Objective:

To develop a multimodal pipeline that integrates structured electronic health record (EHR) data with features extracted from unstructured radiology reports to significantly enhance predictive modeling for renal tumor malignancy.

Key Findings:

• Deep learning models have shown promising predictive capabilities in differentiating benign from malignant tumors using imaging data, with implications for reducing unnecessary surgeries.
• Structured EHR data can capture known RCC risk factors and has been successfully used in risk stratification, indicating its potential for improving patient outcomes.
• Natural language processing (NLP) techniques can extract tumor characteristics from unstructured clinical documentation, enhancing the richness of data available for analysis.
• Combining structured EHR data with unstructured data enhances model performance and supports individualized patient assessments, paving the way for personalized medicine.

Interpretation:

The integration of structured and unstructured data in predictive modeling for renal tumors may improve risk stratification and clinical decision-making.

Limitations:

• Pathology reports were not uniformly available for outcome classification, which may affect the robustness of the findings.
• The study relies on retrospective data, which may introduce biases and limit the generalizability of the results.

Conclusion:

The multimodal approach shows potential for enhancing predictive modeling in renal tumor malignancy assessment.