Ultrasound contrast-enhanced patterns of sentinel lymph nodes: predictive value for nodal status and metastatic burden in early breast cancer

Value of Contrast-Enhanced Ultrasound Combined with Immune-Inflammatory Markers in Predicting Axillary Lymph Node Metastasis of Breast Cancer

RATIONALE AND OBJECTIVES

To evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) combined with immune-inflammatory markers in predicting axillary lymph node metastasis (ALNM) in breast cancer patients.

METHODS

From January 2020 to June 2023, the clinicopathological data and ultrasound features of 401 breast cancer patients who underwent biopsy or surgery were recorded. Patients were randomly divided into a training set (321 patients) and a validation set (80 patients). The risk factors for ALNM were determined using univariate, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis, and prediction models were constructed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess their diagnostic performance.

RESULTS

Logistic regression analysis demonstrated that systemic immunoinflammatory index (SII), CA125, Ki67, pathological type, lesion size, enhancement pattern and Breast Imaging Reporting and Data System (BI-RADS) category were significant risk factors for ALNM. Three different models were constructed, and the combined model yielded an AUC of 0.903, which was superior to the clinical model (AUC=0.790) and ultrasound model (AUC=0.781). A nomogram was constructed based on the combined model, calibration curves and DCA demonstrated its satisfactory performance in predicting ALNM.

CONCLUSION

The nomogram combining ultrasound features and immune-inflammatory markers could serve as a valuable instrument for predicting ALNM in breast cancer patients.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

Evaluating the added value of synthetic magnetic resonance imaging in predicting sentinel lymph node status in breast cancer

Background

The noninvasive prediction of sentinel lymph node (SLN) metastasis using quantitative magnetic resonance imaging (MRI), particularly with synthetic MRI (syMRI), is an emerging field. This study aimed to explore the potential added benefits of syMRI over conventional MRI and diffusion-weighted imaging (DWI) in predicting metastases in SLNs.

Methods

This retrospective study consecutively enrolled 101 patients who were diagnosed with breast cancer (BC) and underwent SLN biopsy from December 2022 to October 2023 at the Affiliated Hospital of Jiangnan University. These patients underwent preoperative MRI including conventional MRI, DWI, and syMRI and were categorized into two groups according to the postoperative pathological results: those with and without metastatic SLNs. MRI morphological features, DWI, and syMRI-derived quantitative parameters of breast tumors were statistically compared between these two groups. Binary logistic regression was used to separately develop predictive models for determining the presence of SLN involvement, with variables that exhibited significant differences being incorporated. The performance of each model was evaluated through receiver operating characteristic (ROC) curve analysis, including the area under the curve (AUC), sensitivity, and specificity.

Results

Compared to the group of 54 patients with BC but no metastatic SLNs, the group of 47 patients with BC and metastatic SLNs had a significantly larger maximum axis diameter [metastatic SLNs: median 2.40 cm, interquartile range (IQR) 1.50-3.00 cm; no metastatic SLNs: median 1.80 cm, IQR 1.37-2.50 cm; P=0.03], a higher proton density (PD) (78.44±11.92 vs. 69.20±10.63 pu; P<0.001), and a lower apparent diffusion coefficient (ADC) (metastatic SLNs: median 0.91×10-3 mm2/s, IQR 0.79-1.01 mm2/s; no metastatic SLNs: median 1.02×10-3 mm2/s, IQR 0.92-1.12 mm2/s; P=0.001). Moreover, the prediction model with maximum axis diameter and ADC yielded an AUC of 0.71 [95% confidence interval (CI): 0.618-0.802], with a sensitivity of 78.72% and a specificity of 51.85%; After addition of syMRI-derived PD to the prediction model, the AUC increased significantly to 0.86 (AUC: 0.86 vs. 0.71; 95% CI: 0.778-0.922; P=0.002), with a sensitivity of 80.85% and a specificity of 81.50%.

Conclusions

Combined with conventional MRI and DWI, syMRI can offer additional value in enhancing the predictive performance of determining SLN status before surgery in patients with BC.

Classification of superficial suspected lymph nodes: non-invasive radiomic model based on multiphase contrast-enhanced ultrasound for therapeutic options of lymphadenopathy

Background

Accurate determination of the types of lymphadenopathy is of great importance in disease diagnosis and treatment and is usually confirmed by pathological findings. Radiomics is a non-invasive tool that can extract quantitative information from medical images. Our study was designed to develop a non-invasive radiomic approach based on multiphase contrast-enhanced ultrasound (CEUS) images for the classification of different types of lymphadenopathy.

Methods

A total of 426 patients with superficial suspected lymph nodes (LNs) from three centres were grouped into a training cohort (n=190), an internal testing cohort (n=127), and an external testing cohort (n=109). The radiomic features were extracted from the prevascular phase, vascular phase, and postvascular phase of the CEUS images. Model 1 (the conventional feature model), model 2 (the multiphase radiomics model), and model 3 (the combined feature model) were established for lymphadenopathy classification. The area under the curve (AUC) and confusion matrix were used to evaluate the performance of the three models. The usefulness of the models was assessed in different threshold probabilities by decision curve analysis.

Results

There were 139 patients (32.6%) with benign LNs, 110 patients (25.8%) with lymphoma, and 177 patients (41.5%) with metastatic LNs in our population. Finally, twenty features were selected to construct the radiomics models for these three types of lymphadenopathy. Model 2 integrating multiphase images of the CEUS yielded the AUCs of 0.838, 0.739, and 0.733 in the training cohort, internal testing cohort, and external testing cohort, respectively. After the combination of conventional features and radiomic features, the AUCs of model 3 improved to 0.943, 0.823 and 0.785 in the training cohort, internal testing cohort, and external testing cohort. Besides, model 3 had an accuracy of 81.05%, sensitivity of 80%, and specificity of 90.43% in the training cohort. Model performance was further confirmed in the internal testing cohort and external testing cohort.

Conclusions

We constructed a combined feature model using a series of CEUS images for the classification of the lymphadenopathies. For patients with superficial suspected LNs, this model can help clinicians make a decision on the LN type noninvasively and choose appropriate treatments.

The Value of Percutaneous Contrast-Enhanced Ultrasound in Sentinel Lymph Node Identification, Metastatic Status and Burden Diagnosis in Early Breast Cancer

OBJECTIVE

The aim of this study was to evaluate the value of percutaneous contrast-enhanced ultrasound (PCEUS) in the identification and characterization of sentinel lymph node (SLN).

METHODS

A total of 102 breast cancer patients were collected and underwent preoperative PCEUS, which was used to identify SLN and lymphatic drainage. SLNs were classified into 4 enhancement patterns, including 6 subtypes: homogeneous (I), featured inhomogeneous (II) including inhomogeneous hypoenhancement (IIa) and annular or semi-annular enhancement (IIb), focal filling defect (III) including filling defect area < 50% (IIIa) and filling defect area ≥ 50% (IIIb), and no enhancement (IV). The enhancement patterns of SLNs were compared with the final pathological diagnosis.

RESULTS

The identification rate of SLNs using PCEUS was 100% (102/102); the rate of identification of LCs was 100% (102/102), and the coincidence rate was 98.0% (100/102). Four lymphatic drainage patterns (LDPs) including 5 subtypes were found: single LC/single SLN(74.5%), multiple LCs/ single SLN (13.7%) including 2 subtypes:2 LCs/1 SLN and 3 LCs/1 SLN, single LC/multiple SLNs (7.8%), and multiple LCs/multiple SLNs (3.9%). A total of 86.3% (44/51) of patients without axillary metastasis could be safely selected for types I, IIa, and IIb, while the axillary metastasis rates of types III and IV were 74.4% and 87.5%, respectively (P < .001). Compared with grayscale US, the PCEUS significant improvement in diagnosing metastatic SLNs (.794 versus .579, P < .001). For the SLN metastatic burden, Types I, IIa, IIb, and IIIa had ≤2 SLNs metastases, with a pathological coincidence rate of (64/67, 95.5%), and types IIIb and IV had >2 SLNs metastases, with a pathological coincidence rate of (25/35, 71.4%) (P < .001). The AUC of PCEUS for the diagnosis of SLN metastatic status and burden was .794 and .879, respectively (P < .001).

CONCLUSION

PCEUS has a high identification rate for SLN and has good potential for diagnosing SLN metastatic status and burden by enhancement patterns.

Predictive value for axillary lymph node metastases in early breast cancer: Based on contrast-enhanced ultrasound characteristics of the primary lesion and sentinel lymph node

OBJECTIVE

To evaluate the value of contrast-enhanced ultrasound (CEUS) characteristics based on primary lesion combined with lymphatic contrast-enhanced ultrasound (LCEUS) patterns of SLN in predicting axillary lymph node metastasis (ALNM) with T1-2N0 breast cancer.

METHODS

A retrospective study was conducted in 118 patients with clinically confirmed T1-2N0 breast cancer. Conventional ultrasound (CUS) and CEUS characteristics of the primary lesion and enhancement patterns of SLN were recorded. The risk factors associated with ALNM were selected by univariate and binary logistic regression analysis, and the receiver operating characteristic (ROC) curve was drawn for the evaluation of predictive ALNM metastasis performance.

RESULTS

Univariate analysis showed that age, HER-2 status, tumor size, nutrient vessels, extended range of enhancement lesion, and the enhancement patterns of SLN were significant predictive features of ALNM. Further binary logistic regression analysis indicated that the extended range of enhancement lesion (p <  0.001) and the enhancement patterns of SLN (p <  0.001) were independent risk factors for ALNM. ROC analysis showed that the AUC of the combination of these two indicators for predicting ALNM was 0.931 (95% CI: 0.887-0.976, sensitivity: 75.0%, specificity: 99.8%).

CONCLUSION

The CEUS characteristics of primary lesion combined with enhancement patterns of SLN are highly valuable in predicting ALNM and can guide clinical axillary surgery decision-making in early breast cancer.

Clinical value of quantitative analysis of contrast-enhanced ultrasonography in the differential diagnosis of benign and malignant pelvic tumors

Background

Cervical cancer, endometrial cancer, and ovarian cancer are among the top 10 most common cancers in women, with ovarian cancer in particular being considered a "silent killer". Therefore, early detection, diagnosis, and treatment constitute important means of care for women's health. This study investigated the clinical value of the quantitative analysis of contrast-enhanced ultrasonography (CEUS) in the differential diagnosis of benign and malignant pelvic tumors.

Methods

CEUS was performed on 151 patients with pelvic masses. Subsequently, a qualitative diagnosis was completed using the image enhancement features and tumor parameters. A multiparametric analysis of CEUS images was performed, which included the following parameters: arrival time (AT), time to peak (TTP), peak intensity (PI), and ascent slope (AS). In addition, the qualitative diagnostic efficiency of CEUS was assessed in a multiparametric analysis, and the results were compared with pathological findings.

Results

The patients in the malignant group were older (P=0.001) and had larger lesion PI values (P<0.01) than those in the benign group. The PI difference (PId) and the AS difference (ASd) showed statistical differences (P<0.01) between the myometrium and lesion tissues in the same patient. Moreover, the PId and ASd showed the largest receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), with sensitivities of 90.9% and 91.7% and specificities of 86.4% and 72.5%, respectively.

Conclusions

The quantitative analysis of CEUS provides a new, simpler, and more accurate method for the differential diagnosis of benign and malignant pelvic masses in clinical practice. The sensitivities and specificities of PId and ASd were higher compared to other parameters from the same patient.