Background:Preoperative non-invasive histologic grading of breast cancer is essential. This study aimed to explore the effectiveness of a machine learning classification method based on Dempster-Shafer (D-S) evidence theory for the histologic grading of breast cancer.Methods:A total of 489 contrast-enhanced magnetic resonance imaging (MRI) slices with breast cancer lesions (including 171 grade Ⅰ, 140 grade Ⅱ, and 178 grade Ⅲ lesions) were used for analysis. All the lesions were segmented by two radiologists in consensus. For each slice, the quantitative pharmacokinetic parameters based on a modified Tofts model and the textural features of the segmented lesion on the image were extracted. Principal component analysis was then used to reduce feature dimensionality and obtain new features from the pharmacokinetic parameters and texture features. The basic confidence assignments of different classifiers were combined using D-S evidence theory based on the accuracy of three classifiers: support vector machine (SVM), Random Forest, and k-nearest neighbor (KNN). The performance of the machine learning techniques was evaluated in terms of accuracy, sensitivity, specificity, and the area under the curve.Results:The three classifiers showed varying accuracy across different categories. The accuracy of using D-S evidence theory in combination with multiple classifiers reached 92.86%, which was higher than that of using SVM (82.76%), Random Forest (78.85%), or KNN (87.82%) individually. The average area under the curve of using the D-S evidence theory combined with multiple classifiers reached 0.896, which was larger than that of using SVM (0.829), Random Forest (0.727), or KNN (0.835) individually.Conclusions:Multiple classifiers can be effectively combined based on D-S evidence theory to improve the prediction of histologic grade in breast cancer.

BackgroundAmide proton transfer (APT) imaging has been increasingly applied in tumor characterization. However, its value in evaluating breast cancer remains undetermined.PurposeTo assess the diagnostic performance of APT imaging in breast cancer and its association with prognostic histopathologic characteristics.Study TypeProspective.SubjectsEighty‐four patients with breast lesions.Field Strength/SequenceA 3.0 T/single‐shot fast spin echo APT imaging.AssessmentAPTw signal in breast lesion was quantified. Lesion malignancy, T stage, grades, Ki‐67 index, molecular biomarkers (estrogen receptor [ER] expression, progesterone receptor [PR] expression, human epidermal growth factor receptor [HER‐2] expression), molecular subtypes (luminal A, luminal B, triple negative, and HER‐2 enriched) were determined.Statistical TestsStudent t‐test, one‐way analysis of variance, receiver operating characteristic analysis, and Pearson's correlation with P < 0.05 as statistical significance.ResultsAPTw signal was significantly higher in malignant lesions (1.55% ± 1.24%) than in benign lesions (0.54% ± 1.13%), and in grade III lesions than in grade II lesions (1.65% ± 0.84% vs. 0.96% ± 0.96%), and in T2‐ (1.57% ± 0.64%) and T3‐stage lesions (1.54% ± 0.63%) than in T1‐stage lesions (0.81% ± 0.64%) for invasive breast carcinoma of no special type. APTw signal significantly correlated with Ki‐67 index (r = 0.364) but showed no significant difference in groups of ER (P = 0.069), PR (P = 0.069), HER‐2 (P = 0.961), and among molecular subtypes (P = 0.073).Data ConclusionAPT imaging shows potential in differentiating breast lesion malignancy and associates with prognosis‐related tumor grade, T stage, and proliferative activity.Evidence Level2Technical EfficacyStage 2