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Multi-modal AI for comprehensive breast cancer prognostication

Abstract Treatment selection in breast cancer is guided by risk assessment using molecular subtypes and clinicopathological characteristics. However, current approaches lack the precision required for optimal clinical decision-making. To address this, we use data from 8161 patients to develop and ev...

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Autores principales: Jan Witowski, Ken G. Zeng, Joseph Cappadona, Jailan Elayoubi, Khalil Choucair, Elena Diana Chiru, Nancy Chan, Young-Joon Kang, Frederick Howard, Irina Ostrovnaya, Carlos Fernandez-Granda, Freya Schnabel, Zoe Steinsnyder, Ugur Ozerdem, Kangning Liu, Waleed Abdulsattar, Yu Zong, Lina Daoud, Rafic Beydoun, Anas M. Saad, Nitya Thakore, Mohammad Sadic, Frank Yeung, Elisa Liu, Theodore Hill, Benjamin Swett, Danielle Rigau, Andrew J. Clayburn, Valerie Speirs, Marcus Vetter, Lina Sojak, Simone Muenst, Daniel Baumhoer, Jia-Wern Pan, Haslina Makmur, Soo-Hwang Teo, Linda M. Pak, Victor Angel, Dovile Zilenaite-Petrulaitiene, Arvydas Laurinavicius, Natalie Klar, Brian D. Piening, Carlo Bifulco, Sun-Young Jun, Jae Pak Yi, Su Hyun Lim, Adam Brufsky, Francisco J. Esteva, Lajos Pusztai, Yann LeCun, Krzysztof J. Geras
Formato: Artigo
Lenguaje:Inglês
Publicado: Nature Portfolio 2026-05-01
Colección:Nature Communications
Acceso en línea:https://doi.org/10.1038/s41467-026-73088-y
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