Inglese M, Conti A, Toschi N
Department of Biomedicine and Prevention, University of Rome Tor Vergata, Italy; Department of Surgery and Cancer, Imperial College London, UK.
Department of Biomedicine and Prevention, University of Rome Tor Vergata, Italy.
Clin Radiol. 2025 Jun;85:106921. doi: 10.1016/j.crad.2025.106921. Epub 2025 Apr 3.
Radiomics allows extraction from medical images of quantitative features that are able to reveal tissue patterns that are generally invisible to human observers. Despite the challenges in visually interpreting radiomic features and the computational resources required to generate them, they hold significant value in downstream automated processing. For instance, in statistical or machine learning frameworks, radiomic features enhance sensitivity and specificity, making them indispensable for tasks such as diagnosis, prognosis, prediction, monitoring, image-guided interventions, and evaluating therapeutic responses. This review explores the application of radiomics in neurodegenerative diseases, with a focus on Alzheimer's disease, Parkinson's disease, Huntington's disease, and multiple sclerosis. While radiomics literature often focuses on magnetic resonance imaging (MRI) and computed tomography (CT), this review also covers its broader application in nuclear medicine, with use cases of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) radiomics. Additionally, we review integrated radiomics, where features from multiple imaging modalities are fused to improve model performance. This review also highlights the growing integration of radiomics with artificial intelligence and the need for feature standardisation and reproducibility to facilitate its translation into clinical practice.
放射组学能够从医学图像中提取定量特征，这些特征能够揭示人类观察者通常难以察觉的组织模式。尽管在视觉上解释放射组学特征存在挑战，且生成这些特征需要计算资源，但它们在下游自动化处理中具有重要价值。例如，在统计或机器学习框架中，放射组学特征可提高敏感性和特异性，使其在诊断、预后、预测、监测、图像引导干预以及评估治疗反应等任务中不可或缺。本综述探讨了放射组学在神经退行性疾病中的应用，重点关注阿尔茨海默病、帕金森病、亨廷顿病和多发性硬化症。虽然放射组学文献通常侧重于磁共振成像（MRI）和计算机断层扫描（CT），但本综述还涵盖了其在核医学中的更广泛应用，包括正电子发射断层扫描（PET）和单光子发射计算机断层扫描（SPECT）放射组学的用例。此外，我们还综述了整合放射组学，即将来自多种成像模态的特征融合以提高模型性能。本综述还强调了放射组学与人工智能日益融合的趋势，以及为促进其转化为临床实践而进行特征标准化和可重复性研究的必要性。
