Ouyang Fu-Sheng, Guo Bao-Liang, Zhang Bin, Dong Yu-Hao, Zhang Lu, Mo Xiao-Kai, Huang Wen-Hui, Zhang Shui-Xing, Hu Qiu-Gen
Department of Radiology, The First People's Hospital of Shunde, Foshan, Guangdong, P.R. China.
Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, P.R. China.
Oncotarget. 2017 Aug 24;8(43):74869-74879. doi: 10.18632/oncotarget.20423. eCollection 2017 Sep 26.
There is no consensus on specific prognostic biomarkers potentially improving survival of nasopharyngeal carcinoma (NPC), especially in advanced-stage disease. The prognostic value of MRI-based radiomics signature is unclear. A total of 970 quantitative features were extracted from the tumor of 100 untreated NPC patients (stage III-IVb) (discovery set: n = 70, validation set: n = 30). We then applied least absolute shrinkage and selection operator (lasso) regression to select features that were most associated with progression-free survival (PFS). Candidate prognostic biomarkers included age, gender, overall stage, hemoglobin, platelet counts and radiomics signature. We developed model 1 (without radiomics signature) and model 2 (with radiomics signature) in the discovery set and then tested in the validation set. Multivariable Cox regression analysis was used to yield hazard ratio (HR) of each potential biomarker. We found the radiomics signature stratified patients in the discovery set into a low or high risk group for PFS (HR = 5.14, < 0.001) and was successfully validated for patients in the validation set (HR = 7.28, = 0.015). However, the other risk factors showed no significantly prognostic value (all p-values for HR, > 0.05). Accordingly, pretreatment MRI-based radiomics signature is a non-invasive and cost-effective prognostic biomarker in advanced NPC patients, which would improve decision-support in cancer care.
对于可能改善鼻咽癌（NPC）患者生存率的特定预后生物标志物，目前尚无共识，尤其是在晚期疾病中。基于MRI的放射组学特征的预后价值尚不清楚。从100例未经治疗的NPC患者（III-IVb期）的肿瘤中提取了总共970个定量特征（发现集：n = 70，验证集：n = 30）。然后，我们应用最小绝对收缩和选择算子（lasso）回归来选择与无进展生存期（PFS）最相关的特征。候选预后生物标志物包括年龄、性别、总分期、血红蛋白、血小板计数和放射组学特征。我们在发现集中开发了模型1（无放射组学特征）和模型2（有放射组学特征），然后在验证集中进行测试。采用多变量Cox回归分析得出每个潜在生物标志物的风险比（HR）。我们发现，放射组学特征在发现集中将患者分为PFS的低风险或高风险组（HR = 5.14，<0.001），并在验证集中成功验证（HR = 7.28，= 0.015）。然而，其他风险因素均未显示出显著的预后价值（所有HR的p值均>0.05）。因此，基于治疗前MRI的放射组学特征是晚期NPC患者一种非侵入性且具有成本效益的预后生物标志物，这将改善癌症治疗中的决策支持。
