In this comprehensive review, we aimed to discuss the current state-of-the-art medical imaging for pheochromocytomas and paragangliomas (PPGLs) diagnosis and treatment. Despite major medical improvements, PPGLs, as with other neuroendocrine tumors (NETs), leave clinicians facing several challenges; their inherent particularities and their diagnosis and treatment pose several challenges for clinicians due to their inherent complexity, and they require management by multidisciplinary teams. The conventional concepts of medical imaging are currently undergoing a paradigm shift, thanks to developments in radiomic and metabolic imaging. However, despite active research, clinical relevance of these new parameters remains unclear, and further multicentric studies are needed in order to validate and increase widespread use and integration in clinical routine. Use of AI in PPGLs may detect changes in tumor phenotype that precede classical medical imaging biomarkers, such as shape, texture, and size. Since PPGLs are rare, slow-growing, and heterogeneous, multicentric collaboration will be necessary to have enough data in order to develop new PPGL biomarkers. In this nonsystematic review, our aim is to present an exhaustive pedagogical tool based on real-world cases, dedicated to physicians dealing with PPGLs, augmented by perspectives of artificial intelligence and big data.
在这篇综合性综述中，我们旨在探讨当前嗜铬细胞瘤和副神经节瘤诊断与治疗领域最前沿的医学影像技术进展。尽管医学已取得重大进步，但PPGL与其他神经内分泌肿瘤一样，因其固有的复杂性给临床医生带来诸多挑战，其诊断与治疗需要多学科团队协作管理。得益于影像组学和代谢成像的发展，传统医学影像概念正在经历范式转变。然而，尽管相关研究活跃，这些新参数的临床意义仍不明确，需要进一步开展多中心研究以验证并促进其在临床实践中的广泛应用。人工智能在PPGL领域的应用可能检测到传统影像生物标志物（如形态、纹理和大小）出现前的肿瘤表型变化。由于PPGL具有罕见性、生长缓慢性和异质性特点，必须通过多中心协作获取足够数据以开发新型生物标志物。本非系统性综述旨在构建基于真实病例的完整教学体系，结合人工智能与大数据的前瞻视角，为临床医生提供PPGL诊疗的全面指导。
肿瘤(癌症)患者之家