Fig. 1

Overall schema of the developed NAC response prediction pipeline. The tile-level histology classification module (first step) consists of A training WSI annotation; B definition of histology classes of interest; C tile preprocessing; D feature extraction and selection; E classifier training, testing, and validation; and F generation of histology classification map. The patient-level NAC response prediction module (second step) consists of G graph node identification; H TME spatial descriptor computation; I graph construction and graph feature selection; J machine learning model training, testing, and validation; and K generation of an attention map with highlighted tissue regions with full feature set. Abbreviations: FE, feature extraction; sTILs, stromal TILs; tTILs, tumor TILs; Feat, feature; MVD, microvessel; PGCC, polyploid giant cancer cell