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

Abstract

Guanming Shao,* Yonghui Ma,* Chao Qu, Ruiqian Gao, Chengzhan Zhu, Linlin Qu, Kui Liu, Na Li, Peng Sun, Jingyu Cao Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Peng Sun; Jingyu Cao, Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266000, People’s Republic of China, Email [email protected]; [email protected]: 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

Keywords

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