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The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma

Xiaoxi Pan
Khalid AbdulJabbar
Jose Coelho-Lima
Anca-Ioana Grapa
Hanyun Zhang
Alvin Ho Kwan Cheung
Juvenal Baena
Takahiro Karasaki
Claire Rachel Wilson
Marco Sereno
Selvaraju Veeriah
Sarah J. Aitken
Allan Hackshaw
Andrew G. Nicholson
Mariam Jamal-Hanjani
TRACERx Consortium
Charles Swanton
Yinyin Yuan
John Le Quesne & David A. Moore

January 10, 2024

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.