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Updated: Mar 6, 2026
06:48
Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research
Published on: June 7, 2024
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A population based statistical model for daily geometric variations in the thorax.
Yenny Z Szeto1, Marnix G Witte1, Marcel van Herk1
1Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Summary
A statistical model was developed to quantify lung cancer patient geometric variations during radiation therapy. This model improves accuracy in probabilistic treatment planning by accounting for systematic interfraction changes.
Area of Science:
- Radiation oncology
- Medical physics
- Image analysis
Background:
- Accurate lung cancer treatment planning requires understanding geometric variations between imaging sessions.
- Quantifying systematic interfraction variations is crucial for probabilistic planning.
Purpose of the Study:
- To develop a population-based statistical model of systematic interfraction geometric variations in lung cancer patients.
- To incorporate this model as an uncertainty term in probabilistic treatment planning.
Main Methods:
- Deformable image registration was used to analyze 235 lung cancer patients' planning CT and first-week CBCT scans.
- Principal Component Analysis (PCA) extracted dominant modes of geometric variation from deformation vector fields (DVFs).
- Leave-one-out cross-validation was performed for model evaluation.
Main Results:
- The first three PCA components captured cranial-caudal, anterior-posterior, and left-right variations.
- Fifty and 112 PCA components described 75% and 90% of the variance, respectively.
- An overall systematic variation of 3.6mm SD was observed, modeled with 1.0mm accuracy.
Conclusions:
- A PCA-based model for systematic geometric variations in the thorax was successfully developed and validated.
- This model provides a foundation for probability-based treatment planning in lung cancer patients.
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