Benign/Cancer Diagnostics Based on X-Ray Diffraction: Comparison of Data Analytics Approaches.
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 c...
| Publicado en: | Cancers Vol. 17; no. 10; pp. 1662 - 1676 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
MDPI
May2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185481050&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185481050 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: May2025 vid: 17 iid: 10 pid: 97109 pub: MDPI artinfo: ui: 185481050 185481050 185481050 10.3390/cancers17101662 185481050 ppf: 1662 ppct: 14 formats: tig: atl: Benign/Cancer Diagnostics Based on X-Ray Diffraction: Comparison of Data Analytics Approaches. aug: au: 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 affil: Matur UK Ltd. 5 New Street Square, London EC4A 3TW, UK sug: subj: Neoplasms Diagnosis X-Rays Evaluation Data Analytics Signal Processing, Computer Assisted Human Descriptive Statistics Machine Learning Biological Markers Sensitivity and Specificity ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|