Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas.
Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas.
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放射组学分析可以精确预测低级别胶质瘤患者的癫痫情况
DOI：
10.1016/j.nicl.2018.04.024
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Tian J
中科院分区：
文献类型：
--
作者：
Liu Z;Wang Y;Liu X;Du Y;Tang Z;Wang K;Wei J;Dong D;Zang Y;Dai J;Jiang T;Tian J
目的：探讨低级别胶质瘤（LGG）相关癫痫与影像学特征的关系，并建立基于放射组学的低级别胶质瘤相关癫痫预测模型。本回顾性研究连续入组286例lgg患者（194例为初级队列，92例为验证队列）。采用t2加权MR图像（T2WI）对lgg相关性癫痫的危险因素进行表征，确定肿瘤位置特征和三维成像特征，分析两者之间的相互作用。应用弹性网将与lgg相关癫痫相关的关键影像学特征与主要队列相结合，生成放射组学特征图，然后开发结合放射组学特征和临床特征的nomogram。在验证队列中验证了放射组学特征和nomogram。每位患者共获得了475项与lgg相关癫痫相关的特征。检测到11个选定特征的放射组学特征可以区分癫痫患者或非癫痫患者，其表现优于位置和3d成像特征。结合放射组学特征和临床特征的nomogram （x线图）在初始队列和验证队列中分别获得了0.8769和0.8152的受试者工作特征（ROC）曲线下面积（area under receiver operating characteristic， AUC）。nomogram也允许在初始队列中进行良好的校准。我们建立并验证了一种有效的lgg相关性癫痫预测模型。我们的研究结果表明放射组学分析可以更精确和个性化地预测lgg相关癫痫。我们确定了一系列定量的癫痫相关放射组学特征。我们利用机器学习方法建立了基于放射组学的lgg相关性癫痫预测模型。提出的基于放射组学的模型可以提供精确和个性化的癫痫风险估计。
To investigate the association between imaging features and low-grade gliomas (LGG) related epilepsy, and to propose a radiomics-based model for the prediction of LGG-associated epilepsy. This retrospective study consecutively enrolled 286 patients with LGGs (194 in the primary cohort and 92 in the validation cohort). T2-weighted MR images (T2WI) were used to characterize risk factors for LGG-related epilepsy: Tumor location features and 3-D imaging features were determined, following which the interactions between these two kinds of features were analyzed. Elastic net was applied to generate a radiomics signature combining key imaging features associated with the LGG-related epilepsy with the primary cohort, and then a nomogram incorporating radiomics signature and clinical characteristics was developed. The radiomics signature and nomogram were validated in the validation cohort. A total of 475 features associated with LGG-related epilepsy were obtained for each patient. A radiomics signature with eleven selected features allowed for discriminating patients with epilepsy or not was detected, which performed better than location and 3-D imaging features. The nomogram incorporating radiomics signature and clinical characteristics achieved a high degree of discrimination with area under receiver operating characteristic (ROC) curve (AUC) at 0.8769 in the primary cohort and 0.8152 in the validation cohort. The nomogram also allowed for good calibration in the primary cohort. We developed and validated an effective prediction model for LGG-related epilepsy. Our results suggested that radiomics analysis may enable more precise and individualized prediction of LGG-related epilepsy. We identified a series of quantitative epilepsy-related radiomics features. We developed a radiomics-based prediction model for LGG-related epilepsy with machine learning method. The proposed radiomics-based model may provide precise and individualized estimation of epilepsy risk.
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DOI：
10.1038/nrclinonc.2012.196
发表时间：
2013-01
期刊：
Nature reviews. Clinical oncology
影响因子：
--
作者：
Lambin P;van Stiphout RG;Starmans MH;Rios-Velazquez E;Nalbantov G;Aerts HJ;Roelofs E;van Elmpt W;Boutros PC;Granone P;Valentini V;Begg AC;De Ruysscher D;Dekker A
通讯作者：
Dekker A
影响因子：
17.1
作者：
Itakura H;Achrol AS;Mitchell LA;Loya JJ;Liu T;Westbroek EM;Feroze AH;Rodriguez S;Echegaray S;Azad TD;Yeom KW;Napel S;Rubin DL;Chang SD;Harsh GR 4th;Gevaert O
通讯作者：
Gevaert O
影响因子：
4.1
作者：
Chang, Edward F.;Potts, Matthew B.;Berger, Mitchel S.
通讯作者：
Berger, Mitchel S.
影响因子：
7.2
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.
影响因子：
2.5
作者：
Kumar V;Gu Y;Basu S;Berglund A;Eschrich SA;Schabath MB;Forster K;Aerts HJ;Dekker A;Fenstermacher D;Goldgof DB;Hall LO;Lambin P;Balagurunathan Y;Gatenby RA;Gillies RJ
通讯作者：
Gillies RJ