EUCLID: an outcome analysis tool for high-dimensional clinical studies

Area of Science:

• Radiation oncology
• Medical physics
• Biostatistics

Background:

• Treatment decisions in 3DCRT and IMRT rely on dose distributions and population-based TCP/NTCP estimates.
• Clinical, biological, and physiological factors influencing radiotherapy outcomes are often overlooked in planning.
• Existing methods lack individualized prediction capabilities for radiotherapy treatment outcomes.

Purpose of the Study:

• To develop and present the EUCLID statistical outcome analysis tool for personalized radiotherapy.
• To enable radiation oncologists and medical physicists to predict individual patient outcome probabilities.
• To integrate diverse clinical, biological, physiological, and dosimetric factors into treatment planning.

Main Methods:

• Developed the EUCLID software for statistical outcome analysis in radiotherapy.
• Built a mathematical model to predict outcome probabilities using multiple patient factors.
• Enabled analysis of large patient datasets to derive regression coefficients for model building.
• Facilitated individual patient outcome prediction by applying the derived model.

Main Results:

• EUCLID analyzes patient data to derive predictive models for radiotherapy outcomes.
• The tool allows for individualized outcome probability assessment for each patient.
• It facilitates the exploration of various factors to identify key predictors of treatment success.
• EUCLID integrates with existing treatment planning and record-and-verify systems for practical data management.

Conclusions:

• EUCLID provides a flexible and evidence-based approach to individualize radiotherapy treatment decisions.
• The tool enhances treatment planning by incorporating a comprehensive set of predictive factors.
• It supports radiation oncologists and medical physicists in optimizing patient care through data-driven insights.