The lack of early detection and a high rate of recurrence/progression after surgery are defined as the most common causes of a very poor prognosis of Gliomas. The developments of quantification systems with special regards to artificial intelligence (AI) on medical images (CT, MRI, PET) are under evaluation in the clinical and research context in view of several applications providing different information related to the reconstruction of imaging, the segmentation of tissues acquired, the selection of features, and the proper data analyses. Different approaches of AI have been proposed as the machine and deep learning, which utilize artificial neural networks inspired by neuronal architectures. In addition, new systems have been developed using AI techniques to offer suggestions or make decisions in medical diagnosis, emulating the judgment of radiologist experts. The potential clinical role of AI focuses on the prediction of disease progression in more aggressive forms in gliomas, differential diagnosis (pseudoprogression vs. proper progression), and the follow-up of aggressive gliomas. This narrative Review will focus on the available applications of AI in brain tumor diagnosis, mainly related to malignant gliomas, with particular attention to the postoperative application of MRI and PET imaging, considering the current state of technical approach and the evaluation after treatment (including surgery, radiotherapy/chemotherapy, and prognostic stratification).
胶质瘤预后极差的最常见原因被定义为缺乏早期检测以及术后高复发/进展率。在临床和研究背景下，量化系统的发展正受到评估，特别是针对医学影像（CT、MRI、PET）的人工智能应用，这些应用提供了与影像重建、获取组织的分割、特征选择以及适当数据分析相关的不同信息。人工智能的不同方法已被提出，如机器学习和深度学习，它们利用受神经元结构启发的人工神经网络。此外，利用人工智能技术开发的新系统能够在医学诊断中提供建议或做出决策，模拟放射学专家的判断。人工智能的潜在临床作用主要集中在预测胶质瘤更具侵袭性形式的疾病进展、鉴别诊断（假性进展与真正进展）以及侵袭性胶质瘤的随访。本叙述性综述将重点关注人工智能在脑肿瘤诊断中的现有应用，主要涉及恶性胶质瘤，特别关注MRI和PET影像的术后应用，同时考虑当前技术方法的现状以及治疗后的评估（包括手术、放疗/化疗和预后分层）。
Artificial Intelligence Analysis Using MRI and PET Imaging in Gliomas: A Narrative Review
肿瘤(癌症)患者之家