CEUS helps to rerate small breast tumors of BI-RADS category 3 and category 4

Current status and future perspectives of contrast-enhanced ultrasound diagnosis of breast lesions

Advances in various imaging modalities for breast lesions have improved diagnostic capabilities not only for tumors but also for non-tumorous lesions. Contrast-enhanced ultrasound (CEUS) plays a crucial role not only in the differential diagnosis of breast lesions, identification of sentinel lymph nodes, and diagnosis of lymph node metastasis but also in assessing the therapeutic effects of neoadjuvant chemotherapy (NAC). In CEUS, two image interpretation approaches, i.e., qualitative analysis and quantitative analysis, are employed and applied in various clinical settings. In this paper, we review CEUS for breast lesions, including its various applications.

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.

Characterizing breast masses using an integrative framework of machine learning and CEUS-based radiomics

AIMS

We evaluated the performance of contrast-enhanced ultrasound (CEUS) based on radiomics analysis to distinguish benign from malignant breast masses.

METHODS

131 women with suspicious breast masses (BI-RADS 4a, 4b, or 4c) who underwent CEUS examinations (using intravenous injection of perflutren lipid microsphere or sulfur hexafluoride lipid-type A microspheres) prior to ultrasound-guided biopsies were retrospectively identified. Post biopsy pathology showed 115 benign and 16 malignant masses. From the cine clip of the CEUS exams obtained using the built-in GE scanner software, breast masses and adjacent normal tissue were then manually segmented using the ImageJ software. One frame representing each of the four phases: precontrast, early, peak, and delay enhancement were selected post segmentation from each CEUS clip. 112 radiomic metrics were extracted from each segmented tissue normalized breast mass using custom Matlab® code. Linear and nonlinear machine learning (ML) methods were used to build the prediction model to distinguish benign from malignant masses. tenfold cross-validation evaluated model performance. Area under the curve (AUC) was used to quantify prediction accuracy.

RESULTS

Univariate analysis found 35 (38.5%) radiomic variables with p < 0.05 in differentiating between benign from malignant masses. No feature selection was performed. Predictive models based on AdaBoost reported an AUC = 0.72 95% CI (0.56, 0.89), followed by Random Forest with an AUC = 0.71 95% CI (0.56, 0.87).

CONCLUSIONS

CEUS based texture metrics can distinguish between benign and malignant breast masses, which can, in turn, lead to reduced unnecessary breast biopsies.

The role of contrast-enhanced ultrasound in the diagnosis of malignant non-mass breast lesions and exploration of diagnostic criteria

OBJECTIVES

To assess the value of contrast-enhanced ultrasound (CEUS) for diagnosing malignant non-mass breast lesions (NMLs) and to explore the CEUS diagnostic criteria.

METHODS

A total of 116 patients with 119 NMLs detected by conventional US were enrolled. Histopathological results were used as the reference standard. The enhancement characteristics of NMLs in CEUS were compared between malignant and benign NMLs. The CEUS diagnostic criteria for malignant NMLs were established using independent diagnostic indicators identified by binary logistic regression analysis. The diagnostic performance of Breast Imaging Reporting and Data System-US (BI-RADS-US), CEUS, and BI-RADS-US combined with CEUS was evaluated and compared.

RESULTS

Histopathological results showed 63 and 56 benign and malignant NMLs. Enhancement degree (OR = 5.75, p = 0.003), enhancement area (OR = 4.25, p = 0.005), and radial or penetrating vessels (OR = 7.54, p = 0.003) were independent diagnostic indicators included to establish the CEUS diagnostic criteria. The sensitivity and specificity of BI-RADS-US, CEUS, and BI-RADS-US combined with CEUS were 100 and 30.2%, 80.4 and 74.6%, and 94.6 and 77.8%, respectively; the corresponding areas under the receiver operating characteristic curve (AUC) were 0.819, 0.775, and 0.885, respectively.

CONCLUSIONS

CEUS has a high specificity in malignant NML diagnosis based on the diagnostic criteria including enhancement degree, enhancement area, and radial or penetrating vessels, but with lower sensitivity than BI-RADS-US. The combination of CEUS and BI-RADS-US is an effective diagnostic tool with both high sensitivity and specificity for the diagnosis of malignant NMLs.

ADVANCES IN KNOWLEDGE

In this study, we assessed the diagnostic value of CEUS for malignant NMLs and constructed a feasible diagnostic criterion. We further revealed that the combination of CEUS and BI-RADS-US has a high diagnostic value for malignant NMLs.

Factors influencing the performance of a diagnostic model including contrast-enhanced ultrasound in 1023 breast lesions: comparison with histopathology

Background

We aimed to investigate the influence of patient and lesion characteristics on our diagnostic model for contrast-enhanced ultrasound (CEUS) of the breast, comparing its accuracy with that of histopathology.

Methods

Conducting a study with eight medical centers, we compared 1,023 breast lesions categorized as BI-RADS 4 or 5 with the score from our newly-established CEUS-based diagnostic model, comparing the results with pathological outcomes. Univariate and multivariate logistic regression analyses were conducted to determine the influence of clinicopathological characteristics on the performance of this CEUS model.

Results

Logistic regression analysis showed that patients' age, maximum lesion diameter, and distance from the lesion's deep edge to the pectoralis major were significant independent influencing factors. The model's diagnostic accuracy was greater for patients >35 y (P=0.005), for maximum lesion diameter >20 mm, and for distance from the lesion's deep edge to the pectoralis major ≤3.05 mm. There was no significant difference in accuracy between lesions with maximum lesion diameter 10-20 and <10 mm (P=0.393).

Conclusions

The diagnostic performance of the proposed CEUS model for breast lesions is influenced by patients' age, maximum lesion diameter, and distance from the lesion's deep edge to the pectoralis major. Consideration of influencing factors is required to optimize clinical use of the CEUS model.

BI-RADS 4 breast lesions: could multi-mode ultrasound be helpful for their diagnosis?

Background

The malignant probability of Breast Imaging Reporting and Data System (BI-RADS) 4 breast lesions is 3-94%, which is a very large span, and thus leads to a high rate of unnecessary biopsy. Therefore, the differential diagnosis of benign and malignant BI-RADS 4 breast lesions has become extremely important. Thus, in this paper, we investigated the diagnostic value of conventional ultrasonography (US), contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) for BI-RADS 4 breast lesions, and tried to figure out a multi-mode ultrasonic method for them.

Methods

From March 2016 to May 2017, 118 breast lesions that were categorized as BI-RADS 4 lesions by US were studied with CEUS and SWE. All the lesions were confirmed by pathology via surgery or vacuum-assisted biopsy. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of US, CEUS and SWE were analyzed. Then the diagnostic efficacies of US, CEUS, SWE and the combination of these modalities were compared. Logistic regression analysis was performed to identify the independent risk factors. A multi-mode method to evaluate BI-RADS 4 lesions based on the logistic regression was developed.

Results

Of the 118 BI-RADS 4 lesions, 74 lesions (62.7%) were benign and 44 lesions (37.3%) were malignant. The diagnostic sensitivity and specificity for US, US + CEUS, US + SWE, US + CEUS + SWE were 88.6% and 75.7%, 86.4% and 94.6%, 88.6% and 90.5%, 97.7% and 93.2%, respectively. The area under the ROC curve (AUC) of US + SWE + CEUS was significantly higher than that of US (P<0.0001), US + CEUS (P=0.020), but there was no significant difference between the AUC of US + SWE + CEUS and the AUC of US + SWE.

Conclusions

US + CEUS + SWE and US + SWE could significantly improve the diagnostic efficiency and accuracy of US in the diagnosis of BI-RADS 4 breast lesions.

A multicenter study of a contrast-enhanced ultrasound diagnostic classification of breast lesions

Purpose

To evaluate a classification model of contrast-enhanced ultrasound (CEUS) and examine the characteristics of patients with false-negative diagnosis.

Patients and methods

A retrospective secondary analysis of a multicenter trial of CEUS for breast cancer diagnosis (from August 2015 to April 2017) was undertaken. Patients (n=1,023) with Breast Imaging Reporting and Data System 4-5 lesions on B-mode ultrasound underwent CEUS. Pathological diagnoses were available from surgical or biopsy specimens for correlation. Lesion maximum diameter (LMD), distance to the papilla (DtP), distance from the superficial edge of the lesion to the skin (DtS), distance from the deep edge of the lesion to the pectoralis muscle (DtPM), and body mass index (BMI) were evaluated.

Results

Median age and BMI were 48.0 and 41.2 years and 23.2 and 22.4 kg/m² for patients with malignant and benign lesions, respectively. Overall sensitivity, specificity, and accuracy of CEUS for malignancy were 89.4%, 65.3%, and 75.8%, respectively. The patients with true-positive and false-negative diagnosis (ie, with malignant lesion) were older than those with false-positive and true-negative diagnosis (ie, with benign lesion). Patients with true-positive and false-positive diagnoses had higher BMI than patients with true-negative and false-negative diagnoses (P=0.004). Patients with true-positive and false-negative diagnoses had larger LMD and DtP, as well as smaller DtS and DtPM.

Conclusion

Older age, higher BMI, larger LMD and DtP, and smaller DtS and DtPM were associated with malignant lesions on CEUS. Patients with these characteristics should undergo further imaging.