| Sumario: | Simple Summary: Breast cancer is the most frequent cancer among women. Currently, histopathological analysis of biopsies is performed for both malignant and benign samples, with no effective triage system to 'fast-track' potential malignant cases. We propose a complementary method of benign/cancer classification based on X-ray scattering. Using and comparing machine learning approaches, we examined over 6000 measurements of benign and cancerous samples from 211 patients, achieving excellent results in distinguishing malignant and benign conditions. This can lead to a significant reduction in the turnaround time for the histopathological analysis and earlier diagnostics of malignancy, with potential impact on the survival rate for breast cancer patients. 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.
|
|---|