文章：

基于X射线衍射的良性/癌症诊断：数据分析方法比较

Benign/Cancer Diagnostics Based on X-Ray Diffraction: Comparison of Data Analytics Approaches

原文发布日期：14 May 2025

DOI: 10.3390/cancers17101662

类型: Article

开放获取: 是

英文摘要：

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.

摘要翻译： 

背景/目的：随着每年检测出的乳腺癌病例数量不断增长，亟需通过快速初步筛查来增强组织病理学分析能力。本研究旨在探讨利用X射线衍射测量技术实现这一目标的可行性。 方法：本研究收集了211名患者超过6000份衍射图谱，并评估了包括傅里叶系数分析在内的标准方法与定制开发方法在数据解析中的应用效果。通过比较多种预处理步骤与机器学习分类器，确定了最优组合方案。 结果：研究证实良性组织与癌变组织在衍射特征上具有显著区分度，特异性和敏感度均超过0.9。在大角度散射分析中，二维傅里叶方法表现更优；而在小角度分析时，基于图像方位角积分的传统分析方法可获得相近的评估指标。 结论：结合机器学习数据分析技术的活检组织X射线衍射检测，有望成为病理服务的重要辅助工具。该方法具有快速、经济的特点，在良恶性分类中展现出优异的评估指标。

原文链接：

Benign/Cancer Diagnostics Based on X-Ray Diffraction: Comparison of Data Analytics Approaches