增强CT影像组学模型可术前评估甲状腺乳头状癌颈部中央区淋巴结转移

  目的  探讨临床、CT影像组学及融合模型等6个模型术前预测甲状腺乳头状癌（PTC）中央区淋巴结转移（CLNM）的价值。  方法  纳入103例PTC患者，根据术后病理结果将患者分为无CLNM组（n=50）和CLNM组（n=53），比较组间临床资料及CT征象差异，按7∶3比例随机将各组分为训练集及测试集，提取训练集CT影像组学的特征，构建临床模型、平扫期（NP）模型、动脉期（AP）模型、静脉期（AP）模型、NP+AP+VP模型及融合模型。模型的效能的评价标准为AUC、敏感度及特异性。  结果  两组的性别差异有统计学意义（P=0.002）；在CT征象中，两组间病灶直径（P=0.001）及甲状腺包膜侵犯（P=0.024）的差异有统计学意义。在NP模型、AP模型、VP模型及NP+AP+VP模型这4个组学模型中NP+AP+VP模型预测PTC患者发生CLNM的效能最佳。比较临床模型、NP+AP+VP模型及融合模型的预测效能，融合模型表现最佳，在训练集和测试集中均具有最高的AUC、敏感度和特异性。  结论  融合模型在训练集和测试集中预测PTC患者发生CLNM的效能均最好，有望对术前评估PTC患者CLNM提供有效的辅助手段。 

Contrast-enhance computed tomography radiomics of papillary thyroid cancer to predictive the central lymph node metastases before surgery

  Objective  To explore the value of six models established according to clinical data, CT imaging radiomics, and combining both for predicting preoperatively central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC).  Methods  A total of 103 PTC patients were enrolled and divided into non-CLNM group (n=50) and CLNM group (n=53) according to pathology. The clinical data and CT signatures were compared between groups. The patients in each group were randomly divided into training set and test set according at the ratio of 7:3. CT radiomics features of PTC were selected from training set, and clinical model, non-contrast phase (NP) model, arterial phase (AP) model, venous phase (VP) model, NP + AP + VP model and the combining model were constructed, respectively. AUC, sensitivity and specificity were calculated to evaluate the effectiveness of these six models.  Results  In clinical data, there was significant difference in gender between the two groups (P=0.002). The lesion diameter (P=0.001) and thyroid capsule invasion (P=0.024) of two groups were significant. Among the NP model, AP model, VP model and NP + AP + VP model these four radiomics models, NP + AP + VP model had the best efficacy for predicting CLNM in PTC patients. Compared with the clinical model and NP+AP+VP model, the combining model performed best and had the highest AUC, sensitivity and specificity in both training set and test set.  Conclusion  All of six models, the combining model high efficacy for predicting preoperatively CLNM, which is expected to provide an effective auxiliary method for preoperative the CLNM in PTC patients.