Methodological considerations in a radiomics–clinical machine‐learning model for predicting cytoreduction completeness

Key Points

• This research aims to address the methodological aspects of integrating radiomics with machine learning to predict the completeness of cytoreduction in clinical settings.
• Analysis of radiomics data in conjunction with clinical data.
• Development of machine learning models based on collected data.
• Evaluation of model performance using relevant statistical measures.
• The integration of radiomics and machine learning enhances prediction accuracy.
• Identified key factors in improving model reliability.
• Recommendations for standardizing methodologies were proposed.