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Evaluation of the Possibility of Diagnosing Female Gynecologic Cancer using X-Ray Fluorescence (XRF)Spectroscopy of Hair and Nail

Jafari Iman, Fatemeh | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56372 (46)
  4. University: Sharif University of Technology
  5. Department: Energy Engineering
  6. Advisor(s): Hosseini, Abulfazl; Salimi, Ehsan
  7. Abstract:
  8. In recent years, indirect methods of cancer detection that utilize changes in trace element levels in various parts of the body, such as hair and nails, have garnered significant attention from researchers.The primary objective of these methods is to identify alterations in trace element levels that serve as biological markers in the presence of disease. While direct examination of affected organs remains the gold standard for cancer diagnosis, it is often expensive, time-consuming, inconvenient, and associated with potential side effects. Therefore, if the effectiveness of this approach is validated, it could serve as a cost-effective, non-invasive, safe, and early diagnostic method in cancer screening and diagnostic centers, either as an alternative or complementary technique. The present study aims to comprehensively investigate the concentrations of rare metals in hair and nail samples obtained from individuals diagnosed with uterine, cervical, and ovarian cancer, in comparison to healthy individuals. X-ray fluorescence spectroscopy was employed at the XRF/XAFS beamline in the SESAME synchrotron facility. The results demonstrate significant changes in cobalt and manganese levels in ovarian cancer, calcium, strontium, and cobalt levels in cervical cancer, and cobalt and strontium levels in uterine cancer, compared to healthy controls. The obtained results were further validated using emission plasma spectroscopy (ICP-Ms). Moreover, the utilization of predictive methods, such as machine learning and artificial intelligence, can assist in analyzing complex datasets and identifying patterns that may indicate the presence of cancer. Combining these innovative approaches with traditional cancer detection techniques has the potential to enhance early detection and provide improved treatment options for patients, highlighting the importance of integrating novel approaches in cancer diagnosis. In this study, the Lunberg-Marquardt neural network algorithm, Bayesian analysis, support vector machine, and principal component regression were utilized. Overall, this research underscores the significance of exploring and integrating new diagnostic strategies in cancer detection, while addressing the limitations of existing approaches
  9. Keywords:
  10. Synchrotron ; Cancer Diagnosis ; X-Ray Fluorescence ; Trace Element ; Gynocology Cancer ; Spectrofluorometry ; Biomarker

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