We have developed a method for simultaneous tomosynthesis and mechanical imaging, called DBTMI. Mechanical imaging measures the stress distribution over the compressed breast surface. Malignant tissue is usually stiffer than benign, which results in higher stress on the compressed breast and enables to distinguish malignant from benign findings. By combining tomosynthesis and mechanical imaging, we could improve cancer detection accuracy by reducing the number of false positive findings. In this study we have analysed clinical DBTMI data, collected from 52 women from an ongoing pilot study at the Skåne University Hospital, Malmö, Sweden. We measured the range of the average stress over the breast surface, the range of average stress over the location of suspected lesions, and the normalized stress over the lesion location. Preliminary results show that the range of stress over the breast surface was 1.23-5.84 kPa, the range over the lesion location 2.10-10.10 kPa, and the normalized stress 1.12-2.44 over the lesion location. Overall, the local stress over malignant lesions was higher than the average stress over the entire breast surface. This is the first step investigating criteria to distinguish between malignant and benign findings based upon clinical DBTMI data.
KEYWORDS: Breast, Tissues, Finite element methods, Tumor growth modeling, Mammography, Sensors, Natural surfaces, Data modeling, Computer simulations, Breast cancer
Purpose: Malignant breast lesions can be distinguished from benign lesions by their mechanical properties. This has been utilized for mechanical imaging in which the stress distribution over the breast is measured. Mechanical imaging has shown the ability to identify benign or normal cases and to reduce the number of false positives from mammography screening. Our aim was to develop a model of mechanical imaging acquisition for simulation purposes. To that end, we simulated mammographic compression of a computer model of breast anatomy and lesions.
Approach: The breast compression was modeled using the finite element method. Two finite element breast models of different sizes were used and solved using linear elastic material properties in open-source virtual clinical trial (VCT) software. A spherical lesion (15 mm in diameter) was inserted into the breasts, and both the location and stiffness of the lesion were varied extensively. The average stress over the breast and the average stress at the lesion location, as well as the relative mean pressure over lesion area (RMPA), were calculated.
Results: The average stress varied 6.2–6.5 kPa over the breast surface and 7.8–11.4 kPa over the lesion, for different lesion locations and stiffnesses. These stresses correspond to an RMPA of 0.80 to 1.46. The average stress was 20% to 50% higher at the lesion location compared with the average stress over the entire breast surface.
Conclusions: The average stress over the breast and the lesion location corresponded well to clinical measurements. The proposed model can be used in VCTs for evaluation and optimization of mechanical imaging screening strategies.
Simultaneous Digital Breast Tomosynthesis (DBT) and mechanical imaging (MI), called DBTMI, is a novel breast imaging method aimed at improving sensitivity and specificity of breast cancer screening. DBTMI combines improved cancer detection by three-dimensional DBT imaging, with the analysis of local stress over the compressed breast by MI, which can reduce false positive findings. The MI signal is affected by various factors, e.g., breast size, composition, tumor depth, etc. Assessing the individual effect of those factors using clinical data is difficult, due to their interdependence. These open clinical questions can be addressed by virtual clinical trials. Our current work is focused on the effects of tumor depth on the DBTMI signal. We simulated the breast anatomy by a matrix of adipose and glandular tissue compartments. Spherical tumors were inserted at various depths. The MI sensor is modeled by a compound material of PMMA and Ag. We calculated the local stress on the compressed breast surface at the tumor location and simulated the MI sensor output. We also simulated the corresponding DBT images and calculated the signal-difference-to-noise ratio (SDNR) with and without pre-processing to analyze the reduction in artifacts. Our preliminary analysis of 24 simulated tumors has shown 16% reduction in the local stress, when increasing tumor depth by 15 mm (10-25 mm from the breast surface). The SDNR improvement was highest for tumors near the sensor and the effect of pre-processing decreased with increasing tumor depth.
Malignant breast tumours can be distinguished from benign lesions and normal tissue based on their mechanical properties. Our pilot studies have demonstrated the potential of using Mechanical Imaging (MI) combined with mammography to reduce recalls and false positives in breast cancer screening by more accurately identifying benign lesions. To enable further optimization of MI we propose a computer simulation of the MI acquisition, for use in a Virtual Clinical Trial (VCT) framework. VCTs are computer simulated clinical trials used to efficiently evaluate clinical imaging systems. A linear elastic finite element (FE) model of the breast under dynamic compression was implemented using an open-source FE solver. A spherical tumour (15 mm in diameter) was inserted into the simulated predominantly adipose breast. The location and stiffness of the tumour was varied. The average stress on the compressed breast surface was calculated and compared with the local average stress at the tumour location and the Relative Mean Pressure over lesion Area (RMPA) was calculated. Preliminary results were within a realistic range with an average stress on the breast (tumour) of 5.9-16.6 kPa which is in agreement with published values between 1.0 – 22.5 kPa. This corresponds to RMPA values of 0.96-2.15 depending on stiffness and location of the tumour. This can lead to more detailed validation of various MI acquisition schemes through VCTs before their use in clinical studies.
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