Kyle Jon Lafata

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
  • 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: kyle.lafata@duke.edu
  • Websites:

Education

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

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

  • Ding, Y; Meyer, M; Lyu, P; Rigiroli, F; Ramirez-Giraldo, JC; Lafata, K; Yang, S; Marin, D, 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, vol 63 no. 6 (2022), pp. 828-838 [10.1177/02841851211010396] [abs].
  • Kierans, AS; Lafata, KJ; Ludwig, DR; Burke, LMB; Chernyak, V; Fowler, KJ; Fraum, TJ; McGinty, KA; McInnes, MDF; Mendiratta-Lala, M; Cunha, GM; Allen, BC; Hecht, EM; Jaffe, TA; Kalisz, KR; Ranathunga, DS; Wildman-Tobriner, B; Cardona, DM; Aslum, A; Gaur, S; Bashir, MR, Comparing Survival Outcomes of Patients With LI-RADS-M Hepatocellular Carcinomas and Intrahepatic Cholangiocarcinomas., J Magn Reson Imaging (2022) [10.1002/jmri.28218] [abs].
  • Hu, Z; Yang, Z; Lafata, KJ; Yin, F-F; Wang, C, A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images., Med Phys, vol 49 no. 5 (2022), pp. 3213-3222 [10.1002/mp.15582] [abs].
  • Hu, Z; Yang, Z; Zhang, H; Vaios, E; Lafata, K; Yin, F-F; Wang, C, A Deep Learning Model with Radiomics Analysis Integration for
    Glioblastoma Post-Resection Survival Prediction
    (2022) [abs].
  • Allphin, AJ; Mowery, YM; Lafata, KJ; Clark, DP; Bassil, AM; Castillo, R; Odhiambo, D; Holbrook, MD; Ghaghada, KB; Badea, CT, Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden., Tomography, vol 8 no. 2 (2022), pp. 740-753 [10.3390/tomography8020061] [abs].