arXiv 2407.205962024-07-30eess.IVcs.CVcs.LG

用于从全切片图像预测卵巢癌贝伐珠单抗治疗反应的组织病理学基础模型基准测试

Benchmarking Histopathology Foundation Models for Ovarian Cancer Bevacizumab Treatment Response Prediction from Whole Slide Images

Faculty of Science, University of British Columbia（不列颠哥伦比亚大学理学院）；School of Biomedical Engineering, University of British Columbia（不列颠哥伦比亚大学生物医学工程学院）；Department of Pathology and Laboratory Medicine, University of British Columbia（不列颠哥伦比亚大学病理学与实验医学系）；Canada’s Michael Smith Genome Sciences Centre, BC Cancer Research Institute（加拿大迈克尔·史密斯基因组科学中心，BC癌症研究所）

Mayur Mallya, Ali Khajegili Mirabadi, Hossein Farahani, Ali Bashashati

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AI总结：

本研究利用组织病理学基础模型结合多实例学习从全切片图像预测卵巢癌贝伐珠单抗治疗反应，最佳模型AUC达0.86，并能显著分层患者风险，突显其作为预后影像生物标志物的潜力。

摘要：

贝伐珠单抗是一种被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。尽管其给药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，一直是其有效应用于个性化治疗的主要障碍。在本研究中，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来提取卵巢肿瘤组织特征，以从WSI预测贝伐珠单抗反应。我们结合不同的组织病理学基础模型和多种实例学习（MIL）策略进行了大量实验，证明了这些大模型在预测卵巢癌患者贝伐珠单抗反应方面的能力，其中最佳模型达到了0.86的AUC分数和72.5%的准确率。此外，我们的生存模型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，即使是在患有高级别浆液性卵巢癌这一侵袭性亚型的患者中也是如此。这项工作突显了组织病理学基础模型在从WSI预测卵巢癌贝伐珠单抗反应任务中的实用性。这些模型突出显示的WSI高注意力区域不仅有助于模型的可解释性，还作为治疗预后的有前景的影像生物标志物。

英文摘要：

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.

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