Deep learning algorithm on H&E whole slide images to characterize <i>TP53</i> alterations frequency and spatial distribution in breast cancer.

ABSTRACT: The tumor suppressor TP53 is frequently mutated in hormone receptor-negative, HER2-positive breast cancer (BC), contributing to tumor aggressiveness. Traditional ancillary methods like immunohistochemistry (IHC) to assess TP53 functionality face pre- and post-analytical challenges. This proof-of-concept study employed a deep learning (DL) algorithm to predict TP53 mutational status from H&E-stained whole slide images (WSIs) of BC tissue. Using a pre-trained convolutional neural network, the model identified tumor areas and predicted TP53 mutations with a Dice coefficient score of 0.82. Predictions were validated through IHC and next-generation sequencing (NGS), confirming TP53 aberrant expression in 92 % of the tumor area, closely matching IHC findings (90 %). The DL m

SUBMITTER: Frascarelli C 

PROVIDER: S-EPMC11638532 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature