Fig. 2: HPCs from Lung adenocarcinoma show consistent enrichment in histomorphological phenotypes. | Nature Communications

Fig. 2: HPCs from Lung adenocarcinoma show consistent enrichment in histomorphological phenotypes.

From: Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides

Fig. 2

A Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction of lung adenocarcinoma tile vector representations labeled by HPC membership (each HPC was assigned a different color for easier visualization). B Percentage of patients from the TCGA cohorts associated with each HPC (100% corresponding to 452 patients). The shades of green are proportional to the percentages (y-axis). C Percentage of institutions associated with each HPC (100% corresponding to 33 institutions). The shades of green are proportional to the percentages (y-axis). D Consensus annotations of each HPC after visual inspection by a panel of 3 expert pathologist of 100 random tiles from each HPC. Stars for detailed consensus indicate the number of agreeing pathologists for the predominant tissue component (a given growth pattern/ non-tumor element, see details in Methods - Cluster Histological Assessment), while the number of stars for patients and institutions quality control (QC), are related to panels B and C with percentage above 50%, above or below 25% for 3, 2 and 1 star respectively). Labels were then projected back to the UMAP in panels E-G. Visual representations of E the distribution of the different tissue categories, F the epithelium:stroma ratio, and G the extent of lymphocytic infiltration are displayed on the UMAP. Source data are provided as a Source Data file.

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