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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