Abstract
The interactions between tumor cells and the tumor microenvironment (TME) dictate therapeutic efficacy of radiation and many systemic therapies in breast cancer. However, to date, there is not a widely available method to reproducibly measure tumor and immune phenotypes for each patient's tumor. Given this unmet clinical need, we applied multiple instance learning (MIL) algorithms to assess activity of ten biologically relevant pathways from the hematoxylin and eosin (H&E) slide of primary breast tumors. We employed different feature extraction approaches and state-of-the-art model architectures. Using binary classification, our models attained area under the receiver operating characteristic (AUROC) scores above 0.70 for nearly all gene expression pathways and on some cases, exceeded 0.80. Attention maps suggest that our trained models recognize biologically relevant spatial patterns of cell sub-populations from H&E. These efforts represent a first step towards developing computational H&E biomarkers that reflect facets of the TME and hold promise for augmenting precision oncology.
Abstract (translated)
肿瘤细胞与肿瘤微环境（TME）之间的相互作用决定了放射治疗和许多系统治疗在乳腺癌中的治疗效果。然而，目前还没有一种可重复测量每个患者肿瘤的肿瘤和免疫表型的广泛可用方法。鉴于这一未满足的临床需求，我们将多实例学习（MIL）算法应用于从原始乳腺癌的哈希和电子显微镜（H&E）切片评估十种生物相关的通路的活动。我们采用了不同的特征提取方法和最先进的模型架构。使用二分类，我们的模型在几乎所有基因表达通路上的接收者操作特征（AUROC）分数都超过了0.70，在某些情况下甚至超过了0.80。注意力图表明，经过训练的模型能够识别H&E中的细胞亚群的空间模式。这些努力代表了解决计算H&E生物标志物的第一步，这些生物标志物可以反映TME的方面，并具有提高精准癌症治疗的精度的潜力。
URL
https://arxiv.org/abs/2404.16397
