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 for lung pathology detection in blood plasma, including lung cancer staging. Raman spectroscopy uses inelastically scattered laser light to obtain molecular information in a reagent-free manner. Obtaining Raman spectral data from liquid samples has long proven challenging, 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 for rapid, low-cost, user-friendly, point-of-care lung pathology analysis in small quantities of blood plasma. A new way of detecting lung cancer in small volumes of liquid blood plasma was developed using laser-based Raman spectroscopy and metallic wells.