Benchmarking histopathology foundation models for ovarian cancer bevacizumab treatment response prediction from whole slide images

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 been shown to increase 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 the capability of these large models in predicting bevacizumab response in ovarian cancer patients with the best models achieving a patient-level balanced accuracy score close to 70%. Furthermore, these models can effectively stratify high- and low-risk patients (p < 0.05) during the first year of bevacizumab treatment.

This work highlights the utility of histopathology foundation models to predict response to bevacizumab treatment  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.