Aim: Monitoring minimal residual disease remains a challenge to the effective medical management of hematological malignancies; yet surface-enhanced Raman spectroscopy (SERS) has emerged as a potential clinical tool to do so. Materials & methods: We developed a cell-free, label-free SERS approach using gold nanoparticles (nanoSERS) to classify hematological malignancies referenced against two control cohorts: healthy and noncancer cardiovascular disease. A predictive model was built using machine-learning algorithms to incorporate disease burden scores for patients under standard treatment upon. Results: Linear- and quadratic-discriminant analysis distinguished three cohorts with 69.8 and 71.4% accuracies, respectively. A predictive nanoSERS model correlated (MSE = 1.6) with established clinical parameters. Conclusion: This study offers a proof-of-concept for the noninvasive monitoring of disease progression, highlighting the potential to incorporate nanoSERS into translational medicine.
Keywords: cancer; lymphoma; machine learning; myeloma; nanochemistry; nanotechnology; plasma.
Cancer patient quality of life is achieved by reassurance from informed doctors using the best clinical tools. Confirming the earliest detection or absence of disease ensures treatment is timely and recovery optimal. Here we show the potential for a new tool to be developed to reassure patients and inform doctors. We examined the ‘chemical fingerprints’ (Raman spectroscopic profiling) of patient's blood, enhanced by gold nanoparticles with a double-referenced machine learning algorithm. Teaching a machine to learn as it works ensures it is improving how it finds clinically important features in the chemical fingerprint. This helps patients live more confidently with cancer or in cancer recovery. Eventually, once fully trained and translated into a real-world hospital application, this could improve patient outcomes and quality of life.