RATE-iPATH: On the design of integrated ultrasonic biomarkers for breast cancer detection. (September 2020)

• Record Type: Journal Article
• Authors: Hossen, Zakir; Abrar, Mohammed Abid; Ara, Sharmin R.; Hasan, Md. Kamrul
• Is Part Of: Biomedical signal processing and control. Volume 62(2020)
• Publication Date: 2020-09
• Subjects: Tissue characterization -- Quantitative ultrasound -- Simulation models -- Indirect pathology -- Integrated biomarker -- Cancer detection -- Computer-aided diagnosis; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102053
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Multi-modal complementary information integrated noninvasive digital biomarkers are emerging with enormous potential for early detection of female breast cancer and reduction of mortality rate among women around the globe. The objective of this study is to design new quantitative ultrasound (QUS) biomarkers integrated from pathological information of tissue microstructure, conventional B-mode imaging-based acoustic and morphological features, and strain imaging-based tissue elasticity features for detecting cancer more effectively. We also develop simulation models to evaluate the estimation accuracy of effective scatterer diameter (ESD), a characteristic parameter of tissue pathology. The proposed biomarkers are designed by optimally integrating 2 QUS indirect pathology ( i PATH) parameters with 27 QUS radiology and tissue elasticity (RATE) parameters. To determine effective biomarkers, several techniques are employed: a wrapper-based feature reduction technique in the original feature domain as well as in the empirical mode decomposition (EMD)-discrete wavelet transforms (DWT) domain, and a genetic algorithm-based feature weighting technique. Simulation phantoms determining the accuracy of the ESD estimation algorithm and explaining the increasing trend of ESD with the carcinogenic transformation of normal tissue are created in MATLAB and analyzed using Field II. The resulting average mean absolute percentage error of the ESD estimation algorithm on the simulation phantoms is 4.06%, proving its efficacy. On 139 in-vivo patient data, the three techniques yield accuracy, sensitivity, and specificity within the range of 97.84–99.28%, 97.84–100%, and 95.45–98.95%, respectively. The high values of these metrics indicate that the RATE- i PATH feature set can be used as non-invasive integrated biomarkers for computer-aided diagnosis of breast cancer.