Combining primary tumor features derived from conventional and contrast-enhanced ultrasound facilitates the prediction of positive axillary lymph nodes in Breast Imaging Reporting and Data System category 4 malignant breast lesions

PURPOSE

To determine whether the primary tumor features derived from conventional ultrasound (US) and contrast-enhanced US (CEUS) facilitate the prediction of positive axillary lymph nodes (ALNs) in breast cancer diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4.

METHODS

A total of 240 women with breast cancer who underwent preoperative conventional US, strain elastography, and CEUS between September 2016 and December 2019 were included. The multiple parameters of the primary tumor were obtained, and univariate and multivariate analyses were performed to predict positive ALNs. Then three prediction models (conventional US features, CEUS features, and the combined features) were developed, and the diagnostic performance was evaluated with receiver operating characteristic curves.

RESULTS

On conventional US, the traits of large size and the non-circumscribed margin of the primary tumor were marked as two independent predictors. On CEUS, the features of vessel perforation or distortion and the enhanced range of the primary tumor were marked as two independent predictors for positive ALNs. Three prediction models were then developed: model A (conventional US features), model B (CEUS features), and model C (model A plus B). Model C yielded the highest area under the curve (AUC) of 0.82 [95% confidence interval (CI), 0.75-0.88] compared with model A (AUC 0.74; 95% CI, 0.68-0.81; P = 0.008) and model B (AUC 0.72; 95% CI, 0.65-0.80; P < 0.001) as per the DeLong test.

CONCLUSION

CEUS, as a non-invasive examination technique, can be used to predict ALN metastasis. Combining conventional US and CEUS may produce favorable predictive accuracy for positive ALNs in BI-RADS category 4 breast cancer.

Comparative analysis between synthetic mammography reconstructed from digital breast tomosynthesis and full-field digital mammography for breast cancer detection and visibility

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2D synthetic mammography images from the DBT dataset do not require additional radiation exposure.

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We compare observer performance between 2DSM and FFDM for breast cancer detection and visibility.

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Diagnostic performances of 2DSM and FFDM images were comparable for detecting breast cancers.

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2DSM may eliminate the need for additional FFDM during DBT-based imaging.

Purpose

To compare observer performance between synthetic mammography (2DSM) and full-field digital mammography (FFDM) for breast cancer detection and visibility.

Method

A retrospective analysis was conducted on 136 histopathologically proven cases of breast cancer in patients who underwent FFDM and digital breast tomosynthesis (DBT). 2DSM images were reconstructed from DBT data, and 2DSM and FFDM images were reviewed and evaluated for mammographic features, probability of malignancy (BI-RADS classification), and lesion conspicuity. DBT images were not reviewed. Statistical differences in cancer detection rates between 2DSM and FFDM images were analyzed using the McNemar test, agreement on BI-RADS assessment between 2DSM and FFDM was assessed using Cohen’s kappa test, and the Wilcoxon’s signed rank test was used to compare visibility scores.

Results

Mean cancer detection rates with 2DSM and FFDM images were 84.6 % and 87.8 %, respectively. In subgroup analyses, differences in breast density, tumor size, and presence of calcifications were not statistically significant. Agreement between 2DSM and FFDM images for BI-RADS classification was graded as good with Cohen’s k-coefficient of 0.78 ± 0.05. Visibility scores in both modalities of images were similar for all lesions combined; however, 2DSM had significantly better visibility scores for calcified cancers (p < 0.01), and in dense breast tissue (p < 0.01).

Conclusions

Diagnostic performances of 2DSM and FFDM images were comparable for detecting breast cancers, and it is possible that 2DSM may eliminate the need for additional FFDM during DBT-based imaging due to advances in image reconstruction methods.

Shear-Wave Elastography: Could it be Helpful for the Diagnosis of Non-Mass-Like Breast Lesions?

The goal of this study was to analyze the diagnostic performance of shear wave elastography (SWE) in differentiation of benign and malignant non-mass-like (NML) breast lesions. Three hundred sixteen consecutive breast lesions in 305 patients who have been scheduled for ultrasound (US)-guided core needle biopsy or vacuum-assisted biopsy or surgical excision between January 2013 and August 2013 were initially included in this study. Finally, 63 patients with 67 lesions classified as NML lesions comprised our study population. The features of SWE and its diagnostic performance in NML lesions were analyzed. Among the 67 NML lesions, 33 were malignant and 34 were benign. The maximum elastic modulus, mean elastic modulus, minimum elastic modulus, elastic modulus ratio and stiff rim sign of the malignant lesions were all significantly higher than those of benign lesions (p < 0.05). The combination of conventional US with maximum elastic modulus and stiff rim sign got significantly higher diagnostic specificity and positive predictive value (PPV) than conventional US (p < 0.05 for both). In the benign lesions, 23 (67.6%) unnecessary biopsies could have been eliminated after the combination of conventional US and SWE. SWE could increase diagnostic specificity and positive predictive values of NML breast lesions. The combination of conventional US and SWE could reduce unnecessary benign biopsies of NML lesions.