Functional-guided radiotherapy using knowledge-based planning.

Area of Science:

• Radiation Oncology
• Medical Physics
• Image-Guided Therapy

Background:

• Implementing functional-guided radiotherapy with 4DCT-ventilation imaging faces challenges in defining patient-specific dosimetric goals for functional lung.
• Ensuring consistent treatment plan quality across different planners is another significant hurdle.

Purpose of the Study:

• To develop and validate a knowledge-based planning (KBP) model for functional-guided radiotherapy.
• To address the challenges of defining dosimetric goals and ensuring plan quality in functional lung treatments.

Main Methods:

• A KBP model was constructed using data from 30 previously planned functional-guided lung cancer patients.
• The model incorporated standard organs at risk (OARs) and a ventilation contour for high-ventilation lung areas.
• Model validation involved comparing dose metrics from 20 independent cases planned with and without KBP.

Main Results:

• KBP-optimized plans showed statistically significant improvements in V20Gy and mean dose to functional lung (p=0.005, p=0.001).
• Significant reductions were observed in V20Gy and mean dose to total lung minus GTV (p=0.002, p=0.01), and mean esophageal dose (p=0.005).

Conclusions:

• A KBP model was successfully developed for functional-guided radiotherapy.
• The study demonstrated modest but statistically significant improvements in functional lung and total lung dose metrics using KBP.