{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:36:44Z","timestamp":1787027804385,"version":"build-2736575974"},"reference-count":145,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T00:00:00Z","timestamp":1766102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science KAKENHI","doi-asserted-by":"crossref","award":["JP23K06487 (KM)"],"award-info":[{"award-number":["JP23K06487 (KM)"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>The integration of molecular features into histopathological diagnoses has become central to the World Health Organization (WHO) classification of central nervous system (CNS) tumors, improving prognostic accuracy and supporting precision medicine. However, unequal access to molecular testing limits the universal application of integrated diagnosis. To address this, artificial intelligence (AI) models are being developed to predict molecular alterations directly from histological data. In gliomas, deep learning applied to whole-slide images (WSIs) of permanent sections achieves neuropathologist-level accuracy in predicting biomarkers such as IDH mutation and 1p\/19q co-deletion, as well as in molecular subtype classification and outcome prediction. Recent advances extend these approaches to intraoperative cryosections, enabling real-time glioma grading, molecular prediction, and label-free tissue analysis using modalities such as stimulated Raman histology and domain-adaptive image translation. Beyond gliomas, AI-powered histology is being explored in other brain tumors, including morphology-based molecular classification of spinal cord ependymomas and intraoperative discrimination of gliomas from primary CNS lymphomas. This review summarizes current progress in AI-assisted molecular profiling prediction of brain tumors from tissue, highlighting opportunities for rapid, accurate, and globally accessible diagnostics. The integration of histology and computational methods holds promise for the development of smart AI-assisted neuro-oncology.<\/jats:p>","DOI":"10.3390\/cancers18010009","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T11:03:46Z","timestamp":1766142226000},"page":"9","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue"],"prefix":"10.3390","volume":"18","author":[{"given":"Maki","family":"Sakaguchi","sequence":"first","affiliation":[{"name":"Department of Diagnostic Pathology, Nara Medical University, Kashihara 634-8521, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akihiko","family":"Yoshizawa","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Pathology, Nara Medical University, Kashihara 634-8521, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5824-6690","authenticated-orcid":false,"given":"Kenta","family":"Masui","sequence":"additional","affiliation":[{"name":"Department of Integrative Neuroscience, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki 852-8523, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomoya","family":"Sakai","sequence":"additional","affiliation":[{"name":"Institute of Integrated Science and Technology, Nagasaki University, Nagasaki 852-8521, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6812-2149","authenticated-orcid":false,"given":"Takashi","family":"Komori","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine and Pathology, Tokyo Metropolitan Neurological Hospital, Tokyo 183-0042, Japan"},{"name":"Department of Laboratory Medicine and Pathology, TMG Asaka Medical Center, Saitama 351-0023, Japan"},{"name":"Department of Neurosurgery, Tokyo Women\u2019s Medical University Hospital, Tokyo 162-8666, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1038\/s41571-019-0252-y","article-title":"Artificial intelligence in digital pathology\u2014New tools for diagnosis and precision oncology","volume":"16","author":"Bera","year":"2019","journal-title":"Nat. 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