Professor Fabio Grizzi graduated in Biological Sciences from the University of Milan in 1995 and, in the year, received the Steven Newburgh Annual Award for his contributions to the field of basic and applied biomedical research. In 2001, he was appointed Adjunct Assistant Professor at the Department of Internal Medicine and Hematology & Oncology at Texas Tech University Health Sciences Center in Lubbock, Texas, USA.
In 2008, he was invited to become a Member of the National Cancer Institute-sponsored Pilot Cancer Antigen Prioritization Process, of the US National Institute of Health.
In 2012, he was invited to become a Member of the worldwide validation of the Immunoscore on colon cancer. He is currently working as Chief of the Histology Laboratory at the Humanitas Research Hospital, Rozzano, Milan, Italy.
In 2015 he was appointed Teacher at the Humanitas University, Faculty of Medicine for the course: “Building bodies: from gametes to organs, SSD BIO/17 Histology”. He is Member of the Editorial Board of various scientific Journals and serves as Reviewer for prestigious Journals.
He has published more than 150 peer-reviewed articles (H-index = 22) and twenty-six book chapters, and made more than 300 presentations at national and international Congresses, and has recently been commissioned by Springer Publishing to prepare a book entitled “The Complexity of Cancer”, which will be published in 2016.
Title: Spectral analysis for tumour diagnosis and classification in surgical pathology and cytopathology
In the pathological sciences a high number of technological devices have been proposed. The main issue remains how organize the huge of produced and available histological images, and how comprehend the relationships among the different cell types making up the complex network governing both natural and tumoral microenvironments.
Science and technology are not interchangeable each other, but technological devices are now capable to define helpful information on the complex nature of human carcinogenesis. It is indubitable that despite their differences, social and biological networks are self-organising, emergent, and complex, and their respective analyses have common features: both focus on local and global patterns of connectivity, search for influential entities, and aim to model the network dynamics.
Translational studies based on multidisciplinary teams that include pathologists, clinicians, biologists and mathematicians might develop computer-aided algorithms promise a more precise diagnosis in pathology and cytopathology toward the goal to discover new predictive and prognostic tissue biomarkers.