Spectroscopy for Cancer Detection: Techniques, Al, and Future Perspectives

Cancer remains one of the most critical global health challenges, and early diagnosis is a key factor in improving treatment outcomes and patient survival. Conventional diagnostic approaches, such as histopathological examination, medical imaging, and molecular assays, have significantly contributed to cancer detection; however, these methods are often limited by invasiveness, time-consuming procedures, dependence on expert interpretation, and reduced sensitivity for detecting early molecular changes. Spectroscopy-based technologies have emerged as powerful and promising tools for cancer diagnosis by providing detailed biochemical and molecular information from tissues, cells, and biofluids. These techniques rely on the interaction between electromagnetic radiation and biological molecules to identify cancer-related alterations in proteins, lipids, nucleic acids, metabolites, and other cellular components. Various spectroscopic approaches, including Raman spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, fluorescence spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and mass spectrometry, have demonstrated considerable potential for cancer classification, biomarker identification, and real-time diagnostic applications. Recent developments in artificial intelligence (AI), machine learning (ML), and deep learning have further advanced spectroscopy-based diagnostic platforms by enabling automated feature extraction, efficient analysis of high-dimensional spectral data, and improved classification accuracy. AI-assisted spectroscopy has shown promising capabilities in differentiating malignant and non-malignant samples, discovering molecular signatures, and supporting clinical decision-making. This review provides a comprehensive overview of spectroscopy-based cancer detection approaches, emphasizing their fundamental principles, biomedical applications, integration with artificial intelligence, current challenges, and future perspectives. Additionally, a comparative evaluation of major spectroscopic techniques is presented to highlight their advantages, limitations, and potential for clinical translation.

کلیدواژه ها:

Cancer detection ، spectroscopy ، Raman spectroscopy ، FTIR spectroscopy ، fluorescence spectroscopy ، NMR spectroscopy ، mass spectrometry ، artificial intelligence ، machine learning ، precision oncology