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

ABSTRACT:

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-f

SUBMITTER: Zhang N 

PROVIDER: S-EPMC11496905 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature