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. Author manuscript; available in PMC: 2021 Mar 23.
Published in final edited form as: Nat Cancer. 2020 Jul 27;1(8):789–799. doi: 10.1038/s43018-020-0087-6

Fig. 3. Inference of putative oncogenic drivers from histological images.

Fig. 3

A deep learning system was trained to predict oncogenic driver genes from histology. Only putative and confirmed drivers were included and variants of unknown significance were pooled with the “wild type” class. On the right-hand side of each panel, a kernel density estimate shows the distribution of all plotted data points. “n” denotes the number of patients with available genetic information and matched histology images in each tumor type. The layout of this figure corresponds to Fig. 2. (a-n) This process uncovered significant predictability of multiple oncogenic drivers, including EGFR, BRAF and TP53.