The introduction of the International Association for the Study of Lung Cancer grading
system has furthered interest in histopathological grading for risk stratification
in lung adenocarcinoma. Complex morphology and high intratumoral heterogeneity present
challenges to pathologists, prompting the development of artificial intelligence (AI)
methods. Here we developed ANORAK (pyrAmid pooliNg crOss stReam Attention networK),
encoding multiresolution inputs with an attention mechanism, to delineate growth patterns
from hematoxylin and eosin-stained slides. In 1,372 lung adenocarcinomas across four
independent cohorts, AI-based grading was prognostic of disease-free survival, and
further assisted pathologists by consistently improving prognostication in stage I
tumors. Tumors with discrepant patterns between AI and pathologists had notably higher
intratumoral heterogeneity. Furthermore, ANORAK facilitates the morphological and
spatial assessment of the acinar pattern, capturing acinus variations with pattern
transition. Collectively, our AI method enabled the precision quantification and morphology
investigation of growth patterns, reflecting intratumoral histological transitions
in lung adenocarcinoma.
