Analysis of heterogeneity of peritumoral T2 hyperintensity in patients with pretreatment glioblastoma: Prognostic value of MRI-based radiomics
Analysis of heterogeneity of peritumoral T2 hyperintensity in patients with pretreatment glioblastoma: Prognostic value of MRI-based radiomics
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DOI：
10.1016/j.ejrad.2019.108642
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发表时间：
2019-11-01
影响因子：
3.3
通讯作者：
Kim, Bum-soo
中科院分区：
文献类型：
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作者：
Choi, Yangsean;Ahn, Kook-Jin;Kim, Bum-soo
目的：在MR成像上，胶质母细胞瘤周围的肿瘤T2高信号包含肿瘤细胞浸润，因此导致预后不良。本研究旨在确定放射组学对治疗前胶质母细胞瘤瘤周T2高信号的增量预后价值。方法：回顾性选择2008年3月至2018年5月期间经病理证实的114例胶质母细胞瘤患者（本机构，n = 61;癌症影像档案，n = 53）。所有患者被随机分为训练组（n = 80）和测试组（n = 34）。手动分割的肿瘤周围T2高信号产生了106放射组学特征，每例患者。使用随机森林变量选择来选择最相关的放射组学特征。四个考克斯比例风险模型与临床特征、临床特征与肿瘤/瘤周体积、放射组学以及所有这些的组合进行拟合。用log-rank检验绘制模型的Kaplan-Meier生存曲线。所有的模型进行了验证的测试集上使用预测误差曲线在survivaltimes.Results：一个随机的森林变量选择产生了5个相关的功能之间的106 radiomic功能（两个形状，两个灰度级和一个一阶功能）。当这些放射组学特征被添加到临床和肿瘤/瘤周体积特征上时，它们增加了生存预测的准确性（组合模型，P = 0.011）。在测试集上，组合模型显示出较低的平均生存预测误差率（0.14）比临床（0.191）或放射组学（0.178）model.Conclusions：放射组学功能的临床模型表现出改善的生存预测性能比模型没有放射组学功能，从而表明肿瘤周围放射组学作为预处理胶质母细胞瘤的MR成像生物标志物的预后价值增加。
Purpose: On MR imaging, peritumoral T2 hyperintensity surrounding glioblastoma is known to contain tumor cell infiltrates, thus contributing to poor prognosis. This study aimed to determine the incremental prognostic value of radiomics on peritumoral T2 hyperintensity in pretreatment glioblastoma.Methods: One hundred fourteen pathologically confirmed glioblastoma patients were retrospectively selected from March 2008 to May 2018 (our institution, n = 61; the Cancer Imaging Archive, n = 53). All patients were randomly divided into either training (n = 80) or test set (n = 34). Manually segmented peritumoral T2 hyperintensity yielded 106 radiomic features per patient. A random forest variable selection was used to select the most relevant radiomic features. Four Cox proportional hazards models were fitted with clinical features, clinical features with tumor/peritumoral volumes, radiomics, and all of them combined. Kaplan-Meier survival curves of the models were plotted with log-rank tests. All models were validated on a test set using prediction error curves over survival times.Results: A random forest variable selection yielded five relevant features among the 106 radiomic features (two shape, two gray-level and one first order features). These radiomic features increased survival prediction accuracy when they were added onto clinical and tumor/peritumoral volumetric features (combined model, P = 0.011). On test set, the combined model showed lower mean survival prediction error rate (0.14) than clinical (0.191) or radiomic (0.178) model.Conclusions: The clinical model with radiomic features demonstrated improved survival predictive performance than the model without radiomic features, thus suggesting incremental prognostic value of peritumoral radiomics as MR imaging biomarker in pretreatment glioblastoma.