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Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer
Subarnarekha Chatterji, Marko van Treeck, Chiara Maria Lavinia Loeffler, Oliver Lester Saldanha, Gregory Patrick Veldhuizen, Didem Çifçi, +4 more
8citations -
Artificial Intelligence–based Detection of FGFR3 Mutational Status Directly from Routine Histology in Bladder Cancer: A Possible Preselection for Molecular Testing?2021 European Urology Focus article Computer Science AI in cancer detection Open access cited by 1 patent
Chiara Maria Lavinia Loeffler, Nadina Ortiz Bruechle, Max Jung, Lancelot Seillier, Michael Rose, Narmin Ghaffari Laleh, +5 more
90citations -
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
Sarah Volinsky-Fremond, Sonali Andani, Jurriaan Barkey Wolf, Jouke Dijkstra, Sinead Melsbach, Jan J. Jobsen, +18 more
134citations -
The Information Technology (IT) Infrastructure of the Multicenter Archipelago of Ovarian Cancer Research Biobank: A Potential Blueprint for Other Biobanks2024 Biopreservation and Biobanking article Computer Science Research Data Management Practices Open access
Hein S. Zelisse, Sander de Ridder, Mignon D J M van Gent, Constantijne Helene Mom, G. Bea A. Wisman, Eva‐Maria Roes, +20 more
2citations -
The Aachen Protocol for Deep Learning Histopathology: A hands-on guide for data preprocessing2020 Zenodo (CERN European Organization for Nuclear Research) article Computer Science AI in cancer detection Open access
Hannah Sophie Muti, Chiara Maria Lavinia Loeffler, Amelie Echle, Lara Rosaline Heij, Roman David Buelow, Jeremias Krause, +8 more
29citations -
Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types
Chiara Maria Lavinia Loeffler, Omar S. M. El Nahhas, Hannah Sophie Muti, Zunamys Itzell Carrero, Tobias Seibel, Marko van Treeck, +10 more
22citations -
Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning2021 Proceedings of the National Academy of Sciences article Computer Science Computational Drug Discovery Methods Open access
Soufiane M. C. Mourragui, Marco Loog, Daniël J. Vis, Kat S Moore, Anna G. Manjón, Mark A. van de Wiel, +2 more
42citations -
Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types
Chiara Maria Lavinia Loeffler, Nadine Therese Gaisa, Hannah Sophie Muti, Marko van Treeck, Amelie Echle, Narmin Ghaffari Laleh, +5 more
26citations
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