PCA-based respiratory motion modeling for individualized PTV margin optimization in NSCLC radiotherapy.

Area of Science:

• Radiation Oncology
• Medical Physics
• Image-guided Therapy

Background:

• Respiratory motion in non-small cell lung cancer (NSCLC) radiotherapy presents challenges for target coverage and normal tissue sparing.
• Current methods often involve a trade-off between adequate tumor coverage and minimizing dose to surrounding healthy tissues.

Purpose of the Study:

• To develop and validate a clinically applicable, individualized planning target volume (PTV) margin strategy for NSCLC radiotherapy.
• To integrate principal component analysis (PCA) with clinical predictors to optimize PTV margins.
• To improve normal tissue sparing, particularly lung sparing, while maintaining target coverage.

Main Methods:

• Utilized 4DCT (four-dimensional computed tomography) data from 61 Stage III NSCLC patients for motion modeling and planning.
• Applied PCA to deformable vector fields to identify dominant tumor motion modes and quantify motion amplitudes (A_mean).
• Developed an individualized margin recipe incorporating motion amplitude, tumor location factor (F_pos), and T stage factor (F_T).

Main Results:

• Tumor location and T stage were significant predictors of superior-inferior motion amplitude, with lower lobe and early T1-T2 stage tumors showing greater mobility.
• Individualized 3D plans significantly reduced PTV volume (410.0 ± 170.0 cm³) compared to conventional 3D plans (460.2 ± 179.1 cm³).
• The individualized margin approach reduced lung dose (V20: 30.0%) compared to conventional 3DCT planning (40.5%), while remaining comparable to 4DCT-based plans (28.9%).

Conclusions:

• Integrating PCA-derived motion characteristics with clinical weighting factors offers a practical approach for individualized margin design in NSCLC radiotherapy.
• This strategy effectively improves lung sparing and maintains target coverage, addressing a key challenge in thoracic radiation therapy.
• The developed method demonstrates potential for enhanced treatment planning and improved outcomes for NSCLC patients.