High-accuracy prediction of mutations in nine genes in lung adenocarcinoma via two-stage multi-instance learning on large-scale whole-slide images.

ABSTRACT:

Background

Lung cancer is widely recognized as a prevalent malignant neoplasm. Traditional genetic testing methods face limitations such as high costs and lengthy procedures. The prediction of clinically relevant genetic mutations via histopathological images could facilitate the expedited identification of genetic mutations in clinical settings.

Methods

We collected 2,221 slides from 1999 patients diagnosed with lung adenocarcinoma. The data include whole-slide images data as well as information on gene mutations in EGFR, KRAS, ALK, HER2, and other rare genes (ROS1, RET, BRAF, PIK3CA, NRAS), and related clinical information. The self-supervised model DINO and the two-stage multi-instance network GAMIL were employed to accurately identify mutation statuses in 9 genes linked

SUBMITTER: Zhao L 

PROVIDER: S-EPMC12128265 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature