Quantitative ultrasound assessment of breast cancer using a multiparameter approach

Early detection and diagnosis of breast cancer leads to improved prognosis. Quantitative ultrasound (QUS) techniques utilizing a multiparameter set have been developed for classifying rodent models of breast cancer. The improvement in detection and diagnosis of breast cancer using QUS will have significant medical impact. Two kinds of mammary tumors, carcinoma and sarcoma, were examined in mice using QUS imaging. Ten tumors for each kind of cancer were scanned with a 20-MHz single-element transducer (f/3). The tumors contained microstructural differences in size, shape, and organizational patterns of the scatterers. Cells were identified as a prominent source of scattering in the tumors. The average scatterer diameter (ASD) and average acoustic concentration (AAC) were estimated by comparing the normalized backscattered power spectra from the tumors with newly developed models of cell scattering. The organizational structure of the tumors was also characterized by a clustering parameter (the ßparameter) and the randomness of the scatterer locations (the S parameter) by comparing the envelope statistics of the backscatter to a homodyned-K distribution. F-tests conducted on the backscattered power spectra from the two kinds of tumors revealed statistically significant differences for frequencies above 16 MHz. QUS images of the tumors utilizing the ASD, AAC, ß, and S parameter estimates from the new model and the envelope statistics were constructed. Statistically significant differences were observed between the carcinomas and sarcomas for all estimated parameters for ultrasonic frequencies above 16 MHz. Feature analysis plots incorporating all four parameters indicated cancer classification was improved compared with analysis using only two parameters. High-frequency QUS utilizing a multiparameter feature set improved the diagnostic potential of ultrasound for breast cancer detection. (Supported by NIH Grants CA 079179 and CA111289)