Zhang Yu-Qing, Chen Jia-Hui, Zhu Tian-Tong, Zhao Ao-Xue, Zhuang Lian-Ting, Lu Chun-Yu, Huang Ying
Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China.
Ann Transl Med. 2022 Oct;10(20):1108. doi: 10.21037/atm-22-4460.
Different from conventional ultrasound, contrast-enhanced ultrasound (CEUS) is better in observing microperfusion. For atypical focal adenomyosis and uterine leiomyomas that are difficult to be distinguished by conventional ultrasound, this study aims to further improve the differential diagnosis performance by using CEUS model.
After screening the cases with difficulties in identifying focal myometrium lesions through conventional ultrasound, the number of cases covered in the focal adenomyosis group and leiomyomas group were 60 and 30 in derivation cohort, 14 and 7 in validation cohort. The qualitative and quantitative characteristics of CEUS were analyzed according to the surgical pathology. The qualitative characteristics include: the enhancement level based on the myometrium, the contrast enhancement pattern, the enhanced time of the lesion based on the myometrium, post-contrast lesion border, the distribution of the contrast agent, and the wash-out time based on the myometrium. The quantitative characteristics include: arrive time (AT), time to peak (TTP), peak intensity (PI), ΔAT, ΔTTP, ΔPI, |ΔAT|, |ΔTTP|, |ΔPI| and lesion temporal variability. Multiple logistic regression analysis was used to screen the independent risk factors, and a risk prediction model for the differential diagnosis of the two diseases was established. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the diagnostic performance of the model. The validation cohort was used to further evaluate the diagnostic performance of the model.
Through the multivariate analysis, it concluded that short-term vessels first enhanced enhancement mode, unclear boundary, lesion temporal variability under CEUS >9.5 s, uneven contrast agent distribution could be independent risk factors for the diagnosis of adenomyosis [AUC =0.908, 95% confidence interval (CI): 0.833-0.982]. We also determined the sensitivity (98.33%), specificity (70.00%), positive predictive value (PPV) (86.76%), negative predictive value (NPV) (95.45%), and accuracy (87.78%) of this model. Based on pathological diagnosis, the sensitivity and specificity of the model in the validation cohort were both 85.71%, with NPV of 75% and PPV of 92.3%. The area under the ROC curve was 0.898 (95% CI: 0.742-1.000).
The establishment of CEUS model has certain clinical application value in differentiating atypical focal adenomyosis from leiomyomas.
与传统超声不同，超声造影（CEUS）在观察微灌注方面表现更佳。对于传统超声难以鉴别的非典型局灶性子宫腺肌病和子宫肌瘤，本研究旨在通过使用CEUS模型进一步提高鉴别诊断性能。
通过传统超声筛选出难以识别局灶性肌层病变的病例，推导队列中子宫腺肌病组和子宫肌瘤组纳入的病例数分别为60例和30例，验证队列中分别为14例和7例。根据手术病理分析CEUS的定性和定量特征。定性特征包括：基于肌层的增强水平、造影剂增强模式、基于肌层的病变增强时间、造影后病变边界、造影剂分布以及基于肌层的消退时间。定量特征包括：到达时间（AT）、峰值时间（TTP）、峰值强度（PI）、ΔAT、ΔTTP、ΔPI、|ΔAT|、|ΔTTP|、|ΔPI|和病变时间变异性。采用多因素logistic回归分析筛选独立危险因素，建立两种疾病鉴别诊断的风险预测模型。采用受试者操作特征（ROC）曲线下面积（AUC）评估模型的诊断性能。使用验证队列进一步评估模型的诊断性能。
通过多因素分析得出，CEUS下短期血管先增强的增强模式、边界不清、病变时间变异性>9.5 s、造影剂分布不均匀可作为子宫腺肌病诊断的独立危险因素[AUC =0.908，95%置信区间（CI）：0.833 - 0.982]。我们还确定了该模型的敏感性（98.33%）、特异性（70.00%）、阳性预测值（PPV）（86.76%）、阴性预测值（NPV）（95.45%）和准确性（87.78%）。基于病理诊断，该模型在验证队列中的敏感性和特异性均为85.71%，NPV为75%，PPV为92.3%。ROC曲线下面积为0.898（95% CI：0.742 - 1.000）。
CEUS模型的建立在鉴别非典型局灶性子宫腺肌病与子宫肌瘤方面具有一定的临床应用价值。
