Accurately calculating the Ki-67 index, a critical biomarker for cellular proliferation, is pivotal in breast cancer (BC) treatment personalization. Variability in manual assessments of this index can lead to inconsistent clinical decisions, underscoring the need for precise, automated methods. This systematic review addresses this imperative by examining advanced Deep Learning (DL) techniques applied to BC histopathology images to predict the Ki-67 index. Following the PRISMA framework, an exhaustive search was conducted across multiple databases without starting-time restrictions, including studies up to February 1, 2025, with stringent inclusion criteria. The review evaluates various DL architectures, such as PathoNet, highlighting their potential to streamline and enhance the accuracy of Ki-67 index predictions. The analysis includes a comprehensive discussion of primary research databases and their accessibility, pointing to significant advancements in the automation of Ki-67 predictions. These advancements are crucial for fostering standardized treatment approaches across oncology practices. Despite notable progress, challenges persist, including the need for more extensive databases and the development of innovative DL models. Clinical adoption remains limited due to difficulties in interpretability, validation, and integration into pathology workflows. Advancing in explainability, standardized validation, and dataset expansion is crucial for facilitating clinical implementation and improving personalized BC treatment.
Arteaga-Arteaga et al. (Sat,) studied this question.