Kyle J. Lafata, Thaddeus V. Samulski Assistant Professor of Radiation Oncology  

Kyle J. Lafata

Office Location: Radiation Physics, Box 3295 DUMC, Durham, NC 27710
Email Address: kyle.lafata@duke.edu
Web Page: https://www.kylelafata.com

Education:
C., Duke University, 2018
Ph.D., Duke University, 2018

Recent Publications   (More Publications)

  1. Rigiroli, F; Hoye, J; Lerebours, R; Lyu, P; Lafata, KJ; Zhang, AR; Erkanli, A; Mettu, NB; Morgan, DE; Samei, E; Marin, D, Exploratory analysis of mesenteric-portal axis CT radiomic features for survival prediction of patients with pancreatic ductal adenocarcinoma., Eur Radiol (March, 2023) [doi]  [abs].
  2. Yang, Z; Hu, Z; Ji, H; Lafata, K; Vaios, E; Floyd, S; Yin, F-F; Wang, C, A neural ordinary differential equation model for visualizing deep neural network behaviors in multi-parametric MRI-based glioma segmentation., Medical Physics (February, 2023) [doi]  [abs].
  3. Kelleher, CB; Macdonald, J; Jaffe, TA; Allen, BC; Kalisz, KR; Kauffman, TH; Smith, JD; Maurer, KR; Thomas, SP; Coleman, AD; Zaki, IH; Kannengiesser, S; Lafata, K; Gupta, RT; Bashir, MR, 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, 2023) [doi]  [abs].
  4. 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; Aslam, A; Gaur, S; Bashir, MR, Comparing Survival Outcomes of Patients With LI-RADS-M Hepatocellular Carcinomas and Intrahepatic Cholangiocarcinomas., J Magn Reson Imaging, vol. 57 no. 1 (January, 2023), pp. 308-317 [doi]  [abs].
  5. DeFreitas, MR; Toronka, A; Nedrud, MA; Cubberley, S; Zaki, IH; Konkel, B; Uronis, HE; Palta, M; Blazer, DG; Lafata, KJ; Bashir, MR, CT-derived body composition measurements as predictors for neoadjuvant treatment tolerance and survival in gastroesophageal adenocarcinoma., Abdom Radiol (Ny), vol. 48 no. 1 (January, 2023), pp. 211-219 [doi]  [abs].

Highlight:

Kyle Lafata is the Thaddeus V. Samulski Assistant Professor at Duke University in the Departments of Radiation Oncology, Radiology, Medical Physics, and Electrical & Computer Engineering. After earning his PhD in Medical Physics in 2018, he completed postdoctoral training at the U.S. Department of Veterans Affairs in the Big Data Scientist Training Enhancement Program. Prof. Lafata has broad expertise in imaging science, digital pathology, computer vision, biophysics, and applied mathematics. His dissertation work focused on the applied analysis of stochastic differential equations and high-dimensional radiomic phenotyping, where he developed physics-based computational methods and soft-computing paradigms to interrogate images. These included stochastic modeling, 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). 

Prof. Lafata has worked in various areas of computational medicine and biology, resulting in 39 peer-reviewed journal publications, 15 invited talks, and more than 50 national conference presentations. At Duke, the Lafata Lab focuses on the theory, development, and application of multiscale computational biomarkers. Using computational and mathematical methods, they study the appearance and behavior of disease across different physical length-scales (i.e., radiomics ~10−3 m, pathomics ~10−6 m, and genomics ~10−9 m) and time-scales (e.g., the natural history of disease, response to treatment). The overarching goal of the lab is to develop and apply new technology that transforms imaging into basic science findings and computational biomarker discovery.