Pourmadadi Mehrab, Shabestari Salar Mohammadi, Abdouss Hamidreza, Rahdar Abbas, Fathi-Karkan Sonia, Pandey Sadanand
Protein Research Center, Shahid Beheshti University, Tehran, Iran.
Department of Polymer, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Semin Oncol. 2025 Dec;52(6):152429. doi: 10.1016/j.seminoncol.2025.152429. Epub 2025 Nov 10.
Artificial intelligence (AI) and nanotechnology are revolutionizing brain cancer theranostics by enhancing drug delivery and diagnostic accuracy. This review examines AI-enhanced engineering strategies for developing intelligent nanocarriers that target glioblastoma and other metastatic central nervous system malignancies. AI encompasses several computational methods, including machine learning (ML) and its subset deep learning (DL). Here, ML algorithms learn design rules for nanocarriers, and DL networks intricate pattern recognition for tumor segmentation and adaptive release. These approaches enable stimuli-responsive nanocarriers to react to tumor microenvironmental signals (eg, pH, enzyme activity) and external stimuli (eg, ultrasound), optimizing targeted medication release while minimizing off-target effects. Magnetic resonance imaging (MRI) and positron emission tomography (PET), in conjunction with AI, enhance tumor detection and segmentation, while the integration of multiomics data facilitates tailored treatment planning. Advanced technologies encompass transferrin-functionalized nanoparticles for traversing the blood-brain barrier (BBB) and dual-stimuli-responsive drug delivery systems. Notwithstanding general progress, apprehensions surrounding batch variability and industrial scalability persist. This review also addresses ethical concerns and cost disparities associated with AI-based therapeutics. The primary development target areas are federated learning for data privacy, explainable artificial intelligence (XAI) for regulatory transparency, and quantum ML for molecular-scale optimization. This paper charts the course to patient-specific, scalable neuro-oncology nanomedicine through the convergence of computational modeling, intelligent materials, and advanced imaging modalities. These themes are explored in greater detail in the introduction, where we lay the groundwork for intelligent nanocarriers, their design with the help of AI, and the clinical need for diagnostics-therapeutics convergence in brain cancer.
人工智能（AI）和纳米技术正在通过提高药物递送和诊断准确性，彻底改变脑癌的诊疗方法。本综述探讨了用于开发靶向胶质母细胞瘤和其他转移性中枢神经系统恶性肿瘤的智能纳米载体的人工智能增强工程策略。人工智能涵盖多种计算方法，包括机器学习（ML）及其子集深度学习（DL）。在这里，机器学习算法学习纳米载体的设计规则，而深度学习网络则用于肿瘤分割和自适应释放的复杂模式识别。这些方法使刺激响应型纳米载体能够对肿瘤微环境信号（如pH值、酶活性）和外部刺激（如超声）做出反应，优化靶向药物释放，同时将脱靶效应降至最低。磁共振成像（MRI）和正电子发射断层扫描（PET）与人工智能相结合，可增强肿瘤检测和分割，而多组学数据的整合则有助于制定个性化的治疗方案。先进技术包括用于穿越血脑屏障（BBB）的转铁蛋白功能化纳米颗粒和双刺激响应药物递送系统。尽管取得了总体进展，但围绕批次变异性和工业可扩展性的担忧依然存在。本综述还讨论了与基于人工智能的治疗方法相关的伦理问题和成本差异。主要的发展目标领域包括用于数据隐私的联邦学习、用于监管透明度的可解释人工智能（XAI）以及用于分子尺度优化的量子机器学习。本文通过计算建模、智能材料和先进成像模态的融合，描绘了通往针对患者的、可扩展的神经肿瘤纳米医学的道路。在引言部分将更详细地探讨这些主题，我们将为智能纳米载体、借助人工智能进行的设计以及脑癌诊断与治疗融合的临床需求奠定基础。
