Fig. 2: Multidimensional visualization of patients’ characteristics in the different cohorts.

a Radial visualization (RadViz) of patients’ characteristics in the internal cohort. RadViz algorithm plots each point normalizing its value on the axes from the center to the arc. No clear separation according to pCR status was observed. b Parallel coordinates plot for the relation between pCR status and patient’s characteristics in the internal cohort. c UMAP in the internal cohort. Different hyperparameters were used to highlight different possible clusters of patients. Columns correspond to different distances (from left to right: Euclidean, Manhattan and Chebyshev) while rows to different numbers of neighbors (from upper to lower: 10, 20, 50, 100). d Heatmap for patients’ characteristics in the training cohort. A hierarchical clustering algorithm with ward distance was used to cluster features and patients. The higher density of pCR was observed in patients with low ER and high Ki67. e Radial visualization (RadViz) of patients’ characteristics in the external validation cohort. Again, no clear separation according to pCR status was observed. f Parallel coordinates plot for the relation between pCR status and patient’s characteristics in the external validation cohort. g UMAP for the external validation cohort. The hyperparameters used were Manhattan distance and neighbors=10, which provided a good discriminative power in the internal cohort. A cluster of pCR patients was observed, nested in a non-pCR region.