Exploration of an Integrative Prognostic Model of Radiogenomics Features With Underlying Gene Expression Patterns in Clear Cell Renal Cell Carcinoma.
Exploration of an Integrative Prognostic Model of Radiogenomics Features With Underlying Gene Expression Patterns in Clear Cell Renal Cell Carcinoma.
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DOI：
10.3389/fonc.2021.640881
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发表时间：
2021
影响因子：
4.7
通讯作者：
Zhao Y
中科院分区：
文献类型：
--
作者：
Huang Y;Zeng H;Chen L;Luo Y;Ma X;Zhao Y
透明细胞肾细胞癌（ccRCC）是泌尿系统最常见的恶性肿瘤之一，放射组学已应用于肾癌的肿瘤分期和预后评估。本研究旨在整合增强 CT 的图像特征和基础基因组学特征来预测 ccRCC 患者的总生存期 (OS)。我们从 TCIA 数据库中获得的可用 CT 图像以及从 TCGA 数据库中获得的相应临床和遗传信息，从 205 名患者中提取了 107 个放射组学特征。 LASSO-COX 和 SVM-RFE 独立用作机器学习算法来选择与预后相关的成像特征 (PRIF)。随后，我们通过 WGCNA 鉴定了与预后相关的基因特征。然后应用随机森林 (RF) 算法将 PRIF 和基因整合到组合成像-基因组预后因素 (IGPF) 模型中。此外，我们构建了一个包含 IGPF 和临床预测因子的列线图，作为 ccRCC 患者的综合预后模型。总共有 4 个 PRIF 和 4 个基因被鉴定为 IGPF，并在 RF 模型中用相应的风险评分表示。综合 IGPF 模型比单独的 PRIF 模型表现出更好的预测性能（测试集中 1 年、3 年和 5 年的平均 AUC 分别为 0.814 与 0.837、0.74 与 0.806 和 0.689 与 0.751）。性别、TNM分期和IGPF等临床特征是独立危险因素。整合临床预测因子和 IGPF 的列线图提供了三个模型中最好的净效益。在这项研究中，我们建立了一个结合影像基因组特征和临床指标的综合预后相关列线图模型。结果表明，IGPF 可能有助于 ccRCC 患者的全面预后评估。
Clear cell renal cell carcinoma (ccRCC) is one of the most common malignancies in urinary system, and radiomics has been adopted in tumor staging and prognostic evaluation in renal carcinomas. This study aimed to integrate image features of contrast-enhanced CT and underlying genomics features to predict the overall survival (OS) of ccRCC patients. We extracted 107 radiomics features out of 205 patients with available CT images obtained from TCIA database and corresponding clinical and genetic information from TCGA database. LASSO-COX and SVM-RFE were employed independently as machine-learning algorithms to select prognosis-related imaging features (PRIF). Afterwards, we identified prognosis-related gene signature through WGCNA. The random forest (RF) algorithm was then applied to integrate PRIF and the genes into a combined imaging-genomics prognostic factors (IGPF) model. Furthermore, we constructed a nomogram incorporating IGPF and clinical predictors as the integrative prognostic model for ccRCC patients. A total of four PRIF and four genes were identified as IGPF and were represented by corresponding risk score in RF model. The integrative IGPF model presented a better prediction performance than the PRIF model alone (average AUCs for 1-, 3-, and 5-year were 0.814 vs. 0.837, 0.74 vs. 0.806, and 0.689 vs. 0.751 in test set). Clinical characteristics including gender, TNM stage and IGPF were independent risk factors. The nomogram integrating clinical predictors and IGPF provided the best net benefit among the three models. In this study we established an integrative prognosis-related nomogram model incorporating imaging-genomic features and clinical indicators. The results indicated that IGPF may contribute to a comprehensive prognosis assessment for ccRCC patients.
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影响因子：
2.4
作者：
Coy, Heidi;Young, Jonathan R.;Raman, Steven S.
通讯作者：
Raman, Steven S.
DOI：
10.1016/j.radonc.2016.04.004
发表时间：
2016-06
期刊：
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
影响因子：
--
作者：
Coroller TP;Agrawal V;Narayan V;Hou Y;Grossmann P;Lee SW;Mak RH;Aerts HJ
通讯作者：
Aerts HJ
影响因子：
17.1
作者：
Allen E;Jabouille A;Rivera LB;Lodewijckx I;Missiaen R;Steri V;Feyen K;Tawney J;Hanahan D;Michael IP;Bergers G
通讯作者：
Bergers G
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：
50.5
作者：
Ho, T. H.;Serie, D. J.;Eckel-Passow, J. E.
通讯作者：
Eckel-Passow, J. E.