Seda CETINKAYA KARABEKIR, Aylin GOKHAN, Hilal ARSLAN, Burak KESKIN, Adile Ferda DAGLI
Annals of Medical Research - 2026;33(1):33-42
Aim: Breast cancer is the most commonly diagnosed malignancy in women, 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. Material 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<20%), the model demonstrated high accuracy (MAE: 5.31%). Binary classification based on a 20% threshold yielded 80% accuracy, 80% sensitivity, 90% precision, and an F1 score of 0.84. Conclusion: The use of artificial intelligence algorithms in Ki-67 assessment demonstrated successful performance, with an MAE of 8.69%, and has the potential to reduce pathologists' workload during the preliminary evaluation phase.
