Noninferiority of Artificial Intelligence-Assisted Analysis of Ki-67 and Estrogen/Progesterone Receptor in Breast Cancer Routine Diagnostics

Abele N, Tiemann K, Krech T, Wellmann A, Schaaf C, Länger F, Peters A, Donner A, Keil F, Daifalla K, Mackens M, Mamilos A, Minin E, Krümmelbein M, Krause L, Stark M, Zapf A, Päpper M, Hartmann A, Lang T (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Modern Pathology Nature Publishing Group: Open Access Hybrid Model Option B

Book Volume: 36

Pages Range: 100033-

Journal Issue: 3

DOI: 10.1016/j.modpat.2022.100033

Abstract

Image analysis assistance with artificial intelligence (AI) has become one of the great promises over recent years in pathology, with many scientific studies being published each year. Nonetheless, and perhaps surprisingly, only few image AI systems are already in routine clinical use. A major reason for this is the missing validation of the robustness of many AI systems: beyond a narrow context, the large variability in digital images due to differences in preanalytical laboratory procedures, staining procedures, and scanners can be challenging for the subsequent image analysis. Resulting faulty AI analysis may bias the pathologist and contribute to incorrect diagnoses and, therefore, may lead to inappropriate therapy or prognosis. In this study, a pretrained AI assistance tool for the quantification of Ki-67, estrogen receptor (ER), and progesterone receptor (PR) in breast cancer was evaluated within a realistic study set representative of clinical routine on a total of 204 slides (72 Ki-67, 66 ER, and 66 PR slides). This represents the cohort with the largest image variance for AI tool evaluation to date, including 3 staining systems, 5 whole-slide scanners, and 1 microscope camera. These routine cases were collected without manual preselection and analyzed by 10 participant pathologists from 8 sites. Agreement rates for individual pathologists were found to be 87.6% for Ki-67 and 89.4% for ER/PR, respectively, between scoring with and without the assistance of the AI tool regarding clinical categories. Individual AI analysis results were confirmed by the majority of pathologists in 95.8% of Ki-67 cases and 93.2% of ER/PR cases. The statistical analysis provides evidence for high interobserver variance between pathologists (Krippendorff's α, 0.69) in conventional immunohistochemical quantification. Pathologist agreement increased slightly when using AI support (Krippendorff α, 0.72). Agreement rates of pathologist scores with and without AI assistance provide evidence for the reliability of immunohistochemical scoring with the support of the investigated AI tool under a large number of environmental variables that influence the quality of the diagnosed tissue images.

Authors with CRIS profile

Arndt Hartmann Lehrstuhl für Allgemeine Pathologie und Pathologische Anatomie

Involved external institutions

Universitätsklinikum Hamburg-Eppendorf (UKE) DE Germany (DE) Pathologische Institut Celle / Institute of Pathology Celle DE Germany (DE) MindPeak GmbH DE Germany (DE) MVZ HPH Institut für Pathologie und Hämatopathologie GmbH / Institut für Hämatopathologie Hamburg DE Germany (DE) Klinikum Osnabrück DE Germany (DE) Technische Universität München (TUM) DE Germany (DE) Universität Regensburg DE Germany (DE) Medizinische Hochschule Hannover (MHH) / Hannover Medical School DE Germany (DE)

How to cite

APA:

Abele, N., Tiemann, K., Krech, T., Wellmann, A., Schaaf, C., Länger, F.,... Lang, T. (2023). Noninferiority of Artificial Intelligence-Assisted Analysis of Ki-67 and Estrogen/Progesterone Receptor in Breast Cancer Routine Diagnostics. Modern Pathology, 36(3), 100033-. https://dx.doi.org/10.1016/j.modpat.2022.100033

MLA:

Abele, Niklas, et al. "Noninferiority of Artificial Intelligence-Assisted Analysis of Ki-67 and Estrogen/Progesterone Receptor in Breast Cancer Routine Diagnostics." Modern Pathology 36.3 (2023): 100033-.

BibTeX: Download