Automated quantification of stil density with h&e-based digital image analysis has prognostic potential in triple-negative breast cancers

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

Research output: Contribution to journalJournal articlepeer-review

1 Downloads (Pure)

Abstract

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 lym-phocytes (sTIL) resurfaced as a strong prognostic biomarker for overall survival (OS) for TNBC patients. Manual assessment has innate limitations that hinder clinical adoption, and the International Immuno-Oncology Biomarker Working Group (TIL-WG) has therefore envisioned that computational assessment of sTIL could overcome these limitations and recommended that any algorithm should follow the manual guidelines where appropriate. However, no existing studies capture all the concepts of the guideline or have shown the same prognostic evidence as manual as-sessment. In this study, we present a fully automated digital image analysis pipeline and demon-strate that our hematoxylin and eosin (H&E)-based pipeline can provide a quantitative and interpretable score that correlates with the manual pathologist-derived sTIL status, and importantly, can stratify a retrospective cohort into two significant distinct prognostic groups. We found our score to be prognostic for OS (HR: 0.81 CI: 0.72–0.92 p = 0.001) independent of age, tumor size, nodal status, and tumor type in statistical modeling. While prior studies have followed fragments of the TIL-WG guideline, our approach is the first to follow all complex aspects, where appropriate, supporting the TIL-WG vision of computational assessment of sTIL in the future clinical setting.
Original languageEnglish
Article number3050
JournalCancers
Volume13
Issue number12
Number of pages16
ISSN2072-6694
DOIs
Publication statusPublished - 2021

Keywords

  • Deep learning
  • Digital pathology
  • Image analysis
  • Prognostic biomarker
  • Survival analysis
  • Triple-negative breast cancer
  • Tumor microenvironment (TME)
  • Tumor-infiltrating lymphocytes
  • Survival analy-sis

Fingerprint

Dive into the research topics of 'Automated quantification of stil density with h&e-based digital image analysis has prognostic potential in triple-negative breast cancers'. Together they form a unique fingerprint.

Cite this