Kyushu University Academic Staff Educational and Research Activities Database
List of Presentations
Yoko Yasuda Last modified date:2024.06.03

Assistant Professor / Faculty of Medical Sciences Division of Medical Technology / Department of Health Sciences / Faculty of Medical Sciences

1. Yoko Yasuda,Kazuaki Tokunaga,Tomoaki Koga,Ilya G. Goldberg,Chiyomi Sakamoto,Noriko Saitoh,Mitsuyoshi Nakao, Evaluation of morphological dissimilarity in H&E images using machine-learning algorithm, 第58回日本臨床細胞学会, 2019.11, Dynamic changes of cell morphology in cancer tissue reflect accumulation of genetic and epigenetic abnormalities. H&E slide is a conventional tool in pathological diagnosis and provide useful information of a morphological context underlying various molecular events. It is important for cancer therapy and diagnosis to understand the molecular mechanism behind morphological changes, however, little is known.
Here, we quantified morphological dissimilarity of H&E images of gastric cancer with machine-learning algorithm, wndchrm. First, we verified that wndchrm algorithm reproduced cancer classification and differentiated grades based on human vision with acceptable accuracy. We next found that both hematoxylin (nuclear) and eosin (cytoplasmic) images equally contain morphological features enough to distinguish cancer grades. To investigate links between morphological change and molecular expression, we performed a fact-driven analysis, an image classification of H&E corresponding to expression level of ATF7IP/MCAF1 (nuclear protein) or PD-L1 (non-nuclear protein), resulting in high classification performance (90% for ATF7IP/MCAF1; 86% for PD-L1).
Our data shows that quantitative evaluation of H&E images by machine-learning algorithm accurately detected morphological differences and propose the usefulness of machine-learning algorithm as a tool to elucidate a link between morphologies and molecular expression patterns..