Department of Ultrasound, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin, Heilongjiang Province, China.
Department of Pathology, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin, Heilongjiang Province, China.
BMC Cancer. 2022 Aug 28;22(1):929. doi: 10.1186/s12885-022-10034-3.
Automated breast ultrasound (ABUS) is a useful choice in breast disease diagnosis. The axillary lymph node (ALN) status is crucial for predicting the clinical classification and deciding on the treatment of early-stage breast cancer (EBC) and could be the primary indicator of locoregional recurrence. We aimed to establish a prediction model using ABUS features of primary breast cancer to predict ALN status.
A total of 469 lesions were divided into the axillary lymph node metastasis (ALNM) group and the no ALNM (NALNM) group. Univariate analysis and multivariate analysis were used to analyze the difference of clinical factors and ABUS features between the two groups, and a predictive model of ALNM was established. Pathological results were as the gold standard.
Ki-67, maximum diameter (MD), posterior feature shadowing or enhancement and hyperechoic halo were significant risk factors for ALNM in multivariate logistic regression analysis (P < 0.05). The four risk factors were used to build the predictive model, and it achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.791 (95% CI: 0.751, 0.831). The accuracy, sensitivity and specificity of the prediction model were 72.5%, 69.1% and 75.26%. The positive predictive value (PPV) and negative predictive value (NPV) were 66.08% and 79.93%, respectively. Distance to skin, MD, margin, shape, internal echo pattern, orientation, posterior features, and hyperechoic halo showed significant differences between stage I and stage II (P < 0.001).
ABUS features and Ki-67 can meaningfully predict ALNM in EBC and the prediction model may facilitate a more effective therapeutic schedule.
自动乳腺超声（ABUS）是一种在乳腺疾病诊断中很有用的选择。腋窝淋巴结（ALN）状态对预测临床分类和决定早期乳腺癌（EBC）的治疗至关重要，并且可能是局部区域复发的主要指标。我们旨在建立一个使用原发性乳腺癌 ABUS 特征预测 ALN 状态的预测模型。
共 469 个病变分为腋窝淋巴结转移（ALNM）组和无 ALNM（NALNM）组。使用单因素分析和多因素分析分析两组之间临床因素和 ABUS 特征的差异，并建立 ALNM 的预测模型。病理结果为金标准。
Ki-67、最大直径（MD）、后特征阴影或增强和高回声晕是多变量逻辑回归分析中 ALNM 的显著危险因素（P<0.05）。四个危险因素用于构建预测模型，其获得的接收器工作特征（ROC）曲线下面积（AUC）为 0.791（95%CI：0.751，0.831）。预测模型的准确性、敏感性和特异性分别为 72.5%、69.1%和 75.26%。阳性预测值（PPV）和阴性预测值（NPV）分别为 66.08%和 79.93%。距离皮肤、MD、边缘、形状、内部回声模式、方向、后特征和高回声晕在 I 期和 II 期之间有显著差异（P<0.001）。
ABUS 特征和 Ki-67 可以对 EBC 中的 ALNM 进行有意义的预测，预测模型可能有助于制定更有效的治疗方案。
