Diagnosis of AL Amyloidosis Versus Monoclonal Gammopathy of Undetermined Significance and Smoldering Multiple Myeloma Using FTIR/ML Analysis of Serum

The prognosis of patients with immunoglobulin light or heavy chain amyloidosis (AL/AH) has improved with the advent of more effective therapies. However, diagnosis is often delayed, with a negative impact on overall survival due to irreversible end-organ damage. Therefore, technologies that could facilitate the diagnosis or raise early suspicion of AL/AH amyloidosis in patients would be beneficial to improve outcomes. We hypothesized that Fourier transform infrared spectroscopic analysis of serum combined with machine learning (FTIR/ML) may be able to correctly diagnose AL/AH amyloidosis compared to patients with monoclonal gammopathy of undetermined significance or smoldering multiple myeloma.

Methods: After institutional Review Board approval, we identified 90 patients with AL/AH amyloidosis, 99 patients with MGUS, and 192 patients with smoldering multiple myeloma (96 each who did/did not progress to active myeloma within two years of diagnosis) that had been diagnosed and followed up at Mayo Clinic in Rochester. We then performed surface- and chemical-enhanced infrared absorption FTIR/ML-based molecular fingerprint analysis on serum samples taken from the time of diagnosis to train an AI algorithm to distinguish between the two cohorts. A randomized 10-fold cross-validation analysis was performed using 253 samples (90% from each group) for training and the remaining 29 for testing to assess the model across ten independent iterations. We performed a Receiver Operator Characteristic analysis of the outcomes to measure the model’s performance.

Results: The patients with immunoglobulin-related amyloidosis had involvement of one or more of the following organs: heart, kidneys, liver, nerve, and gastrointestinal tract. Patients diagnosed with amyloidosis had lower serum albumin (3.0 versus 3.6 g/dl) and higher lambda immunoglobulin-free light chain values compared to those with MGUS (28.4 versus 1.92mg/dl). The predominance of lambda immunoglobulin-free light chains is not unexpected but not diagnostic by itself of AL/AH amyloidosis. Patients with SMM had higher serum M protein, higher immunoglobulin kappa free light chains, and higher bone marrow plasma cell burden (20%) compared to either MGUS (9%) or AL amyloidosis patients (10%). However, none of these are diagnostic features. FTIR/ML was able to correctly classify AL/AH amyloidosis in 80% of samples and MGUS in 83.8%, for an accuracy of 82% with an area under the curve of 89.2%. The precision was 82%, with a recall of 82%. After setting an appropriate threshold, the algorithm was able to correctly identify AL/AH amyloidosis in 86.1% of patients and MGUS in 86.6% of patients. When we compared AL/AH amyloidosis with SMM, FTIR/ML correctly classified AL/AH amyloidosis in 94.4% and SMM in 96.4% for an accuracy of 95.7% and an area under curve of 97.5%. The precision was 95.8%, with a recall of 95.7%. After setting a threshold, the algorithm was able to correctly diagnose SMM in 98.3% and AL/AH amyloidosis in 96.2%.

Discussion: FTIR/ML analysis of serum can distinguish between AL/AH amyloidosis and both MGUS and SMM with a high degree of accuracy. Given that the diagnosis of immunoglobulin related amyloidosis is often delayed, our approach, based on a single analysis of a serum sample could potentially accelerate the diagnosis of AL/AH amyloidosis and implementation of life saving therapy.