From the Department of Biomedical Engineering (M.I., P.P., R.C., G.S., K.B., N.B., R.T., A.M., P.T.), Case Western Reserve University, Cleveland, Ohio
Department of Neuroradiology (V.H., V.S.), Imaging Institute.
AJNR Am J Neuroradiol. 2018 Dec;39(12):2187-2193. doi: 10.3174/ajnr.A5858. Epub 2018 Nov 1.
Differentiating pseudoprogression, a radiation-induced treatment effect, from tumor progression on imaging is a substantial challenge in glioblastoma management. Unfortunately, guidelines set by the Response Assessment in Neuro-Oncology criteria are based solely on bidirectional diametric measurements of enhancement observed on T1WI and T2WI/FLAIR scans. We hypothesized that quantitative 3D shape features of the enhancing lesion on T1WI, and T2WI/FLAIR hyperintensities (together called the lesion habitat) can more comprehensively capture pathophysiologic differences across pseudoprogression and tumor recurrence, not appreciable on diametric measurements alone.
A total of 105 glioblastoma studies from 2 institutions were analyzed, consisting of a training ( = 59) and an independent test ( = 46) cohort. For every study, expert delineation of the lesion habitat (T1WI enhancing lesion and T2WI/FLAIR hyperintense perilesional region) was obtained, followed by extraction of 30 shape features capturing 14 "global" contour characteristics and 16 "local" curvature measures for every habitat region. Feature selection was used to identify most discriminative features on the training cohort, which were evaluated on the test cohort using a support vector machine classifier.
The top 2 most discriminative features were identified as local features capturing total curvature of the enhancing lesion and curvedness of the T2WI/FLAIR hyperintense perilesional region. Using top features from the training cohort (training accuracy = 91.5%), we obtained an accuracy of 90.2% on the test set in distinguishing pseudoprogression from tumor progression.
Our preliminary results suggest that 3D shape attributes from the lesion habitat can differentially express across pseudoprogression and tumor progression and could be used to distinguish these radiographically similar pathologies.
在影像学上区分假性进展,这是一种辐射诱导的治疗效应,与肿瘤进展是胶质母细胞瘤管理中的一个重大挑战。不幸的是,神经肿瘤反应评估标准所制定的指南仅基于 T1WI 上增强扫描和 T2WI/FLAIR 扫描的双向直径测量。我们假设 T1WI 上增强病变和 T2WI/FLAIR 高信号(统称为病变栖息地)的定量 3D 形状特征可以更全面地捕捉假性进展和肿瘤复发之间的病理生理差异,而不仅仅是单独的直径测量。
共分析了来自 2 个机构的 105 项胶质母细胞瘤研究,包括一个训练集(n=59)和一个独立测试集(n=46)。对于每一项研究,我们都获得了病变栖息地(T1WI 增强病变和 T2WI/FLAIR 高信号病变周围区域)的专家勾画,然后提取了 30 个形状特征,这些特征包括 14 个“全局”轮廓特征和 16 个“局部”曲率测量值,用于每个栖息地区域。我们使用特征选择来识别训练集中最具区分性的特征,然后使用支持向量机分类器在测试集中评估这些特征。
前 2 个最具区分性的特征被确定为局部特征,用于捕获增强病变的总曲率和 T2WI/FLAIR 高信号病变周围区域的弯曲度。使用训练集的前 2 个特征(训练准确率=91.5%),我们在测试集上获得了区分假性进展和肿瘤进展的准确率为 90.2%。
我们的初步结果表明,病变栖息地的 3D 形状属性可以在假性进展和肿瘤进展之间产生差异表达,并可用于区分这些影像学相似的病变。