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Author

Sarah Volinsky-Fremond

Observed names: Sarah Fremond, Sarah Fremond-Volinsky, Volinsky-Fremond, Sarah
Observed institution: Leiden University Medical Center
ORCID: 0000-0002-8195-5735
Observed ORCID: 0000-0002-8195-5735
OpenAlex ID: A5093346148
Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts
2022 · Sarah Fremond, Sonali Andani, et al. · The Lancet Digital Health · 135
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Prediction of recurrence risk in endometrial cancer with multimodal deep learning
2024 · Sarah Volinsky-Fremond, Nanda Horeweg, et al. · Nature Medicine · 103
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The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning
2022 · Sarah Fremond, Viktor Hendrik Koelzer, et al. · Frontiers in Oncology · 20
Author Correction: Prediction of recurrence risk in endometrial cancer with multimodal deep learning
2024 · Sarah Volinsky-Fremond, Nanda Horeweg, et al. · Nature Medicine · 4
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POLARIX: POLE-mutation analysis by rapid image-based screening in endometrial cancer
2026 · Nikki van den Berg, Lydia Schoenpflug, et al. · International Journal of Gynecological Cancer · 2
Interpretable Deep Learning Predicts the Molecular Endometrial Cancer Classification from H&E Images: A Combined Analysis of the Portec Randomized Clinical Trials
2022 · Sarah Fremond, Sonali Andani, et al. · SSRN Electronic Journal · 1
Assessing Foundation Models for Computational Pathology in Endometrial Cancer
2026 · Nikki J. van den Berg, Sarah Volinsky-Fremond, et al. · Modern Pathology
Assessing Foundation Models for Computational Pathology in Endometrial Cancer
2026 · Sarah Volinsky-Fremond, Nikki van den Berg, et al. · medRxiv
Deep Learning-Based Screening for POLE mutations on Histopathology Slides in Endometrial Cancer
2026 · Nikki van den Berg, Lydia Schoenpflug, et al. · medRxiv
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HECTOR: multimodal deep learning predicts recurrence risk in endometrial cancer
2023 · Sarah Fremond-Volinsky, Nanda Horeweg, et al. · medRxiv
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Abstract 5695: Deep learning risk prediction model of distant recurrence from H&E endometrial cancer slides
2023 · Sarah Fremond, Sonali Andani, et al. · Cancer Research
2022-RA-648-ESGO Interpretable deep learning provides clues for prognostic refinement of the molecular endometrial cancer classification
2022 · Sarah Fremond, Sonali Andani, et al. · International Journal of Gynecological Cancer
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Works count: 14
Citation count: 265
H-index: 4
I10-index: 3
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