Overview
Kyle Lafata is the Thaddeus V. Samulski Associate Professor at Duke University with faculty appointments in Radiation Oncology, Radiology, Pathology, Medical Physics, and Electrical & Computer Engineering. He joined the faculty at Duke in 2020 following postdoctoral training at the US Department of Veterans Affairs. His dissertation work focused on the applied analysis of stochastic partial differential equations and high-dimensional image 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 over 55 academic papers, 20 invited talks, and more than 60 national conference presentations. At Duke, the Lafata Laboratory focuses on the theory, development, and application of computational oncology. The lab interrogates disease at different length-scales of its biological organization via high-performance computing, multiscale modeling, advanced imaging technology, and the applied analysis of stochastic partial differential equations. Current research interests include tumor topology, cellular dynamics, tumor immune microenvironment, drivers of radiation resistance and immune dysregulation, molecular insight into tissue heterogeneity, and biologically-guided adaptative treatment strategies.
Current Appointments & Affiliations
Recent Publications
Radiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis.
Conference Med Phys · April 2025 BACKGROUND: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinic ... Full text Link to item CiteConcordance-based Predictive Uncertainty (CPU)-Index: Proof-of-concept with application towards improved specificity of lung cancers on low dose screening CT.
Journal Article Artif Intell Med · February 2025 In this paper, we introduce a novel concordance-based predictive uncertainty (CPU)-Index, which integrates insights from subgroup analysis and personalized AI time-to-event models. Through its application in refining lung cancer screening (LCS) predictions ... Full text Link to item CiteLong-term, automated stool monitoring using a novel smart toilet: A feasibility study.
Journal Article Neurogastroenterol Motil · January 2025 BACKGROUND: Patients' report of bowel movement consistency is unreliable. We demonstrate the feasibility of long-term automated stool image data collection using a novel Smart Toilet and evaluate a deterministic computer-vision analytic approach to assess ... Full text Link to item CiteRecent Grants
Computational tumor phenotyping to interrogate treatment resistance and immune dysregulation in head and neck cancer
ResearchPrincipal Investigator · Awarded by National Institutes of Health · 2024 - 2029Disparate Survival, Disparate Workforce: An Integrated Approach to Improving Head and Neck Cancer Outcomes and Diversity in the Oncology Workforce
ResearchCo Investigator · Awarded by National Institute of Dental and Craniofacial Research · 2024 - 2029Targeting the B Cell Response to Treat Antibody-Mediated Rejection
ResearchCo Investigator · Awarded by National Institutes of Health · 2021 - 2028View All Grants