Abstract

Aim: Breast cancer is the most frequently diagnosed malignancy among women worldwide, and early detection plays a critical role in the success of treatment. The Ki 67 proliferation index is widely used to evaluate tumor cell proliferation; however, its manual scoring process is observer-dependent, time-consuming, and inherently subjective. This study aims to assess Ki 67 immunohistochemical staining using deep learning algorithms in an objective, rapid, and reproducible manner, and to compare the model’s performance with conventional scoring methods.
Materials and Methods: In the first phase of the study, a dataset was created using digital images of Ki-67-stained histological sections obtained from patients diagnosed with breast cancer. These images were used to train a machine learning algorithm. In the second phase, 50 new Ki-67-stained tissue sections previously unseen by the model were digitized, and the model’s predictions were compared with Ki 67 index values calculated by conventional manual assessment.
Results: The developed model achieved a mean absolute error (MAE) of 8.69%, a root mean square error (RMSE) of 13.00%, and a coefficient of determination (R²) of 0.540 in overall prediction performance. For cases with low proliferation (Ki-67

Key words: Breast cancer, Ki-67, Artificial Intelligence, Deep Learning, Artificial Neural Network