Strain ratio vs. modulus ratio for the diagnosis of breast cancer using elastography

• Published in: Biomedical engineering letters Vol. 4; no. 3; pp. 292 - 300
• Main Authors: Kwon, Hyock Ju, Shin, Bonghun, Gopaul, Darindra, Fienberg, Samantha
• Format: Journal Article
• Language: English
• Published: Korea The Korean Society of Medical and Biological Engineering 01.09.2014; 대한의용생체공학회
• Subjects: Biological and Medical Physics; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Engineering; Medical and Radiation Physics; Original Article; 의공학; Breast cancer; Eshelby’s solution; Elastogram; Elastography
• ISSN: 2093-9868; 2093-985X
• DOI: 10.1007/s13534-014-0146-y

Purpose In elastography, strain ratio is commonly used as an indicator to detect the malignancy of the lesion, based on palpation principle that pathological lesions are normally stiffer than benign tissues. We investigated the validity of strain ratio by comparing it with modulus ratio which is a true indicator of the malignancy. Methods Strains inside the lesion and the tissue under compressive loading were predicted by Eshelby’s solution which is an analytical method to derive the elastic field within and around an ellipsoidal inclusion embedded in the matrix. Analytical results were also confirmed with finite element analysis (FEA) simulations. Results The analyses using Eshelby’s solution demonstrated that strain ratio not only significantly underestimates the modulus ratio which is the true indicator of malignancy, but also varies with other factors such as the shape and the stiffness of the inclusion. Based on the results from Eshelby’s solution and FEA, we proposed a surface regression model as a cubic polynomial function of strain ratio and ellipticity of the lesion to predict the modulus ratio. Conclusions The proposed model has been successfully applied to gelatin phantoms mimicking breast cancers and clinical ultrasound images of human breasts containing different types of lesions. This study suggests the potential of the modulus ratio predicted by the model as a new indicator in differentiating malignant tumors from benign lesions.