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.

翻译：生物标志物检测是低级别胶质瘤（LGG）诊断与治疗中不可或缺的环节。然而，当前LGG生物标志物检测方法依赖于昂贵且复杂的分子遗传学检测，需要专业人员分析结果，且常存在评估者间差异性问题。为克服这些挑战，我们提出一种可解释的深度学习流程——基于多实例学习（MIL）框架的多生物标志物组织形态学发现模型（Multi-Beholder），仅使用苏木精-伊红染色的全切片图像及切片级生物标志物状态标签，即可预测LGG五种生物标志物的状态。具体而言，通过将单类别分类集成到MIL框架中，实现了实例级监督的精准实例伪标签化，有效补充了切片级标签信息并提升了生物标志物预测性能。在包含不同种族及扫描方案的两个队列（n=607）中，Multi-Beholder对五种LGG生物标志物展现出卓越的预测性能与泛化能力（AUROC=0.6469-0.9735）。此外，Multi-Beholder优异的可解释性使其能够发现生物标志物状态与组织形态学特征之间的量化与定性关联。本流程不仅为生物标志物预测提供了新方法，增强了LGG患者分子治疗的可及性，同时促进了分子功能及LGG进展相关新机制的研究发现。