From organs to algorithms: Redefining cancer classification in the age of artificial intelligence

Abstract

Traditional cancer classification based on organ of origin and histology is increasingly at odds with precision oncology. Tumors in different organs can share molecular features, while those in the same organ can be heterogeneous. This disconnect impacts clinical trials, drug development, and patient care. Recent advances in artificial intelligence (AI), particularly machine learning and deep learning, offer promising avenues for reclassifying cancers through comprehensive integration of molecular, histopathological, imaging, and clinical characteristics. AI-driven approaches have the potential to reveal novel cancer subtypes, identify new prognostic variables, and guide more precise treatment strategies for improving patient outcomes.

Ähnliche Arbeiten