Imaging features of automated breast volume scanner: Correlation with molecular subtypes of breast cancer.

OBJECTIVES: To investigate the correlation between the imaging features obtained by an automated breast volume scanner (ABVS) and molecular subtypes of breast cancer. METHODS: We examined 303 malignant breast tumours by ABVS for specific imaging features and by immunohistochemical analysis to determine the molecular subtype. ABVS imaging features, including retraction phenomenon, shape, margins, echogenicity, post-acoustic features, echogenic halo, and calcifications were analysed by univariate and multivariate logistic regression analyses to determine the significant predictive factors of the molecular subtypes. RESULTS: By univariate logistic regression analysis, the predictive factors of the Luminal-A subtype (n=128) were retraction phenomenon (odds ratio [OR]=10.188), post-acoustic shadowing (OR=5.112), and echogenic halo (OR=3.263, P<0.001). The predictive factors of the Human-epidermal-growth-factor-receptor-2-amplified subtype (n=39) were calcifications (OR=6.210), absence of retraction phenomenon (OR=4.375), non-mass lesions (OR=4.286, P<0.001), absence of echogenic halo (OR=3.851, P=0.035), and post-acoustic enhancement (OR=3.641, P=0.008). The predictors for the Triple-Negative subtype (n=47) were absence of retraction phenomenon (OR=5.884), post-acoustic enhancement (OR=5.255, P<0.001), absence of echogenic halo (OR=4.138, P=0.002), and absence of calcifications (OR=3.363, P=0.001). Predictors for the Luminal-B subtype (n=89) had a relatively lower association (OR≤2.328). By multivariate logistic regression analysis, retraction phenomenon was the strongest independent predictor for the Luminal-A subtype (OR=9.063, P<0.001) when present and for the Triple-Negative subtype (OR=4.875, P<0.001) when absent. CONCLUSIONS: ABVS imaging features, especially retraction phenomenon, have a strong correlation with the molecular subtypes, expanding the scope of ultrasound in identifying breast cancer subtypes with confidence.

Quantitative evaluation of contrast-enhanced ultrasound for differentiation of renal cell carcinoma subtypes and angiomyolipoma.

PURPOSE: To investigate the value of quantitative parameters of contrast-enhanced ultrasound (CEUS) in the differentiation of subtypes of renal cell carcinoma (RCC) and angiomyolipoma (AML). METHODS: The quantitative characteristics of 341 RCCs and 88 AMLs were analyzed with quantitative software (SonoLiver). Quantitative analysis was conducted in the whole tumor (ROItumor) and the maximum enhanced area of the tumor (ROImax), acquiring the parameters of maximum intensity (IMAX), rise time (RT), time to peak (TTP), mean transit time (mTT), and area under the curve (AUC), were derived and analyzed. The difference values between ROImax and normal renal cortex (ΔPar.s, including ΔIMAX, ΔRT, ΔTTP, ΔmTT, ΔAUC) were compared among renal histotypes. RESULTS: All time-related parameters (including RT, TTP and mTT) of ROImax were shorter than the corresponding parameters of ROItumor in RCC subtypes (all p<0.05), but made no statistical difference in AMLs (all p>0.05). There were significant differences of all ΔPar.s among RCC subtypes and AML (all p<0.01). ΔIMAX and ΔAUC showed the trend that ccRCC>AML>pRCC=chRCC. ΔTTP showed AML=pRCC=chRCC>ccRCC, ΔRT and ΔmTT showed AML>pRCC=chRCC=ccRCC. ΔmTT could distinguish RCC from AML with the area under the ROC curve (AUC) of 0.86. The AUC of ΔIMAX and ΔAUC was 0.89 and 0.92 vs 0.85 and 0.85 for discriminating between pRCC (or chRCC) and AML vs ccRCC and AML. CONCLUSIONS: Quantitative analysis of CEUS is a useful modality in AML and RCC subtypes' differentiation, by using ΔmTT, ΔIMAX and ΔAUC.

Comparison of retraction phenomenon and BI-RADS-US descriptors in differentiating benign and malignant breast masses using an automated breast volume scanner.

OBJECTIVE: To compare the diagnostic values of retraction phenomenon in the coronal planes and descriptors in the Breast Imaging Reporting and Data System-Ultrasound (BI-RADS-US) lexicon in differentiating benign and malignant breast masses using an automated breast volume scanner (ABVS). MATERIALS AND METHODS: Two hundred and eight female patients with 237 pathologically proven breast masses (120 benign and 117 malignant) were included in this study. ABVS was performed for each mass after preoperative localization by conventional ultrasonography (US). Multivariate logistic regression analysis was performed to assess independent variables for malignancy prediction. Diagnostic performance was evaluated through the receiver operating characteristic (ROC) curve analysis. RESULTS: Retraction phenomenon (odds ratio [OR]: 76.70; 95% confidence interval [CI]: 12.55, 468.70; P<0.001) was the strongest independent predictor for malignant masses, followed by microlobulated margins (OR: 55.87; 95% CI: 12.56, 248.44; P<0.001), angular margins (OR: 36.44; 95% CI: 4.55, 292.06; P=0.001), calcifications (OR: 5.53; 95% CI: 1.34, 22.88; P=0.018,) and patient age (OR: 1.10; 95% CI: 1.03, 1.17; P=0.004). Mass shape, orientation, echo pattern, indistinct margins, spiculated margins, and mass size were not significantly associated with breast malignancy. Area under the ROC curve (Az) for microlobulated margins and retraction phenomenon was higher than that for other significant independent predictors. Az, sensitivity, and specificity were 0.877 (95% CI: 0.829, 0.926) and 0.838 (95% CI: 0.783, 0.892), 82.9% and 70.1%, and 92.5% and 98.3%, respectively, for microlobulated margins and retraction phenomenon. CONCLUSIONS: Retraction phenomenon and microlobulated margins have high diagnostic values in the differentiation of benign and malignant breast masses using an ABVS.