Benign/Cancer Diagnostics Based on X-Ray Diffraction: Comparison of Data Analytics Approaches
Alekseev A. Shcherbakov V. Avdieiev O. Denisov S.A. Kubytskyi V. Blinchevsky B. Murokh S. Ajeer A. Adams L. Greenwood C. Rogers K. Jones L.J. Mourokh L. Lazarev P.
May 2025Multidisciplinary Digital Publishing Institute (MDPI)
Cancers
2025#17Issue 10
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.
breast cancer diagnostics , Fourier transformation , machine learning , structural biomarkers , X-ray diffraction
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Matur UK Ltd., 5 New Street Square, London, EC4A 3TW, United Kingdom
Department of Physics and Technology, Karaganda Buketov University, Karaganda, 100028, Kazakhstan
Institut de Chimie Physique, UMR8000, CNRS, Université Paris-Saclay, Bât. 349, Orsay, 91405, France
Laboratoire de Physique des 2 Infinis Irène Joliot-Curie, UMR9012, CNRS, Université Paris-Saclay, Bât. 209, Orsay, 91405, France
Stuyvesant High School, 345 Chambers Street, New York, 10282, NY, United States
School of Chemical and Physical Sciences, Keele University, Keele, ST5 5BG, United Kingdom
EosDx, Inc., 1455 Adams Drive, Menlo Park, 94025, CA, United States
Cranfield University, Shrivenham Campus, Swindon, SN6 8LA, United Kingdom
Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, United Kingdom
Physics Department, Queens College, City University of New York, 65-30 Kissena Blvd., Flushing, 11367, NY, United States
Matur UK Ltd.
Department of Physics and Technology
Institut de Chimie Physique
Laboratoire de Physique des 2 Infinis Irène Joliot-Curie
Stuyvesant High School
School of Chemical and Physical Sciences
EosDx
Cranfield University
Barts Cancer Institute
Physics Department
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