一致性
医学
乳腺癌
数字图像分析
分级(工程)
自动化方法
乳腺癌
人工智能
放射科
医学物理学
病理
癌症
计算机科学
内科学
计算机视觉
土木工程
工程类
作者
Talat Zehra,Mahin Shams,Zubair Ahmad,Qurratulain Chundriger,Arsalan Ahmed,Nazish Jaffar
出处
期刊:JCPSP. Journal of the College of Physicians & Surgeons Pakistan
[College of Physicians and Surgeons Pakistan]
日期:2023-05-01
卷期号:33 (05): 544-547
被引量:3
标识
DOI:10.29271/jcpsp.2023.05.544
摘要
To validate the concordance of automated detection of Ki67 in digital images of breast cancer with the manual eyeball / hotspot method.Descriptive study. Place and Duration of the Study: Jinnah Sindh Medical University, Karachi, from 1st January to 15th February 2022.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.The manual and automated scoring methods showed strong positive concordance (p <0.001).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.Artificial intelligence, Algorithms, Breast cancer, Deep learning, Image detection, Ki-67.
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