With the growing number of cancer cases and deaths around the world, fast, non-invasive, and inexpensive screening is paramount. We examine the feasibility of such cancer detection using the X-ray scattering properties of nails in the canine model. A total of 945 samples taken from 266 dogs were measured, with 84 animals diagnosed with cancer. To analyze the obtained X-ray diffraction patterns of keratin, we propose a method based on the two-dimensional Fourier transformation of the images. We compare 745 combinations of data preprocessing steps and machine learning classifiers and determine the corresponding performance metrics. Excellent classification results are demonstrated, with sensitivity or specificity achieving 100% and the best value for balanced accuracy being 87.5%. We believe that our approach can be extended to human samples to develop a non-invasive, convenient, and cheap method for early cancer detection.

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.

As the number of cancer cases and deaths growing around the world, fast, non-invasive, and inexpensive screening is paramount. We examine the feasibility of such cancer detection using the X-ray scattering properties of nails in the canine model. 945 samples taken from 266 dogs were measured, with 84 animals diagnosed with cancer. To analyze the obtained X-ray diffraction patterns of keratin, we propose a method based on the two-dimensional Fourier transformation of the images. We compare 745 combinations of data preprocessing steps and machine learning classifiers and determine corresponding performance metrics. Excellent classification results are demonstrated, with sensitivity or specificity achieving 100% and the best value for balanced accuracy being 87.5%. We believe that our approach can be extended to human samples to develop a non-invasive, convenient, and cheap method for early cancer detection.

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.

Structural biomarkers determined by X-ray scattering of the tissues can complement conventional histopathology and facilitate a fast triage procedure of cancer biopsy samples. It has been shown previously that lipid reflexes can distinguish cancerous from benign samples, except for fibroadenomas. In the present study, we demonstrate that fibroadenoma samples can be recognized using X-ray scattering of collagen. Moreover, we show that modifications in collagen structure are manifested in the water reflexes. Examination of diffraction patterns from water using two-dimensional Fourier transformation and machine learning yields excellent classification metrics in both synchrotron images and laboratory diffractometer data.

Structural biomarkers determined by X-ray scattering of the tissues can complement conventional histopathology and facilitate a fast triage procedure of cancer biopsy samples. It has been shown previously that lipid reflexes can distinguish cancerous from benign samples, except for fibroadenomas. In the present study, we demonstrate that fibroadenoma samples can be recognized using X-ray scattering of collagen. Moreover, we show that modifications in collagen structure are manifested in the water reflexes. Examination of diffraction patterns from water using two-dimensional Fourier transformation and machine learning yields excellent classification metrics in both synchrotron images and laboratory diffractometer data.