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Development and internal-external validation of a risk prediction model for acute pain after HAIC for patients with liver cancer using Logistic Regression and XGBoost Algorithm

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By Jiacheng Cao, Fan Wang, Yina Gong
| apjon.org
Keywords acute pain HAIC prediction model risk classification XGBoost Logistic regression 1 Introduction Liver cancer is one of the most common malignant tumors globally, ranking sixth in incidence and third in mortality1. Radical surgery is considered the optimal treatment for liver cancer2. However, many patients are ineligible for surgery and are thus restricted to non-surgical treatments or conversion therapy for potentially resectable liver cancer2,3.
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