Deep learning-based pathology image analysis predicts cancer progression risk in patients with oral leukoplakia.

ABSTRACT:

Background

Oral leukoplakia (OL) is associated with an increased risk for oral cancer (OC) development. Prediction of OL cancer progression may contribute to decreased OC morbidity and mortality by favoring early intervention. Current OL progression risk assessment approaches face large interobserver variability and is weakly prognostic. We hypothesized that convolutional neural networks (CNN)-based histology image analyses could accelerate the discovery of better OC progression risk models.

Methods

Our CNN-based oral mucosa risk stratification model (OMRS) was trained to classify a set of nondysplastic oral mucosa (OM) and a set of OC H&E slides. As a result, the OMRS model could identify abnormal morphological features of the oral epithelium. By applying this model to OL s

SUBMITTER: Zhang X 

PROVIDER: S-EPMC10067069 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature