A 3D-Printed Patient-Specific Phantom for External Beam Radiation Therapy of Prostate Cancer
Christopher L Lee1, Max C Dietrich1, Uma G Desai1
1Franklin W. Olin College of Engineering, 1000 Olin Way, Needham MA 02492.
Journal of Engineering and Science in Medical Diagnostics and Therapy
|February 19, 2019
Summary
A novel 3D printed phantom for prostate cancer radiation therapy enables personalized treatment planning and validation. This patient-specific tool accurately models relevant anatomy, improving treatment quality assurance.
Area of Science:
- Medical Physics
- Radiotherapy Technology
- Biomedical Engineering
Background:
- Current radiotherapy planning lacks patient-specific phantoms for accurate treatment validation.
- Existing phantoms do not adequately replicate individual prostate cancer anatomy, including critical structures.
Purpose of the Study:
- To design, fabricate, and test a novel, patient-specific 3D printed phantom for prostate cancer external beam radiation therapy.
- To validate the phantom's utility in treatment planning and quality assurance using advanced imaging and dosimetry.
Main Methods:
- Developed a patient-specific phantom using 3D printing based on MRI data of the prostate, seminal vesicles, urethra, and surrounding structures.
- Created and delivered an external beam radiation therapy plan using the CyberKnife System.
- Validated treatment accuracy using radiochromic film measurements and gamma index calculations.
Main Results:
- Successfully designed and fabricated a patient- and organ-specific 3D printed phantom.
- Achieved a 99.8% passing rate in gamma index calculations, confirming accurate radiation dose delivery.
- Demonstrated the phantom's capability for integrating MRI into CT-based radiotherapy planning.
Conclusions:
- The developed 3D printed phantom is a viable tool for personalized prostate cancer radiotherapy.
- This methodology enhances treatment planning accuracy and quality assurance for external beam radiation therapy.
- The phantom facilitates the integration of high-contrast MRI data into CT-based treatment planning workflows.
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