Automated prognosis marker assessment in 2, 004 breast cancers using an artificial intelligence-based framework for BLEACH&STAIN multiplex fluorescence immunohistochemistry. (9th November 2022)

• Record Type: Journal Article
• Authors: Mandelkow, T; Bady, E; Müller, J; Debatin, N F; Lurati, M C; Lennartz, M; Sauter, G; Blessin, N C
• Is Part Of: American journal of clinical pathology. Volume 158(2022)Supplement 1
• Page Start: S90
• Page End: S90
• Publication Date: 2022-11-09
• Subjects: Diagnosis, Laboratory -- Periodicals; Pathology -- Periodicals; 616.07
• DOI: 10.1093/ajcp/aqac126.189
• Languages: English
• ISSNs: 0002-9173

Abstract

Abstract: Introduction/Objective: Introduction: Prognostic markers in routine clinical practice of breast cancer are currently assessed using multi-gene panels. However, the fluctuating tumor purity can reduce the predictive value of such tests. Immunohistochemistry holds the potential for a better risk assessment. Methods/Case Report: Methods: To enable automated prognosis marker detection (i.e. HER2, GATA3, progesterone-[PR], estrogen- [ER], and androgen receptor [AR], TOP2A, Ki-67, TROP2), we have developed and validated a framework for automated breast cancer identification, which comprises three different artificial intelligence analysis steps and an algorithm for cell-distance analysis of 11 + 1 marker BLEACH&STAIN multiplex fluorescence immunohistochemistry (mfIHC) staining in 2, 004 breast cancers. Results (if a Case Study enter NA): Results: The optimal distance between Myosin+ basal cells and benign panCK+ cells was identified as 25 µm and used to exclude benign glands from the analysis combined with several deep learning-based algorithms. Our framework discriminated normal glands from malignant glands with an AUC of 0.96. The accuracy of the approach was also validated by well-characterized biological findings, such as the identification of 13% HER2+, 73% PR+/ER+, and 14 triple negative cases. Furthermore, the automated assessment of GATA3, PR, ER, TOP2A-LI, Ki-67-LI and TROP2 was significantly liked to the tumor grade (p<0.001each). Furthermore, a high expression level of HER2, GATA3, PR, and ER was associated with a prolonged overall survival (p≥0.002 each). Conclusion: Conclusion: A deep learning-based framework for automated breast cancer identification using BLEACH&STAIN multiplex fluorescence IHC facilitates automated prognosis marker quantification in breast cancer.