Abstract：
BACKGROUND: The study of central nervous system (CNS) tumors is particularly relevant in the pediatric population because of their relatively high frequency in this demographic and the significant impact on disease- and treatment-related morbidity and mortality. While both morphological and non-morphological magnetic resonance imaging techniques can give important information concerning tumor characterization, grading, and patient prognosis, increasing evidence in recent years has highlighted the need for personalized treatment and the development of quantitative imaging parameters that can predict the nature of the lesion and its possible evolution. For this purpose, radiomics and the use of artificial intelligence software, aimed at obtaining valuable data from images beyond mere visual observation, are gaining increasing importance. This brief review illustrates the current state of the art of this new imaging approach and its contributions to understanding CNS tumors in children.
METHODS: We searched the PubMed, Scopus, and Web of Science databases using the following key search terms: (\"radiomics\" AND/OR \"artificial intelligence\") AND (\"pediatric AND brain tumors\"). Basic and clinical research literature related to the above key research terms, i.e., studies assessing the key factors, challenges, or problems of using radiomics and artificial intelligence in pediatric brain tumors management, was collected.
RESULTS: A total of 63 articles were included. The included ones were published between 2008 and 2024. Central nervous tumors are crucial in pediatrics due to their high frequency and impact on disease and treatment. MRI serves as the cornerstone of neuroimaging, providing cellular, vascular, and functional information in addition to morphological features for brain malignancies. Radiomics can provide a quantitative approach to medical imaging analysis, aimed at increasing the information obtainable from the pixels/voxel grey-level values and their interrelationships. The \"radiomic workflow\" involves a series of iterative steps for reproducible and consistent extraction of imaging data. These steps include image acquisition for tumor segmentation, feature extraction, and feature selection. Finally, the selected features, via training predictive model (CNN), are used to test the final model.
CONCLUSIONS: In the field of personalized medicine, the application of radiomics and artificial intelligence (AI) algorithms brings up new and significant possibilities. Neuroimaging yields enormous amounts of data that are significantly more than what can be gained from visual studies that radiologists can undertake on their own. Thus, new partnerships with other specialized experts, such as big data analysts and AI specialists, are desperately needed. We believe that radiomics and AI algorithms have the potential to move beyond their restricted use in research to clinical applications in the diagnosis, treatment, and follow-up of pediatric patients with brain tumors, despite the limitations set out.
METHODS: We searched the PubMed, Scopus, and Web of Science databases using the following key search terms: (\"radiomics\" AND/OR \"artificial intelligence\") AND (\"pediatric AND brain tumors\"). Basic and clinical research literature related to the above key research terms, i.e., studies assessing the key factors, challenges, or problems of using radiomics and artificial intelligence in pediatric brain tumors management, was collected.
RESULTS: A total of 63 articles were included. The included ones were published between 2008 and 2024. Central nervous tumors are crucial in pediatrics due to their high frequency and impact on disease and treatment. MRI serves as the cornerstone of neuroimaging, providing cellular, vascular, and functional information in addition to morphological features for brain malignancies. Radiomics can provide a quantitative approach to medical imaging analysis, aimed at increasing the information obtainable from the pixels/voxel grey-level values and their interrelationships. The \"radiomic workflow\" involves a series of iterative steps for reproducible and consistent extraction of imaging data. These steps include image acquisition for tumor segmentation, feature extraction, and feature selection. Finally, the selected features, via training predictive model (CNN), are used to test the final model.
CONCLUSIONS: In the field of personalized medicine, the application of radiomics and artificial intelligence (AI) algorithms brings up new and significant possibilities. Neuroimaging yields enormous amounts of data that are significantly more than what can be gained from visual studies that radiologists can undertake on their own. Thus, new partnerships with other specialized experts, such as big data analysts and AI specialists, are desperately needed. We believe that radiomics and AI algorithms have the potential to move beyond their restricted use in research to clinical applications in the diagnosis, treatment, and follow-up of pediatric patients with brain tumors, despite the limitations set out.
摘要：
背景：中枢神经系统（CNS）肿瘤的研究在儿科人群中特别重要，因为它们在该人口统计学中的频率相对较高，并且对疾病和治疗相关的发病率和死亡率有重大影响。虽然形态学和非形态学磁共振成像技术都可以提供有关肿瘤表征的重要信息，分级,和患者预后，近年来,越来越多的证据强调了个性化治疗的必要性,以及可以预测病变性质及其可能演变的定量成像参数的发展.为此,影像组学和人工智能软件的使用，旨在从图像中获得有价值的数据，而不仅仅是视觉观察，越来越重要。这篇简短的评论说明了这种新的成像方法的最新技术及其对理解儿童中枢神经系统肿瘤的贡献。
方法：我们搜索了PubMed，Scopus,和WebofScience数据库使用以下关键搜索术语：（\"radiomics\"和/或\"人工智能\"）和（\"儿科和脑肿瘤\"）。与上述关键研究术语相关的基础和临床研究文献，即，评估关键因素的研究，挑战,或者在儿科脑肿瘤管理中使用影像组学和人工智能的问题，被收集。
结果：共纳入63篇。所包含的内容在2008年至2024年之间发布。中枢神经肿瘤由于其高频率和对疾病和治疗的影响而在儿科中至关重要。核磁共振成像是神经成像的基石,提供细胞，血管,和功能信息，以及脑恶性肿瘤的形态学特征。影像组学可以提供医学成像分析的定量方法，旨在增加从像素/体素灰度值及其相互关系获得的信息。“影像组学工作流程”涉及一系列迭代步骤，用于可重复和一致地提取成像数据。这些步骤包括用于肿瘤分割的图像采集，特征提取,和特征选择。最后,选定的功能，通过训练预测模型(CNN)，用于测试最终模型。
结论：在个性化医疗领域，影像组学和人工智能（AI）算法的应用带来了新的和重大的可能性。神经成像产生的大量数据远远超过放射科医生可以自己进行的视觉研究。因此，与其他专业专家的新伙伴关系，比如大数据分析师和人工智能专家，迫切需要。我们相信，影像组学和人工智能算法有可能超越其在研究中的限制使用，转向诊断中的临床应用。治疗,以及小儿脑肿瘤患者的随访，尽管存在限制。
方法：我们搜索了PubMed，Scopus,和WebofScience数据库使用以下关键搜索术语：（\"radiomics\"和/或\"人工智能\"）和（\"儿科和脑肿瘤\"）。与上述关键研究术语相关的基础和临床研究文献，即，评估关键因素的研究，挑战,或者在儿科脑肿瘤管理中使用影像组学和人工智能的问题，被收集。
结果：共纳入63篇。所包含的内容在2008年至2024年之间发布。中枢神经肿瘤由于其高频率和对疾病和治疗的影响而在儿科中至关重要。核磁共振成像是神经成像的基石,提供细胞，血管,和功能信息，以及脑恶性肿瘤的形态学特征。影像组学可以提供医学成像分析的定量方法，旨在增加从像素/体素灰度值及其相互关系获得的信息。“影像组学工作流程”涉及一系列迭代步骤，用于可重复和一致地提取成像数据。这些步骤包括用于肿瘤分割的图像采集，特征提取,和特征选择。最后,选定的功能，通过训练预测模型(CNN)，用于测试最终模型。
结论：在个性化医疗领域，影像组学和人工智能（AI）算法的应用带来了新的和重大的可能性。神经成像产生的大量数据远远超过放射科医生可以自己进行的视觉研究。因此，与其他专业专家的新伙伴关系，比如大数据分析师和人工智能专家，迫切需要。我们相信，影像组学和人工智能算法有可能超越其在研究中的限制使用，转向诊断中的临床应用。治疗,以及小儿脑肿瘤患者的随访，尽管存在限制。
