Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Department of Radiology, Myongji Hospital, Goyang, South Korea.
Thorac Cancer. 2020 Sep;11(9):2542-2551. doi: 10.1111/1759-7714.13568. Epub 2020 Jul 22.
A single institution retrospective analysis of 124 non-small cell lung carcinoma (NSCLC) patients was performed to identify whether disease-free survival (DFS) achieves incremental values when radiomic and genomic data are combined with clinical information.
Using the least absolute shrinkage and selection operator (LASSO) Cox regression method, radiomic and genetic features were reduced in number for selection of the most useful prognostic feature. We created four models using only baseline clinical data, clinical data with selected genetic features, clinical data with selected radiomic features, and clinical data with selected genetic and radiomic features together. Multivariate Cox proportional hazards analysis was performed to determine predictors of DFS. Receiver operating characteristic (ROC) calculation was made to compare the discriminative performance for DFS prediction by four constructed models at the five-year time point.
On precontrast scan, improved discrimination performance was obtained in a merging of selected radiomics and genetics (AUC = 0.8638), compared with clinical data only (AUC = 0.7990), selected genetic features (AUC = 0.8497), and selected radiomic features (AUC = 0.8355). On post-contrast scan, discrimination performance was improved (AUC = 0.8672) compared with the clinical variables (AUC = 0.7913), and selected genetic features (AUC = 0.8376) and selected radiomic features (AUC = 0.8399) were considered.
The combination of selected radiomic and genomic features improved stratification of NSCLC patients upon survival. Thus, integrating clinicopathologic model with radiomic and genomic features may lead to improved prognostic accuracy compared to conventional clinicopathological data alone.
SIGNIFICANT FINDINGS OF THE STUDY: Receiver operating characteristic (ROC) calculation was made to compare the discriminative performance for disease-free survival (DFS). The discriminative performance for DFS was better when combining radiomic and genetic features compared to clinical data only, selected genetic features, and selected radiomic features.
The combination of selected radiomic and genomic features improved stratification of NSCLC patients upon survival. Thus, integrating a clinicopathological model with radiomic and genomic features may lead to improved prognostic accuracy compared to conventional clinicopathological data alone.
对 124 例非小细胞肺癌（NSCLC）患者进行了单机构回顾性分析，以确定当放射组学和基因组数据与临床信息相结合时，无病生存期（DFS）是否能获得增值。
使用最小绝对收缩和选择算子（LASSO）Cox 回归方法，减少放射组学和遗传特征的数量，以选择最有用的预后特征。我们仅使用基线临床数据、具有选定遗传特征的临床数据、具有选定放射组学特征的临床数据以及具有选定遗传和放射组学特征的临床数据创建了四个模型。多变量 Cox 比例风险分析用于确定 DFS 的预测因子。计算接收器工作特征（ROC）曲线，以比较四个构建模型在五年时间点对 DFS 预测的判别性能。
在平扫时，与仅临床数据（AUC=0.7990）、选定的遗传特征（AUC=0.8497）和选定的放射组学特征（AUC=0.8355）相比，合并选定的放射组学和遗传学特征可获得更好的区分性能（AUC=0.8638）。在增强扫描时，与临床变量（AUC=0.7913）、选定的遗传特征（AUC=0.8376）和选定的放射组学特征（AUC=0.8399）相比，区分性能得到提高（AUC=0.8672）。
选定的放射组学和基因组特征的结合改善了 NSCLC 患者的生存分层。因此，与仅使用临床病理数据相比，将临床病理模型与放射组学和基因组特征相结合可能会导致预后准确性的提高。
研究的重要发现：计算接收器工作特征（ROC）曲线以比较无病生存期（DFS）的判别性能。与仅临床数据、选定的遗传特征和选定的放射组学特征相比，结合放射组学和遗传特征时，DFS 的判别性能更好。
选定的放射组学和基因组特征的结合改善了 NSCLC 患者的生存分层。因此，与仅使用临床病理数据相比，将临床病理模型与放射组学和基因组特征相结合可能会导致预后准确性的提高。
