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Title: SU-F-T-114: A Novel Anatomically Predictive Extension Model of Computational Human Phantoms for Dose Reconstruction in Retrospective Epidemiological Studies of Second Cancer Risks in Radiotherapy Patients

Abstract

Purpose: Recent advances in cancer treatments have greatly increased the likelihood of post-treatment patient survival. Secondary malignancies, however, have become a growing concern. Epidemiological studies determining secondary effects in radiotherapy patients require assessment of organ-specific dose both inside and outside the treatment field. An essential input for Monte Carlo modeling of particle transport is radiological images showing full patient anatomy. However, in retrospective studies it is typical to only have partial anatomy from CT scans used during treatment planning. In this study, we developed a multi-step method to extend such limited patient anatomy to full body anatomy for estimating dose to normal tissues located outside the CT scan coverage. Methods: The first step identified a phantom from a library of body size-dependent computational human phantoms by matching the height and weight of patients. Second, a Python algorithm matched the patient CT coverage location in relation to the whole body phantom. Third, an algorithm cut the whole body phantom and scaled them to match the size of the patient. Then, merged the two anatomies into one whole body. We entitled this new approach, Anatomically Predictive Extension (APE). Results: The APE method was examined by comparing the original chest-abdomen-pelvis CT images ofmore » the five patients with the APE phantoms developed from only the chest part of the CAP images and whole body phantoms. We achieved average percent differences of tissue volumes of 25.7%, 34.2%, 16.5%, 26.8%, and 31.6% with an average of 27% across all patients. Conclusion: Our APE method extends the limited CT patient anatomy to whole body anatomy by using image processing and computational human phantoms. Our ongoing work includes evaluating the accuracy of these APE phantoms by comparing normal tissue doses in the APE phantoms and doses calculated for the original full CAP images under generic radiotherapy simulations. This research was supported by the NIH Intramural Research Program.« less

Authors:
;  [1];  [2]; ;  [3]
  1. National Cancer Institute, Rockville, MD (United States)
  2. University of Michigan, Ann Arbor, MI (United States)
  3. East Carolina University Greenville, NC (United States)
Publication Date:
OSTI Identifier:
22642356
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ANATOMY; ANIMAL TISSUES; CAT SCANNING; DOSES; IMAGE PROCESSING; MONTE CARLO METHOD; NEOPLASMS; PATIENTS; PHANTOMS; PLANT TISSUES; RADIOTHERAPY; SIMULATION

Citation Formats

Kuzmin, G, Lee, C, Lee, C, Pelletier, C, and Jung, J. SU-F-T-114: A Novel Anatomically Predictive Extension Model of Computational Human Phantoms for Dose Reconstruction in Retrospective Epidemiological Studies of Second Cancer Risks in Radiotherapy Patients. United States: N. p., 2016. Web. doi:10.1118/1.4956250.
Kuzmin, G, Lee, C, Lee, C, Pelletier, C, & Jung, J. SU-F-T-114: A Novel Anatomically Predictive Extension Model of Computational Human Phantoms for Dose Reconstruction in Retrospective Epidemiological Studies of Second Cancer Risks in Radiotherapy Patients. United States. doi:10.1118/1.4956250.
Kuzmin, G, Lee, C, Lee, C, Pelletier, C, and Jung, J. 2016. "SU-F-T-114: A Novel Anatomically Predictive Extension Model of Computational Human Phantoms for Dose Reconstruction in Retrospective Epidemiological Studies of Second Cancer Risks in Radiotherapy Patients". United States. doi:10.1118/1.4956250.
@article{osti_22642356,
title = {SU-F-T-114: A Novel Anatomically Predictive Extension Model of Computational Human Phantoms for Dose Reconstruction in Retrospective Epidemiological Studies of Second Cancer Risks in Radiotherapy Patients},
author = {Kuzmin, G and Lee, C and Lee, C and Pelletier, C and Jung, J},
abstractNote = {Purpose: Recent advances in cancer treatments have greatly increased the likelihood of post-treatment patient survival. Secondary malignancies, however, have become a growing concern. Epidemiological studies determining secondary effects in radiotherapy patients require assessment of organ-specific dose both inside and outside the treatment field. An essential input for Monte Carlo modeling of particle transport is radiological images showing full patient anatomy. However, in retrospective studies it is typical to only have partial anatomy from CT scans used during treatment planning. In this study, we developed a multi-step method to extend such limited patient anatomy to full body anatomy for estimating dose to normal tissues located outside the CT scan coverage. Methods: The first step identified a phantom from a library of body size-dependent computational human phantoms by matching the height and weight of patients. Second, a Python algorithm matched the patient CT coverage location in relation to the whole body phantom. Third, an algorithm cut the whole body phantom and scaled them to match the size of the patient. Then, merged the two anatomies into one whole body. We entitled this new approach, Anatomically Predictive Extension (APE). Results: The APE method was examined by comparing the original chest-abdomen-pelvis CT images of the five patients with the APE phantoms developed from only the chest part of the CAP images and whole body phantoms. We achieved average percent differences of tissue volumes of 25.7%, 34.2%, 16.5%, 26.8%, and 31.6% with an average of 27% across all patients. Conclusion: Our APE method extends the limited CT patient anatomy to whole body anatomy by using image processing and computational human phantoms. Our ongoing work includes evaluating the accuracy of these APE phantoms by comparing normal tissue doses in the APE phantoms and doses calculated for the original full CAP images under generic radiotherapy simulations. This research was supported by the NIH Intramural Research Program.},
doi = {10.1118/1.4956250},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: Epidemiological studies of second cancer risk in radiotherapy patients often require individualized dose estimates of normal tissues. Prior to 3D conformal radiation therapy planning, patient anatomy information was mostly limited to 2D radiological images or not even available. Generic patient CT images are often used in commercial radiotherapy treatment planning system (TPS) to reconstruct normal tissue doses. The objective of the current work was to develop a series of reference size computational human phantoms in DICOM-RT format for direct use in dose reconstruction in TPS. Methods: Contours of 93 organs and tissues were extracted from a series of pediatricmore » and adult hybrid computational human phantoms (newborn, 1-, 5-, 10-, 15-year-old, and adult males and females) using Rhinoceros software. A MATLAB script was created to convert the contours into the DICOM-RT structure format. The simulated CT images with the resolution of 1×1×3 mm3 were also generated from the binary phantom format and coupled with the DICOM-structure files. Accurate volumes of the organs were drawn in the format using precise delineation of the contours in converted format. Due to complex geometry of organs, higher resolution (1×1×1 mm3) was found to be more efficient in the conversion of newborn and 1-year-old phantoms. Results: Contour sets were efficiently converted into DICOM-RT structures in relatively short time (about 30 minutes for each phantom). A good agreement was observed in the volumes between the original phantoms and the converted contours for large organs (NRMSD<1.0%) and small organs (NRMSD<7.7%). Conclusion: A comprehensive series of computational human phantoms in DICOM-RT format was created to support epidemiological studies of second cancer risks in radiotherapy patients. We confirmed the DICOM-RT phantoms were successfully imported into the TPS programs of major vendors.« less
  • Purpose: Epidemiological studies of second cancer risks in breast cancer radiotherapy patients often use generic patient anatomy to reconstruct normal tissue doses when CT images of patients are not available. To evaluate the uncertainty involved in the dosimetry approach, we evaluated the esophagus dose in five sample patients by simulating breast cancer treatments. Methods: We obtained the diagnostic CT images of five anonymized adult female patients in different Body Mass Index (BMI) categories (16– 36kg/m2) from National Institutes of Health Clinical Center. We contoured the esophagus on the CT images and imported them into a Treatment Planning System (TPS) tomore » create treatment plans and calculate esophagus doses. Esophagus dose was calculated once again via experimentally-validated Monte Carlo (MC) transport code, XVMC under the same geometries. We compared the esophagus doses from TPS and the MC method. We also investigated the degree of variation in the esophagus dose across the five patients and also the relationship between the patient characteristics and the esophagus doses. Results: Eclipse TPS using Analytical Anisotropic Algorithm (AAA) significantly underestimates the esophagus dose in breast cancer radiotherapy compared to MC. In the worst case, the esophagus dose from AAA was only 40% of the MC dose. The Coefficient of Variation across the patients was 48%. We found that the maximum esophagus dose was up to 2.7 times greater than the minimum. We finally observed linear relationship (Dose = 0.0218 × BMI – 0.1, R2=0.54) between patient’s BMI and the esophagus doses. Conclusion: We quantified the degree of uncertainty in the esophagus dose in five sample breast radiotherapy patients. The results of the study underscore the importance of individualized dose reconstruction for the study cohort to avoid misclassification in the risk analysis of second cancer. We are currently extending the number of patients up to 30.« less
  • 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 ofmore » 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.« less
  • Purpose: To quantify the dosimetric uncertainty due to organ position errors when using height and weight as phantom selection criteria in the UF/NCI Hybrid Phantom Library for the purpose of out-of-field organ dose reconstruction. Methods: Four diagnostic patient CT images were used to create 7-field IMRT plans. For each patient, dose to the liver, right lung, and left lung were calculated using the XVMC Monte Carlo code. These doses were taken to be the ground truth. For each patient, the phantom with the most closely matching height and weight was selected from the body size dependent phantom library. The patientmore » plans were then transferred to the computational phantoms and organ doses were recalculated. Each plan was also run on 4 additional phantoms with reference heights and or weights. Maximum and mean doses for the three organs were computed, and the DVHs were extracted and compared. One sample t-tests were performed to compare the accuracy of the height and weight matched phantoms against the additional phantoms in regards to both maximum and mean dose. Results: For one of the patients, the height and weight matched phantom yielded the most accurate results across all three organs for both maximum and mean doses. For two additional patients, the matched phantom yielded the best match for one organ only. In 13 of the 24 cases, the matched phantom yielded better results than the average of the other four phantoms, though the results were only statistically significant at the .05 level for three cases. Conclusion: Using height and weight matched phantoms does yield better results in regards to out-of-field dosimetry than using average phantoms. Height and weight appear to be moderately good selection criteria, though this selection criteria failed to yield any better results for one patient.« less
  • Purpose: To correlate dose distributions computed using six algorithms for recurrent early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT), with outcome (local failure). Methods: Of 270 NSCLC patients treated with 12Gyx4, 20 were found to have local recurrence prior to the 2-year time point. These patients were originally planned with 1-D pencil beam (1-D PB) algorithm. 4D imaging was performed to manage tumor motion. Regions of local failures were determined from follow-up PET-CT scans. Follow-up CT images were rigidly fused to the planning CT (pCT), and recurrent tumor volumes (Vrecur) were mapped to themore » pCT. Dose was recomputed, retrospectively, using five algorithms: 3-D PB, collapsed cone convolution (CCC), anisotropic analytical algorithm (AAA), AcurosXB, and Monte Carlo (MC). Tumor control probability (TCP) was computed using the Marsden model (1,2). Patterns of failure were classified as central, in-field, marginal, and distant for Vrecur ≥95% of prescribed dose, 95–80%, 80–20%, and ≤20%, respectively (3). Results: Average PTV D95 (dose covering 95% of the PTV) for 3-D PB, CCC, AAA, AcurosXB, and MC relative to 1-D PB were 95.3±2.1%, 84.1±7.5%, 84.9±5.7%, 86.3±6.0%, and 85.1±7.0%, respectively. TCP values for 1-D PB, 3-D PB, CCC, AAA, AcurosXB, and MC were 98.5±1.2%, 95.7±3.0, 79.6±16.1%, 79.7±16.5%, 81.1±17.5%, and 78.1±20%, respectively. Patterns of local failures were similar for 1-D and 3D PB plans, which predicted that the majority of failures occur in centraldistal regions, with only ∼15% occurring distantly. However, with convolution/superposition and MC type algorithms, the majority of failures (65%) were predicted to be distant, consistent with the literature. Conclusion: Based on MC and convolution/superposition type algorithms, average PTV D95 and TCP were ∼15% lower than the planned 1-D PB dose calculation. Patterns of failure results suggest that MC and convolution/superposition type algorithms predict different outcomes for patterns of failure relative to PB algorithms. Work supported in part by Varian Medical Systems, Palo Alto, CA.« less