Huang Mohan, Law Helen K W, Tam Shing Yau
School of Medical and Health Sciences, Tung Wah College, Hong Kong SAR, China.
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Int J Mol Sci. 2025 Jul 11;26(14):6679. doi: 10.3390/ijms26146679.
Tumor hypoxia involves limited oxygen supply within the tumor microenvironment and is closely associated with aggressiveness, metastasis, and resistance to common cancer treatment modalities such as chemotherapy and radiotherapy. Traditional methodologies for hypoxia assessment, such as the use of invasive probes and clinical biomarkers, are generally not very suitable for routine clinical applications. Radiomics provides a non-invasive approach to hypoxia assessment by extracting quantitative features from medical images. Thus, radiomics is important in diagnosis and the formulation of a treatment strategy for tumor hypoxia. This article discusses the various imaging techniques used for the assessment of tumor hypoxia including magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). It introduces the use of radiomics with machine learning and deep learning for extracting quantitative features, along with its possible clinical use in hypoxic tumors. This article further summarizes the key challenges hindering the clinical translation of radiomics, including the lack of imaging standardization and the limited availability of hypoxia-labeled datasets. It also highlights the potential of integrating radiomics with multi-omics to enhance hypoxia visualization and guide personalized cancer treatment.
肿瘤缺氧涉及肿瘤微环境中有限的氧气供应，并且与侵袭性、转移以及对化疗和放疗等常见癌症治疗方式的抗性密切相关。传统的缺氧评估方法，如使用侵入性探头和临床生物标志物，通常不太适合常规临床应用。放射组学通过从医学图像中提取定量特征，提供了一种非侵入性的缺氧评估方法。因此，放射组学在肿瘤缺氧的诊断和治疗策略制定中具有重要意义。本文讨论了用于评估肿瘤缺氧的各种成像技术，包括磁共振成像（MRI）、正电子发射断层扫描（PET）和计算机断层扫描（CT）。它介绍了将放射组学与机器学习和深度学习相结合以提取定量特征，以及其在缺氧肿瘤中的可能临床应用。本文进一步总结了阻碍放射组学临床转化的关键挑战，包括缺乏成像标准化和缺氧标记数据集的可用性有限。它还强调了将放射组学与多组学整合以增强缺氧可视化并指导个性化癌症治疗的潜力。
