Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer.

ABSTRACT: Small cell lung cancer (SCLC) is a highly aggressive subtype of lung cancer characterized by rapid tumor growth and early metastasis. Accurate prediction of prognosis and therapeutic response is crucial for optimizing treatment strategies and improving patient outcomes. In this study, we conducted a deep-learning analysis of Hematoxylin and Eosin (H&E) stained histopathological images using contrastive clustering and identified 50 intricate histomorphological phenotype clusters (HPCs) as pathomic features. We identified two of 50 HPCs with significant prognostic value and then integrated them into a pathomics signature (PathoSig) using the Cox regression model. PathoSig showed significant risk stratification for overall survival and disease-free survival and successfully identified patient

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10796367 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature