Use of a Novel Deep Learning Open-Source Model for Quantification of Ki-67 in Breast Cancer Patients in Pakistan: A Comparative Study between the Manual and Automated Methods.
Talat Zehra: Department of Pathology, Jinnah Sindh Medical University, Karachi 75001, Pakistan.
Nazish Jaffar: Department of Pathology, Jinnah Sindh Medical University, Karachi 75001, Pakistan.
Mahin Shams: Department of Pathology, United Medical and Dental College, Karachi 71500, Pakistan.
Qurratulain Chundriger: Department of Pathology and Laboratory Medicine, Section of Histopathology, Aga Khan University Hospital, Karachi 3500, Pakistan.
Arsalan Ahmed: Department of Pathology and Laboratory Medicine, Section of Histopathology, Aga Khan University Hospital, Karachi 3500, Pakistan.
Fariha Anum: Research Department, Ziauddin University, Karachi 75600, Pakistan. ORCID
Najah Alsubaie: Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia. ORCID
Zubair Ahmad: Consultant Histopathologist, Sultan Qaboos Comprehensive Cancer Care and Research Centre, Seeb P.O. Box 556, Oman.
Breast cancer is the most common cancer in women; its early detection plays a crucial role in improving patient outcomes. Ki-67 is a biomarker commonly used for evaluating the proliferation of cancer cells in breast cancer patients. The quantification of Ki-67 has traditionally been performed by pathologists through a manual examination of tissue samples, which can be time-consuming and subject to inter- and intra-observer variability. In this study, we used a novel deep learning model to quantify Ki-67 in breast cancer in digital images prepared by a microscope-attached camera. To compare the automated detection of Ki-67 with the manual eyeball/hotspot method. Place and duration of study: This descriptive, cross-sectional study was conducted at the Jinnah Sindh Medical University. Glass slides of diagnosed cases of breast cancer were obtained from the Aga Khan University Hospital after receiving ethical approval. The duration of the study was one month. We prepared 140 digital images stained with the Ki-67 antibody using a microscope-attached camera at 10×. An expert pathologist (P1) evaluated the Ki-67 index of the hotspot fields using the eyeball method. The images were uploaded to the DeepLiif software to detect the exact percentage of Ki-67 positive cells. SPSS version 24 was used for data analysis. Diagnostic accuracy was also calculated by other pathologists (P2, P3) and by AI using a Ki-67 cut-off score of 20 and taking P1 as the gold standard. The manual and automated scoring methods showed a strong positive correlation as the kappa coefficient was significant. The value was <0.001. The highest diagnostic accuracy, i.e., 95%, taking P1 as gold standard, was found for AI, compared to pathologists P2 and P3. Use of quantification-based deep learning models can make the work of pathologists easier and more reproducible. Our study is one of the earliest studies in this field. More studies with larger sample sizes are needed in future to develop a cohort.
