Conversion of computational human phantoms into DICOM-RT for normal tissue dose assessment in radiotherapy patients.

Radiotherapy (RT) treatment planning systems (TPS) are designed for the fast calculation of dose to the tumor bed and nearby organs at risk using x-ray computed tomography (CT) images. However, CT images for a patient are typically available for only a small portion of the body, and in some cases, such as for retrospective epidemiological studies, no images may be available at all. When dose to organs that lie out-of-scan must be estimated, a convenient alternative for the unknown patient anatomy is to use a matching whole-body computational phantom as a surrogate. The purpose of the current work is to connect such computational phantoms to commercial RT TPS for retrospective organ dose estimation. A custom software with graphical user interface (GUI), called the DICOM-RT Generator, was developed in MATLAB to convert voxel computational phantoms into the digital imaging and communications in medicine radiotherapy (DICOM-RT) format, compatible with commercial TPS. DICOM CT image sets for the phantoms are created via a density-to-Hounsfield unit (HU) conversion curve. Accompanying structure sets containing the organ contours are automatically generated by tracing binary masks of user-specified organs on each phantom CT slice. The software was tested on a library of body size-dependent phantoms, the International Commission on Radiological Protection reference phantoms, and a canine voxel phantom, taking only a few minutes per conversion. The resulting DICOM-RT files were tested on several commercial TPS. As an example application, a library of converted phantoms was used to estimate organ doses for members of the National Wilms Tumor Study (NWTS) cohort. The converted phantom library, in DICOM format, and a standalone MATLAB-compiled executable of the DICOM-RT Generator are available for others to use for research purposes (http://ncidose.cancer.gov).

A Novel Method to Extend a Partial-Body CT for the Reconstruction of Dose to Organs beyond the Scan Range.

Epidemiological investigation is an important approach to assessing the risk of late effects after radiotherapy, and organ dosimetry is a crucial part of such analysis. Computed tomography (CT) images, if available, can be a valuable resource for individualizing the dosimetry, because they describe the specific anatomy of the patient. However, CT images acquired for radiation treatment planning purposes cover only a portion of the body near the target volume, whereas for epidemiology, the interest lies in the more distant normal tissues, which may be located outside the scan range. To address this challenge, we developed a novel method, called the Anatomically Predictive Extension (APE), to extend a partial-body CT image stack using images of a computational human phantom matched to the patient based on their height and weight. To test our method, we created five APE phantoms from chest and abdominal images extracted from the chest-abdomen-pelvis (CAP) CT scans of five patients. Organ doses were calculated for simple chest and prostate irradiations that were planned on the reference computational phantom (assumed patient geometry if no CT images are available), APE phantoms (patient-phantom hybrid given a partial-body patient CT) and full patient CAP CT scans (ground truth). The APE phantoms and patient CAP CT scans resulted in nearly identical dosimetry for those organs that were fully included in the partial-body CT used to construct the APE. The calculated doses to these same organs in the reference phantoms differed by up to 20% and 52% for the chest and prostate cases, respectively. For organs outside the scan coverage, the reference phantom showed, on average, dose differences of 31% (chest case) and 41% (prostate case). For the APE phantoms, these values were 26% (chest) and 17% (prostate). The APE method combines patient and phantom images to improve organ dosimetry both inside and outside the scan range. We intend to use the APE method for estimating dose for organs peripheral to the treatment fields; however, this method is quite generalizable with many potential applications.

Reconstruction of organ dose for external radiotherapy patients in retrospective epidemiologic studies.

Organ dose estimation for retrospective epidemiological studies of late effects in radiotherapy patients involves two challenges: radiological images to represent patient anatomy are not usually available for patient cohorts who were treated years ago, and efficient dose reconstruction methods for large-scale patient cohorts are not well established. In the current study, we developed methods to reconstruct organ doses for radiotherapy patients by using a series of computational human phantoms coupled with a commercial treatment planning system (TPS) and a radiotherapy-dedicated Monte Carlo transport code, and performed illustrative dose calculations. First, we developed methods to convert the anatomy and organ contours of the pediatric and adult hybrid computational phantom series to Digital Imaging and Communications in Medicine (DICOM)-image and DICOM-structure files, respectively. The resulting DICOM files were imported to a commercial TPS for simulating radiotherapy and dose calculation for in-field organs. The conversion process was validated by comparing electron densities relative to water and organ volumes between the hybrid phantoms and the DICOM files imported in TPS, which showed agreements within 0.1 and 2%, respectively. Second, we developed a procedure to transfer DICOM-RT files generated from the TPS directly to a Monte Carlo transport code, x-ray Voxel Monte Carlo (XVMC) for more accurate dose calculations. Third, to illustrate the performance of the established methods, we simulated a whole brain treatment for the 10 year-old male phantom and a prostate treatment for the adult male phantom. Radiation doses to selected organs were calculated using the TPS and XVMC, and compared to each other. Organ average doses from the two methods matched within 7%, whereas maximum and minimum point doses differed up to 45%. The dosimetry methods and procedures established in this study will be useful for the reconstruction of organ dose to support retrospective epidemiological studies of late effects in radiotherapy patients.

Mutations in lipoprotein lipase that block binding to the endothelial cell transporter GPIHBP1.

GPIHBP1, a glycosylphosphatidylinositol-anchored protein of capillary endothelial cells, shuttles lipoprotein lipase (LPL) from subendothelial spaces to the capillary lumen. An absence of GPIHBP1 prevents the entry of LPL into capillaries, blocking LPL-mediated triglyceride hydrolysis and leading to markedly elevated triglyceride levels in the plasma (i.e., chylomicronemia). Earlier studies have established that chylomicronemia can be caused by LPL mutations that interfere with catalytic activity. We hypothesized that some cases of chylomicronemia might be caused by LPL mutations that interfere with LPL's ability to bind to GPIHBP1. Any such mutation would provide insights into LPL sequences required for GPIHBP1 binding. Here, we report that two LPL missense mutations initially identified in patients with chylomicronemia, C418Y and E421K, abolish LPL's ability to bind to GPIHBP1 without interfering with LPL catalytic activity or binding to heparin. Both mutations abolish LPL transport across endothelial cells by GPIHBP1. These findings suggest that sequences downstream from LPL's principal heparin-binding domain (amino acids 403-407) are important for GPIHBP1 binding. In support of this idea, a chicken LPL (cLPL)-specific monoclonal antibody, xCAL 1-11 (epitope, cLPL amino acids 416-435), blocks cLPL binding to GPIHBP1 but not to heparin. Also, changing cLPL residues 421 to 425, 426 to 430, and 431 to 435 to alanine blocks cLPL binding to GPIHBP1 without inhibiting catalytic activity. Together, these data define a mechanism by which LPL mutations could elicit disease and provide insights into LPL sequences required for binding to GPIHBP1.