Extracting interpretable features for pathologists using weakly supervised learning to predict p16 expression in oropharyngeal cancer.

ABSTRACT: One drawback of existing artificial intelligence (AI)-based histopathological prediction models is the lack of interpretability. The objective of this study is to extract p16-positive oropharyngeal squamous cell carcinoma (OPSCC) features in a form that can be interpreted by pathologists using AI model. We constructed a model for predicting p16 expression using a dataset of whole-slide images from 114 OPSCC biopsy cases. We used the clustering-constrained attention-based multiple-instance learning (CLAM) model, a weakly supervised learning approach. To improve performance, we incorporated tumor annotation into the model (Annot-CLAM) and achieved the mean area under the receiver operating characteristic curve of 0.905. Utilizing the image patches on which the model focused, we examined the

SUBMITTER: Adachi M 

PROVIDER: S-EPMC10894206 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature