IEEE ICMA 2024 Conference

Plenary Talk 1

Data Harmonization

Image Harmonization and Airway Tree Subtyping on Large Cohorts of CT Scans of the Lung for COPD Risk

Andrew F. Laine, D.Sc.

Percy K. and Vida L. W. Hudson Professor

Department of Biomedical Engineering

Professor of Radiology (Physics), Department of Radiology

Director, Heffner Biomedical Imaging Laboratory

Columbia University, New York, NY, USA

Email: LAINE@columbia.edu

Bio-Sketch: Andrew F. Laine received his D.Sc. degree from Washington University (St. Louis) School of Engineering and Applied Science in Computer Science, in 1989 and BS degree from Cornell University (Ithaca, NY). He was a Professor in the Department of Computer and Information Sciences and Engineering at the University of Florida (Gainesville, FL) from 1990-1997. He joined the Department of Biomedical Engineering in 1997 and served as Vice Chair of the Department of Biomedical Engineering at Columbia University since 2003 – 2011, and Chair of the Department of Biomedical Engineering (2012 – 2017). He is currently Director of the Heffner Biomedical Imaging at Columbia University and the Percy K. and Vida L. W. Hudson Professor of Biomedical Engineering and Professor of Radiology (Physics).

He has served on the program committee for the IEEE-EMBS Workshop on Wavelet Applications in Medicine in 1994, 1998, 1999, and 2004. He was the founding chair of the SPIE conference on “Mathematical Imaging: Wavelet Application in Signal and Image Processing” and served as co-chair during the years 1993-2003. Dr. Laine has served as Chair of Technical Committee (TC-BIIP) on Biomedical Imaging and Image Processing for IEEE EMBS 2004-2009 and has been a member of the TC of IEEE Signal Processing Society, TC-BISP (Biomedical Imaging and Signal Processing) 2003-present. Professor Laine served on the IEEE ISBI (International Symposium on Biomedical Imaging) steering committee, 2006-2009 and 2009 – 2012. He was the Program Chair for the IEEE EMBS annual conference in 2006 held in New York City and served as Program Co-Chair for IEEE ISBI in 2008 (Paris, France). He served as Area Editor for IEEE Reviews in BME in Biomedical Imaging since 2007-2013. He was Program Chair for the EMBS annual conference for 2011 (Boston, MA). Professor Laine Chaired the Steering committee for IEEE ISBI, 2011-2013, and Chaired the Council of Societies for AIMBE (American Institute for Medical and Biological Engineers). He was the General Co-Chair for IEEE ISBI in 2022. Finally, he served as the IEEE EMBS Vice President of Publications 2008 – 2012 and was the President of IEEE EMBS (Engineering in Biology and Medicine Society) 2015 and 2016. He currently serves as the Chair of the Membership Committee for IAMBE (International Academy of Medical and Biological Engineers). He is a Fellow of IEEE, AIMBE and IFMBE.

Abstract: Chronic obstructive pulmonary disease (COPD) defined by irreversible airflow limitation, is the 3rd leading cause of death globally and 4th in the United States. Smoking tobacco is a major extrinsic COPD risk factor, but despite six decades of declining smoking rates in many countries, the corresponding declines in COPD have been modest. Only a minority of lifetime smokers develop COPD, and up to 25% occurs in never smokers. While other factors have been linked to COPD much of the variation in COPD risk remains unexplained.

Airflow obstruction, or reduced airflow from the lungs, is determined in part by airway tree structure and lung volume, both of which can be imaged with high precision by high resolution computed tomographic (HRCT) scans. Emerging evidence by our group suggests that airway tree structure variation is common in the general population and is a major contributor to this unexplained COPD risk. By manual labeling of the airway tree structure, limited to one airway generation in just 2 of the 5 lung lobes (due to complexity of tree structure), we found that 26% of the general population has major airway branch variants that differ from the classical “textbook” structure, increase COPD risk, and have a strong and biologically plausible genetic basis. We further demonstrated that airway tree caliber variation (dysanapsis) measured on CT was a stronger predictor of COPD risk than all known risk factors including smoking. Yet there is no standardized approach to characterize the full scope airway tree variation, making the exact relationship between COPD and individual airway-structure features unclear.

This works applies the power of machine learning methods to the entire airway tree structure imaged on HRCT to build logically upon prior high-impact work to discover new COPD sub-phenotypes for risk stratification and biological pathways of intervention. Also, we apply sophisticated / rigorous mathematical clustering approaches to airway trees derived from over 18,000 computed tomography (CT) scans in three highly characterized cohorts – MESA Lung Study, SPIROMICS, COPDGene Study, in addition to CanCOLD – to discover and replicate novel and clinically significant airway tree subtypes and their genetic basis. By understanding airway tree structure subtypes from lung CT scans, we hope to advance our knowledge of disease susceptibility and improve personalized therapies, prognosis, and identify an underlying genetic basis to COPD risk.