Expert-level detection of M-proteins in serum protein electrophoresis using machine learning.

Elfert E, Kaminski WE, Matek C, Hoermann G, Axelsen EW, Marr C, Piehler AP (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Clinical Chemistry and Laboratory Medicine Walter de Gruyter

Book Volume: 62

Pages Range: 2498-2506

Journal Issue: 12

DOI: 10.1515/cclm-2024-0222

Abstract

OBJECTIVES: Serum protein electrophoresis (SPE) in combination with immunotyping (IMT) is the diagnostic standard for detecting monoclonal proteins (M-proteins). However, interpretation of SPE and IMT is weakly standardized, time consuming and investigator dependent. Here, we present five machine learning (ML) approaches for automated detection of M-proteins on SPE on an unprecedented large and well-curated data set and compare the performance with that of laboratory experts. METHODS: SPE and IMT were performed in serum samples from 69,722 individuals from Norway. IMT results were used to label the samples as M-protein present (positive, n=4,273) or absent (negative n=65,449). Four feature-based ML algorithms and one convolutional neural network (CNN) were trained on 68,722 randomly selected SPE patterns to detect M-proteins. Algorithm performance was compared to that of an expert group of clinical pathologists and laboratory technicians (n=10) on a test set of 1,000 samples. RESULTS: The random forest classifier showed the best performance (F1-Score 93.2 %, accuracy 99.1 %, sensitivity 89.9 %, specificity 99.8 %, positive predictive value 96.9 %, negative predictive value 99.3 %) and outperformed the experts (F1-Score 61.2 ± 16.0 %, accuracy 89.2 ± 10.2 %, sensitivity 94.3 ± 2.8 %, specificity 88.9 ± 10.9 %, positive predictive value 47.3 ± 16.2 %, negative predictive value 99.5 ± 0.2 %) on the test set. Interestingly the performance of the RFC saturated, the CNN performance increased steadily within our training set (n=68,722). CONCLUSIONS: Feature-based ML systems are capable of automated detection of M-proteins on SPE beyond expert-level and show potential for use in the clinical laboratory.

Authors with CRIS profile

Christian Matek

Involved external institutions

Universitätsklinikum Mannheim DE Germany (DE) Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) MLL Münchner Leukämielabor GmbH DE Germany (DE)

How to cite

APA:

Elfert, E., Kaminski, W.E., Matek, C., Hoermann, G., Axelsen, E.W., Marr, C., & Piehler, A.P. (2024). Expert-level detection of M-proteins in serum protein electrophoresis using machine learning. Clinical Chemistry and Laboratory Medicine, 62(12), 2498-2506. https://doi.org/10.1515/cclm-2024-0222

MLA:

Elfert, Eike, et al. "Expert-level detection of M-proteins in serum protein electrophoresis using machine learning." Clinical Chemistry and Laboratory Medicine 62.12 (2024): 2498-2506.

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