Harnessing Deep Learning for Histopathological Subtyping of Ovarian Cancer: The Role of NOTCH3

Abstract

The classification of ovarian cancer into its diverse histopathological subtypes remains a cornerstone of effective clinical management, yet traditional diagnostic approaches often struggle with subjectivity and inter-observer variability. Deep Learning (DL), such as Convolutional Neural Networks (CNNs), has recently made breakthroughs in histopathological image analysis, enabling high-throughput, accurate cancer subtyping. This review combines the use of the deep learning model for histopathological subtyping of ovarian cancer and its relationship with NOTCH3. Up regulation of NOTCH3 correlates with reduced progression-free survival and as a prognostic biomarker, offers a biological relevance for deep learning-based histopathological subtyping. Recent advancements in digital pathology and the incorporation of NOTCH3 Immuno Histo Chemistry (IHC) data have been incorporated into AI workflows. Furthermore, propose future directions for developing robust and explainable AI systems for ovarian cancer classification and biomarker-driven decision-making.