A pipeline of machine learning-driven multi-modal data fusion methods for prognostic risk analysis in bevacizumab-treated metastatic colorectal cancer

Abstract We introduce a comprehensive multi-step machine-learning driven pipeline which fuses multi-modal omics datasets and clinical outcomes with survival and treatment response to predict patient outcome following anti-angiogenic therapy in the metastatic colorectal cancer (mCRC) setting. The app...

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Hauptverfasser:	Valentina Thomas, Gift Nyamundanda, Adrian Lärkeryd, P. S. Hari, Ian S. Miller, Tom Venken, Dominiek Smeets, Bram Boeckx, Johannes Betge, Matthias P. A. Ebert, Timo Gaiser, Verena Murphy, Elaine Kay, Henk M. Verheul, Alice C. O’Farrell, Chiara Cremolini, Federica Marmorino, William M. Gallagher, Ana Barat, Rut Klinger, Bozena Fender, Bauke Ylstra, Nicole van Grieken, Deborah A. McNamara, Bryan T. Hennessy, Sudipto Das, Bruce Moran, Darran P. O’Connor, Rodrigo Dienstmann, Diether Lambrechts, Jochen H. M. Prehn, Anguraj Sadanandam, Annette T. Byrne
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Nature Portfolio 2026-04-01
Schriftenreihe:	Scientific Reports
Schlagworte:	Machine learning Multi-modal data fusion Prognostic risk analysis Metastatic colorectal cancer Personalised medicine Biomarkers
Online-Zugang:	https://doi.org/10.1038/s41598-026-39189-w
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