An observational cohort study to evaluate the use of serum Raman spectroscopy in a rapid diagnosis center setting

• Published in: Clinical spectroscopy. 2022-12, Vol.4, p.100020, Article 100020
• Main Authors: Woods, Freya E.R., Chandler, Susan, Sikora, Natalia, Harford, Rachel, Souriti, Ahmad, Gray, Helen, Wilkes, Heather, Lloyd-Bennett, Catherine, Harris, Dean A., Dunstan, Peter R.
• Format: Article
• Language: English
• Subjects: Cancer; Diagnostics; Raman Spectroscopy; Rapid Diagnosis Center, Non-specific symptoms
• ISSN: 2666-0547; 2666-0547
• DOI: 10.1016/j.clispe.2022.100020

Cancer presenting with non-specific vague symptoms remains a clinical challenge. The purpose of this study was to assess the feasibility of serum Raman spectroscopy for cancer detection in a rapid diagnosis center (RDC) setting. The primary aim was to identify significant spectral peaks of change in sera from cancer patients and the secondary aim was to assign molecular species at Raman peaks. In this prospective observation study of a secondary care RDC, patients referred with vague cancer-related symptoms were recruited. Raman spectra of blood sera of 54 patients was obtained. Of these, 10 patients were diagnosed with cancer, and 44 no significant pathology (control). Common spectral increase/decrease between control and cancer was seen in spectral peaks 830 cm−1, 878 cm−1, 1031 cm−1, 1174 cm−1, 1397 cm−1 tentatively attributed to amino acids, carbohydrates, fatty acids, and proteins. Individual differences between cancer and control via statistical analysis identifies 3 peaks with significance for all 10 of the cancer patients. The peaks are 878 cm−1, 1449 cm−1 and 1519 cm−1, tentatively attributed to proteins, amino acids, lipids, fatty acids, glycoproteins, carbohydrates, and carotenoids. Differences are also seen for at least 9 of the cancers in the peaks at 830 cm−1, 851 cm−1, 1127 cm−1, 1174 cm−1, 1270 cm−1, and 1656 cm−1, tentatively attributed to amino acids, lactate, lipids, triglycerides, carbohydrates, and proteins. Raman spectroscopy has the potential to enhance RDC referral criteria through the detection of peak differences seen commonly with different cancer types. Development of Artificial Intelligence (AI) based models could enable rapid detection and discrimination of different cancer types with more data availability.