Attention-based deep learning for analysis of pathology images and gene expression data in lung squamous premalignant lesions.

ABSTRACT: Molecular and cellular alterations to the normal pseudostratified columnar bronchial epithelium results in the development of bronchial premalignant lesions representing a spectrum of histology from normal to hyperplasia, metaplasia, dysplasia (mild, moderate, and severe), carcinoma in situ and invasive carcinoma. Several studies have identified molecular alterations associated with lesion histology and progression. The broad and continuous spectrum of histologic and molecular changes makes reproducible stratification of lesions across multiple studies challenging. Here we propose a transformer-based framework that flexibly utilizes transcriptomic and histologic patterns to distinguish lesions with bronchial dysplasia or worse from normal, hyperplasia, and metaplasia. We leveraged H&E whol

SUBMITTER: Xu L 

PROVIDER: S-EPMC12204423 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature