Development of a Deep-learning Pipeline to Recognize and Characterize Macrophages in Colo-rectal Liver Metastasis

Abstract

Quantitative analysis of tumor microenvironment (TME) provides prognostic and predictive information in several human cancers but, with few exceptions, it is not performed in the daily clinical practice being time-consuming. We recently showed that the morphology of tumor associated macrophages (TAM) correlates with outcome in patients with colo-rectal liver metastases (CLM). However, as for other TME components, recognizing and characterizing hundreds of TAM in a single histopathological slide is unfeasible. To fasten this process, we explored a deep-learning based solution. We tested three convolutional neural networks (CNN), Unet, SegNet and DeepLab-v3, and compared their results according to IoU (intersection over union), a metric describing the similarity between what CNN predicts as TAM and the ground truth and SBD (symmetric best dice), which indicates the ability of CNN to separate different TAMs. Unet and SegNet showed intrinsic limitations in discriminating single TAMs (highest SBD 61.34 ± 2.21), whereas DeepLab-v3 accurately recognized TAM from the background [IoU (89.13 ± 3.85)] and separated different TAM [SBD (79.00 ± 3.72)]. This deep-learning pipeline to recognize TAMs in digital slides, will allow the characterization of TAM-related metrics in the daily clinical practice, allowing the implementation of prognostic tools