Clinical Relevance of Deep Learning to Facilitate the Diagnosis of Cancer Tissue Biomarkers
Tissue biomarker scoring by pathologists is central to defining the appropriate therapy for patients with cancer. However, inter-pathologist variability in the interpretation of ambiguous cases can affect diagnostic accuracy. Modern artificial intelligence methods such as deep learning have the potential to supplement pathologist expertise to ensure constant diagnostic accuracy. We developed a computational approach based on convolutional neural networks that automatically scores HER2, an immunohistochemistry biomarker that defines patient eligibility for anti-HER2 targeted therapies in breast cancer. Our results show that convolutional neural networks substantially agree with pathologist based diagnosis. Furthermore, we found that convolutional neural networks highlighted cases at risk of misdiagnosis providing preliminary evidence for the clinical utility of deep learning aided diagnosis. More studies are needed to show not only the validity of deep learning, but also its utility i n clinical practice to improve diagnostic accuracy. Beyond correlations of artificial intelligence and human made diagnosis, new study designs should be investigated to enable the demonstration that deep learning can improve clinical decision making.
Michel Vandenberghe is working at AstraZeneca, developing deep learning algorithms to analyse immunohistochemistry biomarkers and evaluating the potential uses of deep learning to support biomarker development and clinical decision making. Prior to that, he gained a PhD in Computer Science at University Pierre and Marie Curie, and a Doctorate in Pharmacy, at the University Paris Sud XI.