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.

通过基于多重实例学习框架的多生物标志物组织形态学发现者（Multi-Beholder）模型，我们提出了一种可解释的深度学习流程，仅使用血液和嗜酸性染色全切片图像和切片级生物标志物状态标签，预测低级别胶质瘤中五种生物标志物的状况，从而克服了当前昂贵且复杂的分子遗传学测试所依赖的专业人员分析结果以及常常报告的内观者差异。

深度学习预测低级别胶质瘤的生物标志物状态并发现相关的组织形态学特征

Deep Learning Predicts Biomarker Status and Discovers Related  Histomorphology Characteristics for Low-Grade Glioma

Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect
spontaneous neural activities in brain and is widely used for brain disorder
analysis.Previous studies propose to extract fMRI representations through
diverse machine/deep learning methods for subsequent analysis. But the learned
features typically lack biological interpretability, which limits their
clinical utility. From the view of graph theory, the brain exhibits a
remarkable modular structure in spontaneous brain functional networks, with
each module comprised of functionally interconnected brain regions-of-interest
(ROIs). However, most existing learning-based methods for fMRI analysis fail to
adequately utilize such brain modularity prior. In this paper, we propose a
Brain Modularity-constrained dynamic Representation learning (BMR) framework
for interpretable fMRI analysis, consisting of three major components: (1)
dynamic graph construction, (2) dynamic graph learning via a novel
modularity-constrained graph neural network(MGNN), (3) prediction and biomarker
detection for interpretable fMRI analysis. Especially, three core
neurocognitive modules (i.e., salience network, central executive network, and
default mode network) are explicitly incorporated into the MGNN, encouraging
the nodes/ROIs within the same module to share similar representations. To
further enhance discriminative ability of learned features, we also encourage
the MGNN to preserve the network topology of input graphs via a graph topology
reconstruction constraint. Experimental results on 534 subjects with rs-fMRI
scans from two datasets validate the effectiveness of the proposed method. The
identified discriminative brain ROIs and functional connectivities can be
regarded as potential fMRI biomarkers to aid in clinical diagnosis.

本文提出了一种基于大脑模块化的动态表示学习框架，利用图神经网络和神经认知模块进行核心区域的提取，以更好地利用脑功能网络的模块化结构，探究有效的 fMRI 生物标记物用于临床诊断。