Abstract
Recent years have seen a growing awareness of the role the immune system plays in successful cancer treatment, especially in novel therapies like immunotherapy. The characterization of the immunological composition of tumors and their micro-environment is thus becoming a necessity. In this paper we introduce a deep learning-based immune cell detection and quantification method, which is based on supervised learning, i.e., the input data for training comprises labeled images. Our approach objectively deals with staining variation and staining artifacts in immunohistochemically stained lung cancer tissue and is as precise as humans. This is evidenced by the low cell count difference to humans of 0.033 cells on average. This method, which is based on convolutional neural networks, has the potential to provide a new quantitative basis for research on immunotherapy.
Highlights
Tumors contain malignant cells and diverse non-malignant cells, such as those from the immune, vascular and lymphatic system, in addition to fibroblasts, pericytes, extracellular matrix and adipocites
In this article we present a robust and quantitative immune cell detection system, which could be used for describing immune system involvement in the cancer micro-environment
The neural network model was applied on a pixel-by-pixel basis on a whole digital slide, yielding a posterior likelihood of a every pixel of being a positive cell (Fig. 3), generating an immune cell localization likelihood map
Summary
Tumors contain malignant cells and diverse non-malignant cells, such as those from the immune, vascular and lymphatic system, in addition to fibroblasts, pericytes, extracellular matrix and adipocites. These cells can even comprise more than 50% of the mass of the primary tumors and their metastases (Balkwill, Capasso & Hagemann, 2012). To date it is not well-studied how the non-malignant cells in the tumor micro-environment regulate tumor progression and its response to treatment (Pietras & Östman, 2010; Herbst et al, 2014; Hoefflin et al, 2016; Michel et al, 2008).
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