and genetic studies and to define novel surrogate targets for drug discovery and development. His group supports the image analytics of multiple Federal and Industry sponsored investigations serving as imaging core for COPDGene, the Framingham Heart Study Pulmonary Research Center, the CARDIA Lung Study, and more recently the American Lung Association (ALA) Lung Health Cohort. He is the original developer and chief architect of the Chest Imaging Platform, an open-source software platform for CT-based lung phenotyping. His current research interest expands from the quantitative study of pulmonary vascular remodeling to the subtyping and modeling of parenchymal lung injury and the prediction of outcomes directly from imaging employing machine learning and artificial intelligence techniques.
Raúl received his Ph.D. in Telecommunications Engineering from the University of Valladolid, Spain, where he specialized in signal processing applied to medical image analysis. He has co-authored over 200 peer-reviewed manuscripts, and he is currently the Principal Investigator of two NIH NHLBI R01 awards.