A method for lung cancer detection and staging from a drop of blood plasma via Raman spectroscopy of well-based samples (ROWS)

We present a new method designed for detection of optical signal associated with lung pathology in blood plasma, including lung cancer stage. Raman spectroscopy uses inelastically scattered laser light to obtain molecular information in a reagent-free manner. Obtaining spontaneous Raman spectral data from small volumes of liquid-form samples poses multiple challenges, but we have developed a novel tool for obtaining spectra from 60 µl liquid samples within two minutes: Raman of Well-based Samples (ROWS). With a low-cost ROWS device, we analyzed 372 blood plasma samples from a national biobank, including controls ( n = 92), patients with stage I-II lung cancer ( n = 99), stage III-IV cancer ( n = 46), benign tumours ( n = 36) and other lung conditions ( n = 99). Machine learning models were built to assess lung cancer stage and lung pathology presence. ROWS achieves up to 94% sensitivity, 90% specificity and 93% accuracy depending on classification. ROWS proves a robust method which could be integrated into rapid, low-cost, user-friendly, point-of-care lung pathology analysis in small quantities of blood plasma.