Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), RWTH Aachen University, Aachen, Germany.
Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany.
J Neurooncol. 2022 Sep;159(3):519-529. doi: 10.1007/s11060-022-04089-2. Epub 2022 Jul 19.
To investigate the potential of radiomics applied to static clinical PET data using the tracer O-(2-[F]fluoroethyl)-L-tyrosine (FET) to differentiate treatment-related changes (TRC) from tumor progression (TP) in patients with gliomas.
One hundred fifty-one (151) patients with histologically confirmed gliomas and post-therapeutic progressive MRI findings according to the response assessment in neuro-oncology criteria underwent a dynamic amino acid PET scan using the tracer O-(2-[F]fluoroethyl)-L-tyrosine (FET). Thereof, 124 patients were investigated on a stand-alone PET scanner (data used for model development and validation), and 27 patients on a hybrid PET/MRI scanner (data used for model testing). Mean and maximum tumor to brain ratios (TBR, TBR) were calculated using the PET data from 20 to 40 min after tracer injection. Logistic regression models were evaluated for the FET PET parameters TBR, TBR, and for radiomics features of the tumor areas as well as combinations thereof to differentiate between TP and TRC. The best performing models in the validation dataset were finally applied to the test dataset. The diagnostic performance was assessed by receiver operating characteristic analysis.
Thirty-seven patients (25%) were diagnosed with TRC, and 114 (75%) with TP. The logistic regression model comprising the conventional FET PET parameters TBR and TBR resulted in an AUC of 0.78 in both the validation (sensitivity, 64%; specificity, 80%) and the test dataset (sensitivity, 64%; specificity, 80%). The model combining the conventional FET PET parameters and two radiomics features yielded the best diagnostic performance in the validation dataset (AUC, 0.92; sensitivity, 91%; specificity, 80%) and demonstrated its generalizability in the independent test dataset (AUC, 0.85; sensitivity, 81%; specificity, 70%).
The developed radiomics classifier allows the differentiation between TRC and TP in pretreated gliomas based on routinely acquired static FET PET scans with a high diagnostic accuracy.
利用示踪剂 O-(2-['F]氟乙基)-L-酪氨酸（FET），研究静态临床 PET 数据的放射组学在区分胶质瘤患者治疗相关变化（TRC）与肿瘤进展（TP）方面的潜力。
151 名经组织学证实的胶质瘤患者和根据肿瘤反应评估标准出现治疗后进展性 MRI 表现的患者，进行了 O-(2-['F]氟乙基)-L-酪氨酸（FET）示踪剂的动态氨基酸 PET 扫描。其中 124 名患者在独立的 PET 扫描仪上进行了检查（用于模型开发和验证的数据），27 名患者在混合 PET/MRI 扫描仪上进行了检查（用于模型测试的数据）。使用示踪剂注射后 20 至 40 分钟的 PET 数据计算肿瘤与脑比值（TBR、TBR）的平均值和最大值。使用逻辑回归模型评估 FET PET 参数 TBR、TBR 以及肿瘤区域的放射组学特征及其组合，以区分 TP 和 TRC。在验证数据集内，对表现最佳的模型进行最终测试数据集应用。通过接受者操作特征分析评估诊断性能。
37 名患者（25%）被诊断为 TRC，114 名患者（75%）为 TP。包含传统 FET PET 参数 TBR 和 TBR 的逻辑回归模型在验证数据集（敏感性，64%；特异性，80%）和测试数据集（敏感性，64%；特异性，80%）中均获得了 0.78 的 AUC。将传统 FET PET 参数与两个放射组学特征相结合的模型在验证数据集（AUC，0.92；敏感性，91%；特异性，80%）中表现出最佳的诊断性能，并在独立的测试数据集（AUC，0.85；敏感性，81%；特异性，70%）中表现出其通用性。
基于常规获取的预处理 FET PET 扫描，所开发的放射组学分类器可实现对治疗相关变化与肿瘤进展的区分，具有较高的诊断准确性。
