Application of Contrast-Enhanced Ultrasound in the Diagnosis of Ductal Carcinoma In Situ: Analysis of 127 Cases

Development of a Multi-Parametric ultrasonography nomogram for prediction of invasiveness in ductal carcinoma in situ

OBJECTIVE

To investigate the independent risk variables associated with the potential invasiveness of ductal carcinoma in situ (DCIS) on multi-parametric ultrasonography, and further construct a nomogram for risk assessment.

METHODS

Consecutive patients from January 2017 to December 2022 who were suspected of having ductal carcinoma in situ (DCIS) based on magnetic resonance imaging or mammography were prospectively enrolled. Histopathological findings after surgical resection served as the gold standard. Grayscale ultrasound, Doppler ultrasound, shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS) examinations were preoperative performed. Binary logistic regression was used for multifactorial analysis to identify independent risk factors from multi-parametric ultrasonography. The correlation between independent risk factors and pathological prognostic markers was analyzed. The predictive efficacy of DCIS associated with invasiveness was assessed by logistic analysis, and a nomogram was established.

RESULTS

A total of 250 DCIS lesions were enrolled from 249 patients, comprising 85 pure DCIS and 165 DCIS with invasion (DCIS-IDC), of which 41 exhibited micro-invasion. The multivariate analysis identified independent risk factors for DCIS with invasion on multi-parametric ultrasonography, including image size (>2cm), Doppler ultrasound RI (≥0.72), SWE's Emax (≥66.4 kPa), hyper-enhancement, centripetal enhancement, increased surrounding vessel, and no contrast agent retention on CEUS. These factors correlated with histological grade, Ki-67, and human epidermal growth factor receptor 2 (HER2) (P < 0.1). The multi-parametric ultrasound approach demonstrated good predictive performance (sensitivity 89.7 %, specificity 73.8 %, AUC 0.903), surpassing single US modality or combinations with SWE or CEUS modalities. Utilizing these factors, a predictive nomogram achieved a respectable performance (AUC of 0.889) for predicting DCIS with invasion. Additionally, a separate nomogram for predicting DCIS with micro-invasion, incorporating independent risk factors such as RI (≥0.72), SWE's Emax (≥65.2 kPa), and centripetal enhancement, demonstrated an AUC of 0.867.

CONCLUSION

Multi-parametric ultrasonography demonstrates good discriminatory ability in predicting both DCIS with invasion and micro-invasion through the analysis of lesion morphology, stiffness, neovascular architecture, and perfusion. The use of a nomogram based on ultrasonographic images offers an intuitive and effective method for assessing the risk of invasion in DCIS. Although the nomogram is not currently considered a clinically applicable diagnostic tool due to its AUC being below the threshold of 0.9, further research and development are anticipated to yield positive outcomes and enhance its viability for clinical utilization.

Contrast-enhanced ultrasound for the preoperative prediction of pathological characteristics in breast cancer

Objective

We aimed to investigate the value of contrast-enhanced ultrasound (CEUS) in the preoperative prediction of the histological grades and molecular subtypes of breast cancer.

Methods

A total of 183 patients with pathologically confirmed breast cancer were included. Contrast enhancement patterns and quantitative parameters were compared in different groups. The receiver operating characteristic (ROC) curve was used to analyze the efficacy of CEUS in the preoperative prediction of pathological characteristics, including histologic grade and molecular subtypes.

Results

Heterogeneous enhancement, perfusion defects, and peripheral radial vessels were mostly observed in higher histologic grade (grade III) breast cancer. Heterogeneous enhancement and perfusion defect were the most effective indicators for grade III breast cancer, with the areas under the ROC curve of 0.768 and 0.756, respectively. There were significant differences in the enhancement intensity, post-enhanced margin, perfusion defects, and peripheral radial vessel among the different molecular subtypes of breast cancer (all P < 0.01). Perfusion defects and clear edge after enhancement were the best qualitative criteria for the diagnosis of HER-2 overexpressed and triple-negative breast cancers, and the corresponding areas under the ROC curves were 0.804 and 0.905, respectively. There were significant differences in PE, WiR, WiPI, and WiWoAUC between grade III vs grade I and II breast cancer (P < 0.05). PE, WiR, WiPI, and WiWoAUC had good efficiency in the diagnosis of high-histologic-grade breast cancer. PE had the highest diagnostic efficiency in Luminal A, while WiPI had the highest diagnostic efficiency in Luminal B subtype breast cancer, and the areas under the ROC curve were 0.825 and 0.838, respectively. WiWoAUC and WiR were the most accurate parameters for assessing triple-negative subtype breast cancers, and the areas under the curve were 0.932 and 0.922, respectively.

Conclusion

Qualitative and quantitative perfusion analysis of contrast-enhanced ultrasound may be useful in the non-invasive prediction of the histological grade and molecular subtypes of breast cancers.

Role of qualitative contrast-enhanced ultrasound in the diagnosis of malignant breast lesions

Background

Carcinoma breast is the commonest cancer among women. Various authors have studied breast cancer with Contrast-Enhanced Ultrasound (CEUS) with promising results. Despite promising results, the additional cost of post-processing software limits its availability. In this study, we evaluated the utility of CEUS in differentiating malignant from benign breast lesions on regular ultrasound equipment without the use of dedicated software.

Methods

We performed CEUS in 121 women with 121 breast lesions. CEUS was done by creating a custom preset on existing ultrasound equipment with the help of an application specialist authorized by the vendor. Lesions were evaluated qualitatively without the use of any commercial software. The pattern of enhancement i.e. homogenous, heterogeneous, peripheral, or no enhancement, and the number of penetrating vessels i.e., few or multiple were recorded. Results were compared with histopathological diagnosis.

Results

There were a total of 121 breast lesions. The study showed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 86.67 %, 54.10 %, 65 %, and 80.49% respectively for differentiating benign vs malignant lesions on the basis of the pattern of contrast enhancement. Using penetrating vessels for differentiating malignant lesions from benign lesions, the sensitivity, specificity, PPV, and NPV were found to be 64%, 67.86%, 78.05%, and 51.35% respectively.

Conclusion

CEUS is useful in differentiating malignant from benign breast lesions. It can be easily performed by creating a custom preset on standard ultrasound equipment without the use of expensive software.

The diagnostic value of contrast-enhanced ultrasonography in breast ductal abnormalities

BACKGROUND

Ductal lesions are an important, often overlooked, and poorly understood issue in breast imaging, which have a risk of underlying malignancy ranging from 5 to 23%. Ultrasonography (US), which has largely replaced galactography or ductography, has become an important imaging method to assess patients with ductal lesions. However, it is difficult to distinguish benign from malignant ductal abnormalities only by ultrasonography, most of which are recommended to be at least in subcategory 4A; these require biopsy according to the ACR BI-RADS®atlas 5th Edition-breast ultrasound. Contrast-enhanced ultrasound (CEUS) has been shown to be valuable for differentiating benign from malignant tumors, but its value is unclear in breast ductal lesions. Therefore, the purposes of this study were to explore the characteristics of malignant ductal abnormalities on US and CEUS imaging and the diagnostic value of CEUS in breast ductal abnormalities.

METHODS

Overall, 82 patients with 82 suspicious ductal lesions were recruited for this prospective study. They were divided into benign and malignant groups according to the pathological results. Morphologic features and quantitative parameters of US and CEUS were analyzed by comparison and multivariate logistic regression to determine the independent risk factors. The diagnostic performance was assessed by receiver operating characteristic (ROC) curve analysis.

RESULTS

Shape, margin, inner echo, size, microcalcification and blood flow classification on US, wash-in time, enhancement intensity, enhancement mode, enhancement scope, blood perfusion defects, peripheral high enhancement and boundary on CEUS were identified as features correlated with malignant ductal lesions. However, multivariate logistic regression showed that only microcalcification (OR = 8.96, P = 0.047) and enhancement scope (enlarged, OR = 27.42, P = 0.018) were independent risk factors for predicting malignant ductal lesions. The sensitivity, specificity, positive predictive value, negative predictive value, accuracy and area under the ROC curve of microcalcifications combined with an enlarged enhancement scope were 0.895, 0.886, 0.872, 0.907, 0.890, and 0.92, respectively.

CONCLUSIONS

Microcalcification and enlarged enhancement scope are independent factors for predicting malignant ductal lesions. The combined diagnosis can greatly improve the diagnostic performance, indicating that CEUS can be useful in the differentiation of benign and malignant lesions to formulate more appropriate management for ductal lesions.

Differential Diagnosis of DCIS and Fibroadenoma Based on Ultrasound Images: a Difference-Based Self-Supervised Approach

Differentiation of ductal carcinoma in situ (DCIS, a precancerous lesion of the breast) from fibroadenoma (FA) using ultrasonography is significant for the early prevention of malignant breast tumors. Radiomics-based artificial intelligence (AI) can provide additional diagnostic information but usually requires extensive labeling efforts by clinicians with specialized knowledge. This study aims to investigate the feasibility of differentially diagnosing DCIS and FA using ultrasound radiomics-based AI techniques and further explore a novel approach that can reduce labeling efforts without sacrificing diagnostic performance. We included 461 DCIS and 651 FA patients, of whom 139 DCIS and 181 FA patients constituted a prospective test cohort. First, various feature engineering-based machine learning (FEML) and deep learning (DL) approaches were developed. Then, we designed a difference-based self-supervised (DSS) learning approach that only required FA samples to participate in training. The DSS approach consists of three steps: (1) pretraining a Bootstrap Your Own Latent (BYOL) model using FA images, (2) reconstructing images using the encoder and decoder of the pretrained model, and (3) distinguishing DCIS from FA based on the differences between the original and reconstructed images. The experimental results showed that the trained FEML and DL models achieved the highest AUC of 0.7935 (95% confidence interval, 0.7900-0.7969) on the prospective test cohort, indicating that the developed models are effective for assisting in differentiating DCIS from FA based on ultrasound images. Furthermore, the DSS model achieved an AUC of 0.8172 (95% confidence interval, 0.8124-0.8219), indicating that our model outperforms the conventional radiomics-based AI models and is more competitive.

Imaging of fibroadenoma: Be careful with imaging follow-up

The present letter to the editor is related to the study titled, “Preoperational diagnosis and management of breast ductal carcinoma in situ arising within fibroadenoma: Two case reports.” Fibroadenoma is the most common benign mass lesion in young females. Based on this study showing that malignancy can develop on fibroadenomas, we want to emphasize that careful sonographic follow-up of fibroadenomas should be done and that each lesion should be followed carefully and separately in cases with multiple fibroadenomas. Additionally, we want to emphasize the critical role of sonographic examination in diagnosing fibroadenoma, the importance of correctly defining benign and malignant sonographic findings, and which lesions should be followed up sonographically and which lesions should be evaluated histopathologically.

Combining contrast-enhanced ultrasound and blood cell analysis to improve diagnostic accuracy of plasma cell mastitis

Plasma cell mastitis is a benign suppurative disease of the breast, lack of specific clinical manifestations, which is easy to be misdiagnosed and mistreated, often confused with mastitis, breast cancer (BC), and other diseases. Thus, we aimed to establish a combined model of promoting diagnostic accuracy of plasma cell mastitis by contrast-enhanced ultrasound (CEUS) patterns and routine blood cell analysis. Eighty-eight plasma cell mastitis, 91 breast cancer, and 152 other benign breast diseases' patients grouped according to pathological diagnosis underwent CEUS and blood cell analysis examination; 100 healthy female donors were involved. All the plasma cell mastitis and breast cancer patients presented hyperenhancement of CEUS breast lesions compared with others. The majority of plasma cell mastitis (65/88) showed perfusion defect of CEUS patterns with smooth edge (56/65) and multiple lesions (49/65); in contrast, fewer breast cancer patients (30/91) displayed perfusion defect. White blood cell count (WBC), neutrophils, and neutrophils/lymphocytes ratio of blood cell analysis in plasma cell mastitis patients increased significantly compared with other patients (P < 0.0001). Combining perfusion defect of CEUS patterns and WBC yielded an area under the receiver operating characteristic curve of 0.831, higher than single 0.720 and 0.774, respectively. The cut-off value of WBC (7.28 × 10⁹/L) helped remaining 65.2% (15/23) atypical cases to be correctly diagnosed as plasma cell mastitis, not misdiagnosed as breast cancer. In conclusion, CEUS presented a clear perfusion defect pattern of plasma cell mastitis lesion for the first time. A precise WBC by routine blood cell analysis test can assist CEUS examination in the differential diagnosis of plasma cell mastitis and breast cancer. It is a promised combination for laboratory diagnostic of PCM.

Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ

BACKGROUND

The molecular biomarkers of breast ductal carcinoma in situ (DCIS) have important guiding significance for individualized precision treatment. This study was intended to explore the significance of radiomics based on ultrasound images to predict the expression of molecular biomarkers of mass type of DCIS.

METHODS

116 patients with mass type of DCIS were included in this retrospective study. The radiomics features were extracted based on ultrasound images. According to the ratio of 7:3, the data sets of molecular biomarkers were split into training set and test set. The radiomics models were developed to predict the expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), Ki67, p16, and p53 by using combination of multiple feature selection and classifiers. The predictive performance of the models were evaluated using the area under the curve (AUC) of the receiver operating curve.

RESULTS

The investigators extracted 5234 radiomics features from ultrasound images. 12, 23, 41, 51, 31 and 23 features were important for constructing the models. The radiomics scores were significantly (P < 0.05) in each molecular marker expression of mass type of DCIS. The radiomics models showed predictive performance with AUC greater than 0.7 in the training set and test set: ER (0.94 and 0.84), PR (0.90 and 0.78), HER2 (0.94 and 0.74), Ki67 (0.95 and 0.86), p16 (0.96 and 0.78), and p53 (0.95 and 0.74), respectively.

CONCLUSION

Ultrasonic-based radiomics analysis provided a noninvasive preoperative method for predicting the expression of molecular markers of mass type of DCIS with good accuracy.