Cancer Centre, First Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, PR China.
Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China; College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, PR China.
EBioMedicine. 2019 Jun;44:289-297. doi: 10.1016/j.ebiom.2019.05.023. Epub 2019 May 23.
Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy.
Independent patient cohorts from two hospitals were included for training (n = 87) and validation (n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for 'Cox' method was used for feature selection. The survival prediction model was constructed based on the sub-region radiomics features using the Cox proportional hazards model. The clinical and biological significance of radiomics features were assessed by correlation analysis of clinical characteristics and copy number alterations(CNAs) in the validation dataset.
The overall survival prediction model combining with seven sub-regional radiomics features was constructed. The C-indexes of the proposed model were 0.729 (0.656-0.801, 95% CI) and 0.705 (0.628-0.782, 95%CI) in the training and validation cohorts, respectively. The 3-year survival receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.811 (0.670-0.952, 95%CI) in training and 0.805 (0.638-0.973, 95%CI) in validation. The correlation analysis showed a significant correlation between radiomics features and CNAs.
The proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. FUND: This work was supported by the Zhejiang Provincial Foundation for Natural Sciences, National Natural Science Foundation of China.
对于食管鳞状细胞癌（OSCC），在同步放化疗前评估临床预后仍然具有挑战性，因为传统的预后标志物是在治疗完成后评估的。在此，我们研究了使用亚区放射组学作为一种新的肿瘤生物标志物预测接受同步放化疗的 OSCC 患者总生存的潜力。
我们纳入了来自两家医院的独立患者队列进行训练（n=87）和验证（n=46）。使用 K-均值方法从患者肿瘤区域聚类的亚区提取放射组学特征。使用 LASSO 回归对“Cox”方法进行特征选择。使用 Cox 比例风险模型基于亚区放射组学特征构建生存预测模型。通过验证数据集的临床特征和拷贝数改变（CNAs）的相关性分析，评估放射组学特征的临床和生物学意义。
构建了一个结合七个亚区放射组学特征的总体生存预测模型。该模型在训练和验证队列中的 C 指数分别为 0.729（0.656-0.801，95%CI）和 0.705（0.628-0.782，95%CI）。3 年生存接收者操作特征（ROC）曲线显示，训练队列的 ROC 曲线下面积为 0.811（0.670-0.952，95%CI），验证队列为 0.805（0.638-0.973，95%CI）。相关性分析显示放射组学特征与 CNAs 之间存在显著相关性。
所提出的亚区放射组学模型可预测接受根治性同步放化疗的 OSCC 患者的总体生存风险。
本工作得到浙江省自然科学基金、国家自然科学基金资助。
