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Article

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

1
Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
2
Visiopharm A/S, 2970 Hørsholm, Denmark
3
Department of Pathology, Herlev and Gentofte Hospital, 2730 Herlev, Denmark
4
Department of Oncology, Herlev and Gentofte Hospital, 2730 Herlev, Denmark
*
Author to whom correspondence should be addressed.
Equal contributors.
Academic Editor: Katja Pinker-Domenig
Cancers 2021, 13(12), 3050; https://doi.org/10.3390/cancers13123050
Received: 12 May 2021 / Revised: 15 June 2021 / Accepted: 17 June 2021 / Published: 18 June 2021
Around 15% of breast cancer patients are diagnosed as triple-negative (TNBC), which have significantly lower 5-year survival rates (77%) than other types of breast cancer (93%). Our study aimed at developing an image analysis-based biomarker to assess how the immune system interacts with the tumor and investigate the potential added value of stromal tumor-infiltrating lymphocytes (sTIL) for the prognosis of overall survival compared to the manual approach. In a large retrospective cohort of 257 patients, we found that our fully automated hematoxylin and eosin (H&E) image analysis pipeline can quantify sTIL density showing both high concordance with manual scoring and association with the prognosis of patients with TNBC. It also overcomes natural limitations of manual assessment that hinder clinical adoption of the immune biomarker. We conclude that sTIL scoring by automated image analysis has prognostic potential comparable to manual scoring and should be further investigated for future use in a clinical setting.
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 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 assessment. In this study, we present a fully automated digital image analysis pipeline and demonstrate 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. View Full-Text
Keywords: deep learning; digital pathology; image analysis; prognostic biomarker; survival analysis; triple-negative breast cancer; tumor microenvironment (TME); tumor-infiltrating lymphocytes deep learning; digital pathology; image analysis; prognostic biomarker; survival analysis; triple-negative breast cancer; tumor microenvironment (TME); tumor-infiltrating lymphocytes
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MDPI and ACS Style

Thagaard, J.; Stovgaard, E.S.; Vognsen, L.G.; Hauberg, S.; Dahl, A.; Ebstrup, T.; Doré, J.; Vincentz, R.E.; Jepsen, R.K.; Roslind, A.; Kümler, I.; Nielsen, D.; Balslev, E. Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers. Cancers 2021, 13, 3050. https://doi.org/10.3390/cancers13123050

AMA Style

Thagaard J, Stovgaard ES, Vognsen LG, Hauberg S, Dahl A, Ebstrup T, Doré J, Vincentz RE, Jepsen RK, Roslind A, Kümler I, Nielsen D, Balslev E. Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers. Cancers. 2021; 13(12):3050. https://doi.org/10.3390/cancers13123050

Chicago/Turabian Style

Thagaard, Jeppe, Elisabeth S. Stovgaard, Line G. Vognsen, Søren Hauberg, Anders Dahl, Thomas Ebstrup, Johan Doré, Rikke E. Vincentz, Rikke K. Jepsen, Anne Roslind, Iben Kümler, Dorte Nielsen, and Eva Balslev. 2021. "Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers" Cancers 13, no. 12: 3050. https://doi.org/10.3390/cancers13123050

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