While traditional TNM staging helps with the clinical treatment decision for patients with muscle-invasive bladder cancer (MIBC), it may not adequately encompass the complex and dynamic behavior of the disease. In this free webinar, learn how machine learning and deep learning can improve TNM staging with the tumor morphology analysis of immunofluorescence (IF) images, providing information on immune contexture and tumor budding to better assess tumor morphology.
TORONTO (PRWEB) May 16, 2019
Join Dr. Nicolas Brieu, Principal Research Scientist at Definiens and Dr. Peter D. Caie, Senior Research Fellow at St. Andrew University, UK in a live webinar on Wednesday, June 5, 2019 at 9am EDT (2pm BST/UK) to learn about a deep learning based image analysis solution capable of automatically quantifying cell populations across multiplex immunofluorescence (IF) labelled tissue slides from muscle-invasive bladder cancer (MIBC) patients.
Muscle-invasive bladder cancer (MIBC) is a highly aggressive disease whose clinical reporting is based on TNM staging. Despite recent research into novel treatment and surgical strategies the mortality rates and prognoses of MIBC patients have remained immutable over the past 30 years. In addition to inter- and intra-reporter variability, TNM staging may not adequately encompass the complex and dynamic behavior of the disease.
An analysis of the tumor morphology and of the immune contexture may hold important pathological information, which could allow for better risk stratification of patients with MIBC than current clinical guidelines based on TNM staging.
In this free webinar, participants will learn about:
For more information or to register for this event, visit Machine Learning for Automated Immune Profiling and Tumor Morphology Analysis on IF Images.
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