Department of Radiology, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, People's Republic of China.
Shantou University Medical College, Shantou, Guangdong, People's Republic of China.
Eur Radiol. 2020 Feb;30(2):833-843. doi: 10.1007/s00330-019-06452-w. Epub 2019 Oct 30.
To develop a radiomics-based model to stratify the risk of early progression (local/regional recurrence or metastasis) among patients with hypopharyngeal cancer undergoing chemoradiotherapy and modify their pretreatment plans.
We randomly assigned 113 patients into two cohorts: training (n = 80) and validation (n = 33). The radiomic significant features were selected in the training cohort using least absolute shrinkage and selection operator and Akaike information criterion methods, and they were used to build the radiomic model. The concordance index (C-index) was applied to evaluate the model's prognostic performance. A Kaplan-Meier analysis and the log-rank test were used to assess risk stratification ability of models in predicting progression. A nomogram was plotted to predict individual risk of progression.
Composed of four significant features, the radiomic model showed good performance in stratifying patients into high- and low-risk groups of progression in both the training and validation cohorts (log-rank test, p = 0.00016, p = 0.0063, respectively). Peripheral invasion and metastasis were selected as significant clinical variables. The combined radiomic-clinical model showed good discriminative performance, with C-indices 0.804 (95% confidence interval (CI), 0.688-0.920) and 0.756 (95% CI, 0.605-0.907) in the training and validation cohorts, respectively. The median progression-free survival (PFS) in the high-risk group was significantly shorter than that in the low-risk group in the training (median PFS, 9.5 m and 19.0 m, respectively; p [log-rank] < 0.0001) and validation (median PFS, 11.3 m and 22.5 m, respectively; p [log-rank] = 0.0063) cohorts.
A radiomics-based model was established to predict the risk of progression in hypopharyngeal cancer with chemoradiotherapy.
• Clinical information showed limited performance in stratifying the risk of progression among patients with hypopharyngeal cancer. • Imaging features extracted from CECT and NCCT images were independent predictors of PFS. • We combined significant features and valuable clinical variables to establish a nomogram to predict individual risk of progression.
开发一种基于放射组学的模型，以对接受放化疗的下咽癌患者的早期进展（局部/区域复发或转移）风险进行分层，并修改其治疗前计划。
我们将 113 名患者随机分为两组：训练组（n=80）和验证组（n=33）。使用最小绝对值收缩和选择算子（LASSO）和赤池信息量准则（AIC）方法从训练组中选择放射组学显著特征，并用于构建放射组学模型。一致性指数（C-index）用于评估模型的预后性能。采用 Kaplan-Meier 分析和对数秩检验评估模型预测进展的风险分层能力。绘制诺莫图预测个体进展风险。
由四个显著特征组成的放射组学模型在训练和验证队列中均能很好地将患者分为进展高风险和低风险组（对数秩检验，p=0.00016，p=0.0063）。周围侵犯和转移被选为显著的临床变量。联合放射组学-临床模型具有良好的判别性能，训练组和验证组的 C 指数分别为 0.804（95%置信区间（CI），0.688-0.920）和 0.756（95%CI，0.605-0.907）。高危组的中位无进展生存期（PFS）明显短于低危组，在训练组（中位 PFS 分别为 9.5 个月和 19.0 个月；p[log-rank]＜0.0001）和验证组（中位 PFS 分别为 11.3 个月和 22.5 个月；p[log-rank]=0.0063）。
建立了基于放射组学的模型，用于预测接受放化疗的下咽癌患者的进展风险。
临床信息在分层下咽癌患者进展风险方面表现有限。
从 CECT 和 NCCT 图像中提取的影像特征是 PFS 的独立预测因子。
我们结合显著特征和有价值的临床变量建立了诺莫图来预测个体进展风险。
