Computational Pathology’s Perspective on Metastatic Tumors
Approaches to Metastatic Tumors in Pathology, NOVA Publications , ss.45-58, 2026
- Yayın Türü: Kitapta Bölüm / Araştırma Kitabı
- Basım Tarihi: 2026
- Yayınevi: NOVA Publications
- Sayfa Sayıları: ss.45-58
- Anahtar Kelimeler: artificial intelligence, cancer diagnosis, computational pathology, digital pathology, machine learning, metastatic tumors, whole slide imaging
- Lokman Hekim Üniversitesi Adresli: Evet
Özet
The increasing workload in pathology laboratories has accelerated the adoption of digital pathology and computational approaches supported by artificial intelligence. Advances in whole slide imaging, computing power, and data storage have enabled the integration of machine learning algorithms into routine histopathological analysis. In particular, computational pathology provides a robust framework for the detection, classification, and prognostic assessment of metastatic tumors. This review summarizes recent developments in artificial intelligence–based methods, including convolutional neural networks, for histopathological image analysis and metastatic tumor evaluation. It also highlights the role of computational pathology in biomarker discovery, tumor heterogeneity analysis, and the integration of genomic and histological data. Despite significant progress, challenges remain regarding data standardization, model validation, and clinical implementation. Addressing these limitations is essential for the translation of computational pathology into routine diagnostic workflows and for improving personalized cancer management.