Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.
Department of Statistics and Data Science, Yonsei University, Seoul, Korea.
Eur Radiol. 2022 Dec;32(12):8089-8098. doi: 10.1007/s00330-022-08941-x. Epub 2022 Jun 28.
To assess whether radiomic features could improve the accuracy of survival predictions of IDH-wildtype (IDHwt) histological lower-grade gliomas (LGGs) over clinicopathological features.
Preoperative MRI data of 61 patients with IDHwt histological LGGs were included as the institutional training set. The test set consisted of 32 patients from The Cancer Genome Atlas. Radiomic features (n = 186) were extracted using conventional MRIs. The radiomics risk score (RRS) for overall survival (OS) was derived from the elastic net. Multivariable Cox regression analyses with clinicopathological features (including epidermal growth factor receptor [EGFR] amplification and telomerase reverse transcriptase promoter [TERTp] mutation status) and the RRS were performed. The integrated area under the receiver operating curves (iAUCs) from the models with and without the RRS were compared. The net reclassification index (NRI) for 1-year OS was also calculated. The prognostic value of the RRS was evaluated using the external validation set.
The RRS independently predicted OS (hazard ratio = 48.08; p = 0.001). Compared with the clinicopathological model alone, adding the RRS had a better OS prediction performance (iAUCs 0.775 vs. 0.910), which was internally validated (iAUCs 0.726 vs. 0.884, 1-year OS NRI = 0.497), and a similar trend was found on external validation (iAUCs 0.683 vs. 0.705, 1-year OS NRI = 0.733). The prognostic significance of the RRS was confirmed in the external validation set (p = 0.001).
Integrating radiomics with clinicopathological features (including EGFR amplification and TERTp mutation status) can improve survival prediction in patients with IDHwt LGGs.
• Radiomics risk score has the potential to improve survival prediction when added to clinicopathological features (iAUCs increased from 0.775 to 0.910). • NRIs for 1-year OS showed that the radiomics risk score had incremental value over the clinicopathological model. • The prognostic significance of the radiomics risk score was confirmed in the external validation set (p = 0.001).
评估放射组学特征是否可以提高 IDH 野生型(IDHwt)组织学低级别胶质瘤(LGG)的生存预测准确性,优于临床病理特征。
纳入 61 例 IDHwt 组织学 LGG 患者的术前 MRI 数据作为机构训练集。测试集由来自癌症基因组图谱的 32 例患者组成。使用常规 MRI 提取放射组学特征(n=186)。基于弹性网络推导整体生存(OS)的放射组学风险评分(RRS)。采用包含表皮生长因子受体(EGFR)扩增和端粒酶逆转录酶启动子(TERTp)突变状态的多变量 Cox 回归分析与临床病理特征和 RRS 进行分析。比较有无 RRS 的模型的综合接收者操作曲线下面积(iAUCs)。还计算了 1 年 OS 的净重新分类指数(NRI)。使用外部验证集评估 RRS 的预后价值。
RRS 独立预测 OS(风险比=48.08;p=0.001)。与单独的临床病理模型相比,添加 RRS 具有更好的 OS 预测性能(iAUCs 0.775 与 0.910),这在内部得到了验证(iAUCs 0.726 与 0.884,1 年 OS NRI=0.497),在外部验证中也发现了类似的趋势(iAUCs 0.683 与 0.705,1 年 OS NRI=0.733)。RRS 的预后意义在外部验证集中得到了证实(p=0.001)。
将放射组学与临床病理特征(包括 EGFR 扩增和 TERTp 突变状态)相结合,可以提高 IDHwt LGG 患者的生存预测。
RRS 具有提高生存预测的潜力,当与临床病理特征(iAUCs 从 0.775 增加到 0.910)相结合时。
1 年 OS 的 NRI 表明,放射组学风险评分比临床病理模型具有增量价值。
外部验证集证实了放射组学风险评分的预后意义(p=0.001)。