Alekseev, AlexanderShcherbakov, ViacheslavAvdieiev, OleksiiDenisov, Sergey A.Kubytskyi, ViacheslavBlinchevsky, BenjaminMurokh, SashaAjeer, AshkanAdams, LoisGreenwood, CharleneRogers, KeithJones, LouiseMourokh, LevLazarev, Pavel2025-06-262025-06-262025-05-14Alekseev A, Shcherbakov V, Avdieiev O, et al., (2025) Benign/Cancer diagnostics based on X-ray diffraction: comparison of data analytics approaches. Cancers, Volume 17, Issue 10, May 2025, Article number 16622072-6694https://doi.org/10.3390/cancers17101662https://dspace.lib.cranfield.ac.uk/handle/1826/24042This article belongs to the Special Issue Application of Biostatistics in Cancer ResearchBackground/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.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/structural biomarkersX-ray diffractionbreast cancer diagnosticsmachine learningFourier transformation32 Biomedical and Clinical Sciences3211 Oncology and CarcinogenesisBioengineeringCancerBreast CancerWomen's HealthMachine Learning and Artificial IntelligenceNetworking and Information Technology R&D (NITRD)4.1 Discovery and preclinical testing of markers and technologiesArticle2072-669467336216621710