A study of the effect of formalin preservation on normal and cancerous breast tissues using small angle X-ray scattering (SAXS)

X-ray dark-field imaging modeling

Dark-field images are formed from x-ray small-angle scattering signals. The small-angle scattering signals are particularly sensitive to structural variation and density fluctuation on a length scale of several tens to hundreds of nanometers, offering a unique contrast mechanism to reveal subtle structural features of an object. In this study, based on the principle of energy conservation, we develop a physical model to describe the relationship between x-ray small-angle scattering coefficients of an object and dark-field intensity images. This model can be used to reconstruct volumetric x-ray small-angle scattering images of an object using classical tomographic algorithms. We also establish a relationship between the small-angle scattering intensity and the visibility function measured with x-ray grating imaging. The numerical simulations and phantom experiments have demonstrated the accuracy and practicability of the proposed model.

Analysis of breast cancer by small angle X-ray scattering (SAXS)

Small angle X-ray scattering (SAXS) images of normal breast tissue and benign and malignant breast tumour tissues, fixed in formalin, were measured at the momentum transfer range of 0.063 nm(-1) < or = q (= 4pisin(theta/2)/lambda) < or = 2.720 nm(-1). Four intrinsic parameters were extracted from the scattering profiles (1D SAXS image reduced) and, from the combination of these parameters, another three parameters were also created. All parameters, intrinsic and derived, were subject to discriminant analysis, and it was verified that parameters such as the area of diffuse scatter at the momentum transfer range 0.50 < or = q < or = 0.56 nm(-1), the ratio between areas of fifth-order axial and third-order lateral peaks and third-order axial spacing provide the most significant information for diagnosis (p < 0.001). Thus, in this work it was verified that by combining these three parameters it was possible to classify human breast tissues as normal, benign lesion or malignant lesion with a sensitivity of 83% and a specificity of 100%.