CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.
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DOI：
10.1016/j.radonc.2015.02.015
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发表时间：
2015-03
期刊：
影响因子：
--
通讯作者：
Aerts HJ
中科院分区：
文献类型：
--
作者：
Coroller TP;Grossmann P;Hou Y;Rios Velazquez E;Leijenaar RT;Hermann G;Lambin P;Haibe-Kains B;Mak RH;Aerts HJ
放射组学通过应用大量的定量成像特征提供了非侵入性地定量肿瘤表型的机会。本研究评估了计算机断层扫描（CT）放射组学特征预测肺腺癌患者远处转移（DM）的能力。我们包括两个数据集：98名患者用于发现，84名患者用于验证。使用635个放射组学特征在治疗前CT扫描上定量原发性肿瘤的表型。使用一致性指数（CI）进行单变量和多变量分析以评价放射组学性能。35个放射学特征被认为是糖尿病的预后（CI > 0.60，FDR < 5%），12个是生存的预后。值得注意的是，在发现队列中，肿瘤体积仅是DM的中度预后（CI=0.55，p值=2.77 × 10−5）。在独立验证数据集中，放射性特征具有很强的预测DM的能力（CI=0.61，p值=1.79 ×10−17）。将这种放射性特征添加到临床模型中，在验证数据集中预测DM的效果显著改善（p值=1.56 × 10−11）。虽然只有基本的指标进行常规定量，这项研究表明，放射组学特征捕获的肿瘤表型的详细信息，可用作临床相关因素，如DM的预后生物标志物。此外，放射性签名为临床数据提供了额外的信息。
Radiomics provides opportunities to quantify the tumor phenotype non-invasively by applying a large number of quantitative imaging features. This study evaluates computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients. We included two datasets: 98 patients for discovery and 84 for validation. The phenotype of the primary tumor was quantified on pre-treatment CT-scans using 635 radiomic features. Univariate and multivariate analysis was performed to evaluate radiomics performance using the concordance index (CI). Thirty-five radiomic features were found to be prognostic (CI > 0.60, FDR < 5%) for DM and twelve for survival. It is noteworthy that tumor volume was only moderately prognostic for DM (CI=0.55, p-value=2.77 × 10−5) in the discovery cohort. A radiomic-signature had strong power for predicting DM in the independent validation dataset (CI=0.61, p-value=1.79 ×10−17). Adding this radiomic-signature to a clinical model resulted in a significant improvement of predicting DM in the validation dataset (p-value=1.56 × 10−11). Although only basic metrics are routinely quantified, this study shows that radiomic features capturing detailed information of the tumor phenotype can be used as a prognostic biomarker for clinically-relevant factors such as DM. Moreover, the radiomic-signature provided additional information to clinical data.
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影响因子：
168.9
作者：
Albain, Kathy S.;Swann, R. Suzanne;Rusch, Valerie W.;Turrisi, Andrew T., III;Shepherd, Frances A.;Smith, Colum;Chen, Yuhchyau;Livingston, Robert B.;Feins, Richard H.;Gandara, David R.;Fry, Willard A.;Darling, Gail;Johnson, David H.;Green, Mark R.;Miller, Robert C.;Ley, Joanne;Sause, Willliam T.;Cox, James D.
通讯作者：
Cox, James D.
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：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
DOI：
10.1016/s0360-3016(02)02814-6
发表时间：
2002-07-15
影响因子：
7
作者：
Etiz, D;Marks, LB;Lind, PA
通讯作者：
Lind, PA
影响因子：
3.8
作者：
Deasy, JO;Blanco, AI;Clark, VH
通讯作者：
Clark, VH