Automatic lung cancer subtyping using rapid on-site evaluation slides and serum biological markers.

ABSTRACT:

Background

Rapid on-site evaluation (ROSE) plays an important role during transbronchial sampling, providing an intraoperative cytopathologic evaluation. However, the shortage of cytopathologists limits its wide application. This study aims to develop a deep learning model to automatically analyze ROSE cytological images.

Methods

The hierarchical multi-label lung cancer subtyping (HMLCS) model that combines whole slide images of ROSE slides and serum biological markers was proposed to discriminate between benign and malignant lesions and recognize different subtypes of lung cancer. A dataset of 811 ROSE slides and paired serum biological markers was retrospectively collected between July 2019 and November 2020, and randomly divided to train, validate, and test the HMLCS mode

SUBMITTER: Chen J 

PROVIDER: S-EPMC11523640 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature