分析无灌注缺损型颈部淋巴结的超声特征，构建淋巴结良恶性预测模型，探讨该模型对良恶性病变的诊断价值。

回顾性分析2013年1月至2020年5月于浙江大学医学院附属杭州市胸科医院就诊的57例患者共计61个颈部淋巴结，所有病变淋巴结均经常规超声及超声造影（CEUS）检查，且CEUS均未显示灌注缺损征象。依据病理及Gene X-pert MTB/RIF检查结果将淋巴结分为良性组45个、恶性组16个，比较2组淋巴结长径与短径比值（L/S）、皮质回声、淋巴门、钙化灶、淋巴结后方回声、彩色多普勒血流成像（CDFI）及CEUS灌注特征，采用单因素χ²检验或Fisher检验及多因素Logistic回归分析，筛选恶性病变的危险因素，建立Logistic回归模型，绘制ROC曲线评价该模型预测能力。

常规超声及CEUS特征单因素分析显示：L/S（χ²=5.785，P＜0.05）、淋巴门（χ²=4.419，P＜0.05）、钙化灶（P＜0.05）、CDFI（χ²=16.775，P＜0.001）、灌注模式（χ²=13.090，P＜0.001）和淋巴门增强（χ²=15.993，P＜0.001），2组间比较差异有统计学意义。二分类Logistic回归分析显示CDFI非门型、向心性灌注模式、淋巴门无增强为恶性病变的危险因素，这3项特征变量构建Logistic回归模型，即Y=-4.732+1.795X₄+1.989X₅+2.734X₆，ROC曲线下面积为0.921（95%CI：0.846~0.996），其预测恶性淋巴结的敏感度、特异度、阳性预测值、阴性预测值分别为81.3%、93.3%、81.3%、93.3%。

基于常规超声及CEUS特征构建的预测模型对无灌注缺损型颈部淋巴结良恶性具有较高的鉴别诊断价值，可为临床提供一定的诊断依据。

To analyze the ultrasound features of cervical lymph nodes without perfusion defects, to develop a predictive model for benign and malignant lymph nodes, and to assess the diagnostic value of the model for benign and malignant lesions.

A retrospective analysis was performed on 57 patients with 61 cervical lymph nodes at the Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine during January 2013 to May 2020. All lymph nodes were examined by conventional ultrasound and contrast-enhanced ultrasound (CEUS), which were cervical lymph nodes without perfusion defects. According to the results of pathology and Gene X-pert MTB/RIF examination, the lymph nodes were divided into either a benign group (45 cases) or a malignant group (16 cases), and the long diameter/short diameter (L/S) ratio of lymph nodes, cortex, hilum, calcification, echo, and color Doppler flow imaging (CDFI) and CEUS perfusion characteristics were compared between the two groups. Univariate χ² or Fisher test and multivariate Logistic regression analysis were performed to identify the risk factors for malignant lymph nodes to build a predictive model. ROC curve analysis was performed to assess the performance of the Logistic model.

Univariate analysis showed significant differences in the following parameters between benign group and malignant group: L/S ratio (χ²=5.785, P<0.05), hilum (χ²=4.419, P＜0.05), calcification (P<0.05), CDFI (χ²=16.775, P<0.001), perfusion pattern (χ²=13.090, P＜0.001), and enhancement of the hilum (χ²=15.993, P<0.001). Binary Logistic regression analysis showed that non-portal type of CDFI, centripetal perfusion mode, and non-enhancement of the hilum were risk factors for malignant lesions, and the three characteristic variables were entered into the Logistic regression model to build a predictive model as Y=-4.732+1.795X₄+1.989X₅+2.734X₆. The area under the ROC curve of this model was 0.921 (95% confidence interval: 0.846-0.996). The sensitivity, specificity, positive predictive value, and negative predictive value of the predictive model in the differential diagnosis of benign and malignant diseases were 81.3%, 93.3%, 81.3%, and 93.3%, respectively.

The conventional ultrasound and CEUS feature prediction model has high diagnostic value in differentiating benign from malignant cervical lymph nodes without perfusion defects.