Semi-automated analysis of digital whole slides from humanized lung-cancer xenograft models for checkpoint inhibitor response prediction.

ABSTRACT: We propose a deep learning workflow for the classification of hematoxylin and eosin stained histological whole-slide images of non-small-cell lung cancer. The workflow includes automatic extraction of meta-features for the characterization of the tumor. We show that the tissue-classification produces state-of-the-art results with an average F1-score of 83%. Manual supervision indicates that experts, in practice, accept a far higher percentage of predictions. Furthermore, the extracted meta-features are validated via visualization revealing relevant biomedical relations between the different tissue classes. In a hypothetical decision-support scenario, these meta-features can be used to discriminate the tumor response with regard to available treatment options with an estimated accuracy of 8

SUBMITTER: Bug D 

PROVIDER: S-EPMC6642041 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature