Department of Medicine, Division of Hematology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
Department of Radiology, Division of Nuclear Medicine, University of Miami Miller School of Medicine, Miami, FL, USA.
Nat Rev Clin Oncol. 2023 Sep;20(9):640-657. doi: 10.1038/s41571-023-00799-2. Epub 2023 Jul 17.
The use of functional quantitative biomarkers extracted from routine PET-CT scans to characterize clinical responses in patients with lymphoma is gaining increased attention, and these biomarkers can outperform established clinical risk factors. Total metabolic tumour volume enables individualized estimation of survival outcomes in patients with lymphoma and has shown the potential to predict response to therapy suitable for risk-adapted treatment approaches in clinical trials. The deployment of machine learning tools in molecular imaging research can assist in recognizing complex patterns and, with image classification, in tumour identification and segmentation of data from PET-CT scans. Initial studies using fully automated approaches to calculate metabolic tumour volume and other PET-based biomarkers have demonstrated appropriate correlation with calculations from experts, warranting further testing in large-scale studies. The extraction of computer-based quantitative tumour characterization through radiomics can provide a comprehensive view of phenotypic heterogeneity that better captures the molecular and functional features of the disease. Additionally, radiomics can be integrated with genomic data to provide more accurate prognostic information. Further improvements in PET-based biomarkers are imminent, although their incorporation into clinical decision-making currently has methodological shortcomings that need to be addressed with confirmatory prospective validation in selected patient populations. In this Review, we discuss the current knowledge, challenges and opportunities in the integration of quantitative PET-based biomarkers in clinical trials and the routine management of patients with lymphoma.
从常规 PET-CT 扫描中提取的功能定量生物标志物在淋巴瘤患者的临床反应特征方面的应用越来越受到关注，这些生物标志物的性能优于既定的临床危险因素。总代谢肿瘤体积能够对淋巴瘤患者的生存结果进行个体化评估，并且已经显示出预测对治疗反应的潜力，适用于临床试验中的风险适应治疗方法。机器学习工具在分子影像学研究中的应用有助于识别复杂模式，并通过图像分类对肿瘤进行识别和对来自 PET-CT 扫描的数据进行分割。使用全自动方法计算代谢肿瘤体积和其他基于 PET 的生物标志物的初步研究已经证明与专家计算具有适当的相关性，值得在大规模研究中进一步测试。通过放射组学从计算机中提取定量肿瘤特征可以提供对表型异质性的全面了解，更好地捕捉疾病的分子和功能特征。此外，放射组学可以与基因组数据集成，提供更准确的预后信息。基于 PET 的生物标志物的进一步改进即将到来，尽管它们目前在纳入临床决策方面存在方法学上的缺陷，需要在选定的患者群体中进行前瞻性验证加以确认。在这篇综述中，我们讨论了在临床试验和淋巴瘤患者常规管理中整合定量基于 PET 的生物标志物的现有知识、挑战和机遇。
