Classification of benign and malignant breast tumors by the contour analysis and scatterers characterization

Abstract

The B-scan reflects the intensity of the reflected echoes, and is clever at a clear description of tumor contour to provide knowledge of morphology, and the Nakagami image reflects the statistical distribution of local backscattered signals, which is associated with the arrangements and concentrations of scatterers in tumors. In this study, we explored the clinical performance of combining the B-scan-based tumor contour analysis and the Nakagami-image-based tumor scatterers characterization in classifying benign and malignant breast tumors. To confirm this concept, rawdata obtained from 60 clinical cases were acquired. The B-mode images were used to calculate the standard deviation of the shortest path for contour feature analysis, and the Nakagami images were applied to estimate the average Nakagami parameters in the region of interests (ROI) in tumors. Overall, malignant tumors were highly irregular in tumor contour, whereas they had lower average Nakagami parameters in scatterers characterization. The receiver operating characteristic (ROC) curve and fuzzy c-means (FCM) clustering were used to estimate the performances of combining two parameters in classifying tumors. The clinical results showed that there would be a tradeoff between the sensitivity and specificity when using a single parameter to differentiate benign and malignant tumors. The ROC analysis demonstrated that the standard deviation (SD) of the shortest distance had a diagnostic accuracy of 81.7%, sensitivity of 76.7%, and specificity of 86.7%. The Nakagami parameter had a diagnostic accuracy of 80%, sensitivity of 86.7%, and specificity of 73.3%. However, the combination of the SD of the shortest distance and the Nakagami parameter concurrently allows both the sensitivity and specificity to exceed 80%, making the performance to diagnose breast tumors better.