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. To accomplish this, the lab develops mathematical methods, computational imaging techniques, and measurement tools to characterize and quantify the appearance and behavior of disease. This technology is applied to interrogate underlying biology, characterize tissue microenvironments, diagnose disease, predict disease progression, quantify treatment response, and enable personalized therapy.

Appointments and Affiliations

  • Assistant Professor of Radiation Oncology
  • Assistant Professor in Radiology
  • Assistant Professor in the Department of Electrical and Computer Engineering
  • Member of the Duke Cancer Institute

Contact Information

  • Office Location: Radiation Physics, Box 3295 DUMC, Durham, NC 27710
  • Office Phone: (919) 660-2180
  • Email Address:
  • Websites:


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

Representative Publications

  • Rigiroli, F; Hoye, J; Lerebours, R; Lafata, KJ; Li, C; Meyer, M; Lyu, P; Ding, Y; Schwartz, FR; Mettu, NB; Zani, S; Luo, S; Morgan, DE; Samei, E; Marin, D, CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study., Radiology, vol 301 no. 3 (2021), pp. 610-622 [10.1148/radiol.2021210699] [abs].
  • Lafata, KJ; Wang, Y; Konkel, B; Yin, F-F; Bashir, MR, Radiomics: a primer on high-throughput image phenotyping., Abdom Radiol (Ny) (2021) [10.1007/s00261-021-03254-x] [abs].
  • Jiang, H; Chen, HC; Lafata, KJ; Bashir, MR, Week 4 Liver Fat Reduction on MRI as an Early Predictor of Treatment Response in Participants with Nonalcoholic Steatohepatitis., Radiology, vol 300 no. 2 (2021), pp. 361-368 [10.1148/radiol.2021204325] [abs].
  • Jiang, H; Song, B; Qin, Y; Wei, Y; Konanur, M; Wu, Y; Zaki, IH; McInnes, MDF; Lafata, KJ; Bashir, MR, Data-Driven Modification of the LI-RADS Major Feature System on Gadoxetate Disodium-Enhanced MRI: Toward Better Sensitivity and Simplicity., J Magn Reson Imaging (2021) [10.1002/jmri.27824] [abs].
  • Lafata, KJ; Chang, Y; Wang, C; Mowery, YM; Vergalasova, I; Niedzwiecki, D; Yoo, DS; Liu, J-G; Brizel, DM; Yin, F-F, Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers., Med Phys, vol 48 no. 7 (2021), pp. 3767-3777 [10.1002/mp.14926] [abs].