癌症是一种致命疾病，也是全球第二大死因。癌症治疗是一个复杂的过程，需要多模态的综合治疗方法。癌症的筛查/诊断是癌症管理的起点，随后进行疾病分期、治疗计划制定和执行、治疗监测以及持续监测和随访，直至患者生命结束。在癌症管理的各个阶段中，影像学发挥着重要作用。传统的肿瘤学实践认为所有患者在疾病类型上都是相似的，而生物标志物则将患者分组在疾病类型中，从而推动了精准肿瘤医学的发展。放射组学过程的运用促进了多样化影像生物标志物的发展，这些生物标志物在精准肿瘤医学中发挥着重要的应用。已有文献显示，影像生物标志物和人工智能（AI）在肿瘤学中的作用已经得到了多位研究人员的探讨。然而，放射组学特征的稳定性也受到了质疑。放射组学界认识到，放射组学特征的不稳定性对基于放射组学的预测模型的全球推广构成了威胁。为了建立基于放射组学的肿瘤学影像生物标志物，放射组学特征的稳健性需要首要确定。这是因为在一个机构开发的放射组学模型往往在其他机构表现不佳，这很可能是由于放射组学特征的不稳定性所致。为了在肿瘤学中推广放射组学的预测模型，已经启动了多个倡议，包括定量成像联盟（QIN）、定量成像生物标志物联盟（QIBA）和图像生物标志物标准化倡议（IBSI），以稳定放射组学特征。© The Author(s) 2023.

Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features.© The Author(s) 2023.