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Proposed here is a breast tumor classification technique using conventional ultrasound B-mode imaging and a new elasticity imaging-based bimodal multiparameter index. A set of conventional ultrasound (US) and ultrasound elastography (UE) parameters are studied, and among those, the effective ones whose independent as well as combined performance is found satisfactory are selected. To improve the combined US performance, two new US parameters are proposed: edge diffusivity, which assesses edge blurriness to differentiate malignant from benign lesions, and the shape asymmetry factor, which quantifies tumor shape irregularity by comparing the tumor boundary with an ellipse fitted to the lesion. Then a new bimodal multiparameter characterization index is defined to discriminate 201 pathologically confirmed breast tumors of which 56 are malignant lesions, 79 are fibroadenomas, 42 are cysts and 24 are inflammatory lesions. The weights of the multiparameter bimodal index are optimally computed using a genetic algorithm (GA). To evaluate the performance variation of the index on different data sets, the tumors are categorized into three classes: malignant lesion versus fibroadenoma, malignant lesion versus fibroadenoma and cyst and malignant lesion versus fibroadenoma, cyst and inflammation. The test results reveal that the proposed bimodal index achieves satisfactory quality metrics ( e.g ., 94.64%–98.21% sensitivity, 97.24%–100.00% specificity and 96.52%–99.44% accuracy) for classification of the aforementioned three classes of breast tumors. Its performance is also observed to be better in totality of the quality metrics sensitivity, specificity, accuracy, positive predictive value and negative predictive value as compared with that of a conventional bimodal index as well as unimodal multiparameter indices based on US or UE. It is suggested that the proposed simple bimodal linear classifier may assist radiologists in better diagnosis of breast tumors and help reduce the number of unnecessary biopsies. [ABSTRACT FROM AUTHOR]

Multi-modal complementary information integrated noninvasive digital biomarkers are emerging with enormous potential for early detection of female breast cancer and reduction of mortality rate among women around the globe. The objective of this study is to design new quantitative ultrasound (QUS) biomarkers integrated from pathological information of tissue microstructure, conventional B-mode imaging-based acoustic and morphological features, and strain imaging-based tissue elasticity features for detecting cancer more effectively. We also develop simulation models to evaluate the estimation accuracy of effective scatterer diameter (ESD), a characteristic parameter of tissue pathology. The proposed biomarkers are designed by optimally integrating 2 QUS indirect pathology (i PATH) parameters with 27 QUS radiology and tissue elasticity (RATE) parameters. To determine effective biomarkers, several techniques are employed: a wrapper-based feature reduction technique in the original feature domain as well as in the empirical mode decomposition (EMD)-discrete wavelet transforms (DWT) domain, and a genetic algorithm-based feature weighting technique. Simulation phantoms determining the accuracy of the ESD estimation algorithm and explaining the increasing trend of ESD with the carcinogenic transformation of normal tissue are created in MATLAB and analyzed using Field II. The resulting average mean absolute percentage error of the ESD estimation algorithm on the simulation phantoms is 4.06%, proving its efficacy. On 139 in-vivo patient data, the three techniques yield accuracy, sensitivity, and specificity within the range of 97.84–99.28%, 97.84–100%, and 95.45–98.95%, respectively. The high values of these metrics indicate that the RATE- i PATH feature set can be used as non-invasive integrated biomarkers for computer-aided diagnosis of breast cancer. [ABSTRACT FROM AUTHOR]

Quantitative ultrasound (QUS) parameters are gaining attention recently as non-invasive biomarkers for soft tissue characterization. In this work, we use two QUS parameters, namely effective scatterer diameter (ESD) and mean scatterer spacing (MSS), for binary classification of breast lesions. To produce improved classification results, we propose a modified frequency-domain technique for ESD estimation of breast tissues from the diffuse component of backscattered radio-frequency (RF) data. Ensemble empirical mode decomposition (EEMD) is performed to separate the diffuse component from the coherent component of the backscattered RF data. A non-parametric Kolmogorov–Smirnov (K-S) test is employed for automatic selection of the intrinsic mode functions (IMFs). A novel multi-step system effect minimization process is also used. The ESD is estimated using a novel nearest neighborhood average regression line fitting (NNARLF) algorithm. Furthermore, we use an ameliorated EEMD domain autoregressive (AR) spectral estimation technique for MSS estimation. On using the ESD for binary classification of 139 lesions, we obtain high sensitivity, specificity, accuracy values of 95.45%, 95.79%, and 95.68%, respectively. The area under the receiver operating characteristics (ROC) curve is 0.95. On combining ESD with MSS we obtain even more improved sensitivity, specificity, and accuracy values of 97.73%, 95.79%, and 96.40%, respectively. The area under the ROC also increases to 0.97. This high classification performance demonstrates the potential of the use of these QUS parameters as non-invasive biomarkers for breast cancer detection. [ABSTRACT FROM AUTHOR]