Benign/Cancer diagnostics based on X-ray diffraction: comparison of data analytics approaches - Alekseev, Alexander; Shcherbakov, Viacheslav; Avdieiev, Oleksii; Denisov, Sergey A.; Kubytskyi, Viacheslav; Blinchevsky, Benjamin; Murokh, Sasha; Ajeer, Ashkan; Adams, Lois; Greenwood, Charlene; Rogers, Keith; Jones, Louise; et al. (MDPI, 2025)
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 6000 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.
This article belongs to the Special Issue Application of Biostatistics in Cancer Research
Keywords: structural biomarkers; X-ray diffraction; breast cancer diagnostics; machine learning; Fourier transformation; 32 Biomedical and Clinical Sciences; 3211 Oncology and Carcinogenesis; Bioengineering; Cancer; Breast Cancer; Women's Health; Machine Learning and Artificial Intelligence; Networking and Information Technology R&D (NITRD); 4.1 Discovery and preclinical testing of markers and technologies