Høibø Maren, Spiske Ute, Pedersen André, Ytterhus Borgny, Akslen Lars A, Wik Elisabeth, Askeland Cecilie, Reinertsen Ingerid, Smistad Erik, Valla Marit
Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
Front Med (Lausanne). 2025 Jun 9;12:1593143. doi: 10.3389/fmed.2025.1593143. eCollection 2025.
The estrogen receptor (ER) is routinely assessed by immunohistochemistry (IHC) in breast cancer to stratify patients into therapeutic and prognostic groups. Pathology laboratories are burdened by an increased number of biopsies, and costly and resource-demanding molecular pathology analyses. Automatic, artificial intelligence-based prediction of biological properties from hematoxylin and eosin (HE)-stained slides could increase efficiency and potentially reduce costs at laboratories. The aim of this study was to develop a model for prediction of ER status from HE-stained tissue microarrays (TMAs). Our methodology can be used as proof-of-concept for the prediction of more complex and costly molecular analyses in cancer.
In this study, TMAs from more than 2,000 Norwegian breast cancer patients were used to train and predict ER status using the clustering-constrained attention multiple-instance learning (CLAM) framework. Two patch sizes were evaluated, multi-branch and single-branch CLAM configurations were compared, and a comprehensive hyperparameter search with more than 16 000 experiments was performed. The models were evaluated on internal and external test sets.
On the internal test set, the proposed model achieved a micro accuracy, a macro accuracy, and an area under the curve of 0.91, 0.86, and 0.95, respectively. The corresponding results on the external test set were 0.93, 0.76, and 0.91, respectively. Using larger patch sizes resulted in significantly better classification performance, while no significant differences were observed when changing CLAM configurations.
在乳腺癌中，雌激素受体（ER）通常通过免疫组织化学（IHC）进行评估，以便将患者分层为不同的治疗和预后组。病理实验室因活检数量增加以及昂贵且资源需求大的分子病理学分析而负担沉重。基于苏木精和伊红（HE）染色玻片的生物特性自动人工智能预测可以提高效率，并有可能降低实验室成本。本研究的目的是开发一种从HE染色组织微阵列（TMA）预测ER状态的模型。我们的方法可用作癌症中更复杂和昂贵的分子分析预测的概念验证。
在本研究中，使用来自2000多名挪威乳腺癌患者的TMA，采用聚类约束注意力多实例学习（CLAM）框架训练和预测ER状态。评估了两种补丁大小，比较了多分支和单分支CLAM配置，并进行了超过16000次实验的全面超参数搜索。在内部和外部测试集上对模型进行评估。
在内部测试集上，所提出的模型分别实现了微准确率、宏准确率和曲线下面积为0.91、0.86和0.95。在外部测试集上的相应结果分别为0.93、0.76和0.91。使用更大的补丁大小导致显著更好的分类性能，而改变CLAM配置时未观察到显著差异。
