ArticleDiscover oncology2025
Benchmarking histopathology foundation models for ovarian cancer bevacizumab treatment response prediction from whole slide images.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Leakage-Controlled Classification of Dataset-Derived Ordinal p53-Signature Size Categories in Fallopian-Tube Immunohistochemistry.Diagnostics (Basel, Switzerland) · 2026Article
- Prognostic RNA-splicing archetypes in breast cancer identified by extended pre-training of histopathology foundation models.Nature communications · 2026Article
- Diversity over scale: Whole-slide image variety enables H&E foundation model training with fewer patches.Journal of pathology informatics · 2026Article
- A cross-validated deep learning framework for automated detection of DMBA-induced ovarian cancer from histopathological images with CA-125 biomarker support.Frontiers in artificial intelligence · 2026Article
- Explainable AI for computational pathology identifies model limitations and tissue biomarkers.ArXiv · 2024Article
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Authors and funding
5 authors.
Funding
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Abstract
purposeBevacizumab 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.
methodsIn 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.
resultsOur 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.
conclusionThis 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.
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