Machine Learning Model Based on the Neutrophil-to-Eosinophil Ratio Predicts the Recurrence of Hepatocellular Carcinoma After Surgery

Abstract

Background

Circulating eosinophils are associated with tumor development. An eosinophil-related index, the neutrophil to eosinophil ratio (NER), can be used to predict the prognosis of patients with tumors. However, there is still a lack of efficient prognostic biomarkers for HCC. In this study, we aimed to investigate the predictive value of the NER and develop an optimal machine learning model for the recurrence of HCC patients. Patients and methods: A retrospective collection of 562 patients who underwent hepatectomy with a pathologic diagnosis of HCC was performed. The relationship between NER and progression-free survival (PFS) was investigated. We developed a new machine learning framework with 10 machine learning algorithms and their 101 combinations to select the best model for predicting recurrence after hepatectomy. The performance of the model was assessed by the area under the curve (AUC) of characteristics and calibration curves, and clinical utility was evaluated by decision curve analysis (DCA).

Results

Kaplan‒Meier curves showed that the PFS in the low NER group was significantly better than that in the high NER group. Multivariate Cox regression analysis showed that NER was an independent risk factor for recurrence after surgery. The random survival forests (RSF) model was selected as the best model that had good predictive efficacy and outperformed the TNM, BCLC, and CNLC staging systems.

Conclusion

The NER has good predictive value for postoperative recurrence in patients with hepatocellular carcinoma. Machine learning model based on NER can be used for accurate predictions.

Keywords:

• hepatocellular carcinoma
• liver resection
• recurrence
• machine learning
• neutrophil-to-eosinophil ratio

Data Sharing Statement

The datasets used during the present study are available from the corresponding author, Dr. Peng Sun (email: [email protected]), upon reasonable request.

Ethics Approval and Consent to Participate

The protocol of this study was approved by the ethical review board of Qingdao University (No. QYFY WZLL 28291).

Consent for Publication

Written informed consent for publication was obtained from all participants.

Acknowledgments

Thank all the staff authors for their contributions to this study.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis, and interpretation, or all these areas; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The author(s) report no conflicts of interest in this work.

Additional information

Funding

This work was supported by the Natural Science Foundation of Qingdao (23-2-1-131-zyyd-jch).