Artificial intelligence-powered optimization of KI-67 assessment in breast cancer: enhancing precision and workflow efficiency. a literature review

• Published in: Journal of the Pakistan Medical Association 2024-04, Vol.74 (4 (Supple-4)), p.S109
• Main Authors: Mooghal, Mehwish, Anjum, Saba, Khan, Wajiha, Tariq, Hassan, Babar, Amna, Vohra, Lubna Mushtaq
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Biomarkers, Tumor - metabolism; Breast Neoplasms - diagnosis; Breast Neoplasms - metabolism; Breast Neoplasms - pathology; Female; Humans; Ki-67 Antigen - metabolism; Workflow
• ISSN: 0030-9982; 0030-9982
• DOI: 10.47391/JPMA.AKU-9S-17

Breast Cancer (BC) has evolved from traditional morphological analysis to molecular profiling, identifying new subtypes. Ki-67, a prognostic biomarker, helps classify subtypes and guide chemotherapy decisions. This review explores how artificial intelligence (AI) can optimize Ki-67 assessment, improving precision and workflow efficiency in BC management. The study presents a critical analysis of the current state of AI-powered Ki-67 assessment. Results demonstrate high agreement between AI and standard Ki-67 assessment methods highlighting AI's potential as an auxiliary tool for pathologists. Despite these advancements, the review acknowledges limitations such as the restricted timeframe and diverse study designs, emphasizing the need for further research to address these concerns. In conclusion, AI holds promise in enhancing Ki-67 assessment's precision and workflow efficiency in BC diagnosis. While challenges persist, the integration of AI can revolutionize BC care, making it more accessible and precise, even in resource-limited settings.