Automated detection of vascular remodeling in tumor-draining lymph nodes by the deep learning tool HEV-finder

ABSTRACT: Vascular remodeling is common in human cancer and has potential as future biomarkers for prediction of disease progression and tumor immunity status. It can also affect metastatic sites, including the tumor-draining lymph nodes (TDLNs). Dilation of the high endothelial venules (HEVs) within TDLNs has been observed in several types of cancer. We recently demonstrated that it is a pre-metastatic effect that can be linked to tumor invasiveness in breast cancer. Manual visual assessment of changes in vascular morphology is a tedious and difficult task, limiting high throughput analysis. Here we present a fully automated approach for detection and classification of HEV dilation. By using 12,524 manually classified HEVs, we trained a deep learning model and created a graphical user interface for

ORGANISM(S): Homo sapiens (human)

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PROVIDER: S-BIAD463 | bioimages |

REPOSITORIES: bioimages