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Rudinei Linck

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Articles

Interpretable machine learning models for oncotypedDX ® prediction using clinicopathologic data: a brazilian cohort study

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By Daniella Castro Araujo, Pedro Souza, Daniela Dornelles Rosa, Artur Katz, Daniele Assad-Suzuki, Solange Moraes Sanches, Laura Testa, José Bines, Rafael Kaliks, Romualdo Barroso-Sousa, Tatiana Strava Correa, Andrea Kazumi Shimada, Daniel Batista, Daniel Gomes, Renata Colombo Bonadio, Zenaide Silva de Souza, Juliana Rodrigues Beal, Mario Lopes, Mário Lopes, Max S Mano, Antonio Carlos Buzaid, Débora de Melo Gagliato, Bruna Los, Daniel Argolo, Rudinei Linck, Marcelle Goldner Cesca, Débora Gaudêncio
| Nature Verified
Abstract Oncotype DX® (ODX) guides adjuvant chemotherapy decisions in early breast cancer (eBC), but its high-cost limits access in resource-constrained settings. We developed and evaluated an interpretable machine learning model using routinely available clinicopathologic variables to predict ODX recurrence score (RS) and optimize genomic testing. We analyzed 935 Brazilian patients with eBC from the GBECAM cohort (2005–2024).
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