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Kenny Guida, DMP, DABR

Kenny Guida portrait
Assistant Professor, Radiation Oncology

Director, Director of Treatment Planning Service, Radiation Oncology

Assistant Director, Assistant Director of Proton Physics Service, Radiation Oncology

kguida@kumc.edu

Professional Background

Dr. Kenny Guida graduated from Vanderbilt University in 2012 with a Professional Doctorate of Medical Physics. In 2008, he received his Bachelor of Science degree in Physics, with a minor in United States History, from the University of Pittsburgh. Since July 2012, Dr. Guida has served as a Clinical Medical Physicist at The University of Kansas Cancer Center Cancer Programs and participates in the CAMPEP-Accredited Therapy Medical Physics Residency Program as a member of the Steering Committee. Currently, Dr. Guida serves as the Director of Treatment Planning Service and Assistant Director of Proton Physics Service.

Dr. Guida is board certified in Therapeutic Radiological Physics by the American Board of Radiology (ABR), and he is a member of the American Association of Physicists in Medicine (AAPM). His research interests include VMAT treatment planning and delivery, Knowledge-Based Planning, and Automation in Treatment Planning.

Education and Training
  • Other, Medical Physics, Vanderbilt University
  • BS, Physics, University of Pittsburgh
Professional Affiliations
  • American Association of Physicists in Medicine , Member, 2008 - Present

Research

Overview

My research is mostly in clinical medical physics. Most of my publications have centered on treatment planning and IMRT optimization strategies to improve a technique that could be used in the clinic. Ongoing research has been done in breast IMRT/VMAT to deliver a simultaneously integrated boost and create a homogenous dose distribution within the breast. Previous research in graduate school focused on brachytherapy for ocular melanoma and utilizing fundus imaging to contour the macula and optic nerve and determine a correlation between dose to those OARs and the tumor and visual acuity changes after treatment. My recent clinical projects have included devising an optimal VMAT strategy for treating difficult breast cancer cases including nodal involvement, as previous methods have delivered too much dose to nearby normal tissues. Another project that is continuously evolving includes the use of machine learning to create disease site-specific models to aid in treatment planning. So far, I have led the charge to create VMAT models for chest wall and nodes, head and neck, craniospinal, and lung, as well as stereotactic models for prostate, brain, abdomen, and lung. I will be working with our team to further improve on these models, and we are looking to publish our data and techniques in the near future.

Publications
  • Jayarathna, Sandun, Shen, Xinglei, Chen, Ronald, Li, H., Harold, Guida, Kenny. 2023. The Effect of integrating knowledge-based planning with multicriteria optimization in treatment planning for prostate SBRT. JACMP, 24, 1-13
  • Haidar-Person, O, Chen, R, Guida, K, Soultan, D, Li, H. 2024. Treatment Planning in Hypofractionated and Stereotactic Radiation Therapy (2nd Edition)