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Breast Tumor Diagnosis Using Finite‐Element Modeling Based on Clinical in vivo Elastographic Data
Author(s) -
Sayed Ahmed M.,
Naser Mohamed A.,
Wahba Ashraf A.,
Eldosoky Mohamed A. A.
Publication year - 2020
Publication title -
journal of ultrasound in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.574
H-Index - 91
eISSN - 1550-9613
pISSN - 0278-4297
DOI - 10.1002/jum.15344
Subject(s) - finite element method , in vivo , medicine , elastography , breast tumor , ultrasound elastography , nonlinear system , compression (physics) , biomedical engineering , ultrasound , radiology , pathology , breast cancer , structural engineering , cancer , physics , materials science , microbiology and biotechnology , quantum mechanics , engineering , composite material , biology
Objectives This study exploited finite‐element modeling (FEM) to simulate breast tissue multicompression during ultrasound elastography to classify breast tumors based on their nonlinear biomechanical properties. Methods Numeric simulations were first calculated by using 3‐dimensional (3D) virtual models with an assumed tumor's geometric dimensions but with actual material properties to test and validate the FEM. Further numeric simulations were used to construct 3D models based on in vivo experimental data to verify our models. The models were designed for each individual in vivo case, emphasizing the geometry, position, and biomechanical properties of the breast tissue. At different compression levels, tissue strains were analyzed between the tumors and the background normal tissues to explore their nonlinearity and classify the tumor type. Tumor classification parameters were deduced by using a power‐law relationship between the applied compressive forces and strain differences. Results Classification parameters were compared between benign and malignant tumors, for which they were found to be statistically significant in classifying the tumor types ( P < .05) by both the validation and verification of FEM. We compared the classification parameters between the in vivo and FEM classifications, for which they were found to be strongly correlated ( R = 0.875; P < .001), with no statistical differences between their outcomes ( P = .909). Conclusions Good agreement between the model outcomes and the in vivo diagnostics was reported. The implemented models were validated and verified. The introduced 3D modeling method may augment elastographic methods to preliminary classify breast tumors at an early stage.