Quantifying variability in radiation dose due to respiratory-induced tumor motion

Area of Science:

• Radiation oncology
• Medical physics
• Computational biology

Background:

• Stereotactic body radiation therapy (SBRT) offers precise tumor targeting but is challenged by respiratory-induced organ motion.
• Current treatment planning struggles to accurately predict and compensate for abdominal organ motion during breathing.
• This motion can lead to significant displacement of target lesions, impacting radiation dose delivery accuracy and clinical outcomes.

Purpose of the Study:

• To develop a computational framework for predicting dose deposition uncertainties caused by respiratory organ motion in SBRT.
• To model patient-specific organ displacement and breathing pattern variations for improved radiation treatment planning.
• To identify tissues at risk of under- or over-dosing due to motion-related uncertainties.

Main Methods:

• Developed a framework combining organ deformation modeling and stochastic breathing models.
• Organ deformation estimated using four-dimensional maximum a posteriori (MAP) estimation based on respiratory-correlated computed tomography (RCCT) images.
• Patient-specific respiration characterized by probability density functions (PDF) of chest wall amplitudes; breathing patterns modeled as random processes.

Main Results:

• Successfully modeled patient-specific organ motion in response to respiration.
• Quantified the variability in radiation dose accumulation resulting from combined motion and breathing models.
• Enabled prediction of dose delivery uncertainties and identification of at-risk tissues.

Conclusions:

• The developed framework accurately predicts dose deposition uncertainties in SBRT due to respiratory organ motion.
• This approach enhances treatment planning and delivery by accounting for patient-specific breathing variations.
• Improved accuracy in radiation dose delivery can lead to better clinical outcomes and reduced toxicity.