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.
将分子特征整合到组织病理学诊断中已成为世界卫生组织（WHO）中枢神经系统肿瘤分类的核心，这提升了预后判断的准确性并支持了精准医疗的发展。然而，分子检测的可及性不均限制了整合诊断的普遍应用。为解决这一问题，人工智能模型正被开发用于直接从组织学数据预测分子改变。在胶质瘤中，深度学习应用于永久切片的全切片图像，在预测IDH突变和1p/19q共缺失等生物标志物、分子亚型分类及预后预测方面已达到神经病理学家水平的准确度。最新进展将这些方法扩展到术中冰冻切片，通过受激拉曼组织学及域自适应图像转换等技术，实现了实时胶质瘤分级、分子预测和无标记组织分析。除胶质瘤外，人工智能辅助的组织学技术正被探索应用于其他脑肿瘤，包括基于形态学的脊髓室管膜瘤分子分类以及术中胶质瘤与原发性中枢神经系统淋巴瘤的鉴别。本综述总结了当前人工智能辅助脑肿瘤组织分子谱预测的研究进展，强调了实现快速、准确且全球可及的诊断的机遇。组织学与计算方法的融合为发展智能人工智能辅助神经肿瘤学带来了希望。
AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue
肿瘤(癌症)患者之家