TY - GEN
T1 - Deep learning assessment of tumor proliferation in breast cancer histological images
AU - Shah, Manan
AU - Wang, Dayong
AU - Rubadue, Christopher
AU - Suster, David
AU - Beck, Andrew
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/15
Y1 - 2017/12/15
N2 - Current analysis of tumor proliferation, the most salient breast cancer prognostic biomarker, is limited to subjective mitosis counting by pathologists in localized regions of tissue images. This study presents the first data-driven integrative approach to characterize the severity of tumor growth and spread on a categorical and molecular level, utilizing multiple biologically salient deep learning classifiers to develop a comprehensive prognostic model. Our approach achieves pathologist-level performance on three-class categorical tumor severity prediction. It additionally pioneers prediction of molecular expression data from a tissue image, obtaining a Spearman's rank correlation coefficient of 0.60 with ex vivo mean calculated RNA expression. Furthermore, our framework is applied to identify over two hundred unprecedented biomarkers critical to the accurate assessment of tumor proliferation, validating our proposed integrative pipeline as the first to holistically and objectively analyze histopathological images.
AB - Current analysis of tumor proliferation, the most salient breast cancer prognostic biomarker, is limited to subjective mitosis counting by pathologists in localized regions of tissue images. This study presents the first data-driven integrative approach to characterize the severity of tumor growth and spread on a categorical and molecular level, utilizing multiple biologically salient deep learning classifiers to develop a comprehensive prognostic model. Our approach achieves pathologist-level performance on three-class categorical tumor severity prediction. It additionally pioneers prediction of molecular expression data from a tissue image, obtaining a Spearman's rank correlation coefficient of 0.60 with ex vivo mean calculated RNA expression. Furthermore, our framework is applied to identify over two hundred unprecedented biomarkers critical to the accurate assessment of tumor proliferation, validating our proposed integrative pipeline as the first to holistically and objectively analyze histopathological images.
UR - http://www.scopus.com/inward/record.url?scp=85045984546&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2017.8217719
DO - 10.1109/BIBM.2017.8217719
M3 - Conference contribution
AN - SCOPUS:85045984546
T3 - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
SP - 600
EP - 603
BT - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
A2 - Yoo, Illhoi
A2 - Zheng, Jane Huiru
A2 - Gong, Yang
A2 - Hu, Xiaohua Tony
A2 - Shyu, Chi-Ren
A2 - Bromberg, Yana
A2 - Gao, Jean
A2 - Korkin, Dmitry
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Y2 - 13 November 2017 through 16 November 2017
ER -