Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer.
Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer.
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DOI：
10.1038/srep11044
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发表时间：
2015-06-05
影响因子：
4.6
通讯作者：
Aerts HJ
中科院分区：
文献类型：
--
作者：
Parmar C;Leijenaar RT;Grossmann P;Rios Velazquez E;Bussink J;Rietveld D;Rietbergen MM;Haibe-Kains B;Lambin P;Aerts HJ
放射组学通过提取和挖掘大量的定量图像特征，提供对肿瘤表型的全面量化。为了减少冗余并比较不同癌症类型的放射组学特征的预后特征，我们研究了四个独立的肺癌和头颈癌（HN）癌症队列（共878例患者）中的癌症特异性放射组学特征集群。从治疗前计算机断层扫描（CT）图像中提取放射组学特征。共识聚类分别产生肺癌和H & N癌的11个和13个稳定的放射组学特征聚类。使用兰德统计在独立的外部验证群组中验证这些聚类（肺RS = 0.92，p < 0.001，H & N RS = 0.92，p < 0.001）。我们的分析表明，共同的以及癌症特异性聚类和放射组学特征的临床关联。与临床参数的最强关联：预后肺CI = 0.60 ± 0.01，预后H & N CI = 0.68 ± 0.01;肺组织学AUC = 0.56 ± 0.03，肺分期AUC = 0.61 ± 0.01，H & N HPV AUC = 0.58 ± 0.03，H & N分期AUC = 0.77 ± 0.02。充分利用图像特征的这些癌症特异性特征可以进一步改善放射组学生物标志物，从而在临床实践中提供量化和监测肿瘤表型特征的非侵入性方式。
Radiomics provides a comprehensive quantification of tumor phenotypes by extracting and mining large number of quantitative image features. To reduce the redundancy and compare the prognostic characteristics of radiomic features across cancer types, we investigated cancer-specific radiomic feature clusters in four independent Lung and Head & Neck (H∓N) cancer cohorts (in total 878 patients). Radiomic features were extracted from the pre-treatment computed tomography (CT) images. Consensus clustering resulted in eleven and thirteen stable radiomic feature clusters for Lung and H & N cancer, respectively. These clusters were validated in independent external validation cohorts using rand statistic (Lung RS = 0.92, p < 0.001, H & N RS = 0.92, p < 0.001). Our analysis indicated both common as well as cancer-specific clustering and clinical associations of radiomic features. Strongest associations with clinical parameters: Prognosis Lung CI = 0.60 ± 0.01, Prognosis H & N CI = 0.68 ± 0.01; Lung histology AUC = 0.56 ± 0.03, Lung stage AUC = 0.61 ± 0.01, H & N HPV AUC = 0.58 ± 0.03, H & N stage AUC = 0.77 ± 0.02. Full utilization of these cancer-specific characteristics of image features may further improve radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor phenotypic characteristics in clinical practice.