Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures

影响因子： 3.962PMID：32500196期刊年卷：Eur Radiol 2020 Jun 04;DOI：10.1007/s00330-020-06962-y作者列表: Mes SW, van Velden FHP, Peltenburg B, Peeters CFW, Te Beest DE, van de Wiel MA, Mekke J, Mulder DC, Martens RM, Castelijns JA, Pameijer FA, de Bree R, Boellaard R, Leemans CR, Brakenhoff RH, de Graaf P,

OBJECTIVES:Head and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic images, termed radiomics. The aim of this study was to develop and validate MRI-based radiomic prognostic models in oral and oropharyngeal cancer.

MATERIALS AND METHODS:Native T1-weighted images of four independent, retrospective (2005-2013), patient cohorts (n = 102, n = 76, n = 89, and n = 56) were used to delineate primary tumors, and to extract 545 quantitative features from. Subsequently, redundancy filtering and factor analysis were performed to handle collinearity in the data. Next, radiomic prognostic models were trained and validated to predict overall survival (OS) and relapse-free survival (RFS). Radiomic features were compared to and combined with prognostic models based on standard clinical parameters. Performance was assessed by integrated area under the curve (iAUC).

RESULTS:In oral cancer, the radiomic model showed an iAUC of 0.69 (OS) and 0.70 (RFS) in the validation cohort, whereas the iAUC in the oropharyngeal cancer validation cohort was 0.71 (OS) and 0.74 (RFS). By integration of radiomic and clinical variables, the most accurate models were defined (iAUC oral cavity, 0.72 (OS) and 0.74 (RFS); iAUC oropharynx, 0.81 (OS) and 0.78 (RFS)), and these combined models outperformed prognostic models based on standard clinical variables only (p < 0.001).

CONCLUSIONS:MRI radiomics is feasible in HNSCC despite the known variability in MRI vendors and acquisition protocols, and radiomic features added information to prognostic models based on clinical parameters.

KEY POINTS:• MRI radiomics can predict overall survival and relapse-free survival in oral and HPV-negative oropharyngeal cancer. • MRI radiomics provides additional prognostic information to known clinical variables, with the best performance of the combined models. • Variation in MRI vendors and acquisition protocols did not influence performance of radiomic prognostic models.

用MRI放射组学征象预测头颈部鳞状细胞癌的预后

目的：头颈部鳞状细胞癌(HNSCC)在肿瘤间表现出明显的异质性，这种异质性可以通过从诊断图像中提取的各种定量特征来捕捉，称为放射组学。本研究的目的是建立和验证基于MRI的口腔和口咽癌放射组学预后模型。

材料和方法：使用4个独立的回顾性(2005-2013年)患者队列(n=102，n=76，n=89，n=56)的自然T1加权图像来勾画原发肿瘤，并从中提取545个定量特征。随后，进行冗余过滤和因子分析来处理数据中的共线性。接下来，对放射组学预后模型进行训练和验证，以预测总生存期(OS)和无复发生存期(RFS)。放射组学特征与基于标准临床参数的预后模型进行比较和结合。采用曲线下积分面积(iAUC)评价治疗效果。

结果：在口腔癌中，放射组学模型在验证队列中的iAUC分别为0.69(OS)和0.70(RFS)，而在口咽癌验证队列中的iAUC分别为0.71(OS)和0.74(RFS)。通过放射学和临床变量的整合，定义了最准确的模型(iAUC口腔，0.72(OS)和0.74(RFS)；iAUC口咽，0.81(OS)和0.78(RFS))，这些组合模型的预测效果优于仅基于标准临床变量的模型(p< 0.001)。

结论：MRI放射组学在HNSCC中是可行的，尽管MRI供应商和采集方案存在已知的差异，而且基于临床参数的放射组学特征为预后模型增加了信息。

要点：·MRI放射组学可以预测口腔癌和HPV阴性口咽癌的总体生存率和无复发生存率。·MRI放射组学为已知的临床变量提供了额外的预后信息，是组合模型中性能最佳的。·MRI供应商和采集方案的差异不影响放射组学预后模型的性能。