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Abstract Background: Although several gene expression-based assays are validated for informing prognosis and treatment decision-making for breast cancer (BC) patients, their uptake has been hampered by technical complexities and cost, particularly in underrepresented and low-resource settings. Here, we explored whether machine learning-based features on standard hematoxylin and eosin (H0.0001), and according to RS categories within both ER+ (P=0.009) and ER- (P=0.006) BC subtypes in the validation set. Conclusion: H 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C022.
Abubakar et al. (Sat,) studied this question.
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