Prediction of hepatic metastasis in esophageal cancer based on machine learning.
Sci Rep
; 14(1): 14507, 2024 06 24.
Article
em En
| MEDLINE
| ID: mdl-38914571
ABSTRACT
This study aimed to establish a machine learning (ML) model for predicting hepatic metastasis in esophageal cancer. We retrospectively analyzed patients with esophageal cancer recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2020. We identified 11 indicators associated with the risk of liver metastasis through univariate and multivariate logistic regression. Subsequently, these indicators were incorporated into six ML classifiers to build corresponding predictive models. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A total of 17,800 patients diagnosed with esophageal cancer were included in this study. Age, primary site, histology, tumor grade, T stage, N stage, surgical intervention, radiotherapy, chemotherapy, bone metastasis, and lung metastasis were independent risk factors for hepatic metastasis in esophageal cancer patients. Among the six models developed, the ML model constructed using the GBM algorithm exhibited the highest performance during internal validation of the dataset, with AUC, accuracy, sensitivity, and specificity of 0.885, 0.868, 0.667, and 0.888, respectively. Based on the GBM algorithm, we developed an accessible web-based prediction tool (accessible at https//project2-dngisws9d7xkygjcvnue8u.streamlit.app/ ) for predicting the risk of hepatic metastasis in esophageal cancer.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Esofágicas
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Aprendizado de Máquina
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Neoplasias Hepáticas
Limite:
Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China