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Your search keyword '"Tan, Ivan En-Howe"' showing total 16 results
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16 results on '"Tan, Ivan En-Howe"'

14. Acute hospital-community hospital care bundle for elderly orthopedic surgery patients: A propensity score-matched economic analysis

Author

Tan, Ivan En-Howe, primary, Chok, Aik Yong, additional, Zhao, Yun, additional, Chen, Yonghui, additional, Koo, Chee Hoe, additional, Aw, Junjie, additional, Soh, Mave Hean Teng, additional, Foo, Chek Hun, additional, Ang, Kwok Ann, additional, Tan, Emile John Kwong Wei, additional, Tan, Andrew Hwee Chye, additional, and Au, Marianne Kit Har, additional

Published
2023
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16. Predictive modeling algorithms for liver metastasis in colorectal cancer: A systematic review of the current literature.

Author

Seow-En I, Koh YX, Zhao Y, Ang BH, Tan IE, Chok AY, Tan EJKW, and Au MKH

Abstract

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

Published
2024
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