Figure 1 from Deep Learning Predicts Mutations and Outcomes in Gastrointestinal Stromal Tumors

Le résumé fourni par la source

DL accurately predicts key molecular mutations in GIST from histopathology. A, AUC with 95% CIs for DL-based prediction of selected molecular mutations in the internal validation cohort (pink) and the external validation cohort (green). B, Representative top-ranked predictive image tiles for three different mutations (PDGFRA exon 18 D842V, KIT exon 11 with two or more codons deleted, and KIT exon 9), as identified by the model’s attention score. Green arrows, lymphocytes for KIT exon 9 and PDGFRA exon 18 D842V; red arrows, vacuolization of cells for PDGFRA exon 18 D842V; blue arrows, mitoses for KIT exon 11 ≥ 2 codons/del–ins. C, AUC with 95% CIs for the same model shown in A when deployed on an external validation cohort consisting exclusively of biopsy specimens; results are reported for all selected mutations. D, Bar plot detailing the sensitivity (pink) and specificity (light pink) for each mutation. E, Heatmaps generated via gradient-weighted class activation mapping (Grad-CAM) illustrate the predictive importance of individual tissue tiles for each mutation: red, high prediction score; blue, low prediction score. In the case of well-characterized mutations such as PDGFRA exon 18 D842V and KIT exon 9, the model focused on tumor regions, in line with reported morphologic correlates. In other cases (PDGFRA exon 18 other mutations), mucosal regions were also emphasized, likely due to vacuolated cells resembling PDGFRA-mutated morphology. Even for more specific variants, such as KIT exon 11 deletions or insertions, the model continued to highlight relevant tumor areas.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.