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| Item Type: | Article |
|---|---|
| Title: | The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma |
| Creators Name: | Pan, X. and AbdulJabbar, K. and Coelho-Lima, J. and Grapa, A.I. and Zhang, H. and Cheung, A.H.K. and Baena, J. and Karasaki, T. and Wilson, C.R. and Sereno, M. and Veeriah, S. and Aitken, S.J. and Hackshaw, A. and Nicholson, A.G. and Jamal-Hanjani, M. and Swanton, C. and Yuan, Y. and Le Quesne, J. and Moore, D.A. |
| 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. |
| Keywords: | Adenocarcinoma of Lung, Adenocarcinoma, Artificial Intelligence, Lung Neoplasms, Neoplasm Staging |
| Source: | Nature Cancer |
| ISSN: | 2662-1347 |
| Publisher: | Springer Nature |
| Volume: | 5 |
| Number: | 2 |
| Page Range: | 347-363 |
| Date: | February 2024 |
| Additional Information: | Tom L. Kaufmann (19184) is a member of the TRACERx Consortium. |
| Official Publication: | https://doi.org/10.1038/s43018-023-00694-w |
| PubMed: | View item in PubMed |
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