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Poster session 14

1219P - Artificial intelligence-based breast cancer detection facilitates automated prognosis marker assessment using multiplex fluorescence immunohistochemistry

Date

21 Oct 2023

Session

Poster session 14

Topics

Cancer Diagnostics

Tumour Site

Breast Cancer

Presenters

Tim Mandelkow

Citation

Annals of Oncology (2023) 34 (suppl_2): S711-S731. 10.1016/S0923-7534(23)01942-7

Authors

T. Mandelkow1, E. Bady1, M. Lennartz1, A. Lebeau1, S. Steurer1, E. Burandt1, T.S. Clauditz1, G. Sauter1, M. Graefen2, S. Minner3, C. Bernreuther3, N.C. Blessin3

Author affiliations

  • 1 Institute Of Pathology, University Medical Center Hamburg-Eppendorf, 20246 - Hamburg/DE
  • 2 Prostate Cancer Center, Martini-Clinic, University Medical Center Hamburg-Eppendorf, 20251 - Hamburg/DE
  • 3 Institute Of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg/DE

Resources

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Abstract 1219P

Background

Prognostic markers in routine clinical practice of breast cancer are often assessed using RNA based multi-gene panels that are depending on a fluctuating tumor purity. Multiplex fluorescence immunohistochemistry (mfIHC) holds the potential for improved risk assessment.

Methods

To enable automated prognosis marker quantification, we have developed and validated a framework for automated breast cancer detection involving three different artificial intelligence analysis steps and an algorithm for cell-distance analysis using BLEACH&STAIN multiplex fluorescence immunohistochemistry. Pan-cytokeratin (panCK) antibodies were used to detect epithelial cells and antibodies directed against Myosin and p63 were used to identify basal cells.

Results

The optimal distance between Myosin+ and p63+ basal cells and benign panCK+ cells was identified as 25 μm in breast cancer and used – combined with deep learning-based algorithms – to exclude benign glands from the analysis. Our framework discriminated normal glands from malignant glands with an accuracy of 98.4% (95% confidence interval [CI]: 97.4 – 99.3). The approach for automated breast cancer detection improved the predictive performance of several prognosis markers significantly (each p<0.05) and a comparison with manually assessed data using conventional brightfield immunohistochemistry showed a high concordance for a multitude of different prognosis marker such as PR, ER, GATA3, HER2, and PD-L1 (each p<0.0001).

Conclusions

The combined assessment of up to 5 markers in a prognosis score showed strong prognostic relevance (p<0.001) and was an independent risk factor in multivariate analysis (p=0.005). Thus, the data from this study show that automated breast cancer detection in combination with artificial intelligence-based analysis of multiplex fluorescence immunohistochemistry enables a rapid and reliable analysis of multiple prognostic parameters. The major advantage of this method is the analysis of malignant cells exclusively that cannot be achieved using RNA-based panel analysis.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

G. Sauter: Other, Personal, Other, The GATA3 (MSVA-450M), PD-L1 (MSVA-711R), PR (MSVA-570R), AR (MSVA-367R), ER (MSVA-564R), TROP2 (MSVA-733R), TOP2A (MSVA-802R), Myosin (MSVA-375R), panCK (MSVA-000R), Ki-67 (MSVA-267M) antibodies were provided by MS Validated Antibodies GmbH (owned by a family member of GS): MSVA. All other authors have declared no conflicts of interest.

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