Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Ultrasound, Jiangsu Cancer Hospital, Nanjing, China.
Br J Radiol. 2021 Sep 1;94(1125):20210518. doi: 10.1259/bjr.20210518. Epub 2021 Jul 28.
This study aimed to develop a model to predict the risk of malignancy in solid renal parenchymal lesions based on the imaging features of combined conventional and contrast-enhanced ultrasound (CEUS).
A retrospective review was performed among patients with focal solid renal parenchymal lesions on ultrasound images. Ultrasound features were characterized by two experienced radiologists independently. A multiple logistic regression analysis was performed to determine the most relevant features and to estimate the risk of malignancy. Scoring and counting methods were developed based on the most relevant features. The diagnostic performance was evaluated by the sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC).
A total of 519 renal lesions were included in this study. The conventional ultrasound features of diameter, echogenicity, hypoechoic rim and the CEUS feature of heterogeneity were identified as the most relevant features for prediction of malignancy. The sensitivity and specificity for the logistic regression model, the scoring method and the counting method were 95.3 and 93.4%, 93.8 and 87.8%, 88.8 and 93.9%, respectively. The logistic model had the best performance for diagnosing malignant renal lesions with AUC of 0.978, compared with the scoring method and the counting method with AUCs of 0.958 and 0.965.
The combination of contrast-enhanced ultrasound with conventional ultrasound improved the diagnostic performance of solid renal lesions based on the logistic regression model.
In this study, we revealed that the combination of CEUS and conventional ultrasound provided higher accuracy for diagnosing malignant renal tumors.
本研究旨在基于常规超声和超声造影（CEUS）联合成像特征，建立预测肾脏实体占位性病变恶性风险的模型。
本研究回顾性分析了超声图像上存在局灶性肾脏实性占位性病变的患者。由两位有经验的放射科医生独立对超声特征进行描述。采用多因素逻辑回归分析确定最相关的特征，并评估恶性肿瘤的风险。基于最相关的特征制定评分和计数方法。通过灵敏度、特异度、阳性预测值、阴性预测值和受试者工作特征曲线下面积（AUC）评估诊断效能。
本研究共纳入 519 个肾脏病变。常规超声特征（直径、回声强度、低回声晕环）和 CEUS 特征（异质性）被确定为预测恶性肿瘤的最相关特征。逻辑回归模型、评分法和计数法的灵敏度和特异度分别为 95.3%和 93.4%、93.8%和 87.8%、88.8%和 93.9%。与评分法和计数法相比，逻辑模型对恶性肾脏病变的诊断效能最高，AUC 为 0.978。
CEUS 与常规超声联合应用可提高基于逻辑回归模型诊断肾脏实体占位性病变的效能。
本研究揭示了 CEUS 和常规超声联合应用可为诊断肾脏恶性肿瘤提供更高的准确性。
