Our mission is to maximize the clinical value of every gynecological pathology diagnosis. We aim to transform pathology from a discipline that classifies disease into one that provides comprehensive, clinically actionable information for patients and their treating physicians. Every diagnosis should help predict disease behavior, identify hereditary cancer syndromes where appropriate, and support the selection of the most effective treatment.
To achieve this, we perform translational research with a strong emphasis on clinical implementation. Rather than studying biomarkers in isolation, we develop, validate, and implement diagnostic tools that can be seamlessly integrated into routine pathology workflows. Our ambition is to ensure that scientific discoveries rapidly translate into tangible improvements in patient care.
For this our teams research integrates large, well-characterised patient cohorts with extensive tumour tissue collections, many originating from international randomised clinical trials (eg PORTEC and RAINBO). Over the past decade, we have established one of the world's largest collections of gynecological cancer tissue linked to comprehensive clinical, pathological, and molecular data, providing a unique platform for translational research.
Our work is inherently multidisciplinary. We collaborate closely with clinicians, molecular biologists, computational scientists, bioinformaticians, immunologists, epidemiologists, and fellow gynecologic pathologists through an extensive international network. These collaborations include initiatives such as AIRMEC, TransPORTEC, as well as numerous academic and clinical partners worldwide.
Beyond generating scientific knowledge, we are committed to changing clinical practice. Our research is designed to answer clinically relevant questions and to deliver biomarkers and diagnostic innovations that can be adopted in routine pathology, ultimately improving care for women with gynecological cancer across the world.
Selected publications
- Volinsky-Fremond S, et al. Prediction of recurrence risk in endometrial cancer with multimodal deep learning. Nature Medicine. 2024.
- Kramer C, et al. Causality and functional relevance of BRCA1 and BRCA2 pathogenic variants in non-high-grade serous ovarian carcinomas. The Journal of Pathology. 2024.
- Fremond S, et al. Interpretable deep learning model to predict the molecular classification of endometrial cancer from H&E whole-slide images. The Lancet Digital Health. 2023.
- Peters EEM, et al. Defining substantial lymphovascular space invasion in endometrial cancer. International Journal of Gynecological Pathology. 2022.
- León-Castillo A, et al. Molecular Classification of the PORTEC-3 Trial for High-Risk Endometrial Cancer: Impact on Prognosis and Benefit from Adjuvant Therapy. Journal of Clinical Oncology. 2020.
A complete overview of our publications is available through PubMed.
Join us
We are always interested in collaborating with clinicians, researchers, data scientists, and industry partners who share our ambition to improve the diagnosis and treatment of gynecological cancer. We also welcome enquiries from talented PhD candidates, postdoctoral researchers, medical students, and research interns who are passionate about molecular pathology, computational pathology, translational oncology, or artificial intelligence in medicine.
