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

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Title: Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study.
Authors: Kjällquist U; Department of Oncology/Pathology, Karolinska Institutet, Stockholm, Sweden; Theme Cancer, Karolinska University Hospital, Stockholm, Sweden. Electronic address: una.kjallquist@ki.se., Tsiknakis N; Department of Oncology/Pathology, Karolinska Institutet, Stockholm, Sweden., Acs B; Department of Oncology/Pathology, Karolinska Institutet, Stockholm, Sweden; Theme Cancer, Karolinska University Hospital, Stockholm, Sweden., Margolin S; Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden; Department of Oncology, Södersjukhuset, Stockholm, Sweden., Kessler LE; Breast Center, Capio St:Göran's Hospital, Stockholm, Sweden., Levy S; Breast Center, Capio St:Göran's Hospital, Stockholm, Sweden., Ekholm M; Department of Oncology, Ryhov County Hospital, Jönköping, Sweden; Department of Biomedical and Clinical Sciences, Division of Oncology, Linköping University, Linköping, Sweden., Lundgren C; Department of Oncology, Ryhov County Hospital, Jönköping, Sweden; Department of Biomedical and Clinical Sciences, Division of Oncology, Linköping University, Linköping, Sweden., Olsson E; Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden., Lindman H; Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden., Valachis A; Department of Oncology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden., Hartman J; Department of Oncology/Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden., Foukakis T; Department of Oncology/Pathology, Karolinska Institutet, Stockholm, Sweden; Theme Cancer, Karolinska University Hospital, Stockholm, Sweden., Matikas A; Department of Oncology/Pathology, Karolinska Institutet, Stockholm, Sweden; Theme Cancer, Karolinska University Hospital, Stockholm, Sweden. Electronic address: alexios.matikas@ki.se.
Source: Breast (Edinburgh, Scotland) [Breast] 2025 Aug; Vol. 82, pp. 104489. Date of Electronic Publication: 2025 May 07.
Publication Type: Journal Article; Multicenter Study
Journal Info: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 9213011 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-3080 (Electronic) Linking ISSN: 09609776 NLM ISO Abbreviation: Breast Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1532-3080
DOI:10.1016/j.breast.2025.104489