A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas.
A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas.
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放射组学特征作为低级别神经胶质瘤患者无进展生存的非侵入性预测因子。
DOI：
10.1016/j.nicl.2018.10.014
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Wang Y
中科院分区：
文献类型：
--
作者：
Liu X;Li Y;Qian Z;Sun Z;Xu K;Wang K;Liu S;Fan X;Li S;Zhang Z;Jiang T;Wang Y
本研究的目的是开发一种用于预测低级别胶质瘤无进展生存(PFS)的放射组学特征，并调查该放射组学特征背后的遗传背景。在这项回顾研究中，训练组(n = 2 16)和验证组(n = 84)分别来自中国脑胶质瘤基因组图谱和肿瘤基因组图谱。对于每个患者，从术前T2加权磁共振图像中提取了总共431个放射组学特征。在训练队列中生成放射组学签名，并在训练队列和验证队列中评估其预后价值。对高危人群进行放射基因组学分析，确定高危人群的遗传特征，建立预测PFS的诺模图。在训练(P <； 0.001，多变量COX回归)和验证(P = 0.045，多变量COX回归)队列中，放射组学特征(包括9个筛选的放射组学特征)与PFS之间存在显著的相关性，PFS独立于其他临床病理因素。放射基因组学分析表明，放射组学特征与免疫反应、细胞程序性死亡、细胞增殖和血管发育有关。使用放射组学特征和临床病理风险因素建立的诺模图显示，在训练(C指数，0.684)和验证(C指数，0.823)队列中，对于预测PFS，具有很高的准确性和良好的校准。通过一组能够反映这些肿瘤生物学过程的放射组学特征，可以无创地预测LGGS患者的PFS。我们开发了一种非侵入性模型来预测低级别胶质瘤患者的PFS。我们利用全面的放射基因组分析进一步揭示了放射基因组特征背后的生物学过程。基于放射组学模型可以有效地预测低级别胶质瘤的PFS。
The aim of this study was to develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and to investigate the genetic background behind the radiomics signature. In this retrospective study, training (n = 216) and validation (n = 84) cohorts were collected from the Chinese Glioma Genome Atlas and the Cancer Genome Atlas, respectively. For each patient, a total of 431 radiomics features were extracted from preoperative T2-weighted magnetic resonance images. A radiomics signature was generated in the training cohort, and its prognostic value was evaluated in both the training and validation cohorts. The genetic characteristics of the group with high-risk scores were identified by radiogenomic analysis, and a nomogram was established for prediction of PFS. There was a significant association between the radiomics signature (including 9 screened radiomics features) and PFS, which was independent of other clinicopathologic factors in both the training (P < 0.001, multivariable Cox regression) and validation (P = 0.045, multivariable Cox regression) cohorts. Radiogenomic analysis revealed that the radiomics signature was associated with the immune response, programmed cell death, cell proliferation, and vasculature development. A nomogram established using the radiomics signature and clinicopathologic risk factors demonstrated high accuracy and good calibration for prediction of PFS in both the training (C-index, 0.684) and validation (C-index, 0.823) cohorts. PFS can be predicted non-invasively in patients with LGGs by a group of radiomics features that could reflect the biological processes of these tumors. We developed a non-invasive model for the prediction of PFS in patients with lower-grade gliomas. We further revealed the biological processes underlying the radiomic signature by using comprehensive radiogenomic analysis. PFS of lower-grade gliomas could be predicted effectively based on the radiomics model.
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DOI：
10.1158/1078-0432.ccr-16-0702
发表时间：
2016-12-01
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Kickingereder P;Götz M;Muschelli J;Wick A;Neuberger U;Shinohara RT;Sill M;Nowosielski M;Schlemmer HP;Radbruch A;Wick W;Bendszus M;Maier-Hein KH;Bonekamp D
通讯作者：
Bonekamp D
影响因子：
3.7
作者：
Grove O;Berglund AE;Schabath MB;Aerts HJ;Dekker A;Wang H;Velazquez ER;Lambin P;Gu Y;Balagurunathan Y;Eikman E;Gatenby RA;Eschrich S;Gillies RJ
通讯作者：
Gillies RJ
影响因子：
5
作者：
Balagurunathan, Yoganand;Gu, Yuhua;Gillies, Robert J.
通讯作者：
Gillies, Robert J.
影响因子：
4.1
作者：
Wang, Yongheng;Wang, Yinyan;Jiang, Tao
通讯作者：
Jiang, Tao
CGCG 成人弥漫性胶质瘤治疗临床实践指南。
影响因子：
9.7
作者：
Jiang, Tao;Mao, Ying;Wang, Qixue
通讯作者：
Wang, Qixue