Predicting breast cancer molecular subtypes from H &E-stained histopathological images using a spatial-transcriptomics-based patch filter

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yuqi Chen, Juan Liu, Lang Wang, Peng Jiang, Baochuan Pang, Dehua Cao
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引用次数: 0

Abstract

The molecular subtype of breast cancer plays an important role in the prognosis of patients and guides physicians to develop scientific therapeutic regimes. In clinical practice, physicians classify molecular subtypes of breast cancer with immunohistochemistry(IHC) technology, which requires a long cycle for diagnosis, resulting in a delay in effective treatment of patients with breast cancer. To improve the diagnostic rate, we proposed a machine learning method that predicted molecular subtypes of breast cancer from H&E-stained histopathological images. Although some molecular subtype prediction methods have been suggested, they are noisy and lack clinical evidence. To address these issues, we introduced a patch filter-based molecular subtype prediction (PFMSP) method using spatial transcriptomics data, training a patch filter with spatial transcriptomics data first, and then the trained filter was used to select valuable patches for molecular subtype prediction in other H&E-stained histopathological images. These valuable patches contained one or more genes expressed of ESR1, ESR2, PGR, and ERBB2. We evaluated the performance of our method on the spatial transcriptomics(ST) dataset and the TCGA-BRCA dataset, and the patches filtered by the patch filter achieved accuracies of 80% and 73.91% in predicting molecular subtypes on the ST and TCGA-BRCA datasets, respectively. Experimental results showed that the use of the trained patch filter to filter patches was beneficial to improving precision in predicting molecular subtypes of breast cancer.

Abstract Image

利用基于空间转录组学的补丁过滤器从 H & E 染色组织病理学图像中预测乳腺癌分子亚型
乳腺癌的分子亚型对患者的预后起着重要作用,并指导医生制定科学的治疗方案。在临床实践中,医生通过免疫组化(IHC)技术对乳腺癌分子亚型进行分类,诊断周期较长,延误了乳腺癌患者的有效治疗。为了提高诊断率,我们提出了一种机器学习方法,从H&E染色的组织病理图像中预测乳腺癌的分子亚型。虽然已经提出了一些分子亚型预测方法,但这些方法存在噪声,而且缺乏临床证据。为了解决这些问题,我们利用空间转录组学数据引入了基于斑块过滤器的分子亚型预测(PFMSP)方法,首先用空间转录组学数据训练斑块过滤器,然后用训练好的过滤器在其他H&E染色组织病理学图像中选择有价值的斑块进行分子亚型预测。这些有价值的斑块包含一个或多个表达 ESR1、ESR2、PGR 和 ERBB2 的基因。我们在空间转录组学(ST)数据集和TCGA-BRCA数据集上评估了我们的方法的性能,经补丁过滤器过滤的补丁在ST和TCGA-BRCA数据集上预测分子亚型的准确率分别达到了80%和73.91%。实验结果表明,使用训练有素的补丁过滤器过滤补丁有利于提高预测乳腺癌分子亚型的精确度。
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来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
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