Laboratory of Computational Biology and Bioinformatics, CIPE/A.C.Camargo Cancer Center, São Paulo, São Paulo, 01508-010, Brazil.
Institute of Mathematics and Computer Sciences, Universidade de São Paulo, São Carlos, São Paulo, 13566-590, Brazil.
Breast Cancer Res. 2024 Aug 19;26(1):124. doi: 10.1186/s13058-024-01863-0.
Human epidermal growth factor receptor 2 (HER2)-low breast cancer has emerged as a new subtype of tumor, for which novel antibody-drug conjugates have shown beneficial effects. Assessment of HER2 requires several immunohistochemistry tests with an additional in situ hybridization test if a case is classified as HER2 2+. Therefore, novel cost-effective methods to speed up the HER2 assessment are highly desirable.
We used a self-supervised attention-based weakly supervised method to predict HER2-low directly from 1437 histopathological images from 1351 breast cancer patients. We built six distinct models to explore the ability of classifiers to distinguish between the HER2-negative, HER2-low, and HER2-high classes in different scenarios. The attention-based model was used to comprehend the decision-making process aimed at relevant tissue regions.
Our results indicate that the effectiveness of classification models hinges on the consistency and dependability of assay-based tests for HER2, as the outcomes from these tests are utilized as the baseline truth for training our models. Through the use of explainable AI, we reveal histologic patterns associated with the HER2 subtypes.
Our findings offer a demonstration of how deep learning technologies can be applied to identify HER2 subgroup statuses, potentially enriching the toolkit available for clinical decision-making in oncology.
人表皮生长因子受体 2（HER2）-低乳腺癌已成为一种新的肿瘤亚型，新型抗体药物偶联物已显示出有益的效果。HER2 的评估需要进行多次免疫组织化学测试，如果病例被分类为 HER2 2+，则需要额外进行原位杂交测试。因此，非常需要新的具有成本效益的方法来加快 HER2 的评估。
我们使用基于自我监督注意力的弱监督方法，直接从 1351 名乳腺癌患者的 1437 张组织病理学图像中预测 HER2-低。我们构建了六个不同的模型，以探索分类器在不同情况下区分 HER2 阴性、HER2 低和 HER2 高的能力。基于注意力的模型用于理解针对相关组织区域的决策过程。
我们的结果表明，分类模型的有效性取决于基于检测的 HER2 测试的一致性和可靠性，因为这些测试的结果被用作训练模型的基准真相。通过使用可解释的 AI，我们揭示了与 HER2 亚型相关的组织学模式。
我们的研究结果展示了深度学习技术如何应用于识别 HER2 亚组状态，这可能丰富肿瘤学中临床决策的工具包。
