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 494: Projects in Electrical and Computer Engineering
  • ECE 891: Internship
  • MEDPHY 507: Radiation Biology
  • MEDPHY 791: Independent Study in Medical Physics

Representative Publications

  • Kelleher, Colm B., Jacob Macdonald, Tracy A. Jaffe, Brian C. Allen, Kevin R. Kalisz, Travis H. Kauffman, Jordan D. Smith, et al. “A Faster Prostate MRI: Comparing a Novel Denoised, Single-Average T2 Sequence to the Conventional Multiaverage T2 Sequence Regarding Lesion Detection and PI-RADS Score Assessment.” J Magn Reson Imaging, January 6, 2023. https://doi.org/10.1002/jmri.28577.
  • 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 57, no. 1 (January 2023): 308–17. https://doi.org/10.1002/jmri.28218.
  • Wang, Yuqi, Xiang Li, Meghana Konanur, Brandon Konkel, Elisabeth Seyferth, Nathan Brajer, Jian-Guo Liu, Mustafa R. Bashir, and Kyle J. Lafata. “Towards optimal deep fusion of imaging and clinical data via a model-based description of fusion quality.” Med Phys, December 22, 2022. https://doi.org/10.1002/mp.16181.
  • 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 49, no. 11 (November 2022): 7278–86. https://doi.org/10.1002/mp.15837.
  • Carpenter, David J., Brahma Natarajan, Muzamil Arshad, Divya Natesan, Olivia Schultz, Michael J. Moravan, Charlotte Read, et al. “Prognostic Model for Intracranial Progression after Stereotactic Radiosurgery: A Multicenter Validation Study.” Cancers (Basel) 14, no. 21 (October 22, 2022). https://doi.org/10.3390/cancers14215186.