Fig. 5: Cell type modeling correctly classifies synovial stromal and immune cells in RA synovial biopsies.

A A subset of cells from 13 RA synovial biopsies were annotated (n = 2,341) using an active learning strategy. After nuclei detection and custom feature extraction from each cell, a gradient boosted decision tree was trained using a parameter grid search with a nested, stratified, 5-fold cross-validation training strategy. The F1 scores (M ± SD) of the five folds for each cell class are presented with the overall weighted F1 of 0.85 ± 0.01 (M ± SD). B The confusion matrix from the most performant model demonstrates the typical misclassification in this dataset (data is cell counts). Stromal cells can be mistaken for other stromal cells (vascular endothelial cells, synovial lining cells, and fibroblast) and lymphocytes can be mistaken for plasma cells. F: Fibroblast, L: Lymphoid, M/H: Macrophage/Histocyte, PC: Plasma Cell, S/C: Stromal/Connective Cell, SLC: Synovial Lining Cell; VEC: Vascular Endothelial Cell. C, D The most performant model was used to predict the cell type of the remaining cells from all RA synovial biopsies (n = 60). C Adjacent sections to the H&E-stained slides were stained with either CD3 (T-Cells), CD20 (B-Cells) and CD138 (Plasma Cells) or CLIC5 (Synovial Lining), CD3 (T-Cells), CD68 (Macrophages), and CD34 (Vascular Endothelial Cells) (n = 15). Representative images of plasma cells (Cⁱ), lymphocytes (Cⁱⁱ), synovial lining cells (Cⁱⁱⁱ) and vascular endothelial cells (C^iv) with the original H&E in the left column, prediction overlays in the middle, and adjacent slide IF in the right column. Immunostains and magnification are denoted within the image. D Correlation of machine learning predictions with quantitative histomorphometry of IF+ cells from adjacent sections of n = 15 RA synovial biopsy pieces. Top: CD138+ cells vs ML Predictions of Plasms cells; Bottom: CD3+ and CD20+ cells vs ML Predictions of Lymphocytes; Spearman’s Correlations (n = 15). Please note the log scale. E Correlation of machine learning predictions of lymphocytes (as a percent of total cells) vs the Krenn Inflammation Score, Spearman’s Correlations (n = 60). Please note the log scale on the x-axis.