Deep Learning Predicts Biomarker Status and Discovers Related
  Histomorphology Characteristics for Low-Grade Glioma
Zijie Fang Yihan Liu Yifeng Wang Xiangyang Zhang Yang Chen Changjing Cai Yiyang Lin Ying Han Zhi Wang Shan Zeng Hong Shen Jun Tan Yongbing Zhang
Biomarker detection is an indispensable part in the diagnosis and treatment
of low-grade glioma (LGG). However, current LGG biomarker detection methods
rely on expensive and complex molecular genetic testing, for which
professionals are required to analyze the results, and intra-rater variability
is often reported. To overcome these challenges, we propose an interpretable
deep learning pipeline, a Multi-Biomarker Histomorphology Discoverer
(Multi-Beholder) model based on the multiple instance learning (MIL) framework,
to predict the status of five biomarkers in LGG using only hematoxylin and
eosin-stained whole slide images and slide-level biomarker status labels.
Specifically, by incorporating the one-class classification into the MIL
framework, accurate instance pseudo-labeling is realized for instance-level
supervision, which greatly complements the slide-level labels and improves the
biomarker prediction performance. Multi-Beholder demonstrates superior
prediction performance and generalizability for five LGG biomarkers
(AUROC=0.6469-0.9735) in two cohorts (n=607) with diverse races and scanning
protocols. Moreover, the excellent interpretability of Multi-Beholder allows
for discovering the quantitative and qualitative correlations between biomarker
status and histomorphology characteristics. Our pipeline not only provides a
novel approach for biomarker prediction, enhancing the applicability of
molecular treatments for LGG patients but also facilitates the discovery of new
mechanisms in molecular functionality and LGG progression.