Samuel Ingram

Samuel Ingram is a Clinical Scientist (Proton Therapy) at The Christie NHS Foundation Trust and holds an honorary research position as a Researcher at The University of Manchester. He is also a Topol Digital Health Fellow for Health Education England. His work focuses on computational radiobiology, translational radiotherapy, and machine learning applications in healthcare. Ingram earned his Doctor of Philosophy from The University of Manchester (2022), specializing in in silico modelling of radiobiological mechanisms in proton beam therapy, alongside MSc degrees from the University of Liverpool (Clinical Science) and Swansea University (Medical Radiation Physics), and a BSc in Theoretical Physics from Swansea University (2013).

His research interests include developing computational models to understand DNA damage and repair mechanisms induced by radiation, optimizing proton therapy protocols, and leveraging AI for medical image analysis. He collaborates with a multidisciplinary team at The Christie and the University of Manchester, including Karen Kirkby (Group Lead), Michael Taylor, and Michael Merchant (both Lecturers in Proton Therapy Physics). Key expertise areas include QC physics, medical physics, and biological modelling. His work contributes to UN Sustainable Development Goals related to health and innovation.

Ingram’s recent publications explore topics such as real-world evidence in radiotherapy, DNA damage dynamics, and AI-driven auto-contouring techniques. His group’s research aims to improve treatment precision, reduce side effects, and advance personalized radiotherapy strategies. Notably, he has contributed to datasets on genome structure modeling for radiation-induced DNA damage and computational approaches to quantifying immunofluorescent foci miscounting.

Ingram’s achievements include the Topol Digital Health Fellowship, recognizing his contributions to digital health innovation. His work bridges clinical practice and academic research, with a focus on translating computational models into practical healthcare solutions.