Abstract P5-07-01: Computational scoring of tumor infiltrating lymphocytes in triple-negative breast cancer
Authors: M Amgad, A Sarkar, C Srinivas, R Redman, S Ratra, CJ Bechert, BC Calhoun, K Mrazeck, U Kurkure, LA Cooper, M Barnes
Journal: Cancer Research (2019)

Abstract Background: Stromal Tumor Infiltrating Lymphocytes (sTIL) are an established prognostic feature in triple-negative breast cancer, yet manual assessment or visual estimation of sTILs with conventional light microscopy may be subject to inter-pathologist variability. Recently published guidelines by the International TIL Working Group help address inter-pathologist variability, yet there remains a need for more objective and quantitative computational sTIL scoring. Methods: Our study used 120 triple-negative breast cancer slides (one slide per patient). A deep-learning based image analysis workflow is used to perform segmentation and classification of tissue regions and cells on the digital whole slide image. We used 14 annotated slides to train and validate the deep learning model, and to obtain image segmentation and classification accuracy statistics. Non-training slides were used to evaluate the concordance of manual (m-sTIL) and computationally derived (c-sTIL) scores. To generate data to create the model we manually annotated tissue regions in FFPE H&amp;E stained digital slides, including: tumor, stroma, and necrosis. Initial classification of cell nuclei was performed using a semi-automated image analysis method, and then manually corrected to generate ground truth for tumor, stroma (fibroblasts), and lymphocytes. All annotations were performed by a trained research fellow and reviewed by a board-certified pathologist. Corresponding region and nucleus-level annotations were combined to train and validate a fully-convolutional neural network that jointly classifies tissue regions and cell nuclei. Tissue region segmentation accuracy was assessed by the Dice coefficient to measure degree of overlap between predicted tissue regions and ground truth annotations. Cell classification accuracy was assessed using area under curve (AUC). Two board-certified pathologists independently generated an m-sTIL score for all slides according to clinical guidelines, and discrepancies between pathologists were resolved by consensus. c-sTIL scores were calculated as the percentage of classified stromal areas occupied by nuclei classified as lymphocytic infiltrates. Results: Tissue region segmentation was accurate for both stroma (0.77 Dice) and tumor (0.83 Dice) regions, and accurate overall (0.78 Dice). Cell classification was highly accurate for lymphocytes (0.89 AUC), tumor cells (0.90 AUC), stromal cells (0.78 AUC), and overall (0.89 AUC, micro average). Inter observer spearman correlation between the m-sTIL scores of our two pathologists was 0.66 (p &amp;lt; 0.001). By comparison, the correlation between c-sTIL and consensus m-sTIL was higher at 0.73 (p &amp;lt; 0.001). Dichotomizing at a threshold sTIL score of 10%, c-sTIL scoring identifies low-sTIL patients with an accuracy of 85%. High- and Low- sTIL score patient groups show clear separation on a Kaplan-Meier curve for both c-sTIL and m-sTIL scoring approaches. Conclusions: Our pipeline quantifies stromal TILs with high concordance with manual pathologist scores, and sheds light on the ability of computational approaches in standardizing diagnostic pathology workflows. Future work will investigate how other computationally driven histology biomarkers can predict outcomes and help prognosticate breast cancer patients. Citation Format: Amgad M, Sarkar A, Srinivas C, Redman R, Ratra S, Bechert CJ, Calhoun BC, Mrazeck K, Kurkure U, Cooper LA, Barnes M. Computational scoring of tumor infiltrating lymphocytes in triple-negative breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-07-01.