Objective: To evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) combined with conventional ultrasound (US) of axillary lymph nodes (ALNs) in predicting metastatic ALNs in patients with breast cancer. Methods: This retrospective study included 259 patients with breast cancer who underwent conventional US and CEUS. The parameters and patterns evaluated on conventional US included short axis diameter (S), long axis/short axis (L/S) ratio, cortical thickness, resistive index (RI), lymph node (LN) morphology of grey-scale US, hilum and vascular pattern. Meanwhile, enhancement pattern, wash-in time, time to peak (TP), maximum signal intensity, and duration of contrast enhancement were evaluated on CEUS. Univariate and multiple logistic regression analyses were performed to identify independent factors of ALN status. Three models (conventional US, CEUS, and combined parameters) were established. Receiver operating characteristic (ROC) curves were applied to evaluate the accuracy of the three predictive models. Results: On conventional axillary US, LN morphology and vascular pattern were independent factors in predicting metastatic ALNs. On CEUS, maximum signal intensity, duration of contrast enhancement, and TP were independent factors in predicting metastatic ALNs. When combining conventional US and CEUS features, five independent factors obtained from the conventional US and CEUS were associated with ALN status. ROC curve analysis showed that the use of CEUS markers combined with conventional US features (AUC = 0.965) was superior to the use of CEUS markers (AUC = 0.936) and conventional US features alone (AUC = 0.851). Conclusion: Combining conventional US and CEUS features can enable discrimination of ALN status better than the use of CEUS and conventional US features alone. Advances in knowledge: The axillary lymph node status in breast cancer patients impacts the treatment decision. Our ultrasonic data demonstrated that CEUS features of ALNs in breast cancer patients could be image markers for predicting ALN status. Combining conventional US and CEUS features of ALNs can improve specificity discrimination of ALN status better than the use of CEUS and the conventional US features alone, which will help the treatment planning optimization.
Objective: This study aimed to develop a radiomics nomogram that incorporates radiomics, conventional ultrasound (US) and clinical features in order to differentiate triple-negative breast cancer (TNBC) from fibroadenoma. Methods: A total of 182 pathology-proven fibroadenomas and 178 pathology-proven TNBCs, which underwent preoperative US examination, were involved and randomly divided into training (n = 253) and validation cohorts (n = 107). The radiomics features were extracted from the regions of interest of all lesions, which were delineated on the basis of preoperative US examination. The least absolute shrinkage and selection operator model and the maximum relevance minimum redundancy algorithm were established for the selection of tumor status-related features and construction of radiomics signature (Rad-score). Then, multivariate logistic regression analyses were utilized to develop a radiomics model by incorporating the radiomics signature and clinical findings. Finally, the usefulness of the combined nomogram was assessed by using the receiver operator characteristic curve, calibration curve, and decision curve analysis (DCA). Results: The radiomics signature, composed of 12 selected features, achieved good diagnostic performance. The nomogram incorporated with radiomics signature and clinical data showed favorable diagnostic efficacy in the training cohort (AUC 0.986, 95% CI, 0.975–0.997) and validation cohort (AUC 0.977, 95% CI, 0.953–1.000). The radiomics nomogram outperformed the Rad-score and clinical models (p < 0.05). The calibration curve and DCA demonstrated the good clinical utility of the combined radiomics nomogram. Conclusion: The radiomics signature is a potential predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models. Advances in knowledge: Recent advances in radiomics-based US are increasingly showing potential for improved diagnosis, assessment of therapeutic response and disease prediction in oncology. Rad-score is an independent predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models.
In the current study, we sought to evaluate the diagnostic efficacies of conventional ultrasound (US), contrast-enhanced US (CEUS), combined US and CEUS and magnetic resonance imaging (MRI) in detecting focal solid breast lesions. Totally 117 patients with 120 BI-RADS category 4A-5 breast lesions were evaluated by conventional US and CEUS, and MRI, respectively. SonoVue was used as contrast agent in CEUS and injected as an intravenous bolus; nodule scan was performed 4 minutes after bolus injection. A specific sonographic quantification software was used to obtain color-coded maps of perfusion parameters for the investigated lesion, namely the time-intensity curve. The pattern of contrast enhancement and related indexes regarding the time-intensity curve were used to describe the lesions, comparatively with pathological results. Histopathologic examination revealed 46 benign and 74 malignant lesions. Sensitivity, specificity, and accuracy of US in detecting malignant breast lesions were 90.14%, 95.92%, and 92.52%, respectively. Meanwhile, CE-MRI showed sensitivity, specificity, and accuracy of 88.73%, 95.92%, and 91.67%, respectively. The area under the ROC curve for combined US and CEUS in discriminating benign from malignant breast lesions was 0.936, while that of MRI was 0.923, with no significant difference between them, as well as among groups. The time-intensity curve of malignant hypervascular fibroadenoma and papillary lesions mostly showed a fast-in/fast-out pattern, with no good correlation between them (kappa<0.20). In conclusion, the combined use of conventional US and CEUS displays good agreement with MRI in differentiating benign from malignant breast lesions.
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