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15 May 2019 Phenotyping tumor infiltrating lymphocytes (PhenoTIL) on H&E tissue images: predicting recurrence in lung cancer
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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 ).
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Cristian Barrera, Germán Corredor, Xiangxue Wang, Kurt A. Schalper, David L. Rimm, Vamsidhar Velcheti, Anant Madabhushi, and Eduardo Romero Castro M.D. "Phenotyping tumor infiltrating lymphocytes (PhenoTIL) on H&E tissue images: predicting recurrence in lung cancer", Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 1095607 (15 May 2019);

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