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Phenotyping tumor infiltrating lymphocytes (PhenoTIL) on H&E tissue images: predicting recurrence in lung cancer
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Paper Abstract

A number of papers have established that a high density of tumor-infiltrating lymphocytes (TILs) is highly correlated with a better prognosis for many different cancer types. More recently, some studies have shown that the spatial interplay between different subtypes of TILs (e.g. CD3, CD4, CD8) is more prognostic of disease outcome compared to just metrics related to TIL density. A challenge with TIL subtyping is that it relies on quantitative immunofluoresence or immunohistochemistry, complex and tissue-destructive technologies. In this paper we present a new approach called PhenoTIL to identify TIL sub-populations and quantify the interplay between these sub-populations and show the association of these interplay features with recurrence in early stage lung cancer. The approach comprises a Dirichlet Process Gaussian Mixture Model that clusters lymphocytes on H&E images. The approach was evaluated on a cohort of N=178 early stage non-small cell lung cancer patients, N=100 being used for model training and N=78 being used for independent validation. A Linear Discriminant Analysis classifier was trained in conjunction with 186 PhenoTIL features to predict the likelihood of recurrence in the test set. The PhenoTIL features yielded an AUC=0.84 compared to an approach involving just TIL density alone (AUC=0.58). In addition, a Kaplan-Meier analysis showed that the PhenoTIL features were able to statistically significantly distinguish early from late recurrence (p = 4 ∗ 10 −5 ).

Paper Details

Date Published: 15 May 2019
PDF: 8 pages
Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 1095607 (15 May 2019); doi: 10.1117/12.2513048
Show Author Affiliations
Cristian Barrera, Univ. Nacional de Colombia (Colombia)
Germán Corredor, Univ. Nacional de Colombia (Colombia)
Xiangxue Wang, Case Western Reserve Univ. (United States)
Kurt A. Schalper, Yale School of Medicine (United States)
David L. Rimm, Yale School of Medicine (United States)
Vamsidhar Velcheti, Cleveland Clinic (United States)
Anant Madabhushi, Ctr. for Computational Imaging and Personalized Diagnostics, Case Western Reserve Univ. (United States)
Eduardo Romero Castro M.D., Univ. Nacional de Colombia Sede Bogotá (Colombia)


Published in SPIE Proceedings Vol. 10956:
Medical Imaging 2019: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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