通过H&E染色的整张滑片图像评估早期乳腺癌复发风险
Assessing the risk of recurrence in early-stage breast cancer through H&E stained whole slide images
AI总结：
本研究利用深度学习分析H&E染色的整张滑片图像，以预测早期乳腺癌复发风险，展示了该方法在风险分层中的潜力。
摘要：
准确预测复发的可能性对于选择术后治疗方案至关重要。在本研究中，我们探讨了深度学习算法是否能通过分析癌症组织病理图像来预测患者的复发风险。我们分析了来自两个机构（国家癌症中心和韩国大学医学中心格鲁医院）的125名患者共125张经苏木精和伊红染色的整张滑片图像（WSIs），利用深度学习预测乳腺癌复发风险。低、中、高风险类别中的灵敏度分别为0.857、0.746和0.529，特异度分别为0.816、0.803和0.972，与组织学分级的皮尔逊相关性为0.61。类激活图突出显示了管状形成和有丝分裂率等特征，表明了一种经济有效的风险分层方法，有待更广泛验证。这些发现表明，仅使用苏木精和伊红染色的整张滑片图像训练的深度学习模型可以近似基因组检测结果，提供了一种经济高效且可扩展的乳腺癌复发风险评估工具。然而，需要进一步使用更大且更平衡的数据集来验证本方法的临床适用性。
英文摘要：
Accurate prediction of the likelihood of recurrence is important in the selection of postoperative treatment for patients with early-stage breast cancer. In this study, we investigated whether deep learning algorithms can predict patients' risk of recurrence by analyzing the pathology images of their cancer histology.We analyzed 125 hematoxylin and eosin-stained whole slide images (WSIs) from 125 patients across two institutions (National Cancer Center and Korea University Medical Center Guro Hospital) to predict breast cancer recurrence risk using deep learning. Sensitivity reached 0.857, 0.746, and 0.529 for low, intermediate, and high-risk categories, respectively, with specificity of 0.816, 0.803, and 0.972, and a Pearson correlation of 0.61 with histological grade. Class activation maps highlighted features like tubule formation and mitotic rate, suggesting a cost-effective approach to risk stratification, pending broader validation. These findings suggest that deep learning models trained exclusively on hematoxylin and eosin stained whole slide images can approximate genomic assay results, offering a cost-effective and scalable tool for breast cancer recurrence risk assessment. However, further validation using larger and more balanced datasets is needed to confirm the clinical applicability of our approach.
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