Wu Zhida, Wang Tao, Lan Junlin, Wang Jianchao, Chen Gang, Tong Tong, Zhang Hejun
Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China.
College of Physics and Information Engineering, Fuzhou University, Xueyuan Road No.2, Fuzhou, Fujian, 350108, China.
J Transl Med. 2025 Jan 6;23(1):13. doi: 10.1186/s12967-024-06034-5.
First-line treatment for advanced gastric adenocarcinoma (GAC) with human epidermal growth factor receptor 2 (HER2) is trastuzumab combined with chemotherapy. In clinical practice, HER2 positivity is identified through immunohistochemistry (IHC) or fluorescence in situ hybridization (FISH), whereas deep learning (DL) can predict HER2 status based on tumor histopathological features. However, it remains uncertain whether these deep learning-derived features can predict the efficacy of anti-HER2 therapy.
We analyzed a cohort of 300 consecutive surgical specimens and 101 biopsy specimens, all undergoing HER2 testing, along with 41 biopsy specimens receiving trastuzumab-based therapy for HER2-positive GAC.
We developed a convolutional neural network (CNN) model using surgical specimens that achieved an area under the curve (AUC) value of 0.847 in predicting HER2 amplification, and achieved an AUC of 0.903 in predicting HER2 status specifically in patients with HER2 2 + expression. The model also predicted HER2 status in gastric biopsy specimens, achieving an AUC of 0.723. Furthermore, our classifier was trained using 41 HER2-positive gastric biopsy specimens that had undergone trastuzumab treatment, our model demonstrated an AUC of 0.833 for the (CR + PR) / (SD + PD) subgroup.
This work explores an algorithm that utilizes hematoxylin and eosin (H&E) staining to accurately predict HER2 status and assess the response to trastuzumab in GAC, potentially facilitating clinical decision-making.
人表皮生长因子受体2（HER2）阳性的晚期胃腺癌（GAC）一线治疗方案是曲妥珠单抗联合化疗。在临床实践中，HER2阳性通过免疫组织化学（IHC）或荧光原位杂交（FISH）来确定，而深度学习（DL）可以根据肿瘤组织病理学特征预测HER2状态。然而，这些深度学习衍生的特征能否预测抗HER2治疗的疗效仍不确定。
我们分析了连续的300例手术标本和101例活检标本，所有标本均接受HER2检测，以及41例接受基于曲妥珠单抗治疗的HER2阳性GAC活检标本。
我们使用手术标本开发了一个卷积神经网络（CNN）模型，该模型在预测HER2扩增时曲线下面积（AUC）值为0.847，在预测HER2 2+表达患者的HER2状态时AUC为0.903。该模型还预测了胃活检标本中的HER2状态，AUC为0.723。此外，我们的分类器使用41例接受曲妥珠单抗治疗的HER2阳性胃活检标本进行训练，我们的模型在（完全缓解+部分缓解）/（疾病稳定+疾病进展）亚组中的AUC为0.833。
这项工作探索了一种利用苏木精和伊红（H&E）染色准确预测HER2状态并评估GAC中曲妥珠单抗反应的算法，可能有助于临床决策。
