Recently, the feasibility of visualizing the characteristics of bonding at an inclusion-background boundary using axial-shear strain elastography was demonstrated. In this paper, we report a feasibility study on the utility of the axial-shear strain elastograms in the classification of in vivo breast tumor as being benign or malignant. The study was performed using data sets obtained from 15 benign and 15 malignant cases that were biopsy proven. A total of three independent observers were trained, and their services were utilized for the study. A total of 9 cases were used as training set and the remaining cases were used as testing set. The feature from the axial-shear strain elastogram, namely, the area of the axial-shear region, was extracted by the observers. The observers also outlined the tumor area on the corresponding sonogram, which was used to normalize the area of the axial-shear strain region. There are several observations that can be drawn from the results. First, the result indicates that the observers consistently ( approximately 82% of the cases) noticed the characteristic pattern of the axial-shear strain distribution data as predicted in the previous simulation studies, i.e. alternating regions of positive and negative axial-shear strain values around the tumor-background interface. Second, the analysis of the result suggests that in approximately 57% of the cases in which the observers did not visualize tumor in the sonogram, the elastograms helped them to locate the tumor. Finally, the analysis of the result suggests that for the discriminant feature value of 0.46, the number of unnecessary biopsies could be reduced by 56.3% without compromising on sensitivity and on negative predictive value (NPV). Based on the results in this study, feature values greater than 0.75 appear to be indicative of malignancy, while values less than 0.46 to be indicative of benignity. Feature values between 0.46 and 0.75 may result in an overlap between benign and malignant cases.

Lymphedema is a common condition involving an abnormal accumulation of lymphatic fluid in the interstitial space that causes swelling, most often in the arm(s) and leg(s). Lymphedema is a significant lifelong concern that can be congenital or develop following cancer treatment or cancer metastasis. Common methods of evaluation of lymphedema are mostly qualitative making it difficult to reliably assess the severity of the disease, a key factor in choosing the appropriate treatment. In this paper, we investigate the feasibility of using novel elastographic techniques to differentiate between lymphedematous and normal tissues. This study represents the first step of a larger study aimed at investigating the combined use of elastographic and sonographic techniques for the detection and staging of lymphedema. In this preliminary study, poroelastographic images were generated from the leg (8) and arm (4) subcutis of five normal volunteers and seven volunteers having lymphedema, and the results were compared using statistical analyses. The preliminary results reported in this paper suggest that it may be feasible to perform poroelastography in different lymphedematous tissues in vivo and that poroelastography techniques may be of help in differentiating between normal and lymphedematous tissues.

The purpose of this work was to investigate the potential of the normalized axial-shear strain area (NASSA) feature, derived from axial-shear strain elastograms (ASSE), for breast lesion classification of fibroadenoma and cancer. This study consisted of previously-acquired in vivo digital RF-data of breast lesions. A total of 33 biopsy-proven malignant tumors and 30 fibroadenoma cases were included in the study that involved 3 observers blinded to the original BIRADS®-ultrasound scores. The observers outlined the lesions on the sonograms. The ASSEs were segmented and color-overlaid on the sonograms, and the NASSA feature from the ASSE was computed semi-automatically. Receiver operating characteristic (ROC) curves were then generated and the area under the curve (AUC) was calculated for each observer performance. A logistic regression classifier was built to compare the improvement in the AUC when using BIRADS scores plus NASSA values as opposed to BIRADS scores alone. BIRADS score ROC had an AUC of 0.89 (95% CI = 0.81 -0.97). In comparison, the average of the AUC for all the three observers using ASSE feature alone was 0.84. However, the AUC increased to 0.94 (average of 3 observers) when BIRADS score and ASSE feature were combined. The results demonstrate that the NASSA feature derived from ASSE has the potential to improve BIRADS breast lesion classification of fibroadenoma and malignant tumors.