ArticleCancer medicine2024
Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long-Term Outcomes in Patients With HCC Undergoing Ablation.
Article in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic and clinicopathological value of prognostic nutritional index in patients with multiple myeloma: a meta-analysis.Frontiers in oncology · 2025Pooled it
- Article
- The role of the advanced lung cancer inflammation index (ALI) in the risk of liver fibrosis and mortality among US adult MAFLD patients: a cross-sectional study of NHANES 1999-2018.BMC gastroenterology · 2025Article
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10 authors.
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Abstract
aimsTo 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.
methodsFrom 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.
resultsAlkaline 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 and decision curve analysis demonstrated that the Aorsf model provided superior clinical benefits compared to other models.
conclusionThe PNI-based Aorsf model effectively predicts long-term survival outcomes after ablation therapy, making a significant contribution to HCC research by improving surveillance, prevention, and treatment strategies.
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