Figure 1.

Constructing an experimental approach to identify an AI-derived histologic biomarker associated with outcomes following adjuvant gemcitabine

(A) AI-derived biomarkers could be identified from digitized slides of pancreatic tumor resections, which might guide adjuvant treatment selection.

(B) Data from three patient cohorts were used for this study: (1) TCGA (n = 93 patients), which served as the source for a training set (n = 46) to develop a histologic signature and for a test set (n = 47) to evaluate the performance of the histologic signature; (2) a retrospective cohort from UPMC, which served as a test set external to the data source used for training; and (3) a cohort from a study in Copenhagen, which included patients who received no adjuvant treatment, serving as a negative control.