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

Pan, X; Abduljabbar, K; Coelho-Lima, J; Grapa, AI; Zhang, H; Cheung, AHK; Baena, J; Karasaki, T; Wilson, CR; Sereno, M; Veeriah, S; Aitken, SJ; Hackshaw, A; Nicholson, AG; Jamal-Hanjani, M; TRACERx Consortium; Swanton, C; Yuan, Y; Le Quesne, J; Moore, DA

Author(s): Pan, X; Abduljabbar, K; Coelho-Lima, J; Grapa, AI; Zhang, H; Cheung, AHK; Baena, J; Karasaki, T; Wilson, CR; Sereno, M; Veeriah, S; Aitken, SJ; Hackshaw, A; Nicholson, AG; Jamal-Hanjani, M; TRACERx Consortium; Swanton, C; Yuan, Y; Le Quesne, J; Moore, DA;
Details: Publication Year 2024,Volume 5,Issue #2,Page 347-363
Journal Title: Nature Cancer
Publication Type: Research article
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: Humans; *Adenocarcinoma; Artificial Intelligence; Neoplasm Staging; *Adenocarcinoma of Lung; *Lung Neoplasms/pathology
Department(s): Laboratory Research
PubMed ID: 38200244
Publisher's Version: https://doi.org/10.1038/s43018-023-00694-w
Open Access at Publisher's Site: https://doi.org/10.1038/s43018-023-00694-w
Terms of Use/Rights Notice: Refer to copyright notice on published article.

Creation Date: 2024-08-20 06:17:44

Last Modified: 2024-08-20 06:56:39