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Using a large set of ultrasound features does not necessarily ensure improved quantitative classification of breast tumors; rather, it often degrades the performance of a classifier. In this paper, we propose an effective feature reduction approach in the transform domain for improved multi-class classification of breast tumors. Feature transformation methods, such as empirical mode decomposition (EMD) and discrete wavelet transform (DWT), followed by a filter- or wrapper-based subset selection scheme are used to extract a set of non-redundant and more potential transform domain features through decorrelation of an optimally ordered sequence of N ultrasonic bi-modal (i.e., quantitative ultrasound and elastography) features. The proposed transform domain bi-modal reduced feature set with different conventional classifiers will classify 201 breast tumors into benign-malignant as well as BI-RADS⩽3, 4, and 5 categories. For the latter case, an inadmissible error probability is defined for the subset selection using a wrapper/filter. The classifiers use train truth from histopathology/cytology for binary (i.e., benign-malignant) separation of tumors and then bi-modal BI-RADS scores from the radiologists for separating malignant tumors into BI-RADS category 4 and 5. A comparative performance analysis of several widely used conventional classifiers is also presented to assess their efficacy for the proposed transform domain reduced feature set for classification of breast tumors. The results show that our transform domain bimodal reduced feature set achieves improvement of 5.35%, 3.45%, and 3.98%, respectively, in sensitivity, specificity, and accuracy as compared to that of the original domain optimal feature set for benign-malignant classification of breast tumors. In quantitative classification of breast tumors into BI-RADS categories⩽3, 4, and 5, the proposed transform domain reduced feature set attains improvement of 3.49%, 9.07%, and 3.06%, respectively, in likelihood of malignancy and 4.48% in inadmissible error probability compared to that of the original domain optimal subset. In summary, the construction of a transform domain reduced feature set by extracting complementary information from a large set of available bi-modal features and use of qualitative bi-modal BI-RADS can contribute to improved quantitative classification of breast tumors and thereby help reduce the number of unnecessary biopsies, securing a nearly minimum chance of a life-endangering diagnosis.

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.

In this paper, a phase-based direct average strain estimation method is developed. A mathematical model is presented to calculate axial strain directly from the phase of the zero-lag cross-correlation function between the windowed precompression and stretched post-compression analytic signals. Unlike phase-based conventional strain estimators, for which strain is computed from the displacement field, strain in this paper is computed in one step using the secant algorithm by exploiting the direct phase-strain relationship. To maintain strain continuity, instead of using the instantaneous phase of the interrogative window alone, an average phase function is defined using the phases of the neighboring windows with the assumption that the strain is essentially similar in a close physical proximity to the interrogative window. This method accounts for the effect of lateral shift but without requiring a prior estimate of the applied strain. Moreover, the strain can be computed both in the compression and relaxation phases of the applied pressure. The performance of the proposed strain estimator is analyzed in terms of the quality metrics elastographic signal-to-noise ratio (SNRe), elastographic contrast-to-noise ratio (CNRe), and mean structural similarity (MSSIM), using a finite element modeling simulation phantom. The results reveal that the proposed method performs satisfactorily in terms of all the three indices for up to 2.5% applied strain. Comparative results using simulation and experimental phantom data, and in vivo breast data of benign and malignant masses also demonstrate that the strain image quality of our method is better than the other reported techniques.

Ethnopharmacological relevance: Tabebuia pallida (Lindl.) Miers (T. pallida) is a well-known native Caribbean medicinal plant. The leaves and barks of T. pallida are used as traditional medicine in the form of herbal or medicinal tea to manage cancer, fever, and pain. Moreover, extracts from the leaves of T. pallida showed anticancer activity. However, the chemical profile and mechanism of anticancer activity of T. pallida leaves (TPL), stem bark (TPSB), root bark (TPRB) and flowers (TPF) remain unexplored. Aim of the study: The present study was designed to explore the regulation of apoptosis by T. pallida using Ehrlich Ascites Carcinoma (EAC) cultured cells and an EAC mouse model. LC-ESI-MS/MS was used for compositional analysis of T. pallida extracts. Materials and methods: Dried and powdered TPL, TPSB, TPRB and TPF were extracted with 80% methanol. Using cultured EAC cells and EAC-bearing mice with and without these extracts, anticancer activities were studied by assessing cytotoxicity and tumor cell growth inhibition, changes in life span of mice, and hematological and biochemical parameters. Apoptosis was analyzed by microscopy and expression of selected apoptosis-related genes (Bcl-2, Bcl-xL, NFκ-B, PARP-1, p53, Bax, caspase-3 and -8) using RT-PCR. LC-ESI-MS analysis was performed to identify the major compounds from active extracts. Computer aided analyses was undertaken to sort out the best-fit phytoconstituent of total ten isolated compounds of this plant for antioxidant and anticancer activity. Results: In EAC mice compared with untreated controls, the TPL extract exhibited the highest cancer cell toxicity with significant tumor cell growth inhibition (p < 0.001), reduced ascites by body weight (p < 0.01), increased the life span (p < 0.001), normalized blood parameters (RBC/WBC counts), and increased the levels of superoxide dismutase and catalase. TPL-treated EAC cells showed increased apoptotic characteristics of membrane blebbing, chromatin condensation and nuclear fragmentation, and caspase-3 activation, compared with untreated EAC cells. Moreover, annexin V-FITC and propidium iodide signals were greatly enhanced in response to TPL treatment, indicating apoptosis induction. Pro- and anti-apoptotic signaling after TPL treatment demonstrated up-regulated p53, Bax and PARP-1, and down-regulated NFκ-B, Bcl-2 and Bcl-xL expression, suggesting that TPL shifts the balance of pro- and anti-apoptotic genes towards cell death. LC-ESI-MS data of TPL showed a mixture of glycosides, lapachol, and quercetin antioxidant and its derivatives that were significantly linked to cancer cell targets. The compound, pelargonidin-3-O-glucoside was found to be most effective in computer aided models. Conclusions: In conclusion, the TPL extract of T. pallida possesses significant anticancer activity. The tumor suppressive mechanism is due to apoptosis induced by activation of antioxidant enzymes and caspases and mediated by a change in the balance of pro- and anti-apoptotic genes that promotes cell death.