A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories.
Nat Med
; 29(5): 1113-1122, 2023 05.
Article
en En
| MEDLINE
| ID: mdl-37156936
ABSTRACT
Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 pancreatic cancer cases) in Denmark (Danish National Patient Registry (DNPR)) and from 3 million patients (3,900 cases) in the United States (US Veterans Affairs (US-VA)). We trained machine learning models on the sequence of disease codes in clinical histories and tested prediction of cancer occurrence within incremental time windows (CancerRiskNet). For cancer occurrence within 36 months, the performance of the best DNPR model has area under the receiver operating characteristic (AUROC) curve = 0.88 and decreases to AUROC (3m) = 0.83 when disease events within 3 months before cancer diagnosis are excluded from training, with an estimated relative risk of 59 for 1,000 highest-risk patients older than age 50 years. Cross-application of the Danish model to US-VA data had lower performance (AUROC = 0.71), and retraining was needed to improve performance (AUROC = 0.78, AUROC (3m) = 0.76). These results improve the ability to design realistic surveillance programs for patients at elevated risk, potentially benefiting lifespan and quality of life by early detection of this aggressive cancer.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias Pancreáticas
/
Aprendizaje Profundo
Tipo de estudio:
Diagnostic_studies
/
Etiology_studies
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Prognostic_studies
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Risk_factors_studies
/
Screening_studies
Límite:
Humans
/
Middle aged
Idioma:
En
Revista:
Nat Med
Asunto de la revista:
BIOLOGIA MOLECULAR
/
MEDICINA
Año:
2023
Tipo del documento:
Article
País de afiliación:
Dinamarca