Abstract Background: Ki67 is a key proliferation marker in solid tumors, particularly relevant for HR+/HER2- breast cancer when guiding adjuvant therapy. Despite its clinical importance, Ki67 immunohistochemistry (IHC) scoring lacks standardization. International guidelines aim to reduce variability among pathologists, and AI-driven image analysis solutions have recently emerged as rapid and reliable alternatives. This study compares Ki67 scoring using Aiforia® (AI platform) and Halo® (supervised image analysis software) against three independent pathologists across a large solid tumor cohort. Methods: We stained 192 tumors of various origins, including breast and prostate, with Ki67 (clone 30-9). Pathologists were trained per International Ki67 Working Group (IKWG) recommendations and scored tissues accordingly. Aiforia®, based on deep learning, automatically quantified Ki67-positive tumor cells within minutes. Halo® employed a random forest classifier to segment tumor, non-tumor, and background regions, verified by a pathologist. After cell segmentation, Ki67 positivity was determined by thresholding. Results: Ki67 scoring showed strong agreement between Aiforia® and Halo® across all solid tumors (r2 = 0.95). Inter-pathologist correlations were weaker (A-B: r2 = 0.78; A-C: r2 = 0.86; B-C: r2 = 0.85) despite standardized training, though still acceptable. Among 19 tumor types analyzed, only thyroid and stomach showed software correlation inferior to 0.75, with inter-pathologist agreement fair for stomach (r2 0.80) and lower for thyroid (r2 = 0.65-0.82). Organ-specific variability was notable among pathologists, while software scores remained consistent. For breast tumors (n = 16), Aiforia® and Halo® correlated strongly (r2 = 0.96), whereas pathologist agreement ranged from r2 = 0.60 to 0.87. Conclusions: AI-based platforms like Aiforia® and supervised image analysis tools such as Halo® provide robust, reproducible Ki67 scoring and significantly reduce inter-observer variability. These technologies offer valuable assistance for IHC-based clinical analysis and may serve as arbitration tools or standardize Ki67 evaluation in solid tumors. This text has been revised with the assistance of Microsoft Copilot to comply with the specified character limit. Citation Format: Rania Gaspo, Xavier Pichon, Maroua Tliba, Sabine Iglesias, Darshan Kumar, Renaud Burrer, Amanda Finan-Marchi, Marie Gérus-Durand. AI-assisted Ki67 evaluation in solid tumors: Consistency and reliability compared to expert pathologists abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5495.
Gaspo et al. (Fri,) studied this question.