The value of automated breast volume scanner combined with virtual touch tissue quantification in the differential diagnosis of benign and malignant breast lesions: A comparative study with mammography

Multimodal ultrasound imaging for diagnostic differentiation of sclerosing adenosis from invasive ductal carcinoma

Background

Sclerosing adenosis (SA) is a common proliferative benign lesion without atypia in the breast that may mimic invasive ductal carcinoma (IDC) on medical imaging, leading to it often being misdiagnosed and mistreated. Consequently, the purpose of this study was to assess the diagnostic value of multimodal ultrasound imaging in distinguishing SA from IDC.

Methods

Multimodal ultrasound imaging, including automated breast volume scan (ABVS), elasticity imaging (EI), and color Doppler flow imaging (CDFI), were performed on 120 consecutive patients comprising 122 breast lesions (54 SA, 68 IDC). All lesions were pathologically confirmed. Multimodal ultrasound imaging features were compared between the two groups. Binary logistic regression analysis based on ABVS, EI, and CDFI was conducted to formulate a logistic regression equation for differentiating SA from IDC. The diagnostic performances of ABVS, EI, CDFI, and their combination were compared by the receiver operating characteristic (ROC) curve analysis.

Results

The sensitivity, specificity, and accuracy of ABVS, EI, CDFI, and their combination in differentiating SA from IDC were, respectively, 75.00%, 72.22%, and 73.77%; 86.76%, 72.22%, and 80.33%; 73.53%, 64.81%, and 69.67%; and 88.24%, 74.07%, and 81.97%. Combining multimodal ultrasound imaging yielded an area under the curve (AUC) of 0.895 (95% confidence interval: 0.827-0.943), which was higher than that of ABVS, EI, and CDFI, with AUC values of 0.736, 0.795, and 0.692, respectively, and the difference was statistically significant (ABVS vs. combined model, P<0.001; CDFI vs. combined model, P<0.001; EI vs. combined model, P<0.001). There was no significant difference in the diagnostic efficacy among the three imaging modalities (ABVS vs. EI, P=0.266; ABVS vs. CDFI, P=0.4671; EI vs. CDFI, P=0.051). Compared with those in IDC, the calcification (16.67% vs. 57.35%; P<0.001) and retraction phenomena in the coronal planes (18.52% vs. 57.35%; P<0.001) were less common in patients with SA, while circumscribed margin (38.89% vs. 5.88%; P<0.001), vascularity grade 0-I (64.81% vs. 26.47%; P<0.001), and elasticity scores 1-3 (72.22% vs. 13.24%; P<0.001) were more frequently found in patients with SA. Patients with SA were significantly younger than were patients with IDC (43±11 vs. 54±11 years; P<0.001), and the lesion size was smaller in patients with SA than in those with IDC (median size 1.0 cm; interquartile range (IQR), 0.9 cm vs. median size 1.3 cm; IQR, 1.3 cm; P<0.001).

Conclusions

The preliminary results suggested that multimodal ultrasound imaging can improve the diagnostic accuracy of SA and provide additional information for differential diagnosis of SA and IDC.

Comparative analysis of automated breast volume scanner (ABVS) combined with conventional hand-held ultrasound and mammography in female breast cancer detection

Objective and background

This study aimed to compare the diagnostic value of an automated breast volume scanner (ABVS) combined with conventional hand-held ultrasound and mammography in detecting female breast cancer. Early detection is vital in improving patient outcomes for this prevalent disease.

Methods

Seventy-eight suspicious breast lesions from 60 patients were examined between August 2019 and July 2020. Each patient underwent ABVS, conventional hand-held ultrasound, and mammography. Diagnostic values, including coincidence rate, sensitivity, specificity, positive predictive value, and negative predictive value, were calculated using histopathology results as the "gold standard."

Results

Histopathology confirmed 55 malignant (70.51%) and 23 benign lesions (29.48%). ABVS combined with conventional hand-held ultrasound identified 56 malignant (52 confirmed, 4 benign) and 22 benign nodules (3 confirmed, 19 benign). Mammography detected 48 malignant (45 confirmed, 3 benign) and 30 benign nodules (10 confirmed, 20 benign). ABVS combined with conventional hand-held ultrasound had a sensitivity of 94.5%, specificity of 82.6%, positive predictive value of 92.9%, and negative predictive value of 86.4%. Mammography showed a sensitivity of 81.8%, specificity of 87.0%, positive predictive value of 93.8%, and negative predictive value of 66.7%.

Conclusion

ABVS combined with conventional hand-held ultrasound showed high diagnostic value in detecting female breast cancer. The "convergence sign" in the coronal section played a significant role. It slightly outperformed mammography and offered advantages in terms of cost, convenience, comfort, and absence of radiation. Further promotion and implementation are supported.

An Optimized Radiomics Model Based on Automated Breast Volume Scan Images to Identify Breast Lesions: Comparison of Machine Learning Methods: Comparison of Machine Learning Methods

OBJECTIVES

To develop and test an optimized radiomics model based on multi-planar automated breast volume scan (ABVS) images to identify malignant and benign breast lesions.

METHODS

Patients (n = 200) with breast lesions who underwent ABVS examinations were included. For each patient, 208 radiomics features were extracted from the ABVS images, including axial plane and coronal plane. Recursive feature elimination, random forest, and chi-square test were used to select features. A support vector machine, logistic regression, and extreme gradient boosting were utilized as classifiers to differentiate malignant and benign breast lesions. The area under the curve, sensitivity, specificity, accuracy, and precision was used to evaluate the performance of the radiomics models. Generalization of the radiomics models was verified through 5-fold cross-validation.

RESULTS

For a single plane or a combination of planes, a combination of recursive feature elimination, and support vector machine yielded the best performance when identifying breast lesions. The machine learning models based on a combination of planes performed better than those based on a single plane. Regarding the axial plane and coronal plane, the machine learning model using a combination of recursive feature elimination and support vector machine yielded the optimal identification performance: average area under the curve (0.857 ± 0.058, 95% confidence interval, 0.763-0.957); the average values of sensitivity, specificity, accuracy, and precision were 87.9, 68.2, 80.7, and 82.9%, respectively.

CONCLUSIONS

The optimized radiomics model based on ABVS images can provide valuable information for identifying benign and malignant breast lesions preoperatively and guide the accurate clinical treatment. Further external validation is required.