Abstract
The diffusion signal in breast tissue has primarily been modelled using apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and diffusion tensor (DT) models, which may be too simplistic to describe the underlying tissue microstructure. Formalin‐fixed breast cancer samples were scanned using a wide range of gradient strengths, durations, separations and orientations. A variety of one‐ and two‐compartment models were tested to determine which best described the data. Models with restricted diffusion components and anisotropy were selected in most cancerous regions and there were no regions in which conventional ADC or DT models were selected. Maps of ADC generally related to cellularity on histology, but maps of parameters from more complex models suggest that both overall cell volume fraction and individual cell size can contribute to the diffusion signal, affecting the specificity of ADC to the tissue microstructure. The areas of coherence in diffusion anisotropy images were small, approximately 1 mm, but the orientation corresponded to stromal orientation patterns on histology.
Highlights
Breast cancer screening allows the early detection of cancerous lesions, but improved technology increases the likelihood of detecting small, slow‐growing cancers that do not require aggressive treatment
Model parameters were compared with histology, first examining parameters associated with compartment size and restriction, and those associated with orientational structure
The data from most cellular cancer regions and the adjacent fibroglandular tissue were best explained using a Tensor–Sphere or Zeppelin–Sphere model, indicating that both restriction and anisotropy are present in breast cancer tissues
Summary
Breast cancer screening allows the early detection of cancerous lesions, but improved technology increases the likelihood of detecting small, slow‐growing cancers that do not require aggressive treatment. A previous study found large differences in the ADC of epithelial cell regions compared with surrounding stroma, as well as qualitative differences in anisotropy.[38] The ex vivo approach permitted longer scan times to obtain data over a broad range of gradient strengths, durations, orientations and diffusion times. This rich dataset was fitted with a set of candidate models which describe the intracellular and extracellular spaces with different shapes and degrees of restriction. This information can be used to optimise clinical scan protocols[39] and to select a biologically relevant signal model with parameters that might allow for higher specificity in tumour characterisation
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