Hypoxia occurs when tumour cells outgrow their blood supply, leading to
regions of low oxygen levels within the tumour. Calculating hypoxia levels can
be an important step in understanding the biology of tumours, their clinical
progression and response to treatment. This study demonstrates a novel
application of deep learning to evaluate hypoxia in the context of breast
cancer histomorphology. More precisely, we show that Weakly Supervised Deep
Learning (WSDL) models can accurately detect hypoxia associated features in
routine Hematoxylin and Eosin (H&E) whole slide images (WSI). We trained and
evaluated a deep Multiple Instance Learning model on tiles from WSI H&E tissue
from breast cancer primary sites (n=240) obtaining on average an AUC of 0.87 on
a left-out test set. We also showed significant differences between features of
hypoxic and normoxic tissue regions as distinguished by the WSDL models. Such
DL hypoxia H&E WSI detection models could potentially be extended to other
tumour types and easily integrated into the pathology workflow without
requiring additional costly assays.

本研究展示了一种使用深度学习评估乳腺癌组织学形态学下低氧相关特征的新方法。通过对组织切片图像的训练和评估，我们证明了弱监督深度学习模型可以准确检测常规 H&E 全切片图像中的低氧相关特征，并展示了低氧和正常氧组织区域特征之间的显著差异。该方法具有潜在可将其扩展到其他肿瘤类型，并能轻松集成到病理工作流程中而不需要额外昂贵的化验分析。

基于深度学习的 H&E 乳腺癌全切片图像中与缺氧有关的形态特征检测

Deep learning-based detection of morphological features associated with  hypoxia in H&E breast cancer whole slide images

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

Despite the advances in machine learning and digital pathology, it is not yet
clear if machine learning methods can accurately predict molecular information
merely from histomorphology. In a quest to answer this question, we built a
large-scale dataset (185538 images) with reliable measurements for Ki67, ER,
PR, and HER2 statuses. The dataset is composed of mirrored images of H\&E and
corresponding images of immunohistochemistry (IHC) assays (Ki67, ER, PR, and
HER2. These images are mirrored through registration. To increase reliability,
individual pairs were inspected and discarded if artifacts were present (tissue
folding, bubbles, etc). Measurements for Ki67, ER and PR were determined by
calculating H-Score from image analysis. HER2 measurement is based on binary
classification: 0 and 1+ (IHC scores representing a negative subset) vs 3+ (IHC
score positive subset). Cases with IHC equivocal score (2+) were excluded. We
show that a standard ViT-based pipeline can achieve prediction performances
around 90% in terms of Area Under the Curve (AUC) when trained with a proper
labeling protocol. Finally, we shed light on the ability of the trained
classifiers to localize relevant regions, which encourages future work to
improve the localizations. Our proposed dataset is publicly available:
this https URL

通过使用机器学习方法和数字病理学，本研究建立了一个大规模数据集（185,538 张图像），通过可靠的 Ki67、ER、PR 和 HER2 状态测量结果，验证了基于 ViT 的标准流水线能够在正确的标注协议下达到大约 90% 的预测性能，同时揭示了训练分类器定位相关区域的能力，为未来改进本领域的本地化工作提供了启示。