  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.
