Machine learning diagnosis by immunoglobulin N-glycan signatures for precision diagnosis of urological diseases.
Cancer Sci
; 113(7): 2434-2445, 2022 Jul.
Article
em En
| MEDLINE
| ID: mdl-35524940
ABSTRACT
Early diagnosis of urological diseases is often difficult due to the lack of specific biomarkers. More powerful and less invasive biomarkers that can be used simultaneously to identify urological diseases could improve patient outcomes. The aim of this study was to evaluate a urological disease-specific scoring system established with a machine learning (ML) approach using Ig N-glycan signatures. Immunoglobulin N-glycan signatures were analyzed by capillary electrophoresis from 1312 serum subjects with hormone-sensitive prostate cancer (n = 234), castration-resistant prostate cancer (n = 94), renal cell carcinoma (n = 100), upper urinary tract urothelial cancer (n = 105), bladder cancer (n = 176), germ cell tumors (n = 73), benign prostatic hyperplasia (n = 95), urosepsis (n = 145), and urinary tract infection (n = 21) as well as healthy volunteers (n = 269). Immunoglobulin N-glycan signature data were used in a supervised-ML model to establish a scoring system that gave the probability of the presence of a urological disease. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). The supervised-ML urologic disease-specific scores clearly discriminated the urological diseases (AUC 0.78-1.00) and found a distinct N-glycan pattern that contributed to detect each disease. Limitations included the retrospective and limited pathological information regarding urological diseases. The supervised-ML urological disease-specific scoring system based on Ig N-glycan signatures showed excellent diagnostic ability for nine urological diseases using a one-time serum collection and could be a promising approach for the diagnosis of urological diseases.
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Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Próstata
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Neoplasias da Bexiga Urinária
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Neoplasias Renais
Tipo de estudo:
Diagnostic_studies
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Observational_studies
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Prognostic_studies
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Screening_studies
Limite:
Humans
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Male
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article