Predicting the risk of early-stage breast cancer recurrence using H\&E-stained tissue images

Document detail

ID

oai:arXiv.org:2406.06650

Topic

Electrical Engineering and Systems... Computer Science - Computer Vision...

Author

Lee, Geongyu Lee, Joonho Kwak, Tae-Yeong Kim, Sun Woo Kwon, Youngmee Kim, Chungyeul Chang, Hyeyoon

Year

2024

listing date

6/19/2024

Keywords

breast images recurrence predicting cancer 0

Metrics

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.

A total of 125 hematoxylin and eosin stained breast cancer whole slide images labeled with the risk prediction via genomics assays were used, and we obtained sensitivity of 0.857, 0.746, and 0.529 for predicting low, intermediate, and high risk, and specificity of 0.816, 0.803, and 0.972.

When compared to the expert pathologist's regional histology grade information, a Pearson's correlation coefficient of 0.61 was obtained.

When we checked the model learned through these studies through the class activation map, we found that it actually considered tubule formation and mitotic rate when predicting different risk groups.

;Comment: 12 pages, 7 figures

Lee, Geongyu,Lee, Joonho,Kwak, Tae-Yeong,Kim, Sun Woo,Kwon, Youngmee,Kim, Chungyeul,Chang, Hyeyoon, 2024, Predicting the risk of early-stage breast cancer recurrence using H\&E-stained tissue images