Assessing the risk of recurrence in early-stage breast cancer through H&E stained whole slide images.

Geongyu Lee¹, Joonho Lee¹, Tae-Yeong Kwak¹, Sun Woo Kim¹, Youngmee Kwon², Chungyeul Kim³, Hyeyoon Chang⁴

¹Research Team, Deep Bio Inc., Seoul, Seoul, 08380, Republic of Korea.

²Department of Pathology, National Cancer Center, Goyang, Goyang, 10408, Republic of Korea.

³Department of Pathology, Korea University Guro Hospital, Seoul, Seoul, 08380, Republic of Korea. idea1@korea.ac.kr.

⁴Department of Pathology, Deep Bio Inc., Seoul, Seoul, 08380, Republic of Korea. hychang@deepbio.co.kr.

PubMed PMC DOI PDF

Abstract

Accurate prediction of the likelihood of recurrence is important in the selection of postoperative treatment for patients with early-stage breast cancer. In this study, we investigated whether deep learning algorithms can predict patients' risk of recurrence by analyzing the pathology images of their cancer histology. We analyzed 125 hematoxylin and eosin-stained whole slide images (WSIs) from 125…

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