Shen Chen, Liu Zhenyu, Guan Min, Song Jiangdian, Lian Yucheng, Wang Shuo, Tang Zhenchao, Dong Di, Kong Lingfei, Wang Meiyun, Shi Dapeng, Tian Jie
School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China.
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China.
Transl Oncol. 2017 Dec;10(6):886-894. doi: 10.1016/j.tranon.2017.08.007. Epub 2017 Sep 18.
To compare 2D and 3D radiomics features prognostic performance differences in CT images of non-small cell lung cancer (NSCLC).
We enrolled 588 NSCLC patients from three independent cohorts. Two sets of 463 patients from two different institutes were used as the training cohort. The remaining cohort with 125 patients was set as the validation cohort. A total of 1014 radiomics features (507 2D features and 507 3D features correspondingly) were assessed. Based on the dichotomized survival data, 2D and 3D radiomics indicators were calculated for each patient by trained classifiers. We used the area under the receiver operating characteristic curve (AUC) to assess the prediction performance of trained classifiers (the support vector machine and logistic regression). Kaplan-Meier and Cox hazard survival analyses were also employed. Harrell's concordance index (C-Index) and Akaike's information criteria (AIC) were applied to assess the trained models.
Radiomics indicators were built and compared by AUCs. In the training cohort, 2D_AUC=0.653, 3D_AUC=0.671. In the validation cohort, 2D_AUC=0.755, 3D_AUC=0.663. Both 2D and 3D trained indicators achieved significant results (P<.05) in the Kaplan-Meier analysis and Cox regression. In the validation cohort, 2D Cox model had a C-Index=0.683 and AIC=789.047; 3D Cox model obtained a C-Index=0.632 and AIC=799.409.
Both 2D and 3D CT radiomics features have a certain prognostic ability in NSCLC, but 2D features showed better performance in our tests. Considering the cost of the radiomics features calculation, 2D features are more recommended for use in the current study.
比较非小细胞肺癌（NSCLC）CT图像中二维和三维放射组学特征的预后性能差异。
我们从三个独立队列中纳入了588例NSCLC患者。来自两个不同机构的两组共463例患者用作训练队列。其余125例患者的队列作为验证队列。总共评估了1014个放射组学特征（相应地，507个二维特征和507个三维特征）。基于二分法生存数据，通过训练的分类器为每位患者计算二维和三维放射组学指标。我们使用受试者操作特征曲线下面积（AUC）来评估训练分类器（支持向量机和逻辑回归）的预测性能。还采用了Kaplan-Meier和Cox风险生存分析。应用Harrell一致性指数（C指数）和赤池信息准则（AIC）来评估训练模型。
通过AUC构建并比较放射组学指标。在训练队列中，二维AUC = 0.653，三维AUC = 0.671。在验证队列中，二维AUC = 0.755，三维AUC = 0.663。二维和三维训练指标在Kaplan-Meier分析和Cox回归中均取得显著结果（P <.05）。在验证队列中，二维Cox模型的C指数= 0.683，AIC = 789.047；三维Cox模型的C指数= 0.632，AIC = 799.409。
二维和三维CT放射组学特征在NSCLC中均具有一定的预后能力，但在我们的测试中二维特征表现更好。考虑到放射组学特征计算的成本，在本研究中更推荐使用二维特征。
