Evidential deep learning-based ALK-expression screening using H&E-stained histopathological images.

ABSTRACT: Efficient and accurate identification of genetic alterations of non-small cell lung cancer is a critical diagnostic process for targeted therapies. Utilizing advanced modern deep learning is a potential solution that can accurately predict genetic alterations from H&E-stained pathological images without additional testing procedures and costs. However, clinically applicable predictive power for Anaplastic Lymphoma Kinase (ALK) rearrangement has yet to succeed. To tackle these issues, we have developed a pathologically interpretable, evidence-based deep learning algorithm to screen ALK alterations to reduce unnecessary medical costs and understand the association between genetic alterations and pathological phenotypes. The proposed model resulted in +95% accuracy with both resection and bio

SUBMITTER: Kosaraju SC 

PROVIDER: S-EPMC12521586 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature