乳腺癌是影响全世界女性的最常见恶性肿瘤，以其形态和生物学多样性而闻名，治疗后复发的风险也各不相同。 Oncotype DX 乳腺癌复发评分测试是雌激素受体阳性/HER2 阴性乳腺癌的重要预测和预后基因组检测，可指导治疗策略；然而，此类测试可能价格昂贵、延误护理，而且无法广泛使用。本研究的目的是开发一种多模型方法，整合全幻灯片图像和临床病理数据的分析，以预测其相关的乳腺癌复发风险，并根据预测评分将这些患者分为两个风险组：低风险和高风险。所提出的新颖方法使用卷积神经网络进行特征提取，使用视觉转换器进行上下文聚合，并辅以逻辑回归模型，分析临床病理数据以将其分类为两个风险类别。该方法在 950 名 ER /HER2- 乳腺癌患者的 956 张苏木精和伊红染色的全玻片图像上进行了训练和测试，这些患者具有先前进行过 Oncotype DX 测试的相应临床病理特征。该模型的性能使用达特茅斯健康中心 192 名患者的内部测试集和芝加哥大学 405 名患者的外部测试集进行评估。多模型方法在预测低位和高位乳腺癌方面，内部组的 AUC 为 0.91（95% CI：0.87-0.95），外部组的 AUC 为 0.84（95% CI：0.78-0.89）基于 Oncotype DX 复发评分的复发风险类别。经过进一步验证，所提出的方法可以提供一种替代方案，帮助临床医生对乳腺癌患者进行个性化治疗，并有可能改善其治疗结果。© 2024。作者。

Breast cancer is the most common malignancy affecting women worldwide and is notable for its morphologic and biologic diversity, with varying risks of recurrence following treatment. The Oncotype DX Breast Recurrence Score test is an important predictive and prognostic genomic assay for estrogen receptor positive/HER2 negative breast cancer that guides therapeutic strategies; however, such tests can be expensive, delay care, and are not widely available. The aim of this study was to develop a multi-model approach integrating the analysis of whole-slide images and clinicopathologic data to predict their associated breast cancer recurrence risks and categorize these patients into two risk groups according to the predicted score: low-risk and high-risk. The proposed novel methodology uses convolutional neural networks for feature extraction and vision transformers for contextual aggregation, complemented by a logistic regression model that analyzes clinicopathologic data for classification into two risk categories. This method was trained and tested on 956 hematoxylin and eosin-stained whole-slide images of 950 ER+/HER2- breast cancer patients with corresponding clinicopathological features that had prior Oncotype DX testing. The model's performance was evaluated using an internal test set of 192 patients from Dartmouth Health and an external test set of 405 patients from the University of Chicago. The multi-model approach achieved an AUC of 0.91 (95% CI: 0.87-0.95) on the internal set and an AUC of 0.84 (95% CI: 0.78-0.89) on the external cohort for predicting low- and high-breast cancer recurrence risk categories based on the Oncotype DX recurrence score. With further validation, the proposed methodology could provide an alternative to assist clinicians in personalizing treatment for breast cancer patients and potentially improving their outcomes.© 2024. The Author(s).