Abstract 7442: Transforming breast cancer treatment decisions: AI tools improve Ki67 risk assessments with 90 pathologists

Abstract A Ki67 proliferation index (PI) of ≥20% determines high-risk status and guides treatment decisions in HR+/HER2− early breast cancer. The Ki67 PI, defined as the ratio of Ki67+ tumor cells to total malignant cells, is labor-intensive and subjective to score. AI support is necessary for routine and reliable clinical use. This is the first large-scale, international study on AI-aided Ki67 PI risk assessments in breast cancer. Ninety pathologists scored the continuous PI from 0% to 100% for 10 breast cancer tissue microarrays (TMA) with and without AI support. The AI tool provided a continuous PI and a Ki67+/- tumor nuclei overlay. The AI tool was developed and validated independently of the pathologists and data used in this study (PMID: 38218973). Using gold-standard manual counts, two pathologists established the ground-truth (GT) PI scores for each TMA, ranging from 7% to 28%. The pathologists’ PI scores were classified into low (<20%) and high risk (≥20%). The risk classification accuracy, sensitivity and specificity relative to the GT were obtained for manual and AI-aided scores, yielding 900 paired classification metrics (90 pathologists × 10 TMAs). Table 1 summarizes the classification metrics showing improvements with AI support for all demographics. The AI tool achieved 100% risk classification accuracy. Manual misclassification and AI-aided correction rates are also summarized in Table 1. The overall manual misclassification rate was 26%, and AI support successfully corrected 95% of these cases. The McNemar’s test, a nonparametric statistical test for paired binary data, assessed statistical differences in risk classification between manual and AI-aided scoring, an asterisk denotes significance (p<0.05). AI support can improve the clinical reliability of PI scoring for risk classification and treatment decisions. AI integration into routine practice can ensure consistent Ki67 use, improving drug response predictions and patient outcomes. Citation Format: Amanda Dy, Ngoc-Nhu Jennifer Nguyen, Melanie Dawe, Dimitrios Androutsos, Susan Done, April Khademi. Transforming breast cancer treatment decisions: AI tools improve Ki67 risk assessments with 90 pathologists [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7442.