Abstract
Lung adenocarcinoma (LUAD) tumour tissue grows into variable morphological architecture called growth patterns (GPs). The GPs are clinically linked to the biological behaviour of the tumour. However, due to the complex heterogeneity of the tumours, there is high inter-and intra-observer variability in the pathologist reporting of GPs. This paper proposes a deep-learning model for automatically classifying the LUAD growth patterns in whole slide images (WSIs). The model is trained and tested on 78 cases of LUAD in the digitised WSI of the sample. For each case, all the growth patterns were automatically classified and quantified. Our multivariate analysis shows that lepidic and micropapillary patterns are independent predictors for five-year survival (p<0.05). The proposed model splits our study cohort into short- and long-term survival with p=0.009.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
