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A Novel Method to Estimate Normal Tissue Dose for Radiotherapy Patients to Support Epidemiologic Studies of Second Cancer Risk

C Lee

C Lee1*, J Jung2 , C Pelletier2 , J Kim3 , C Lee4 , (1) University of Michigan, Ann Arbor, MI, (2) East Carolina University, Greenville, NC, (3) University of Pittsburgh Medical Center, Pittsburgh, PA, (4) National Cancer Institute, Bethesda, MD


SU-D-16A-1 Sunday 2:05PM - 3:00PM Room: 16A

Purpose: Patient cohort of second cancer study often involves radiotherapy patients with no radiological images available: We developed methods to construct a realistic surrogate anatomy by using computational human phantoms. We tested this phantom images both in a commercial treatment planning system (Eclipse) and a custom Monte Carlo (MC) transport code.

Methods: We used a reference adult male phantom defined by International Commission on Radiological Protection (ICRP). The hybrid phantom which was originally developed in Non-Uniform Rational B-Spline (NURBS) and polygon mesh format was converted into more common medical imaging format. Electron density was calculated from the material composition of the organs and tissues and then converted into DICOM format. The DICOM images were imported into the Eclipse system for treatment planning, and then the resulting DICOM-RT files were imported into the MC code for MC-based dose calculation. Normal tissue doses were calculation in Eclipse and MC code for an illustrative prostate treatment case and compared to each other.

Results: DICOM images were generated from the adult male reference phantom. Densities and volumes of selected organs between the original phantom and ones represented within Eclipse showed good agreements, less than 0.6%. Mean dose from Eclipse and MC code match less than 7%, whereas maximum and minimum doses were different up to 45%.

Conclusion: The methods established in this study will be useful for the reconstruction of organ dose to support epidemiological studies of second cancer in cancer survivors treated by radiotherapy. We also work on implementing body size-dependent computational phantoms to better represent patient’s anatomy when the height and weight of patients are available.

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