UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
J Neurooncol. 2020 Mar;147(1):135-145. doi: 10.1007/s11060-020-03407-w. Epub 2020 Jan 24.
To examine whether the rate of change in maximum F-FDOPA PET uptake and the rate of change in non-enhancing tumor volume could predict malignant transformation and residual overall survival (OS) in low grade glioma (LGG) patients who received serial F-FDOPA PET and MRI scans.
27 LGG patients with ≥ 2 F-FDOPA PET and MRI scans between 2003 and 2016 were included. The rate of change in FLAIR volume (uL/day) and maximum normalized F-FDOPA specific uptake value (nSUV/month), were compared between histological and molecular subtypes. General linear models (GLMs) were used to integrate clinical information with MR-PET measurements to predict malignant transformation. Cox univariate and multivariable regression analyses were performed to identify imaging and clinical risk factors related to OS.
A GLM using patient age, treatment, the rate of change in FLAIR and F-FDOPA nSUV could predict malignant transformation with > 67% sensitivity and specificity (AUC = 0.7556, P = 0.0248). A significant association was observed between OS and continuous rates of change in PET uptake (HR = 1.0212, P = 0.0034). Cox multivariable analysis confirmed that continuous measures of the rate of change in PET uptake was an independent predictor of OS (HR = 1.0242, P = 0.0033); however, stratification of patients based on increasing or decreasing rate of change in FLAIR (HR = 2.220, P = 0.025), PET uptake (HR = 2.148, P = 0.0311), or both FLAIR and PET (HR = 2.354, P = 0.0135) predicted OS.
The change in maximum normalized F-FDOPA PET uptake, with or without clinical information and rate of change in tumor volume, may be useful for predicting the risk of malignant transformation and estimating residual survival in patients with LGG.
探讨连续 F-FDOPA PET 和 MRI 扫描中最大 F-FDOPA 摄取率的变化率和无强化肿瘤体积的变化率是否可以预测低级别胶质瘤(LGG)患者的恶性转化和残留总生存期(OS)。
纳入 2003 年至 2016 年间接受过≥2 次 F-FDOPA PET 和 MRI 扫描的 27 例 LGG 患者。比较组织学和分子亚型之间 FLAIR 体积(uL/天)和最大标准化 F-FDOPA 摄取值(nSUV/月)的变化率。使用广义线性模型(GLM)将临床信息与 MR-PET 测量结果相结合,以预测恶性转化。进行 Cox 单变量和多变量回归分析,以确定与 OS 相关的影像学和临床危险因素。
使用患者年龄、治疗、FLAIR 和 F-FDOPA nSUV 变化率的 GLM 可以预测恶性转化,具有 >67%的敏感性和特异性(AUC=0.7556,P=0.0248)。观察到 OS 与 PET 摄取率的连续变化之间存在显著相关性(HR=1.0212,P=0.0034)。Cox 多变量分析证实,PET 摄取率的连续变化率是 OS 的独立预测因子(HR=1.0242,P=0.0033);然而,根据 FLAIR(HR=2.220,P=0.025)、PET 摄取率(HR=2.148,P=0.0311)或两者(HR=2.354,P=0.0135)的变化率升高或降低对患者进行分层,可以预测 OS。
最大标准化 F-FDOPA PET 摄取率的变化,无论是否结合临床信息和肿瘤体积的变化率,都可能有助于预测 LGG 患者恶性转化的风险和估计残留的生存时间。