ArticleScientific data2022
Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed, 30 citations in OpenAlex.
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- A generalizable pathology foundation model using a unified knowledge distillation pretraining framework.Nature biomedical engineering · 2026Article
- Development of a PANoptosis-Related Pathomics Prognostic Model in Ovarian Cancer: A Multi-Omics Study.Journal of cellular and molecular medicine · 2025Article
- Benchmarking histopathology foundation models for ovarian cancer bevacizumab treatment response prediction from whole slide images.Discover oncology · 2025Article
- Bevacizumab in ovarian cancer therapy: current advances, clinical challenges, and emerging strategies.Frontiers in bioengineering and biotechnology · 2025Review
- Article
- Utilizing a Pathomics Biomarker to Predict the Effectiveness of Bevacizumab in Ovarian Cancer Treatment.Bioengineering (Basel, Switzerland) · 2024Article
- CAISHI: A benchmark histopathological H&E image dataset for cervical adenocarcinomaData in brief · 2024Article
- Annotation-Free Deep Learning-Based Prediction of Thyroid Molecular Cancer Biomarker BRAF (V600E) from Cytological Slides.International journal of molecular sciences · 2023Article
- Vascular normalization: reshaping the tumor microenvironment and augmenting antitumor immunity for ovarian cancer.Frontiers in immunology · 2023Review
- Transferable and Interpretable Treatment Effectiveness Prediction for Ovarian Cancer via Multimodal Deep Learning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer.Scientific data · 2022Article
- PathNarratives: Data annotation for pathological human-AI collaborative diagnosis.Frontiers in medicine · 2022Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Ovarian cancer is the leading cause of gynecologic cancer death among women. Regardless of the development made in the past two decades in the surgery and chemotherapy of ovarian cancer, most of the advanced-stage patients are with recurrent cancer and die. The conventional treatment for ovarian cancer is to remove cancerous tissues using surgery followed by chemotherapy, however, patients with such treatment remain at great risk for tumor recurrence and progressive resistance. Nowadays, new treatment with molecular-targeted agents have become accessible. Bevacizumab as a monotherapy in combination with chemotherapy has been recently approved by FDA for the treatment of epithelial ovarian cancer (EOC). Prediction of therapeutic effects and individualization of therapeutic strategies are critical, but to the authors' best knowledge, there are no effective biomarkers that can be used to predict patient response to bevacizumab treatment for EOC and peritoneal serous papillary carcinoma (PSPC). This dataset helps researchers to explore and develop methods to predict the therapeutic effect of patients with EOC and PSPC to bevacizumab.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.