Vertical cutoff methods in serum protein electrophoresis for the measurement of monoclonal protein concentrations: Which is best?

Fernández, Mc. Cárdenas; Surribas, David Pérez; Garay, Raquel Pérez; Jiménez, Juana Jiménez; Gella, Alejandro; Muñiz, Edgar Zapico; Iglesias, Esther Cruz; García, Marta; Duran, Salvador; Benito, Goitzane Marcaida; Ortiz, Carmen

doi:10.1016/j.cca.2020.07.051

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…In addition, the detection of monoclonal gammopathy by PEP is subjective and false-positive cases of monoclonal gammopathy by the EP method have occasionally occurred [ 14 ]. Furthermore, there are no standardized clinical practice guidelines for the interpretation of PEP [ 35 ]. In this study, all monoclonal gammopathy specimens confirmed by the IFE study were trained, and both the typical peak and atypical peak, including a small peak or peak outside the gamma region, could be detected with high accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, all monoclonal gammopathy specimens confirmed by the IFE study were trained, and both the typical peak and atypical peak, including a small peak or peak outside the gamma region, could be detected with high accuracy. In the case of monoclonal proteins, not only detection of monoclonal components but also standardization of the M protein measurement process and establishment of clinical practice guidelines are important issues [ 35 ]. DL-based classification algorithms are helpful for the standardization of M component detection and quantification as they provide a more objective interpretation.…”

Section: Discussionmentioning

confidence: 99%

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Development and validation of a deep learning-based protein electrophoresis classification algorithm

et al. 2022

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Background Protein electrophoresis (PEP) is an important tool in supporting the analytical characterization of protein status in diseases related to monoclonal components, inflammation, and antibody deficiency. Here, we developed a deep learning-based PEP classification algorithm to supplement the labor-intensive PEP interpretation and enhance inter-observer reliability. Methods A total of 2,578 gel images and densitogram PEP images from January 2018 to July 2019 were split into training (80%), validation (10%), and test (10.0%) sets. The PEP images were assessed based on six major findings (acute-phase protein, monoclonal gammopathy, polyclonal gammopathy, hypoproteinemia, nephrotic syndrome, and normal). The images underwent processing, including color-to-grayscale and histogram equalization, and were input into neural networks. Results Using densitogram PEP images, the area under the receiver operating characteristic curve (AUROC) for each diagnosis ranged from 0.873 to 0.989, and the accuracy for classifying all the findings ranged from 85.2% to 96.9%. For gel images, the AUROC ranged from 0.763 to 0.965, and the accuracy ranged from 82.0% to 94.5%. Conclusions The deep learning algorithm demonstrated good performance in classifying PEP images. It is expected to be useful as an auxiliary tool for screening the results and helpful in environments where specialists are scarce.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%