Guillaud, Martial
PhD
Academic Rank(s): Assistant Professor (Partner), Department of Pathology & Laboratory Medicine, Faculty of Medicine, and Adjunct Professor, Dpt. of Statistics UBC, Senior Scientist, Dept of Integrative Oncology, BC Cancer Research Institute
Affiliation(s): BC Cancer Research Institute
Research and Scholarly Interests:Cancer biomarkers, digital pathology, machine learning, lung cancer,breast cancer, prosttae cancer, oral cancer, cervical cancer, Epithelial Pre-cancers, Spatial
Clinical Interests: Integration of digital pathology and advanced image analysis techniques to enhance the understanding and treatment of cancer.
In addition to serving as a Senior Scientist at the BC Cancer Research Centre, Dr. Guillaud has an appointment as an Adjunct Professor in the UBC Department of Statistics. He has previously provided instruction in data analysis, image analysis, and computer science at the Joseph Fourier University (Biological Sciences), Stendhal University (Social Sciences), and the French Institute National of Health and Medical Research (INSERM). He completed his PhD in the field of Biological and Medical Informatics at Joseph Fourier University, developing new statistical and image analysis techniques that could be applied to tissue biopsy cross-sections to determine patient prognosis and diagnosis. He also completed a Master’s degree in Cellular Biology (Animal Genetics) at Clermont Ferrand University. Dr. Guillaud holds patents on multiple techniques for automated assessment of tissue specimens and has authored dozens of research manuscripts in the areas of biostatistics and image processing.
Academic Background
- PhD, Biological and Medical Informatics, Joseph Fourier University (France), 1993
- MSc, Cellular Biology (Animal Genetics), Clemont University (France), 1986
- BSc, Cellular Biology and Physiology, Joseph Fourier University (France), 1985
Awards and Recognition
Publications
Research Interest
Dr. Martial Guillaud Laboratory is focused in part on developing three dimensional computer models that will help determine how populations of malignant cells progress through different disease stages and become aggressive, invasive tumours. Healthy human tissue is typically ordered and arranged in a very specific fashion, while cancerous and precancerous tissues show radical changes to this order that increase in severity as disease progresses. Dr. Guillaud’s models and simulations – and other platforms that assess the spatial arrangement of cells – are yielding new insights into how populations of cancer cells evolve and progress, helping researchers and physicians better understand cancer biology and behavior.
The lab is also focused on creating automated methods for reading clinical histopathology results. A cancer or precancer diagnosis typically depends on review of tissue cross sections by an expert pathologist. This process can be time consuming and costly. Further, for precancers, it is not always easy to accurately stage disease. Programs developed by Dr. Guillaud and his team work to objectively quantify histopathology results, rapidly measuring over 100 different features of individual cells on any tissue cross-section. The goal of these efforts is to provide more accurate and consistent diagnoses, but it also may reduce health care costs and the amount of time needed to give a diagnosis