Pitfalls and Emerging Trends in AI-driven Pathological Analysis of Metastatic Brain Tumors

The integration of artificial intelligence (AI) and computational pathology has the potential to refine prognostic stratification in patients with metastatic brain tumors (MBTs). However, current AI models, which are trained predominantly on extracranial tissues, encounter significant challenges in the unique intracranial microenvironment. Unlike previous reviews that focus on primary brain tumors, this article addresses the challenges of applying AI pathology to MBTs. We discuss three major limitations in AI-driven MBT pathology. First, models struggle with cellular lineage distinction between resident microglia and bone marrow-derived macrophages. Second, there is a need for topological analysis of immune cell distribution within the immune-privileged context of the brain. Third, domain shift arises from neuropil texture and metabolic adaptation. Emerging approaches, such as multiplex immunohistochemistry (mIHC) and graph neural networks (GNNs), can enable lineage-specific, spatially resolved, and metabolically contextualized analysis. However, prospective validation in MBT cohorts remains necessary. We propose a brain-optimized, multidimensional framework that integrates clinical parameters with spatial immune and metabolic features. Such models can refine prognosis beyond conventional Stage IV classification and support individualized therapeutic strategies for patients with MBTs.