Automated Ki‐67 labeling index assessment in prostate cancer using artificial intelligence and multiplex fluorescence immunohistochemistry

Author:

Blessin Niclas C1ORCID,Yang Cheng1,Mandelkow Tim1,Raedler Jonas B12,Li Wenchao13,Bady Elena1,Simon Ronald1ORCID,Vettorazzi Eik4,Lennartz Maximilian1,Bernreuther Christian1,Fraune Christoph1,Jacobsen Frank1,Krech Till1,Marx Andreas5,Lebok Patrick1,Minner Sarah1,Burandt Eike1,Clauditz Till S1,Wilczak Waldemar1,Sauter Guido1,Heinzer Hans6,Haese Alexander6,Schlomm Thorsten7,Graefen Markus6,Steurer Stefan1

Affiliation:

1. Institute of Pathology University Medical Center Hamburg‐Eppendorf Hamburg Germany

2. College of Arts and Sciences Boston University Boston MA USA

3. Department of Urology Affiliated Zhongda Hospital of Southeast University Nanjing PR China

4. Department of Medical Biometry and Epidemiology University Medical Center Hamburg‐Eppendorf Hamburg Germany

5. Institute of Pathology Klinikum Fürth Fürth Germany

6. Martini‐Clinic Prostate Cancer Center University Medical Center Hamburg‐Eppendorf Hamburg Germany

7. Department of Urology Charité – Universitätsmedizin Berlin Berlin Germany

Abstract

AbstractThe Ki‐67 labeling index (Ki‐67 LI) is a strong prognostic marker in prostate cancer, although its analysis requires cumbersome manual quantification of Ki‐67 immunostaining in 200–500 tumor cells. To enable automated Ki‐67 LI assessment in routine clinical practice, a framework for automated Ki‐67 LI quantification, which comprises three different artificial intelligence analysis steps and an algorithm for cell‐distance analysis of multiplex fluorescence immunohistochemistry (mfIHC) staining, was developed and validated in a cohort of 12,475 prostate cancers. The prognostic impact of the Ki‐67 LI was tested on a tissue microarray (TMA) containing one 0.6 mm sample per patient. A ‘heterogeneity TMA’ containing three to six samples from different tumor areas in each patient was used to model Ki‐67 analysis of multiple different biopsies, and 30 prostate biopsies were analyzed to compare a ‘classical’ bright field‐based Ki‐67 analysis with the mfIHC‐based framework. The Ki‐67 LI provided strong and independent prognostic information in 11,845 analyzed prostate cancers (p < 0.001 each), and excellent agreement was found between the framework for automated Ki‐67 LI assessment and the manual quantification in prostate biopsies from routine clinical practice (intraclass correlation coefficient: 0.94 [95% confidence interval: 0.87–0.97]). The analysis of the heterogeneity TMA revealed that the Ki‐67 LI of the sample with the highest Gleason score (area under the curve [AUC]: 0.68) was as prognostic as the mean Ki‐67 LI of all six foci (AUC: 0.71 [p = 0.24]). The combined analysis of the Ki‐67 LI and Gleason score obtained on identical tissue spots showed that the Ki‐67 LI added significant additional prognostic information in case of classical International Society of Urological Pathology grades (AUC: 0.82 [p = 0.002]) and quantitative Gleason score (AUC: 0.83 [p = 0.018]). The Ki‐67 LI is a powerful prognostic parameter in prostate cancer that is now applicable in routine clinical practice. In the case of multiple cancer‐positive biopsies, the sole automated analysis of the worst biopsy was sufficient. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Publisher

Wiley

Subject

Pathology and Forensic Medicine

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