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Fig. 1 | Breast Cancer Research

Fig. 1

From: Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response

Fig. 1

Study design. The Gene expression analysis cohort containing both RNA-seq and clinical data was used to develop TME phenotypes. The Radiomics discovery cohort, regarded as a training set, was used for radiomics signature training of TME phenotypes while the Radiomics validation cohort was for repeatability validation. Data from these two cohort were both derived from the TCIA dataset. The Immune phenotype cohort was divided into immune-inflamed tumors and immune-desert tumors according to the IHC-CD8 outcomes of the enrolled subjects. The Immunotherapy-treated cohort receiving anti-PD-1/PD-L1 therapy was used to predict the prognostic response to immunotherapy. These two external validation sets were recruited from the Guangdong People's Hospital. *TME, Tumor microenvironment; RNA-Seq, RNA sequencing; IHC, Immunohistochemistry; PD-1, programmed cell death protein-1; PD-L1, programmed cell death ligand 1

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