Vibrational spectroscopy with machine learning for accurate cancer detection

Abstract

Cancer remains a global health crisis, significantly impacting individuals and societies worldwide. In 2020, approximately 19.3 million new cancer cases and 10 million cancer-related deaths were reported globally. Screening and triaging are crucial in the early detection, diagnosis, and management of cancer, targeting different stages to improve patient outcomes. Despite being one of the leading causes of mortality, many cancers lack effective screening methods. While conventional screening techniques are available for some cancers, they have varying accuracy and limitations. Identifying cancer or precancerous conditions early can significantly reduce mortality and enhance treatment outcomes. The analysis of biofluids to detect cancer-related signals—liquid biopsy, has garnered considerable attention over the past decade. Although promising, many current liquid biopsies lack the sensitivity needed for early-stage cancer detection. Raman spectroscopy (RS) is a non-destructive, real-time technique for molecular analysis. Our study investigated the impact of optimising selected parameters and assessed various spectral processing methods on the reliability and accuracy of spectral analyses, and demonstrated that manual extension of the sampled volume significantly enhanced the detection of low-concentration cancer biomolecules, improving spectral resolution in half the measurement time compared to conventional settings. Additionally, we examined chemical changes associated with acquired radioresistance in HR+ and HR− breast cancer cell lines. Combining RS with machine learning, we achieved high accuracy in distinguishing between parental cell lines and their radioresistant phenotypes, regardless of hormonal status. The radioresistant phenotypes exhibited similar difference spectra and formed a single cluster, suggesting common biochemical changes during the acquisition of radioresistance. We also integrated RS with advanced machine learning techniques for accurate cancer detection in blood plasma, using both liquid and dried samples. Our results showed high sensitivity and specificity in classifying stage Ia breast cancer, with an Area Under the Curve (AUC) of 1.00. Hierarchical clustering validated the reproducibility of our results. This research highlights the potential of combining vibrational spectroscopy with AI for cost-effective, non-invasive, and personalised early cancer detection, emphasising the need for standardised protocols and robust data processing techniques to facilitate clinical translation in liquid biopsy applications.