The aim of this study was to evaluate diagnostic performance of radiomics models of MRI in the detection of differentiation degree (DD) and lymph node metastases (LNM) of extrahepatic cholangiocarcinoma (ECC). We retrospectively enrolled 100 patients with ECC confirmed by pathology from January 2011 to December 2018. Three hundred radiomics features were extracted from each region of interest using MaZda software. Next, the radiomics model was developed by incorporating the optimal radiomics signatures and ADC values of tumors to predict DD (model A) and LNM (model B) of ECC, respectively, through the random forest algorithm. After which, the performance of the radiomics models were further evaluated. The model A showed better performance in both training and testing cohorts to discriminate high and medium-low differentiation groups of ECC, with an average AUC of 0.78 and 0.80, respectively. The model B also yielded the good average AUC of 0.80 and 0.90 to predict the LNM of ECC in training and testing cohorts. The radiomics models based on MRI performed well in predicting DD and LNM of ECC and have significant potential in clinical noninvasive diagnosis and in the prediction of ECC.

中文翻译：

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磁共振成像的放射线学模型可预测肝外胆管癌的病理分级和淋巴结转移。

这项研究的目的是评估MRI放射学模型在肝外胆管癌（ECC）的分化程度（DD）和淋巴结转移（LNM）检测中的诊断性能。我们回顾性研究了2011年1月至2018年12月经病理证实的ECC患者100例。使用MaZda软件从每个目标区域提取了300个放射学特征。接下来，通过结合最佳肿瘤的放射学特征和肿瘤的ADC值，通过随机森林算法分别预测ECC的DD（模型A）和LNM（模型B），从而建立了放射学模型。之后，进一步评估了放射线模型的性能。模型A在训练和测试队列中可以更好地区分ECC的高分化和中低分化组，平均AUC分别为0.78和0.80。模型B还产生了0.80和0.90的良好平均AUC，以预测训练和测试队列中ECC的LNM。基于MRI的放射学模型在预测ECC的DD和LNM方面表现良好，并且在临床非侵入性诊断和ECC预测中具有巨大的潜力。