DataSheet_2_Using Ultrasound-Based Multilayer Perceptron to Differentiate Early Breast Mucinous Cancer and its Subtypes From Fibroadenoma.docx

ObjectivesMucinous breast cancer (MBC), particularly pure MBC (pMBC), often tend to be confused with fibroadenoma (FA) due to their similar images and firm masses, so some MBC cases are misdiagnosed to be FA, which may cause poor prognosis. We analyzed the ultrasonic features and aimed to identify the ability of multilayer perceptron (MLP) to classify early MBC and its subtypes and FA.
Materials and MethodsThe study consisted of 193 patients diagnosed with pMBC, mMBC, or FA. The area under curve (AUC) was calculated to assess the effectiveness of age and 10 ultrasound features in differentiating MBC from FA. We used the pairwise comparison to examine the differences among MBC subtypes (pure and mixed types) and FA. We utilized the MLP to differentiate MBC and its subtypes from FA.
ResultsThe nine features with AUCs over 0.5 were as follows: age, echo pattern, shape, orientation, margin, echo rim, vascularity distribution, vascularity grade, and tumor size. In subtype analysis, the significant differences were obtained in 10 variables (p-value range, 0.000–0.037) among pMBC, mMBC, and FA, except posterior feature. Through MLP, the AUCs of predicting MBC and FA were both 0.919; the AUCs of predicting pMBC, mMBC, and FA were 0.875, 0.767, and 0.927, respectively.
ConclusionOur study found that the MLP models based on ultrasonic characteristics and age can well distinguish MBC and its subtypes from FA. It may provide a critical insight into MBC preoperative clinical management.

研究目的：黏液性乳腺癌（mucinous breast cancer, MBC），尤其是纯黏液性乳腺癌（pure MBC, pMBC），因其影像学表现相似且肿块质地偏硬，常与乳腺纤维腺瘤（fibroadenoma, FA）相混淆，部分MBC病例会被误诊为FA，进而可能导致不良预后。本研究分析其超声特征，旨在明确多层感知机（multilayer perceptron, MLP）对早期MBC及其亚型与FA的分类能力。
材料与方法：本研究纳入193例分别被诊断为pMBC、混合型黏液性乳腺癌（mixed MBC, mMBC）或FA的患者。以曲线下面积（area under curve, AUC）评估年龄及10项超声特征区分MBC与FA的效能。采用两两比较法检验MBC亚型（纯型与混合型）与FA之间的差异。利用MLP实现MBC及其亚型与FA的鉴别诊断。
结果：9项AUC大于0.5的特征依次为年龄、回声模式、形态、方位、边缘、回声晕、血流分布、血流分级及肿瘤大小。亚型分析显示，除后方回声特征外，pMBC、mMBC与FA三组间在其余10项变量中均存在显著差异（P值范围：0.000~0.037）。通过MLP模型，区分MBC与FA的AUC均为0.919；区分pMBC、mMBC与FA的AUC分别为0.875、0.767及0.927。
结论：本研究表明，基于超声特征与年龄构建的MLP模型可有效区分MBC及其亚型与FA，可为MBC术前临床管理提供重要参考依据。