Kyle Jon Lafata

Assistant Professor of Radiation Oncology

Kyle Lafata is an Assistant Professor of Radiology, Radiation Oncology, and Electrical & Computer Engineering at Duke University. As an imaging physicist and data scientist, Dr. Lafata’s research interests are in image-based phenotyping and computational biomarkers. His dissertation work focused on nature-inspired computational methods and soft-computing paradigms, including the applied analysis of stochastic differential equations, self-organization, and quantum machine learning (i.e., an emerging branch of research that explores the methodological and structural similarities between quantum systems and learning systems). He has broad expertise in imaging science, digital pathology, computer vision, feature engineering, and applied mathematics.

The Lafata Laboratory focuses on multi-scale imaging biomarkers. They study the imaging phenotype across multiple physical length-scales, including radiological (i.e., ~10-3 m), pathological (i.e., ~10-6 m), and molecular (i.e., ~10-9 m) domains. The lab develops mathematical methods, physics-based models, computational imaging techniques, and data fusion algorithms to quantify the appearance and behavior of disease across space and time. This technology is applied to interrogate underlying biology, characterize tissue microenvironments, diagnose disease, quantify treatment response, and enable personalized therapy.

Appointments and Affiliations

  • Assistant Professor of Radiation Oncology
  • Assistant Professor in Radiology
  • Member of the Duke Cancer Institute

Contact Information

  • Office Location: Radiation Physics, Box 3295 DUMC, Durham, NC 27710
  • Email Address: kyle.lafata@duke.edu
  • Websites:

Education

  • Ph.D. Duke University, 2018
  • C. Duke University, 2018
  • Duke University School of Medicine, 2020

Research Interests

Biomedical imaging, image processing, computer vision, computational pathology, applied analysis of stochastic differential equations, high dimensional data analysis, feature engineering, adaptive imaging, multi-scale data fusion, Uncertainty Quantification

Courses Taught

  • ECE 392: Projects in Electrical and Computer Engineering
  • ECE 891: Internship

Representative Publications

  • Lafata, Kyle J., Yuqi Wang, Brandon Konkel, Fang-Fang Yin, and Mustafa R. Bashir. “Radiomics: a primer on high-throughput image phenotyping.” Abdom Radiol (Ny) 47, no. 9 (September 2022): 2986–3002. https://doi.org/10.1007/s00261-021-03254-x.
  • Jiang, Hanyu, Bin Song, Yun Qin, Meghana Konanur, Yuanan Wu, Matthew D. F. McInnes, Kyle J. Lafata, and Mustafa R. Bashir. “Modifying LI-RADS on Gadoxetate Disodium-Enhanced MRI: A Secondary Analysis of a Prospective Observational Study.” J Magn Reson Imaging 56, no. 2 (August 2022): 399–412. https://doi.org/10.1002/jmri.28056.
  • Yang, Zhenyu, Kyle J. Lafata, Xinru Chen, James Bowsher, Yushi Chang, Chunhao Wang, and Fang-Fang Yin. “Quantification of lung function on CT images based on pulmonary radiomic filtering.” Med Phys, June 30, 2022. https://doi.org/10.1002/mp.15837.
  • Ding, Yuqin, Mathias Meyer, Peijie Lyu, Francesca Rigiroli, Juan Carlos Ramirez-Giraldo, Kyle Lafata, Siyun Yang, and Daniele Marin. “Can radiomic analysis of a single-phase dual-energy CT improve the diagnostic accuracy of differentiating enhancing from non-enhancing small renal lesions?” Acta Radiol 63, no. 6 (June 2022): 828–38. https://doi.org/10.1177/02841851211010396.
  • Kierans, Andrea S., Kyle J. Lafata, Daniel R. Ludwig, Lauren M. B. Burke, Victoria Chernyak, Kathryn J. Fowler, Tyler J. Fraum, et al. “Comparing Survival Outcomes of Patients With LI-RADS-M Hepatocellular Carcinomas and Intrahepatic Cholangiocarcinomas.” J Magn Reson Imaging, May 5, 2022. https://doi.org/10.1002/jmri.28218.