Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers

Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers

Triple-negative breast cancer (TNBC) is an aggressive and difficult-to-treat cancer type that represents approximately 15% of all breast cancers. Recently, stromal tumor-infiltrating lymphocytes (sTIL) resurfaced as a strong prognostic biomarker for overall survival (OS) for TNBC patients. Manual as...

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Bibliographic Details
Main Authors: Jeppe Thagaard, Elisabeth Specht Stovgaard, Line Grove Vognsen, Søren Hauberg, Anders Dahl, Thomas Ebstrup, Johan Doré, Rikke Egede Vincentz, Rikke Karlin Jepsen, Anne Roslind, Iben Kümler, Dorte Nielsen, Eva Balslev
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Cancers
Subjects:
deep learning
digital pathology
image analysis
prognostic biomarker
survival analysis
triple-negative breast cancer
Online Access:https://www.mdpi.com/2072-6694/13/12/3050

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