ObjectiveThe aim of this study was to evaluate the diagnostic value between contrast-enhanced spectral mammography (CESM) and breast magnetic resonance imaging (MRI) in breast disease.MethodsTwo hundred thirty-five patients who were suspected of having breast abnormalities by clinical examination or mammography underwent CESM and MRI examination. Using histopathologic results as the criterion standard, the diagnostic performance of CESM and MRI was investigated. The areas under receiver operating characteristic curves were applied to analyze diagnostic efficiency. The Pearson correlation coefficients between CESM versus pathology and MRI versus pathology were calculated.ResultsTwo hundred sixty-three breast lesions were found in 235 patients, in which 177 were malignant and 86 were benign. By evaluating the diagnostic value, sensitivity, positive predictive value, negative predictive value, and false-negative rate from CESM examination were comparable to those from MRI (91.5%, 94.7%, 83.7%, and 8.5% vs 91.5%, 90.5%, 82.1%, and 8.5%). Importantly, the accuracy and the specificity were higher for CESM than those for MRI (81% and 89.5% vs 80.2% and 71.7%), whereas the false-positive rate was lower (10.5% vs 19.8%). The areas under receiver operating characteristic curves of CESM and MRI were 0.950 and 0.939, displaying the equivalent diagnostic efficiency (P = 0.48).For the agreement between measurements, mean tumor sizes were 3.1 cm for CESM and 3.4 cm for MRI compared with 3.2 cm on histopathologic results. The Pearson correlation coefficient of CESM versus histopathology (r = 0.774, P = 0.000) was consistent with MRI versus histopathology (r = 0.771, P = 0.000).ConclusionsOur results show better accuracy, specificity, and false-positive rate of CESM in breast cancer detection than MRI. Contrast-enhanced spectral mammography displayed a good correlation with histopathology in assessing the lesion size of breast cancer, which is consistent with MRI.

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Background To establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer. Methods A total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models. Results Seven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer. Conclusions The model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer.