Abstract：
To evaluate single- and multiparametric MRI models to differentiate recurrent glioblastoma (GBM) and treatment-related changes (TRC) in clinical routine imaging. Selective and unselective apparent diffusion coefficient (ADC) and minimum, mean, and maximum cerebral blood volume (CBV) measurements in the lesion were performed. Minimum, mean, and maximum ratiosCBV (CBVlesion to CBVhealthy white matter) were computed. All data were tested for lesion discrimination. A multiparametric model was compiled via multiple logistic regression using data demonstrating significant difference between GBM and TRC and tested for its diagnostic strength in an independent patient cohort. A total of 34 patients (17 patients with recurrent GBM and 17 patients with TRC) were included. ADC measurements showed no significant difference between both entities. All CBV and ratiosCBV measurements were significantly higher in patients with recurrent GBM than TRC. A minimum CBV of 8.5, mean CBV of 116.5, maximum CBV of 327 and ratioCBV minimum of 0.17, ratioCBV mean of 2.26 and ratioCBV maximum of 3.82 were computed as optimal cut-off values. By integrating these parameters in a multiparametric model and testing it in an independent patient cohort, 9 of 10 patients, i.e., 90%, were classified correctly. The multiparametric model further improves radiological discrimination of GBM from TRC in comparison to single-parameter approaches and enables reliable identification of recurrent tumors.
摘要：
评估单参数和多参数MRI模型在临床常规成像中区分复发性胶质母细胞瘤（GBM）和治疗相关变化（TRC）。选择性和非选择性表观扩散系数（ADC）和最小值，意思是，并进行病灶内最大脑血容量(CBV)测量。最小值,意思是，并计算最大CBV比（CBV损伤与CBV健康白质）。对所有数据进行病变辨别测试。使用证明GBM和TRC之间存在显着差异的数据，通过多元逻辑回归编制了多参数模型，并在独立患者队列中测试了其诊断强度。共纳入34例患者(17例复发性GBM患者和17例TRC患者)。ADC测量显示两个实体之间没有显著差异。复发性GBM患者的所有CBV和CBV比值均明显高于TRC。计算出的最小CBV为8.5,平均CBV为116.5,最大CBV为327,比率CBV为最小0.17,比率CBV为平均2.26,比率CBV为最大3.82作为最佳截止值。通过将这些参数集成到多参数模型中并在独立的患者队列中进行测试，10名患者中的9名，即，90%,分类正确。与单参数方法相比，多参数模型进一步改善了GBM与TRC的放射学区分，并能够可靠地识别复发性肿瘤。
