Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy.
Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy.
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DOI：
10.1186/s12885-017-3847-7
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发表时间：
2017-12-06
期刊：
影响因子：
3.8
通讯作者：
Wang PM
中科院分区：
文献类型：
--
作者：
Cozzi L;Dinapoli N;Fogliata A;Hsu WC;Reggiori G;Lobefalo F;Kirienko M;Sollini M;Franceschini D;Comito T;Franzese C;Scorsetti M;Wang PM
评价以放射组学为基础的分析预测肝细胞癌患者局部反应和总体生存的能力。对138例巴塞罗那临床肝癌(BCLC)A-C期患者(男112例，女26例，平均年龄66岁)进行了回顾性研究。对于这些患者中的一部分(106例)，可以获得关于治疗结果的完整信息，即局部对照。计算临床靶区的放射学特征。共提取并分析了35个特征。单因素分析用于确定临床和放射组学的显著特征。用COX回归风险模型建立局部控制和生存结局的多因素模型。通过受试者工作特征(ROC)曲线的曲线下面积(AUC)对模型进行评价。对于LC分析，分析了选择两组不相关特征的两个模型，而建立了一个用于OS分析的单一模型。单变量分析确定了15个显着的放射组学特征，但交叉相关分析显示了几个交叉相关的协变量。不相关的变量被用来建立两个独立的模型；两者都导致了一个显著的放射学协变量：模型1：能量p <； 0.05，ROC 0.6659的AUC0.05，C.I.：0.5585-0.7732；模型2：GLNUp <； 0.05，AUC0.6396，C.I.：0.5266-0.7526。对与局部控制显著相关的协变量进行单变量分析，得到9个临床特征和13个放射组学特征，这些特征具有多重和复杂的交叉相关性。经弹性网络正则化后，最显著的协变量是COX模型的COX模型似然比检验p <； 0.0001，致密性p <； 0.00001；AUC值为0.8014(C.I.=0.7232~0.8797)。最终确定了由一个单一特征产生的稳健的放射组学特征。计划进行基于独立患者集的验证阶段，以确认结果。
To appraise the ability of a radiomics based analysis to predict local response and overall survival for patients with hepatocellular carcinoma. A set of 138 consecutive patients (112 males and 26 females, median age 66 years) presented with Barcelona Clinic Liver Cancer (BCLC) stage A to C were retrospectively studied. For a subset of these patients (106) complete information about treatment outcome, namely local control, was available. Radiomic features were computed for the clinical target volume. A total of 35 features were extracted and analyzed. Univariate analysis was used to identify clinical and radiomics significant features. Multivariate models by Cox-regression hazards model were built for local control and survival outcome. Models were evaluated by area under the curve (AUC) of receiver operating characteristic (ROC) curve. For the LC analysis, two models selecting two groups of uncorrelated features were analyzes while one single model was built for the OS analysis. The univariate analysis lead to the identification of 15 significant radiomics features but the analysis of cross correlation showed several cross related covariates. The un-correlated variables were used to build two separate models; both resulted into a single significant radiomic covariate: model-1: energy p < 0.05, AUC of ROC 0.6659, C.I.: 0.5585–0.7732; model-2: GLNU p < 0.05, AUC 0.6396, C.I.:0.5266–0.7526. The univariate analysis for covariates significant with respect to local control resulted in 9 clinical and 13 radiomics features with multiple and complex cross-correlations. After elastic net regularization, the most significant covariates were compacity and BCLC stage, with only compacity significant to Cox model fitting (Cox model likelihood ratio test p < 0.0001, compacity p < 0.00001; AUC of the model is 0.8014 (C.I. = 0.7232–0.8797)). A robust radiomic signature, made by one single feature was finally identified. A validation phases, based on independent set of patients is scheduled to be performed to confirm the results.
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DOI：
10.1186/1748-717x-6-76
发表时间：
2011-06-21
期刊：
Radiation oncology (London, England)
影响因子：
--
作者：
Kuo YC;Chiu YM;Shih WP;Yu HW;Chen CW;Wong PF;Lin WC;Hwang JJ
通讯作者：
Hwang JJ
影响因子：
2.6
作者：
Sala E;Mema E;Himoto Y;Veeraraghavan H;Brenton JD;Snyder A;Weigelt B;Vargas HA
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Vargas HA
DOI：
10.1016/s0360-3016(03)00091-9
发表时间：
2003-05-01
影响因子：
7
作者：
Cheng, JCH;Wu, JK;Huang, AT
通讯作者：
Huang, AT
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：
45.3
作者：
Altekruse, Sean F.;McGlynn, Katherine A.;Reichman, Marsha E.
通讯作者：
Reichman, Marsha E.