Background/Objectives: Artificial intelligence (AI) is transforming neuroimaging by enhancing diagnostic precision and treatment planning. However, its applications in pediatric cancer neuroimaging remain limited. This review assesses the current state, potential applications, and challenges of AI in pediatric neuroimaging for cancer, emphasizing the unique needs of the pediatric population. Methods: A comprehensive literature review was conducted, focusing on AI’s impact on pediatric neuroimaging through accelerated image acquisition, reduced radiation, and improved tumor detection. Key methods include convolutional neural networks for tumor segmentation, radiomics for tumor characterization, and several tools for functional imaging. Challenges such as limited pediatric datasets, developmental variability, ethical concerns, and the need for explainable models were analyzed. Results: AI has shown significant potential to improve imaging quality, reduce scan times, and enhance diagnostic accuracy in pediatric neuroimaging, resulting in improved accuracy in tumor segmentation and outcome prediction for treatment. However, progress is hindered by the scarcity of pediatric datasets, issues with data sharing, and the ethical implications of applying AI in vulnerable populations. Conclusions: To overcome current limitations, future research should focus on building robust pediatric datasets, fostering multi-institutional collaborations for data sharing, and developing interpretable AI models that align with clinical practice and ethical standards. These efforts are essential in harnessing the full potential of AI in pediatric neuroimaging and improving outcomes for children with cancer.
背景/目的：人工智能（AI）正通过提升诊断精度和治疗规划能力，深刻改变神经影像学领域。然而，其在儿童癌症神经影像中的应用仍较为有限。本综述评估了AI在儿童癌症神经影像中的现状、潜在应用及挑战，特别关注儿科群体的独特需求。 方法：通过系统性文献回顾，重点分析AI在加速图像采集、降低辐射剂量及提升肿瘤检测能力方面对儿童神经影像的影响。关键技术方法包括用于肿瘤分割的卷积神经网络、用于肿瘤特征分析的影像组学，以及多种功能成像工具。研究同时剖析了儿科数据集有限、发育变异性、伦理问题及模型可解释性需求等核心挑战。 结果：AI在提升儿童神经影像质量、缩短扫描时间、提高诊断准确性方面展现出显著潜力，尤其在肿瘤分割精度和治疗预后预测方面取得进展。然而，儿科数据稀缺、数据共享障碍以及在脆弱群体中应用AI的伦理问题，制约了该领域的进一步发展。 结论：为突破当前局限，未来研究应聚焦于构建高质量的儿科数据集，推动多机构数据共享合作，并开发符合临床实践与伦理规范的可解释AI模型。这些努力对充分发挥AI在儿童神经影像中的潜力、改善癌症患儿临床结局至关重要。
Artificial Intelligence for Neuroimaging in Pediatric Cancer
肿瘤(癌症)患者之家