Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long‐Term Outcomes in Patients With HCC Undergoing Ablation

作者：Nan Zhang, Ke Lin, Bin Qiao, Liwei Yan, Dongdong Jin, Daopeng Yang, Yue Yang, Xiaohua Xie, Xiaohua Xie, Xiaoyan Xie, Xiaoyan Xie, Bowen Zhuang · 发表于：Cancer Medicine · 年份：2024 · DOI：10.1002/cam4.70344 · 被引用次数：5 · 研究领域：Inflammatory Biomarkers in Disease Prognosis、Hepatocellular Carcinoma Treatment and Prognosis、Ferroptosis and cancer prognosis

AIMS: To develop multiple machine learning (ML) models based on the prognostic nutritional index (PNI) and determine the optimal model for predicting long-term survival outcomes in hepatocellular carcinoma (HCC) patients after local ablation. METHODS: From January 2009 to December 2019, we analyzed data from 848 primary HCC patients who underwent local ablation. ML models were constructed and evaluated using the concordance index (C-index), concordance-discordance area under curve (C/D AUC), and Brier scores. The optimal ML model was interpreted using the partial dependence plot (PDP) and SHapley Additive exPlanations (SHAP) framework. Additionally, the prognostic performance of our model was compared with other models. RESULTS: Alkaline phosphatase, preoperation alpha-fetoprotein level, PNI, tumor number, and tumor size were identified as independent prognostic factors for ML model construction. Among the 19 ML algorithms tested, the Aorsf model showed superior performance in both the training cohort (C/D AUC: 0.733; C-index: 0.736; Brier score: 0.133) and validation cohort (C/D AUC: 0.713; C-index: 0.793; Brier score: 0.117). The time-dependent AUC of the Aorsf model for predicting overall survival was as follows: 1-, 3-, 5-, 7-, and 9-year were 0.828, 0.765, 0.781, 0.817, and 0.812 in the training cohort, 0.846, 0.859, 0.824, 0.845, and 0.874 in the validation cohort, respectively. The PDP and SHAP algorithms were employed for visual interpretation. Furthermore, time-AUC a...