Prediction of Invasive Breast Cancer Using Mass Characteristic Frequency and Elasticity in Correlation with Prognostic Histologic Features and Immunohistochemical Biomarkers

Tumor-Infiltrating Lymphocyte Level Consistently Correlates with Lower Stiffness Measured by Shear-Wave Elastography: Subtype-Specific Analysis of Its Implication in Breast Cancer

Background: We aimed to elucidate the clinical significance of tumor stiffness across breast cancer subtypes and establish its correlation with the tumor-infiltrating lymphocyte (TIL) levels using shear-wave elastography (SWE). Methods: SWE was used to measure tumor stiffness in breast cancer patients from January 2016 to August 2020. The association of tumor stiffness and clinicopathologic parameters, including the TIL levels, was analyzed in three breast cancer subtypes. Results: A total of 803 patients were evaluated. Maximal elasticity (Emax) showed a consistent positive association with an invasive size and the pT stage in all cases, while it negatively correlated with the TIL level. A subgroup-specific analysis revealed that the already known parameters for high stiffness (lymphovascular invasion, lymph node metastasis, Ki67 levels) were significant only in hormone receptor-positive and HER2-negative breast cancer (HR + HER2-BC). In the multivariate logistic regression, an invasive size and low TIL levels were significantly associated with Emax in HR + HER2-BC and HER2 + BC. In triple-negative breast cancer, only TIL levels were significantly associated with low Emax. Linear regression confirmed a consistent negative correlation between TIL and Emax in all subtypes. Conclusions: Breast cancer stiffness presents varying clinical implications dependent on the tumor subtype. Elevated stiffness indicates a more aggressive tumor biology in HR + HER2-BC, but is less significant in other subtypes. High TIL levels consistently correlate with lower tumor stiffness across all subtypes.

A Force-Matched Approach to Large-Strain Nonlinearity in Elasticity Imaging for Breast Lesion Characterization

OBJECTIVE

Ultrasound elasticity imaging is a class of ultrasound techniques with applications that include the detection of malignancy in breast lesions. Although elasticity imaging traditionally assumes linear elasticity, the large strain elastic response of soft tissue is known to be nonlinear. This study evaluates the nonlinear response of breast lesions for the characterization of malignancy using force measurement and force-controlled compression during ultrasound imaging.

METHODS

54 patients were recruited for this study. A custom force-instrumented compression device was used to apply a controlled force during ultrasound imaging. Motion tracking derived strain was averaged over lesion or background ROIs and matched with compression force. The resulting force-matched strain was used for subsequent analysis and curve fitting.

RESULTS

Greater median differences between malignant and benign lesions were observed at higher compressional forces (p-value < 0.05 for compressional forces of 2-6N). Of three candidate functions, a power law function produced the best fit to the force-matched strain. A statistically significant difference in the scaling parameter of the power function between malignant and benign lesions was observed (p-value = 0.025).

CONCLUSIONS

We observed a greater separation in average lesion strain between malignant and benign lesions at large compression forces and demonstrated the characterization of this nonlinear effect using a power law model. Using this model, we were able to differentiate between malignant and benign breast lesions.

SIGNIFICANCE

With further development, the proposed method to utilize the nonlinear elastic response of breast tissue has the potential for improving non-invasive lesion characterization for potential malignancy.

Noninvasive prediction of axillary lymph node breast cancer metastasis using morphometric analysis of nodal tumor microvessels in a contrast-free ultrasound approach

PURPOSE

Changes in microcirculation of axillary lymph nodes (ALNs) may indicate metastasis. Reliable noninvasive imaging technique to quantify such variations is lacking. We aim to develop and investigate a contrast-free ultrasound quantitative microvasculature imaging technique for detection of metastatic ALN in vivo.

EXPERIMENTAL DESIGN

The proposed ultrasound-based technique, high-definition microvasculature imaging (HDMI) provides superb images of tumor microvasculature at sub-millimeter size scales and enables quantitative analysis of microvessels structures. We evaluated the new HDMI technique on 68 breast cancer patients with ultrasound-identified suspicious ipsilateral axillary lymph nodes recommended for fine needle aspiration biopsy (FNAB). HDMI was conducted before the FNAB and vessel morphological features were extracted, analyzed, and the results were correlated with the histopathology.

RESULTS

Out of 15 evaluated quantitative HDMI biomarkers, 11 were significantly different in metastatic and reactive ALNs (10 with P << 0.01 and one with 0.01 < P < 0.05). We further showed that through analysis of these biomarkers, a predictive model trained on HDMI biomarkers combined with clinical information (i.e., age, node size, cortical thickness, and BI-RADS score) could identify metastatic lymph nodes with an area under the curve of 0.9 (95% CI [0.82,0.98]), sensitivity of 90%, and specificity of 88%.

CONCLUSIONS

The promising results of our morphometric analysis of HDMI on ALNs offer a new means of detecting lymph node metastasis when used as a complementary imaging tool to conventional ultrasound. The fact that it does not require injection of contrast agents simplifies its use in routine clinical practice.

The Utility of Quantitative Parameters of Shear-Wave Elastography to Predict Prognostic Histologic Features of Breast Cancer

ABSTRACT

In this study, we aimed to investigate the correlation of stiffness values of shear-wave elastography (SWE) and histopathological prognostic factors in patients with breast cancer. Between January 2021 and June 2022, SWE images of 138 core-biopsy proven breast cancer lesions from 132 patients were retrospectively reviewed. Histopathogic prognostic factors, including tumor size, histologic grade, histologic type, hormone receptor positivity, human epidermal growth factor receptor (HER2) status, immunohistochemical subtype and Ki-67 index were documented. Elasticity values including mean and maximum elasticity ( Emean and Emax ) and lesion-to-fat ratio ( Eratio ) were recorded. The association between histopathological prognostic factors and elasticity values were assessed using Mann-Whitney U and Kruskal-Wallis test, and multiple linear regression analysis. Tumor size, histological grade, and Ki-67 index were significantly associated with the Eratio ( P < 0.05). Larger tumor size and higher Ki-67 index also showed significantly higher Emean and Emax values ( P < 0.05). However, hormone receptor positivity, HER2 status, and immunohistochemical subtype were not significantly associated with elasticity values ( P > 0.05). Multivariate logistic regression analysis revealed that tumor size was significantly associated with Emean , Emax , and Eratio values ( P < 0.05). A high Ki-67 index was also significantly associated with high Eratio values. Larger tumor size and higher Ki-67 index are independently associated with high Eratio values. Preoperative SWE may improve the performance of conventional ultrasound in predicting prognosis and treatment planning.

Relationship between strain elastography and histopathological parameters in breast cancer

Purpose: This study aimed to evaluate the relationship between strain elastography and immunohistochemical markers, the histologic grade, and molecular subtyping in invasive breast cancer. Materials and Methods: The relationships between the elastography index values and progesterone receptor, estrogen receptor, human epidermal growth factor receptor 2, Ki-67, the histologic grade, and molecular subtype in 171 patients who had not received neoadjuvant treatment and underwent breast-conserving surgery were evaluated. Strain elastosonography were used to evaluate elastography index. Results: The mean patient age was 46.871 ± 11.949 years. There were 135, 129, and 90 estrogen receptor-positive, progesterone receptor-positive, and human epidermal growth factor receptor 2-positive patients, respectively. Forty-seven patients had the worst histological grade. Based on molecular subtyping, human epidermal growth factor receptor 2-positive, luminal A, luminal B, and triple-negative classifications were made for 25 (14.5%), 29 (17.0%), 109 (63.7%), and 8 (4.7%) cases, respectively. There was no statistically significant correlation between the elastography index values and estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, Ki-67, histologic grade, or molecular subtype among these breast resection cases. Conclusion: The elastography index value was insufficient to predict the specified histopathological parameters.

Clinical Significance of Ultrasound Elastography and Fibrotic Focus and Their Association in Breast Cancer

(1) Background: Ultrasound (US) elastography is an imaging technology that reveals tissue stiffness. This study aimed to investigate whether fibrotic focus (FF) affects elastographic findings in breast cancer, and to evaluate the clinical significance of US elastography and FF in breast cancer. (2) Methods: In this study, 151 patients with breast cancer who underwent surgery were included. Strain elastography was performed and an elasticity scoring system was used to assess the findings. The elasticity scores were classified as negative, equivocal, or positive. FF was evaluated in the surgical specimens. Medical records were reviewed for all patients. (3) Results: Elastographic findings were equivocal in 30 patients (19.9%) and positive in 121 patients (80.1%). FF was present in 68 patients (46.9%). There was no correlation between elastographic findings and FF. Older age, larger tumor size, lymph node metastasis, and higher tumor stage were associated with positive elastographic results. FF showed a positive correlation with age, postmenopausal status, tumor size, lymphovascular invasion, lymph node metastasis, tumor stage, and intratumoral and peritumoral inflammation. (4) Conclusions: Our study showed that positive elastographic results and FF were associated with poor prognostic factors for breast cancer. FF did not affect the elastographic findings of this study.

Added value of mass characteristic frequency to 2-D shear wave elastography for differentiation of benign and malignant thyroid nodules

Mass characteristic frequency (f_mass) is a novel shear wave (SW) parameter that represents the ratio of the averaged minimum SW speed within the regions of interest to the largest dimension of the mass. Our study objective was to evaluate if the addition of f_mass to conventional 2-D shear wave elastography (SWE) parameters would improve the differentiation of benign from malignant thyroid nodules. Our cohort comprised 107 patients with 113 thyroid nodules, of which 67 (59%) were malignant. Two-dimensional SWE data were obtained using the Supersonic Imagine Aixplorer ultrasound system equipped with a 44- to 15-MHz15-MHz linear array transducer. A receiver operating characteristic curve was generated based on a multivariable logistic regression analysis to evaluate the ability of SWE parameters with/without f_mass and with/without clinical factors to discriminate benign from malignant thyroid nodules. The addition of f_mass to conventional SW elasticity parameters increased the area under the curve from 0.808 to 0.871 (p = 0.02). The combination of SW elasticity parameters plus f_mass plus clinical factors provided the strongest thyroid nodule malignancy probability estimate, with a sensitivity of 93.4% and specificity of 91.1% at the optimal threshold. In summary, f_mass can be a valuable addition to conventional 2-D SWE parameters.

Hybrid high-definition microvessel imaging/shear wave elastography improves breast lesion characterization

BACKGROUND

Low specificity in current breast imaging modalities leads to increased unnecessary follow-ups and biopsies. The purpose of this study is to evaluate the efficacy of combining the quantitative parameters of high-definition microvasculature imaging (HDMI) and 2D shear wave elastography (SWE) with clinical factors (lesion depth and age) for improving breast lesion differentiation.

METHODS

In this prospective study, from June 2016 through April 2021, patients with breast lesions identified on diagnostic ultrasound and recommended for core needle biopsy were recruited. HDMI and SWE were conducted prior to biopsies. Two new HDMI parameters, Murray's deviation and bifurcation angle, and a new SWE parameter, mass characteristic frequency, were included for quantitative analysis. Lesion malignancy prediction models based on HDMI only, SWE only, the combination of HDMI and SWE, and the combination of HDMI, SWE and clinical factors were trained via elastic net logistic regression with 70% (360/514) randomly selected data and validated with the remaining 30% (154/514) data. Prediction performances in the validation test set were compared across models with respect to area under the ROC curve as well as sensitivity and specificity based on optimized threshold selection.

RESULTS

A total of 508 participants (mean age, 54 years ± 15), including 507 female participants and 1 male participant, with 514 suspicious breast lesions (range, 4-72 mm, median size, 13 mm) were included. Of the lesions, 204 were malignant. The SWE-HDMI prediction model, combining quantitative parameters from SWE and HDMI, with AUC of 0.973 (95% CI 0.95-0.99), was significantly higher than the result predicted with the SWE model or HDMI model alone. With an optimal cutoff of 0.25 for the malignancy probability, the sensitivity and specificity were 95.5% and 89.7%, respectively. The specificity was further improved with the addition of clinical factors. The corresponding model defined as the SWE-HDMI-C prediction model had an AUC of 0.981 (95% CI 0.96-1.00).

CONCLUSIONS

The SWE-HDMI-C detection model, a combination of SWE estimates, HDMI quantitative biomarkers and clinical factors, greatly improved the accuracy in breast lesion characterization.