Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer
Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer
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放射组学特征：预测早期（I 期或 II 期）非小细胞肺癌无病生存的潜在生物标志物
DOI：
10.1148/radiol.2016152234
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发表时间：
2016-12-01
期刊：
影响因子：
19.7
通讯作者：
Liang, Changhong
中科院分区：
文献类型：
--
作者：
Huang, Yanqi;Liu, Zaiyi;Liang, Changhong
目的：建立一种影像组学特征来评估早期（I - II期）非小细胞肺癌（NSCLC）患者的无病生存期（DFS），并评估其相对于传统分期系统以及临床病理风险因素在个体DFS评估中的增值价值。
材料与方法：本回顾性分析已获得机构审查委员会的伦理批准，且免除了获取知情同意的要求。本研究纳入了282例连续的IA - IIB期NSCLC患者。通过使用最小绝对收缩和选择算子（LASSO）Cox回归模型生成影像组学特征。探究了影像组学特征与DFS之间的关联。通过多变量Cox回归进一步验证影像组学特征作为一种独立的生物标志物。构建了一个包含影像组学特征的影像组学列线图，以展示影像组学特征相对于传统分期系统以及其他临床病理风险因素在个体化DFS评估中的增值价值，然后从校准、区分度、再分类以及临床实用性方面对其进行评估。
结果：影像组学特征与DFS显著相关，且独立于临床 - 病理风险因素。将影像组学特征纳入基于影像组学的列线图中，在DFS评估方面（C指数：0.72；95%置信区间[CI]：0.71，0.73）比临床 - 病理列线图（C指数：0.691；95%CI：0.68，0.70）表现更好（P <.0001），并且校准更好，生存结果分类的准确性也提高了（净再分类改善：0.182；95%CI：0.02，0.31；P =.02）。决策曲线分析表明，就临床实用性而言，影像组学列线图优于传统分期系统和临床 - 病理列线图。
结论：影像组学特征是评估早期NSCLC患者DFS的一种独立生物标志物。影像组学特征、传统分期系统以及其他临床 - 病理风险因素相结合，在早期NSCLC患者的个体化DFS评估中表现更好，这可能推动精准医学向前迈进了一步。（C）美国放射学会，2016
Purpose: To develop a radiomics signature to estimate disease-free survival (DFS) in patients with early-stage (stage I-II) non-small cell lung cancer (NSCLC) and assess its incremental value to the traditional staging system and clinicalpathologic risk factors for individual DFS estimation.Materials and Methods: Ethical approval by the institutional review board was obtained for this retrospective analysis, and the need to obtain informed consent was waived. This study consisted of 282 consecutive patients with stage IA-IIB NSCLC. A radiomics signature was generated by using the least absolute shrinkage and selection operator, or LASSO, Cox regression model. Association between the radiomics signature and DFS was explored. Further validation of the radiomics signature as an independent biomarker was performed by using multivariate Cox regression. A radiomics nomogram with the radiomics signature incorporated was constructed to demonstrate the incremental value of the radiomics signature to the traditional staging system and other clinicalpathologic risk factors for individualized DFS estimation, which was then assessed with respect to calibration, discrimination, reclassification, and clinical usefulness.Results: The radiomics signature was significantly associated with DFS, independent of clinical-pathologic risk factors. Incorporating the radiomics signature into the radiomics-based nomogram resulted in better performance (P < .0001) for the estimation of DFS (C-index: 0.72; 95% confidence interval [CI]: 0.71, 0.73) than with the clinical-pathologic nomogram (C-index: 0.691; 95% CI: 0.68, 0.70), as well as a better calibration and improved accuracy of the classification of survival outcomes (net reclassification improvement: 0.182; 95% CI: 0.02, 0.31; P = .02). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinical-pathologic nomogram.Conclusion: The radiomics signature is an independent biomarker for the estimation of DFS in patients with early-stage NSCLC. Combination of the radiomics signature, traditional staging system, and other clinical-pathologic risk factors performed better for individualized DFS estimation in patients with early-stage NSCLC, which might enable a step forward precise medicine. (C) RSNA, 2016