Quality Assessment of Ki67 Staining Using Cell Line Proliferation Index and Stain Intensity Features

About the Presenter

Alex Skovsbo Jørgensen
Assistant Professor
Department of Health Science and Technology
Aalborg University

Alex is an assistant professor in the Department of Health Science and Technology at Aalborg University. His research focus is using machine learning and image analysis within the domain of digital pathology. His current research topics of interest within digital pathology is automated cancer detection and grading, artificial intelligence, and quality assessment of staining protocols. 

Webinar Summary

Breast cancer is the most frequent cancer among women worldwide. Ki67 can be used as an immunohistochemical pseudo marker for cell proliferation to determine how aggressive the cancer is and thereby the treatment of the patient. No standard Ki67 staining protocol exists, resulting in interlaboratory stain variability. Therefore, it is important to determine the quality control of a staining protocol to ensure correct diagnosis and treatment of patients. Currently, quality control is performed by the organization NordiQC that use an expert panel-based qualitative assessment system. However, no objective method exists to determine the quality of a staining protocol.

Learning Objectives

•     Understand the challenges of staining quality assessment.
•     Use cell lines for assessment of stain quality.
•     How to use image analysis and machine learning for quality assessment of staining protocols.
•     Understand validation challenges.

Who Should Attend

Pathologists and engineers within medial image analysis and machine learning.

CMLE Credit: 1.0