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 J, Mourokh Lev, Lazarev Pavel
Matur UK Ltd., 5 New Street Square, London EC4A 3TW, UK.
Department of Physics and Technology, Karaganda Buketov University, Karaganda 100028, Kazakhstan.
Cancers (Basel). 2025 May 14;17(10):1662. doi: 10.3390/cancers17101662.
: 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. 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. 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. : 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.
随着每年检测出的乳腺癌病例数量不断增加，需要通过快速初步筛查来加强组织病理学分析。我们研究了为此目的使用X射线衍射测量的可行性。在这项工作中，我们从211名患者那里获得了6000多个衍射图案，并研究了包括傅里叶系数分析在内的标准方法和定制开发的方法用于其解释。比较了各种预处理步骤和机器学习分类器以确定最佳组合。我们证明良性和癌性簇能够很好地分离，特异性和敏感性超过0.9。对于广角散射，二维傅里叶方法更优越，而对于小角度，基于图像方位积分的传统分析提供了类似的指标。活检组织的X射线衍射，辅以机器学习方法进行数据分析，可以成为病理服务的重要工具。该方法快速且成本低廉，为良性/癌症分类提供了出色的指标。
