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<p>Early detection of cancer in low income countries is still a major health problem. This study discovers a novel diagnostic technique based on Nanoparticle-Enhanced Laser-Induced Breakdown Spectroscopy (NE-LIBS), which combines machine learning, nanotechnology, and laser-based elemental analysis as a potential early screening tool reported for the first time to our knowledge. We have explored that the incorporation of silver and copper oxide nanoparticles significantly enhances the intensity of emission signals of laser induced blood plasma, particularly of metallic biomarkers sodium and calcium, which have previously been known to reflect cancer-related metabolic changes. The use of advanced machine learning models to analyze these improved spectral features enables the accurate classification of cancerous and non-cancerous samples with an accuracy of nearly 95%. In low-income countries where conventional methods are still unavailable, machine learning assisted NE-LIBS has the potential to develop into a future platform that would enable scalable, reasonably priced initial clinical cancer screening when combined with machine learning.</p> Graphical Abstract <p></p>

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On exploring the potential of machine learning assisted nanoparticles enhanced laser induced breakdown spectroscopy as an initial screening tool for early breast cancer detection

  • 期刊论文

  • 发表日期：

    2025年9月11日

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  • Shahwal Sabir,
  • Ayesha Israr,
  • Muhammad Faheem,
  • Ghulam Rasool Sani,
  • Aqsa Khalid,
  • Sajid Bashir,
  • Tania Jabbar,
  • Yasir Jamil

摘要

Early detection of cancer in low income countries is still a major health problem. This study discovers a novel diagnostic technique based on Nanoparticle-Enhanced Laser-Induced Breakdown Spectroscopy (NE-LIBS), which combines machine learning, nanotechnology, and laser-based elemental analysis as a potential early screening tool reported for the first time to our knowledge. We have explored that the incorporation of silver and copper oxide nanoparticles significantly enhances the intensity of emission signals of laser induced blood plasma, particularly of metallic biomarkers sodium and calcium, which have previously been known to reflect cancer-related metabolic changes. The use of advanced machine learning models to analyze these improved spectral features enables the accurate classification of cancerous and non-cancerous samples with an accuracy of nearly 95%. In low-income countries where conventional methods are still unavailable, machine learning assisted NE-LIBS has the potential to develop into a future platform that would enable scalable, reasonably priced initial clinical cancer screening when combined with machine learning.

Graphical Abstract