Barroilhet Lisa, Vitonis Allison, Shipp Thomas, Muto Michael, Benacerraf Beryl
Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, MA, USA.
J Clin Ultrasound. 2013 Jun;41(5):269-74. doi: 10.1002/jcu.22014. Epub 2013 Mar 16.
To identify a combination of sonographic features that best predicts ovarian malignancy.
Subjects included 249 women who had a transvaginal sonogram for a pelvic mass at Brigham and Women's Hospital between December 2005 and February 2010. Subjects underwent surgery for removal of the mass and pathologic diagnosis was available. Images were reviewed retrospectively by one sonologist blinded to diagnosis and clinical information. Twelve sonographic features were scored for each mass. The dataset was divided into training (n = 149) and testing (n = 100) sets. Within the training set, a stepwise logistic regression was used to weigh each variable and combination of features to identify those associated with malignancies. Using the results from the logistic regression analyses, we created a three-level risk stratification that was applied to the sonograms of subjects in the testing set to assess its ability to distinguish benign lesions from invasive and borderline cancers.
High risk lesions included all masses with internal vascularity. In our testing set, this feature was present in 9 out of 12 (75%) invasive cancers, 1 out of 6 (16.7%) borderline lesions, and 9 out of 82 (11%) benign masses. The intermediate risk level included lesions with a thick wall or thick septa without internal blood flow. This combination of features identified one additional invasive cancer and 5 out of 6 (83.3%) borderline tumors. Masses with low risk features had a 2/49 (4.0%) incidence of malignancy.
In the absence of high or intermediate risk sonographic features, the risk of malignancy is low.
确定最能预测卵巢恶性肿瘤的超声特征组合。
研究对象包括249名女性,她们于2005年12月至2010年2月期间在布莱根妇女医院因盆腔肿块接受经阴道超声检查。这些研究对象均接受了肿块切除手术且有病理诊断结果。由一名对诊断和临床信息不知情的超声科医生对图像进行回顾性审查。对每个肿块的12项超声特征进行评分。数据集被分为训练集(n = 149)和测试集(n = 100)。在训练集中,采用逐步逻辑回归对每个变量和特征组合进行加权,以确定与恶性肿瘤相关的因素。利用逻辑回归分析的结果,我们创建了一个三级风险分层,并将其应用于测试集研究对象的超声图像,以评估其区分良性病变与浸润性癌和交界性癌的能力。
高风险病变包括所有具有内部血管的肿块。在我们的测试集中,12例浸润性癌中有9例(75%)、6例交界性病变中有1例(16.7%)以及82例良性肿块中有9例(11%)存在这一特征。中风险水平包括具有厚壁或厚间隔且无内部血流的病变。这种特征组合又识别出1例浸润性癌和6例交界性肿瘤中的5例(83.3%)。具有低风险特征的肿块恶性肿瘤发生率为2/49(4.0%)。
在没有高风险或中风险超声特征的情况下,恶性肿瘤的风险较低。