Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study

Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study

Purpose: Gene expression profiles are used for decision making in the adjuvant setting in hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer. While algorithms to optimize testing exist for RS/Oncotype Dx, no such efforts have focused on ROR/Prosigna. This study aims to enhance pre-se...

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Main Authors: Una Kjällquist, Nikos Tsiknakis, Balazs Acs, Sara Margolin, Luisa Edman Kessler, Scarlett Levy, Maria Ekholm, Christine Lundgren, Erik Olsson, Henrik Lindman, Antonios Valachis, Johan Hartman, Theodoros Foukakis, Alexios Matikas
Format: Article
Language:English
Published: Elsevier 2025-08-01
Series:Breast
Subjects:
Adjuvant
Breast cancer
Machine learning
Prosigna
Risk of recurrence
Online Access:http://www.sciencedirect.com/science/article/pii/S0960977625005065
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http://www.sciencedirect.com/science/article/pii/S0960977625005065

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