Bevacizumab is a widely studied targeted therapeutic drug used in conjunction with standard chemotherapy for the treatment of recurrent ovarian cancer. While its administration has shown to increase the progression-free survival (PFS) in patients with advanced stage ovarian cancer, the lack of identifiable biomarkers for predicting patient response has been a major roadblock in its effective adoption towards personalized medicine. In this work, we leverage the latest histopathology foundation models trained on large-scale whole slide image (WSI) datasets to extract ovarian tumor tissue features for predicting bevacizumab response from WSIs. Our extensive experiments across a combination of different histopathology foundation models and multiple instance learning (MIL) strategies demonstrate capability of these large models in predicting bevacizumab response in ovarian cancer patients with the best models achieving an AUC score of 0.86 and an accuracy score of 72.5%. Furthermore, our survival models are able to stratify high- and low-risk cases with statistical significance (p < 0.05) even among the patients with the aggressive subtype of high-grade serous ovarian carcinoma. This work highlights the utility of histopathology foundation models for the task of ovarian bevacizumab response prediction from WSIs. The high-attention regions of the WSIs highlighted by these models not only aid the model explainability but also serve as promising imaging biomarkers for treatment prognosis.

翻译：贝伐珠单抗是一种广泛研究的靶向治疗药物，与标准化疗联合用于治疗复发性卵巢癌。虽然其应用已被证明能提高晚期卵巢癌患者的无进展生存期（PFS），但缺乏可识别的生物标志物来预测患者反应，一直是其在个性化医疗中有效应用的主要障碍。在本工作中，我们利用在大规模全切片图像（WSI）数据集上训练的最新组织病理学基础模型，提取卵巢肿瘤组织特征，以从WSI中预测贝伐珠单抗反应。我们结合不同组织病理学基础模型与多实例学习（MIL）策略进行的广泛实验表明，这些大型模型能够预测卵巢癌患者的贝伐珠单抗反应，其中最佳模型的AUC得分达到0.86，准确率得分达到72.5%。此外，我们的生存模型即使在侵袭性亚型的高级别浆液性卵巢癌患者中，也能够以统计学显著性（p < 0.05）对高风险和低风险病例进行分层。这项工作凸显了组织病理学基础模型在基于WSI的卵巢癌贝伐珠单抗反应预测任务中的实用性。这些模型所高亮显示的WSI高关注区域不仅有助于模型的可解释性，而且可作为有前景的治疗预后影像学生物标志物。