Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.

ABSTRACT: Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area unde

SUBMITTER: Wang W 

PROVIDER: S-EPMC10567721 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature