Gutierrez Robert L, DeMartini Wendy B, Eby Peter R, Kurland Brenda F, Peacock Sue, Lehman Constance D
Department of Radiology, University of Washington Medical Center, University of Washington, Seattle, WA.
AJR Am J Roentgenol. 2009 Oct;193(4):994-1000. doi: 10.2214/AJR.08.1983.
The purpose of our study was to evaluate the predictive features of BI-RADS lesion characteristics and the risk of malignancy for mammographically and clinically occult lesions detected initially on breast MRI.
We reviewed 1,523 consecutive breast MRI examinations performed from January 1, 2003, to June 30, 2005, to identify all lesions initially detected on MRI and assessed as BI-RADS 4 or 5 for which the patient underwent subsequent imaging-guided needle or excisional biopsy. BI-RADS lesion features were recorded for each case, and the risk of malignancy was assessed using generalized estimating equations. Separate multivariate models were constructed for lesions classified as masses.
Included in the analysis were 258 suspicious lesions in 196 women. Among all lesions, those of 1 cm or greater were significantly more often malignant (50/147, 34%) than lesions of less than 1 cm (22/111, 20%; odds ratio, 2.09; 95% CI, 1.13-3.83). For masses, size, BI-RADS margin, and enhancement pattern predicted malignancy. In multivariate analysis of combinations of features, masses of 1 cm or greater with heterogeneous enhancement and irregular margins had a 68% probability of malignancy. Masses of 1 cm or greater with smooth margins and homogeneous enhancement had the lowest predicted probability of malignancy of 3%. BI-RADS descriptors and size were not significant predictors of malignancy for nonmasslike enhancement (NMLE).
Combinations of BI-RADS lesion descriptors can predict the probability of malignancy for breast MRI masses but not for NMLE. If our model is validated, masses with a low probability of malignancy may be eligible for short-interval follow-up rather than biopsy. Further research focused on predictive features of NMLE is needed.
我们研究的目的是评估乳腺影像报告和数据系统(BI-RADS)病变特征对最初在乳腺磁共振成像(MRI)上发现的乳腺钼靶和临床隐匿性病变的预测特征以及恶性风险。
我们回顾了2003年1月1日至2005年6月30日期间连续进行的1523例乳腺MRI检查,以识别所有最初在MRI上发现并评估为BI-RADS 4或5类的病变,这些患者随后接受了影像引导下的穿刺或切除活检。记录每个病例的BI-RADS病变特征,并使用广义估计方程评估恶性风险。针对分类为肿块的病变构建了单独的多变量模型。
分析纳入了196名女性的258个可疑病变。在所有病变中,直径1厘米或更大者的恶性率(50/147,34%)显著高于直径小于1厘米者(22/111,20%;优势比,2.09;95%置信区间,1.13 - 3.83)。对于肿块,大小、BI-RADS边缘和强化模式可预测恶性。在特征组合的多变量分析中,直径1厘米或更大、强化不均匀且边缘不规则的肿块恶性概率为68%。直径1厘米或更大、边缘光滑且强化均匀的肿块预测恶性概率最低,为3%。对于非肿块样强化(NMLE),BI-RADS描述符和大小不是恶性的显著预测因素。
BI-RADS病变描述符的组合可预测乳腺MRI肿块的恶性概率,但不能预测NMLE的恶性概率。如果我们的模型得到验证,恶性概率低的肿块可能适合短期随访而非活检。需要针对NMLE的预测特征开展进一步研究。