Prediction model of ocular metastasis from primary liver cancer: Machine learning-based development and interpretation study

Jin-Qi Sun, Shi-Nan Wu, Zheng-Lin Mou, Jia-Yi Wen, Hong Wei, Jie Zou, Qing-Jian Li, Zhao-Lin Liu, San Hua Xu, Min Kang, Qian Ling, Hui Huang, Xu Chen, Yi-Xin Wang, Xu-Lin Liao, Gang Tan*, Yi Shao*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Background: Ocular metastasis (OM) is a rare metastatic site of primary liver cancer (PLC). The purpose of this study was to establish a clinical predictive model of OM in PLC patients based on machine learning (ML). Methods: We retrospectively collected the clinical data of 1540 PLC patients and divided it into a training set and an internal test set in a 7:3 proportion. PLC patients were divided into OM and non-ocular metastasis (NOM) groups, and univariate logistic regression analysis was performed between the two groups. The variables with univariate logistic analysis p < 0.05 were selected for the ML model. We constructed six ML models, which were internally verified by 10-fold cross-validation. The prediction performance of each ML model was evaluated by receiver operating characteristic curves (ROCs). We also constructed a web calculator based on the optimal performance ML model to personalize the risk probability for OM. Results: Six variables were selected for the ML model. The extreme gradient boost (XGB) ML model achieved the optimal differential diagnosis ability, with an area under the curve (AUC) = 0.993, accuracy = 0.992, sensitivity = 0.998, and specificity = 0.984. Based on these results, an online web calculator was constructed by using the XGB ML model to help clinicians diagnose and treat the risk probability of OM in PLC patients. Finally, the Shapley additive explanations (SHAP) library was used to obtain the six most important risk factors for OM in PLC patients: CA125, ALP, AFP, TG, CA199, and CEA. Conclusion: We used the XGB model to establish a risk prediction model of OM in PLC patients. The predictive model can help identify PLC patients with a high risk of OM, provide early and personalized diagnosis and treatment, reduce the poor prognosis of OM patients, and improve the quality of life of PLC patients.

Original languageEnglish
Pages (from-to)20482-20496
Number of pages15
JournalCancer Medicine
Volume12
Issue number20
Early online date1 Oct 2023
DOIs
Publication statusPublished - 1 Oct 2023

Keywords

  • machine learning
  • ocular metastasis
  • primary liver cancer
  • Shapley additive explanations
  • XGBoost
  • HEPATOCELLULAR-CARCINOMA
  • CHOROIDAL METASTASIS
  • ORBITAL METASTASIS
  • RISK-FACTORS
  • MASS

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