Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Eur J Radiol. 2019 Aug;117:193-198. doi: 10.1016/j.ejrad.2019.06.019. Epub 2019 Jun 25.
To develop magnetic resonance imaging (MRI)-based radiomic signature and nomogram for preoperatively predicting prognosis in head and neck squamous cell carcinoma (HNSCC) patients.
This retrospective study consisted of a training cohort (n = 85) and a validation cohort (n = 85) of patients with HNSCC. LASSO Cox regression model was used to select the most useful prognostic features with their coefficients, upon which a radiomic signature was generated. The receiver operator characteristics (ROC) analysis and association of the radiomic signature with overall survival (OS) of patients was assessed in both cohorts. A nomogram incorporating the radiomic signature and independent clinical predictors was then constructed. The incremental prognostic value of the radiomic signature was evaluated.
The radiomic signature, consisted of 7 selected features from MR images, was significantly associated with OS of patients with HNSCC (P < 0.0001 for training cohort, P = 0.0013 for validation cohort). The radiomic signature and TNM stage were proved to be independently associated with OS of HNSCC patients, which therefore were incorporated to generate the radiomic nomogram. In the training cohort, the nomogram showed a better prognostic capability than TNM stage only (P = 0.005), which was confirmed in the validation cohort (P = 0.01). Furthermore, the calibration curves of the nomogram demonstrated good agreement with actual observation.
MRI-based radiomic signature is an independent prognostic factor for HNSCC patients. Nomogram based on radiomic signature and TNM stage shows promising in non-invasively and preoperatively predicting prognosis of HNSCC patient in clinical practice.
开发基于磁共振成像(MRI)的放射组学特征和列线图,用于预测头颈部鳞状细胞癌(HNSCC)患者的术前预后。
本回顾性研究包括 HNSCC 患者的训练队列(n=85)和验证队列(n=85)。使用 LASSO Cox 回归模型选择最有用的预后特征及其系数,在此基础上生成放射组学特征。在两个队列中评估了放射组学特征与患者总生存(OS)的接收者操作特征(ROC)分析和相关性。然后构建了一个包含放射组学特征和独立临床预测因子的列线图。评估了放射组学特征的增量预后价值。
由 MR 图像中 7 个选定特征组成的放射组学特征与 HNSCC 患者的 OS 显著相关(训练队列 P<0.0001,验证队列 P=0.0013)。放射组学特征和 TNM 分期被证明与 HNSCC 患者的 OS 独立相关,因此将其纳入生成放射组学列线图。在训练队列中,列线图显示出比 TNM 分期更好的预后能力(P=0.005),在验证队列中也得到了证实(P=0.01)。此外,列线图的校准曲线显示出与实际观察结果的良好一致性。
基于 MRI 的放射组学特征是 HNSCC 患者的独立预后因素。基于放射组学特征和 TNM 分期的列线图在临床实践中具有非侵入性和术前预测 HNSCC 患者预后的潜力。