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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.

Titel:	Prediction of hepatic metastasis in esophageal cancer based on machine learning.
Autor/in / Beteiligte Person:	Wan, J ; Zeng, Y
Link:	Full Text Finder (Volltext)
Zeitschrift:	Scientific reports, Jg. 14 (2024-06-24), Heft 1, S. 14507
Veröffentlichung:	London : Nature Publishing Group, copyright 2011-, 2024
Medientyp:	academicJournal
ISSN:	2045-2322 (electronic)
DOI:	10.1038/s41598-024-63213-6
Schlagwort:	Humans Male Female Middle Aged Aged Retrospective Studies Risk Factors ROC Curve SEER Program Machine Learning Esophageal Neoplasms pathology Liver Neoplasms secondary
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Sci Rep] 2024 Jun 24; Vol. 14 (1), pp. 14507. <i>Date of Electronic Publication: </i>2024 Jun 24. MeSH Terms: Machine Learning* ; Esophageal Neoplasms* / pathology ; Liver Neoplasms* / secondary ; Humans ; Male ; Female ; Middle Aged ; Aged ; Retrospective Studies ; Risk Factors ; ROC Curve ; SEER Program References: Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 71, 209–249 (2021). (PMID: 10.3322/caac.2166033538338) ; Lagergren, J., Smyth, E., Cunningham, D. & Lagergren, P. Oesophageal cancer. Lancet 390, 2383–2396 (2017). (PMID: 10.1016/S0140-6736(17)31462-928648400) ; Intenational Agency for Research on Cancer (IARC). Global Cancer Observatory (Globocan). ; Uhlenhopp, D. J., Then, E. O., Sunkara, T. & Gaduputi, V. 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Prediction of hepatic metastasis in esophageal cancer based on machine learning.