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OBJECTIVE: To validate the concordance of automated detection of Ki67 in digital images of breast cancer with the manual eyeball / hotspot method. STUDY DESIGN: Descriptive study. Place and Duration of the Study: Jinnah Sindh Medical University, Karachi, from 1st January to 15th February 2022. METHODOLOGY: Glass slides of cases diagnosed as invasive ductal carcinoma (IDC) were obtained from the Agha Khan Medical University Hospital, selected retrospectively and randomly from 60 patients. They were stained with the Ki67 antibody. An expert pathologist evaluated the Ki67 index in the hotspot fields using eyeball method. Digital images were taken from the hotspots using a camera attached to the microscope. The images were uploaded in the Mindpeak software to detect the exact percentage of Ki67-positive cells. The results obtained through automated detection were compared with the results reported by expert pathologists to see the differential outcome. RESULTS: The manual and automated scoring methods showed strong positive concordance (p <0.001). CONCLUSION: Automated scoring of Ki-67 staining has tremendous potential as the issues of lack of consistency, reproducibility, and accuracy can be eliminated. In the era of personalised medicine, pathologists can efficiently give a precise clinical diagnosis with the support of AI. KEY WORDS: Artificial intelligence, Algorithms, Breast cancer, Deep learning, Image detection, Ki-67.
Zehra et al. (Mon,) studied this question.