Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

© 2017 The Author(s). This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic

Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities

© 2017 The Author(s). Anti-cancer therapies including chemotherapy aim to induce tumour cell death. Cell death introduces alterations in cell morphology and tissue micro-structures that cause measurable changes in tissue echogenicity. This study investigated the effectiveness of quantitative ultrasound (QUS) parametric imaging to characterize intra-tumour heterogeneity and monitor the pathological response of breast cancer to chemotherapy in a large cohort of patients (n = 100). Results demonstrated that QUS imaging can non-invasively monitor pathological response and outcome of breast cancer patients to chemotherapy early following treatment initiation. Specifically, QUS biomarkers quantifying spatial heterogeneities in size, concentration and spacing of acoustic scatterers could predict treatment responses of patients with cross-validated accuracies of 82 ± 0.7%, 86 ± 0.7% and 85 ± 0.9% and areas under the receiver operating characteristic (ROC) curve of 0.75 ± 0.1, 0.80 ± 0.1 and 0.89 ± 0.1 at 1, 4 and 8 weeks after the start of treatment, respectively. The patients classified as responders and non-responders using QUS biomarkers demonstrated significantly different survivals, in good agreement with clinical and pathological endpoints. The results form a basis for using early predictive information on survival-linked patient response to facilitate adapting standard anti-cancer treatments on an individual patient basis

Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer

Aim: We aimed to identify quantitative ultrasound (QUS)-radiomic markers to predict radiotherapy response in metastatic lymph nodes of head and neck cancer. Materials & methods: Node-positive head and neck cancer patients underwent pretreatment QUS imaging of their metastatic lymph nodes. Imaging features were extracted using the QUS spectral form, and second-order texture parameters. Machine-learning classifiers were used for predictive modeling, which included a logistic regression, naive Bayes, and k-nearest neighbor classifiers. Results: There was a statistically significant difference in the pretreatment QUS-radiomic parameters between radiological complete responders versus partial responders (p < 0.05). The univariable model that demonstrated the greatest classification accuracy included: spectral intercept (SI)-contrast (area under the curve = 0.741). Multivariable models were also computed and showed that the SI-contrast + SI-homogeneity demonstrated an area under the curve = 0.870. The three-feature model demonstrated that the spectral slope-correlation + SI-contrast + SI-homogeneity-predicted response with accuracy of 87.5%. Conclusion: Multivariable QUS-radiomic features of metastatic lymph nodes can predict treatment response a priori

Tissue characterization using A-mode ultrasound for beef quality grading, 1992-1995

Many of the modern medical diagnostic and material characterization technologies are a direct result of clever use of ultrasound. Ultrasound, for example, is used very widely as a tool for characterizing tissue in medical applications. It is also used extensively in other applications such as nondestructive testing and fluid flow measurements. The objective of the research described in this thesis is to develop a computer based ultrasound system for characterizing beef tissue for quality grading. Extensive research has been performed with regard to tissue characterization in medical applications. Similar work has also been carried out using ultrasound for structural analysis in material science applications. However, little has been done in applying ultrasound for characterizing beef tissue (quality grading). Thus, the requirements of an ultrasound system and the data processing algorithms are unknown. However, since beef tissue has physical properties that are similar to human tissue, the instrumentation and algorithms for analysis should be similar. This thesis describes the work and results obtained to date in attempting to achieve the stated objective

Is early detection of liver and breast cancers from ultrasound scans possible

Abstract This paper presents an integral approach for the tissue characterization problem. Such an approach includes a model, estimation algorithms and an evaluation method. This work focuses on liver and breast tissue characterization but it may be applicable to other tissue types after proper modifications. Liver and breast tissue is composed of two major kinds of scattering structure, i.e., the liver and breast parenchyma, which is relatively large and thus resolvable using the current ultrasonic transducers, and liver and breast cells which are not resolvable. In this work, we propose a decomposition approach for the RF echo into two components, namely the coherent and diffuse component, which are related to the resolvable and unresolvable scatterers in the liver and breast structure, respectively. Structural differences between the liver and breast, related to the resolvable scatterers properties, led us to develop two different decomposition algorithms. The first algorithm was developed for the liver RF echo and was based on the quasi-periodic structure of the liver lobules. Breast tissue decomposition was based on a more general model for the resolvable scatterers echo, because the breast tissue parenchyma is far from regular. By using the proposed decomposition we were able to estimate structural parameters of the liver and breast such as the average spacing of the liver lobules, the energy of the resolvable and unresolvable scatterers, and the correlation between neighboring unresolvable scatterers in the tissue. Empirical receiver operating characteristics analysis was applied to the parameters estimated from a large database of liver and breast B-scan images, to evaluate their diagnostic power. Single parameters of the liver and breast tissue showed good discriminating power between cancerous and normal liver and breast tissue, and also between malignant and benign breast tissue. The ability to identify small breast lesions (4 mm) is also demonstrated