Abstract C022: Pathomics-based classifiers for inferring breast cancer genomic assays and prognosis in sub-Saharan Africa: Results from the Ghana Breast Health Study

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.