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.

Objectives Nodular sclerosing adenoses (NSAs) and malignant tumors (MTs) may coexist and are often classified into the same Breast Imaging Reporting and Data System (BI‐RADS) category. We aimed to build and validate an ultrasound‐based nomogram to distinguish MT from NSA for building a precise sequence of biopsies. Materials and Methods The training cohort included 156 patients (156 masses) with NSA or MT at one study institution. We used best subset regression to determine the predictors for building a nomogram from ultrasonic characteristics and patients' age. Model performance and clinical utility were evaluated using Brier score, concordance (C)‐index, calibration curve, and decision curve analysis. The independent validation cohort consisted of 162 patients (162 masses) from a separate institution. Results Through best subset regression, we selected 6 predictors to develop nomogram: age, calcification, echogenic rim, vascularity distribution, tumor size, and thickness of breast parenchyma. Brier score and C‐index of the nomogram in the training cohort were 0.068 and 0.967 (95% confidence interval [CI]: 0.941–0.993), respectively. In addition, calibration curve demonstrated good agreement between prediction and pathological result. In the validation cohort, the nomogram still obtained a favorable C‐index score of 0.951 (95% CI: 0.919–0.983) and fine calibration. Decision curve analysis showed that the model was clinically useful. Conclusions If multiple NSA and MT masses are present in the same patient and are classified into the same BI‐RADS category, our nomogram can be used as a supplement to the BI‐RADS category for accurate biopsy of the mass most likely to be MT.

Background: The American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) used with ultrasonography cannot guide the individual management of solid breast tumors, but preoperative core biopsy categories (CBCs) can. We aimed to use machine learning to analyze clinical and ultrasonic features for predicting CBCs and to aid in the development of a new ultrasound (US) imaging reporting system for solid tumors of the breast.Methods: This retrospective study included women with solid breast tumors who underwent US-guided core needle biopsy from March 1, 2019, to December 31, 2019. All patients were randomly assigned to a training or validation cohort (7:3 ratio). CBC was predicted using 5 machine learning models: random forest (RF), support vector machine (SVM), k-nearest-neighbor (KNN), multilayer perceptron (MLP), and ridge regression (RR). In the validation cohort, the area under the curve (AUC) and accuracy were ascertained for every algorithm. Based on AUC values, the optimal algorithm was determined, and the features' importance was depicted.Results: A total of 1,082 female patients were included (age range, 12-96 years; mean age ± standard deviation, 42.22±13.37 years). The proportion of the 4 CBCs was 4% (44/1,185) for the B1 group, 60% (714/1,185) for the B2 group, 5% (57/1,185) for the B3 group, and 31% (370/1,185) for the B5 group. In the validation cohort, AUCs of the optimal algorithm constructed RF were 0.78, 0.88, 0.64, and 0.92 for B1, B2, B3, and B5, respectively, with an accuracy of 0.82.Conclusions: Machine learning could strongly predict CBC, particularly in B2 and B5 categories of solid breast tumors, with RF being the optimal machine learning model.