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首页> 外文期刊>Chinese journal of academic radiology. >Clinical-radiomic features predict survival in patients with extranodal nasal-type natural killer/T cell lymphoma
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Clinical-radiomic features predict survival in patients with extranodal nasal-type natural killer/T cell lymphoma

机译：临床影像组学特征可预测结外鼻型自然杀伤/T 细胞淋巴瘤患者的生存率

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Purpose To investigate the value of MRI-based radiomic features integrated with clinical indicators for survival prediction in patients with extranodal natural killer/T-cell lymphoma, nasal-type (ENKTL). Materials and methods One-hundred and sixty-five patients with ENKTL who underwent pretreatment MRI were enrolled. Patients were randomly divided into training (n= 115) and validation (n = 50) sets. A radiomic signature (R-signature) was generated using the least absolute shrinkage and selection operator regression. Kaplan-Meier analysis and univariate Cox proportional hazards model were used to determine the association of the R-signature and clinical variables with overall survival (OS) and progression-free survival (PFS). Clinical models and combined clinical-R-signature models were constructed by multivariable Cox regression analysis, respectively. Results The R-signature achieved C-index of 0.666 and 0.684 (training set) and 0.679 and 0.691 (test set) for the prediction of OS and PFS, respectively. For both OS and PFS prediction, the C-index was comparable between the R-signature and clinical model both in the training cohort (OS: C-index = 0.666 vs. 0.719, p = 0.284; PFS: C-index = 0.684 vs. 0.725, p = 0.439) and the validation cohort (OS: C-index = 0.679 vs. 0.665,p = 0.878; PFS: C-index = 0.691vs.0.668,p = 0.803), respectively. The combined clinical-R-signature models achieved better predictive performance than the R-signature in the training cohort (OS: C-index = 0.741 vs.0.666,p = 0.032; PFS: C-index = 0.762 vs. 0.684/7 = 0.020), respectively. The differences did not reach statistical significance in the validation cohort (p> 0.2). Conclusion The radiomic signature extracted from baseline MRI can predict outcomes of patients with ENKTL, and the combination of MRI radiomic signature and clinical predictors may further improve the predictive performance in patients with ENKTL.
机译：目的探讨mri的价值结合临床radiomic特性指标预测患者的生存与淋巴结外侵犯自然杀伤/ t细胞淋巴瘤,nasal-type (ENKTL)。一百年和六十五年ENKTL患者接受预处理MRI被录取。病人被随机分为培训(n =115)和验证(n = 50)集。签名(R-signature)生成的使用至少绝对收缩和选择算子回归。Cox比例风险模型被用来确定R-signature和协会总生存期(OS)和临床变量无进展生存(PFS)。和clinical-R-signature模式相结合由多变量Cox回归分别分析。0.666和0.684的实现c指数(培训集)和0.679和0.691(测试集)分别预测操作系统和PFS。操作系统和PFS预测,c指数是可比的R-signature和临床之间的模型在训练队列(OS: c指数= 0.666 vs。0.719, p = 0.284;p = 0.439)和验证队列(OS: c指数= 0.679和0.665,p = 0.878;分别0.691 vs.0.668, p = 0.803)。clinical-R-signature模式相结合实现比这个更好的预测性能R-signature训练队列(OS: c指数=0.741 vs.0.666, p = 0.032;分别比0.684/7 = 0.020)。没有达到统计学差异意义验证组(p > 0.2)。结论提取radiomic签名患者的基线MRI能预测结果ENKTL, MRI radiomic的结合签名和临床预测可能会进一步改善患者的预测性能

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