放射组学分析揭示胶质母细胞瘤患者T1加权基线磁共振成像中的预后信息。
Radiomic Analysis Reveals Prognostic Information in T1-Weighted Baseline Magnetic Resonance Imaging in Patients With Glioblastoma.
作者信息
Ingrisch Michael, Schneider Moritz Jörg, Nörenberg Dominik, Negrao de Figueiredo Giovanna, Maier-Hein Klaus, Suchorska Bogdana, Schüller Ulrich, Albert Nathalie, Brückmann Hartmut, Reiser Maximilian, Tonn Jörg-Christian, Ertl-Wagner Birgit
机构信息
From the *Josef Lissner Laboratory for Biomedical Imaging, and †Institute for Clinical Radiology, Ludwig-Maximilians University Hospital Munich, Munich; ‡Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg; §Department of Neurosurgery, and ∥Center for Neuropathology and Prion Research, Ludwig-Maximilian University Hospital Munich, Munich, Germany; ¶Research Institute Children's Cancer Center, Hamburg; Departments of #Nuclear Medicine, and **Neuroradiology, Ludwig-Maximilian University Hospital, Munich, Germany.
出版信息
Invest Radiol. 2017 Jun;52(6):360-366. doi: 10.1097/RLI.0000000000000349.
OBJECTIVES
The aim of this study was to investigate whether radiomic analysis with random survival forests (RSFs) can predict overall survival from T1-weighted contrast-enhanced baseline magnetic resonance imaging (MRI) scans in a cohort of glioblastoma multiforme (GBM) patients with uniform treatment.
MATERIALS AND METHODS
This retrospective study was approved by the institutional review board and informed consent was waived. The MRI scans from 66 patients with newly diagnosed GBM from a previous prospective study were analyzed. Tumors were segmented manually on contrast-enhanced 3-dimensional T1-weighted images. Using these segmentations, P = 208 quantitative image features characterizing tumor shape, signal intensity, and texture were calculated in an automated fashion. On this data set, an RSF was trained using 10-fold cross validation to establish a link between image features and overall survival, and the individual risk for each patient was predicted. The mean concordance index was assessed as a measure of prediction accuracy. Association of individual risk with overall survival was assessed using Kaplan-Meier analysis and a univariate proportional hazards model.
RESULTS
Mean overall survival was 14 months (range, 0.8-85 months). Mean concordance index of the 10-fold cross-validated RSF was 0.67. Kaplan-Meier analysis clearly distinguished 2 patient groups with high and low predicted individual risk (P = 5.5 × 10). Low predicted individual mortality was found to be a favorable prognostic factor for overall survival in a univariate Cox proportional hazards model (hazards ratio, 1.038; 95% confidence interval, 1.015-1.062; P = 0.0059).
CONCLUSIONS
This study demonstrates that baseline MRI in GBM patients contains prognostic information, which can be accessed by radiomic analysis using RSFs.
目的
本研究旨在调查采用随机生存森林（RSF）的放射组学分析能否根据多形性胶质母细胞瘤（GBM）患者队列中经过统一治疗的T1加权对比增强基线磁共振成像（MRI）扫描预测总生存期。
材料与方法
本回顾性研究经机构审查委员会批准，无需获得知情同意。分析了来自先前一项前瞻性研究的66例新诊断GBM患者的MRI扫描。在对比增强三维T1加权图像上手动分割肿瘤。利用这些分割结果，以自动化方式计算了P = 208个表征肿瘤形状、信号强度和纹理的定量图像特征。在该数据集上，使用10折交叉验证训练RSF以建立图像特征与总生存期之间的联系，并预测每位患者的个体风险。评估平均一致性指数作为预测准确性的指标。使用Kaplan-Meier分析和单变量比例风险模型评估个体风险与总生存期的相关性。
结果
平均总生存期为14个月（范围0.8 - 85个月）。10折交叉验证的RSF的平均一致性指数为0.67。Kaplan-Meier分析清楚地区分了预测个体风险高和低的两组患者（P = 5.5×10）。在单变量Cox比例风险模型中，预测个体死亡率低被发现是总生存期的有利预后因素（风险比，1.038；95%置信区间，1.015 - 1.062；P = 0.0059）。
结论
本研究表明GBM患者的基线MRI包含预后信息，可通过使用RSF的放射组学分析获取。
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