Can Machine Learning Predict Favorable Outcome After Radiofrequency Ablation of Hepatocellular Carcinoma?

Area of Science:

• Hepatobiliary medicine
• Oncology
• Artificial Intelligence in Medicine

Background:

• Standard treatments for limited-stage hepatocellular carcinoma (HCC) include resection and radiofrequency ablation (RFA).
• Treatment outcomes following RFA are influenced by patient health, liver function, and cancer stage.
• Predicting RFA response in HCC is complex and crucial for effective patient management.

Purpose of the Study:

• To develop and evaluate a machine learning model for predicting treatment response in HCC patients undergoing RFA.
• To identify key factors influencing the success of RFA for localized HCC.
• To assess the accuracy of machine learning in forecasting favorable outcomes 12 months post-RFA.

Main Methods:

• Retrospective analysis of 111 HCC patients treated with RFA between 2018 and 2022.
• Utilized Python and XGBoost to build a predictive model using clinical, radiologic, and laboratory data.
• Data was divided into 70% training and 30% validation sets to assess model performance.

Main Results:

• The study included 111 patients (70.3% male, median age 57).
• A favorable outcome (alive with controlled HCC) was observed in 55.9% of patients at 12 months.
• The machine learning model achieved high accuracy (90.6% training, 78.9% validation) and AUC (0.95 training, 0.80 validation).

Conclusions:

• Machine learning models show promise as predictive tools for RFA outcomes in HCC.
• The developed model demonstrated significant predictive capability.
• Further validation in larger patient cohorts is recommended to confirm these findings.