AI and Digital Pathology Reshape Diagnosis of Adult Brain Tumors

For more than a century, the pathologist peering through a microscope delivered the final word on a brain tumor’s identity. That hierarchy shifted with the 2021 World Health Organization classification of central nervous system tumors, fifth edition, which introduced a layered, integrated diagnosis in which molecular findings take precedence over histology when the two conflict. A new narrative review in the Journal of Neuro-Oncology examines how artificial intelligence applied to digitized slides adds a computational layer to this framework and what it means for adult neuro-oncology.

The review, led by Mirna Lechpammer and colleagues including Jonathan H. Sherman, surveys literature indexed in PubMed both before and after the 2021 WHO classification. The authors synthesized evidence on diagnostic performance, molecular prediction, prognostic modeling, workflow integration, and ethical considerations, focusing specifically on adult CNS tumors rather than pediatric disease.

The central finding is one of potential rather than proven clinical benefit. According to the authors, AI and digital pathology show potential to improve diagnostic consistency, shorten turnaround time, and integrate histopathological, molecular, and radiological information within a single computational workflow. The review covers whole-slide imaging (WSI) as part of the digital pathology landscape in adult neuro-oncology.

The reported applications span several domains of adult neuro-oncology. AI models may support glioma grading, prediction of molecular alterations from routine histology, survival stratification, prediction of brain metastasis, and intraoperative decisions.

The review also emphasizes collaboration between neuropathologists and neurosurgeons, including intraoperative decision support, as critical to embedding AI in clinical practice. The authors frame this as an opportunity that remains contingent on validation.

The review identifies limited generalizability, lack of transparency, data privacy, and evolving regulatory oversight as the principal obstacles. Models trained on slides from single institutions may fail when applied elsewhere, a problem the authors tie directly to the need for multi-institutional testing.

The conclusion drawn by the authors is measured. AI and digital pathology are transforming adult CNS tumor diagnostics and could improve precision, efficiency, and multidisciplinary decision-making, but successful implementation will require rigorous multi-institutional clinical validation, transparent governance, equitable access, and collaboration among patients and health care professionals. The review is positioned as a map of the field’s opportunities and challenges rather than a declaration that any specific tool is ready for routine use.

A distinctive feature of the review is its emphasis on patient-centered implementation. The authors state that transparency, equitable access, and inclusion of patient perspectives are essential for responsible adoption.

Because this is a narrative review, its synthesis reflects the authors’ selection and interpretation of published literature rather than a systematic meta-analysis with quantified effect sizes. The performance claims summarized here describe what published studies have reported for AI models in adult CNS tumor pathology, and the authors themselves condition every application on the validation and governance requirements they outline. For clinicians and researchers, the review’s practical message is that the computational layer atop the 2021 WHO framework is advancing quickly, but its translation into clinical practice depends on evidence that does not yet exist at scale.

Subject of Research: Applications of artificial intelligence and digital pathology in the diagnosis of adult central nervous system tumors

Article Title: Opportunities and challenges of artificial intelligence and digital pathology in adult CNS tumors

Article References: Lechpammer, M., Norrell, J. T., Parnas, J. N., Vojnic, M., & Sherman, J. H. (2026). Opportunities and challenges of artificial intelligence and digital pathology in adult CNS tumors. Journal of Neuro-Oncology, 180(2), Article 25. https://doi.org/10.1007/s11060-026-05833-8

Image Credits: AI Generated

DOI: 10.1007/s11060-026-05833-8

Keywords: artificial intelligence, digital pathology, whole-slide imaging, glioma, brain tumors, WHO classification 2021, neuro-oncology, molecular prediction, intraoperative decision support, clinical validation, data privacy, regulatory oversight