Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
J Clin Ultrasound. 2022 Jun;50(5):675-684. doi: 10.1002/jcu.23205. Epub 2022 Apr 27.
To explore the value of ultrasonic multimodality imaging for characterizing nonpuerperal mastitis (NPM) lesions and feasibility of distinguishing different subtypes.
Thirty-eight NPM lesions were assessed using conventional ultrasonography (US), strain elastography (SE), and contrast-enhanced ultrasound (CEUS). The lesions were confirmed pathologically and classified as granulomatous lobular mastitis (GLM), plasma cell mastitis (PCM), or nonspecific mastitis (NSM). Furthermore, diagnostic indicators were evaluated. The diagnostic performances of the modalities were compared using the area under the receiver operating characteristic curve (AUC).
The overall morphological features on US differed significantly between the GLM and PCM groups (p = 0.002). Lesion size (≤10 mm) (p = 0.003) and mean SE score (p = 0.001) differed significantly between the PCM and NSM groups. The frequent NPM characteristic on CEUS was hyperenhancement with (or without) increased lesion size; intergroup differences were not significant. Breast Imaging Reporting and Data System > 3 was considered to indicate malignancy; accordingly, the accuracy of US alone, US with CEUS, and US with SE was 10.5%, 21.1%, and 65.8%, respectively. Moreover, the AUC for US with SE for classifying GLM and PCM was 0.616.
CEUS cannot accurately classify NPM subtypes, while US and SE are valuable for classification.
探讨超声多模态成像在非产褥期乳腺炎（NPM）病变特征分析中的价值，以及区分不同亚型的可行性。
对 38 例 NPM 病变进行常规超声（US）、应变弹性成像（SE）和超声造影（CEUS）检查。对病灶进行病理证实并分为肉芽肿性小叶乳腺炎（GLM）、浆细胞性乳腺炎（PCM）和非特异性乳腺炎（NSM）。进一步评估诊断指标。采用受试者工作特征曲线（ROC）下面积（AUC）比较各模态的诊断效能。
GLM 和 PCM 组 US 整体形态特征差异有统计学意义（p=0.002）。病变大小（≤10mm）（p=0.003）和平均 SE 评分（p=0.001）在 PCM 和 NSM 组之间差异有统计学意义。CEUS 上 NPM 的常见特征为高增强（伴或不伴）病灶增大；组间差异无统计学意义。乳腺影像报告和数据系统（BI-RADS）>3 被认为提示恶性；因此，单独 US、US 联合 CEUS 和 US 联合 SE 的准确率分别为 10.5%、21.1%和 65.8%。此外，US 联合 SE 对 GLM 和 PCM 的分类 AUC 为 0.616。
CEUS 不能准确分类 NPM 亚型，而 US 和 SE 对分类有价值。
