Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods

Abstract

Purpose Accurate and timely diagnosis of breast cancer is extremely important because of its high incidence and high morbidity. Early diagnosis of breast cancer through screening can improve overall prognosis. Currently, biopsy remains as the gold standard for tumor pathological confirmation. Development of diagnostic imaging techniques for rapid and accurate characterization of breast lesions is required. We aim to evaluate the usefulness of texture-derivate features of QUS spectral parametric images for non-invasive characterization of breast lesions. Methods QUS Spectroscopy was used to determine parametric images of mid-band fit (MBF), spectral slope (SS), spectral intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) in 204 patients with suspicious breast lesions. Subsequently, texture analysis techniques were used to generate texture maps from parametric images to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS parameters, texture-parameters and texture-derivate parameters were determined from both tumor core and a 5-mm tumor margin and were used in comparison to histopathological analysis in order to develop a diagnostic model for classifying breast lesions as either benign or malignant. Both leave-one-out and hold-out cross-validations were used to evaluate the performance of the diagnostic model. Three standard classification algorithms including a linear discriminant analysis (LDA), k -nearest neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated. Results Core and margin information using the SVM-RBF attained the best classification performance of 90% sensitivity, 92% specificity, 91% accuracy, and 0.93 AUC utilizing QUS parameters and their texture derivatives, evaluated using leave-one-out cross-validation. Implementation of hold-out cross-validation using combination of both core and margin information and SVM-RBF achieved average accuracy and AUC of 88% and 0.92, respectively. Conclusions QUS-based framework and derivative texture methods enable accurate classification of breast lesions. Evaluation of the proposed technique on a large cohort using hold-out cross-validation demonstrates its robustness and its generalization.

Comparison of methods for texture analysis of QUS parametric images in the characterization of breast lesions

“…Earlier, the cohort consisted of 78 patients with breast lesions (46 benign and 32 malignant cases) [8]. Recently, novel derivative texture methods were also evaluated on a larger cohort of patients with breast lesions [9]. In those studies, however, only the GLCM method was used to quantify texture of the parametric images.…”

“…However, DCE-MRI is not always available for rapid diagnosis purposes. The development of imaging techniques that can perform rapid and accurate characterization of breast lesions is highly beneficial for early detection of breast carcinoma and triaging patients in a screening workflow [8,9].…”

“…Furthermore, QUS spectroscopy have also been used in clinical research to differentiate breast tumours from the surrounding normal tissues in patients with locally-advanced breast cancer (LABC) [38]. Recently, QUS spectral parametric imaging, along with texture and novel derivative texture analysis, have been used in the characterization of breast lesions [8,9].…”

“…In earlier studies, we only used the GLCM method to analyze texture of QUS spectral parametric images [8,9]. In this study, different texture methods were applied to QUS spectral parametric images encompassing the breast mass and its 5-mm margin.…”

“…QUS-based texture analysis of tumour margins has been demonstrated in the a priori prediction of response and survival in LABC patients undergoing neoadjuvant chemotherapy (NAC) [62]. Margin information is further potentially useful for characterizing breast lesions as has been shown recently using QUS spectroscopy [9], US Nakagami shape parameters and texture features of B-mode US images [63].…”