  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.
