Benign/Cancer Diagnostics Based on X-Ray Diffraction: Comparison of Data Analytics Approaches

Background/Objectives: With the number of detected breast cancer cases growing every year, there is a need to augment histopathological analysis with fast preliminary screening. We examine the feasibility of using X-ray diffraction measurements for this purpose; Methods: In this work, we obtained more than 6,000 diffraction patterns from 211 patients and examined both standard and custom-developed methods, including Fourier coefficient analysis, for their interpretation. Various preprocessing steps and machine-learning classifiers were compared to determine the optimal combination; Results: We demonstrated that benign and cancerous clusters are well-separated, with specificity and sensitivity exceeding 0.9. For wide-angle scattering, the two-dimensional Fourier method is superior, while for small angles, the conventional analysis based on azimuthal integration of the images provides similar metrics; Conclusions: X-ray diffraction of biopsy tissues, supported by machine-learning approaches to data analytics, can be an essential tool for pathological services. The method is rapid and inexpensive, providing excellent metrics for benign/cancer classification.