Faculty, Staff and Student Publications
Publication Date
2-1-2024
Journal
Nature Cancer
Abstract
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.
Trial registration: ClinicalTrials.gov NCT01888601.
Keywords
Humans, Adenocarcinoma, Artificial Intelligence, Neoplasm Staging, Adenocarcinoma of Lung, Lung Neoplasms
DOI
10.1038/s43018-023-00694-w
PMID
38200244
PMCID
PMC10899116
PubMedCentral® Posted Date
1-10-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
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