The Influence of Storage and Cooking on the Vitamin D Content of 25-Hydroxyvitamin D3-Enriched Eggs.

Food fortification is an effective approach to improve vitamin D (VD) concentrations in foods. Eggs are a useful food vehicle for enrichment with VD via its hydroxylated metabolite, 25-hydroxyvitamin D (25-D3), in hen feed. This study determined the impact of time of lay, storage conditions (ambient and refrigeration) and common cooking methods (boiling, frying, scrambling, poaching and microwaving) on the vitamin D metabolite concentration of eggs enriched with 25-D3. Processed samples were freeze-dried and analysed for D3 and 25-D3 using an HPLC-MS(/MS) method. The results indicated that storage and cooking practices influence VD metabolites, with 25-D3 showing true retention of 72-111% and concentrations of 0.67-0.96 µg/100 g of whole egg. Vitamin D3 showed true retention of 50-152% and concentrations of 0.11-0.61 µg/100 g of whole egg. Depending on the storage and method of cooking applied, the calculated total VD activity of enriched eggs ranged from 3.45 to 5.43 µg/100 g of whole egg and was 22-132% higher in comparison to standardised VD content for non-enriched British eggs. The study suggests that 25-D3 is a stable metabolite in eggs following storage and cooking, and that 25-D3-enriched eggs may serve as a potent dietary source of VD.

Personalizing Radiotherapy Prescription Dose Using Genomic Markers of Radiosensitivity and Normal Tissue Toxicity in NSCLC.

INTRODUCTION: Cancer sequencing efforts have revealed that cancer is the most complex and heterogeneous disease that affects humans. However, radiation therapy (RT), one of the most common cancer treatments, is prescribed on the basis of an empirical one-size-fits-all approach. We propose that the field of radiation oncology is operating under an outdated null hypothesis: that all patients are biologically similar and should uniformly respond to the same dose of radiation. METHODS: We have previously developed the genomic-adjusted radiation dose, a method that accounts for biological heterogeneity and can be used to predict optimal RT dose for an individual patient. In this article, we use genomic-adjusted radiation dose to characterize the biological imprecision of one-size-fits-all RT dosing schemes that result in both over- and under-dosing for most patients treated with RT. To elucidate this inefficiency, and therefore the opportunity for improvement using a personalized dosing scheme, we develop a patient-specific competing hazards style mathematical model combining the canonical equations for tumor control probability and normal tissue complication probability. This model simultaneously optimizes tumor control and toxicity by personalizing RT dose using patient-specific genomics. RESULTS: Using data from two prospectively collected cohorts of patients with NSCLC, we validate the competing hazards model by revealing that it predicts the results of RTOG 0617. We report how the failure of RTOG 0617 can be explained by the biological imprecision of empirical uniform dose escalation which results in 80% of patients being overexposed to normal tissue toxicity without potential tumor control benefit. CONCLUSIONS: Our data reveal a tapestry of radiosensitivity heterogeneity, provide a biological framework that explains the failure of empirical RT dose escalation, and quantify the opportunity to improve clinical outcomes in lung cancer by incorporating genomics into RT.

Anatomic structure-based deformable image registration of brachytherapy implants in the treatment of locally advanced cervix cancer.

PURPOSE: To examine the impact of anatomic structure-based image sets in deformable image registration (DIR) for cervical cancer patients. METHODS AND MATERIALS: CT examinations of 7 patients previously treated for locally advanced cervical cancer with external beam radiation therapy and from three to five fractions of high-dose-rate brachytherapy (HDR-BT) were used. Structure-based image sets were created from "free" structures already made for planning purposes, with each structure of interest assigned a unique, homogeneous Hounsfield number. Subsequent HDR fractions were registered to the pretreatment external beam radiation therapy and/or the first HDR fraction using commercially available software by rigid alignment (RIG) followed by DIR. Comparison methods included quantification of external contour displacement between source and target images and calculation of mean voxel displacement values. Registration results for structure-based image sets were then compared and contrasted to intensity-based registrations of the original grayscale images. RESULTS: Utilization of anatomic structure-based image sets resulted in better initial rigid matching (A-RIG) with more importance on applicator positioning and soft tissue structures. Subsequent DIR of anatomic structure-based images allowed for intermodality registrations, whereas all intermodality registrations using original CT images failed to produce anatomically feasible results. CONCLUSIONS: We have investigated the use of structure-based CT image sets for image registrations and have produced anatomically favorable registrations with excellent matching of external contours as compared to registrations of original grayscale images. Commercial software registrations using treatment-planning structures required no manual tweaking on a per-patient basis, suggesting results are reproducible and broadly applicable.

A novel, reproducible, and objective method for volumetric magnetic resonance imaging assessment of enhancing glioblastoma.

OBJECT: Robust methodology that allows objective, automated, and observer-independent measurements of brain tumor volume, especially after resection, is lacking. Thus, determination of tumor response and progression in neurooncology is unreliable. The objective of this study was to determine if a semi-automated volumetric method for quantifying enhancing tissue would perform with high reproducibility and low interobserver variability. METHODS: Fifty-seven MR images from 13 patients with glioblastoma were assessed using our method, by 2 neuroradiologists, 1 neurosurgeon, 1 neurosurgical resident, 1 nurse practitioner, and 1 medical student. The 2 neuroradiologists also performed traditional 1-dimensional (1D) and 2-dimensional (2D) measurements. Intraclass correlation coefficients (ICCs) assessed interobserver variability between measurements. Radiological response was determined using Response Evaluation Criteria In Solid Tumors (RECIST) guidelines and Macdonald criteria. Kappa statistics described interobserver variability of volumetric radiological response determinations. RESULTS: There was strong agreement for 1D (RECIST) and 2D (Macdonald) measurements between neuroradiologists (ICC = 0.42 and 0.61, respectively), but the agreement using the authors' novel automated approach was significantly stronger (ICC = 0.97). The volumetric method had the strongest agreement with regard to radiological response (κ = 0.96) when compared with 2D (κ = 0.54) or 1D (κ = 0.46) methods. Despite diverse levels of experience of the users of the volumetric method, measurements using the volumetric program remained remarkably consistent in all users (0.94). CONCLUSIONS: Interobserver variability using this new semi-automated method is less than the variability with traditional methods of tumor measurement. This new method is objective, quick, and highly reproducible among operators with varying levels of expertise. This approach should be further evaluated as a potential standard for response assessment based on contrast enhancement in brain tumors.

Voxel clustering for quantifying PET-based treatment response assessment.

PURPOSE: Imaging biomarkers are crucial in managing treatment options for cancer patients. They are extremely powerful tools since they allow personalized treatment assessment early during therapy by using repeated imaging to detect and quantify tumor response. Currently, treatment response assessment from consecutive imaging is measured by simple global measures that do not capture a tumor's heterogeneous response. The authors present an automated, multivoxel metric that groups voxels into clusters of changes for a local definition of radiation treatment efficiency from multiple PET imaging studies acquired at different time periods for assessing therapeutic response. METHODS: The algorithm employs level-set mathematics to extract changing features to classify voxels into response patterns. First, pretreatment and post-treatment PET images were aligned using a deformable registration to correct for posture and soft tissue changes. The detailed mapping was modeled by free form deformations B-spline optimized using the limited memory L-BFGS algorithm. The posture-corrected datasets are then subtracted to produce an image of molecular changes embedded with noise. Once images were aligned and subtracted, a segmentation algorithm combining the concepts of voxel and distance-based techniques classified voxels into patterns of signal reduction or enhancement. Although signal reduction is evidence of successful treatment, signal-enhancing regions are an indication of treatment failure. For an in depth analysis of potential treatment errors, patterns of signal enhancement were correlated with the radiation treatment dose and anatomical structures from the treatment plan using image registration methods. RESULTS: The algorithm was retrospectively applied to PET∕CT and radiotherapy (RT) oncology data from an NCI-sponsored clinical trial (81 clinical cases from RTOG 0522 Trial) for combined drug and radiation therapy in head and neck carcinomas. This clinical trial dataset presented a realistic environment for implementing and validating our algorithm to correlate local response as observed in serial PET with delivered dose. The technique was instrumental in detecting geographical and segmentation misses on the actual clinical cases by providing accurate voxel-by-voxel analysis of metabolic changes. Results of the level-set based clustering algorithm are saved as a detailed report of enhancing∕nonenhancing regions and their location, and can be further displayed as a colorwash laid over the original anatomy for in depth analysis. CONCLUSIONS: The automated technique was instrumental in analyzing treatment response in the clinical cases and provided an useful tool for accurate, outcome-based response assessment of the radiation treatment process. The developed method is general and should be extendable to other high-resolution diagnostic imaging with minor modifications.

A measure to evaluate deformable registration fields in clinical settings.

Deformable registration has migrated from a research topic to a widely used clinical tool that can improve radiotherapeutic treatment accuracy by tracking anatomical changes. Although various mathematical formulations have been reported in the literature and implemented in commercial software, we lack a straightforward method to verify a given solution in routine clinical use. We propose a metric using concepts derived from vector analysis that complements the standard evaluation tools to identify unrealistic wrappings in a displacement field. At the heart of the proposed procedure is identification of vortexes in the displacement field that do not correspond to underlying anatomical changes. Vortexes are detected and their intensity quantified using the CURL operator and presented as a vortex map overlaid on the original anatomy for rapid identification of problematic regions. We show application of the proposed metric on clinical scenarios of adaptive radiotherapy and treatment response assessment, where the CURL operator quantitatively detected errors in the displacement field and identified problematic regions that were invisible to classical voxel-based evaluation methods. Unrealistic warping not visible to standard voxel-based solution assessment can produce erroneous results when the deformable solution is applied on a secondary dataset, such as dose matrix in adaptive therapy or PET data for treatment response assessment. The proposed metric for evaluating deformable registration provides increased usability and accuracy of detecting unrealistic deformable registration solutions when compared to standard intensity-based approaches. It is computationally efficient and provides a valuable platform for the clinical acceptance of image-guided radiotherapy.

Quantitative analysis of pre- and post-treatment PET-CT scans using deformable image registration methods.

BACKGROUND AND PURPOSE: To investigate the utility of quantitative PET analysis for early prediction of local control following stereotactic body radiation therapy (SBRT). MATERIAL AND METHODS: An initial test cohort of fourteen cases and a validation cohort of twenty-three cases were analyzed. All patients had metastatic or recurrent cancer and underwent PET-CTs pre- and post- SBRT to a variety of sites. Local failure was defined as biopsy proven persistent/recurrent disease or progressive disease on radiologic imaging. Using deformable registration, radiation dose was transferred to the PET-CTs. Using the prescription isodose as the volume of interest (VOI), response was assessed by generating metabolic volume histograms (MVH). MVH curves examine metabolic heterogeneity in the VOI. Exploratory analyses of the test cohort evaluated the viability of multiple iso-SUV and iso-volumetric points selected from the MVH curves to serve as novel markers of response. Standard PET response markers (maximum/mean SUV and qualitative analysis) were also assessed. RESULTS: In the initial cohort, ten of fourteen patients achieved local control at last follow-up, a median of 225 days following post-SBRT PET. Three out of four local failures had an increase in max SUV, while all patients who achieved local control had a reduction in max SUV (p=0.01). Exploratory analyses using multiple iso-SUV and iso-volumetric points did not yield any factors associated with local control (p>0.05). In the validation cohort, lower post- treatment max SUV (p=.03) and reduction in max SUV (p<0.05) were significantly associated with local control. CONCLUSIONS: Reduction in max SUV following SBRT is associated with local control.

A novel method for volumetric MRI response assessment of enhancing brain tumors.

Current radiographic response criteria for brain tumors have difficulty describing changes surrounding postoperative resection cavities. Volumetric techniques may offer improved assessment, however usually are time-consuming, subjective and require expert opinion and specialized magnetic resonance imaging (MRI) sequences. We describe the application of a novel volumetric software algorithm that is nearly fully automated and uses standard T1 pre- and post-contrast MRI sequences. T1-weighted pre- and post-contrast images are automatically fused and normalized. The tumor region of interest is grossly outlined by the user. An atlas of the nasal mucosa is automatically detected and used to normalize levels of enhancement. The volume of enhancing tumor is then automatically calculated. We tested the ability of our method to calculate enhancing tumor volume with resection cavity collapse and when the enhancing tumor is obscured by subacute blood in a resection cavity. To determine variability in results, we compared narrowly-defined tumor regions with tumor regions that include adjacent meningeal enhancement and also compared different contrast enhancement threshold levels used for the automatic calculation of enhancing tumor volume. Our method quantified enhancing tumor volume despite resection cavity collapse. It detected tumor volume increase in the midst of blood products that incorrectly caused decreased measurements by other techniques. Similar trends in volume changes across scans were seen with inclusion or exclusion of meningeal enhancement and despite different automated thresholds for tissue enhancement. Our approach appears to overcome many of the challenges with response assessment of enhancing brain tumors and warrants further examination and validation.

Evaluation of automatic atlas-based lymph node segmentation for head-and-neck cancer.

PURPOSE: To evaluate if automatic atlas-based lymph node segmentation (LNS) improves efficiency and decreases inter-observer variability while maintaining accuracy. METHODS AND MATERIALS: Five physicians with head-and-neck IMRT experience used computed tomography (CT) data from 5 patients to create bilateral neck clinical target volumes covering specified nodal levels. A second contour set was automatically generated using a commercially available atlas. Physicians modified the automatic contours to make them acceptable for treatment planning. To assess contour variability, the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm was used to take collections of contours and calculate a probabilistic estimate of the "true" segmentation. Differences between the manual, automatic, and automatic-modified (AM) contours were analyzed using multiple metrics. RESULTS: Compared with the "true" segmentation created from manual contours, the automatic contours had a high degree of accuracy, with sensitivity, Dice similarity coefficient, and mean/max surface disagreement values comparable to the average manual contour (86%, 76%, 3.3/17.4 mm automatic vs. 73%, 79%, 2.8/17 mm manual). The AM group was more consistent than the manual group for multiple metrics, most notably reducing the range of contour volume (106-430 mL manual vs. 176-347 mL AM) and percent false positivity (1-37% manual vs. 1-7% AM). Average contouring time savings with the automatic segmentation was 11.5 min per patient, a 35% reduction. CONCLUSIONS: Using the STAPLE algorithm to generate "true" contours from multiple physician contours, we demonstrated that, in comparison with manual segmentation, atlas-based automatic LNS for head-and-neck cancer is accurate, efficient, and reduces interobserver variability.

PET lesion segmentation using automated iso-intensity contouring in head and neck cancer.

To improve the objectivity of the integration of positron emission tomography (PET), we used the conformality index (CI) to measure the goodness of fit of a given PET iso-SUV (standardized uptake value) level with the GTV defined on PET (GTV(PET)) and CT (GTV(CT)). Twenty-two datasets involving 20 head and neck cancer patients were identified. GTV(PET) and GTV(CT) were delineated manually.An iso-intensity method was developed to automatically segment GTV(PET-ISO) using (a) SUV and (b) maximum intensity thresholding (% Max), over a range of intensities. For each intensity, GTV(PET-ISO) was compared to GTV(PET) using the conformality index CI(PET) (and, similarly, to GTV(CT) using CICT). Comparing GTV(PET) to GTV(PET-ISO) vs comparing GTV(CT) to GTV(PET-ISO), the average peak CI was 0.68 +/- 0.09 vs 0.49 +/- 0.12 (p < 0.001), the optimum iso-SUV was 2.7 +/- 0.7 vs 2.9 +/- 1.0 (p=0. 253), and the % Max SUV was 21.8% +/- 7.6% vs 23.8% +/- 8.6% (p=0. 310), respectively. The radiation oncologist's volumes corresponded to a lower iso-SUV (3.02 +/- 0.58 vs 4.36 +/- 0.77, p< 0.001) and lower % Max SUV (24.1 +/- 9.1% vs 34.3 +/- 11.2%, p<0.001) than those drawn by the nuclear medicine physician. Though manual editing may still be necessary, PET iso-contouring is one method to improve the objectivity of GTV definition in head and neck cancer patients. Iso-SUV's can also be used to study the differences between PET's role as a nuclear medicine diagnostic test versus a radiation oncology treatment planning tool.

Multileaf collimator-based linear accelerator radiosurgery: five-year efficiency analysis.

BACKGROUND: In 1989, Emory University initiated a linear accelerator (linac) radiosurgery program using circular collimators. In 2001, the program converted to a multileaf collimator. Since then, the treatment parameters of each patient have been stored in the record-and-verify system. Three major changes have occurred in the radiosurgery program in the past 6 years: in 2002, treatment was changed from static conformal beams to dynamic conformal arc (DCA) therapy, and all patients were imaged before treatment. Beginning in 2005, a linac was used, with the opportunity to treat at higher dose rates (600-1,000 monitor units/min). The aim of this study was to analyze the time required to deliver radiosurgery and the factors affecting treatment delivery. Benchmark data are provided for centers contemplating initiating linac radiosurgery programs. MATERIALS AND METHODS: Custom software was developed to mine the record-and-verify system database and automatically perform a chart review on patients who underwent stereotactic radiosurgery from March 2001 to October 2006. The software extracted 510 patients who underwent stereotactic radiosurgery, and the following information was recorded for each patient: treatment technique, treatment time (from initiation of imaging, if done, to completion of therapy), number of isocenters, number of fields, total monitor units, and dose rate. RESULTS: Of the 510 patients, 395 were treated with DCA therapy and 115 with static conformal beams. The average number of isocenters treated was 1.06 (range, 1-4). The average times to deliver treatment were 24.1 minutes for patients who underwent DCA therapy and 19.3 minutes for those treated with static conformal beams, reflecting the lack of imaging in the latter patients. Eighty percent of patients were treated in <30 minutes. For the patients who underwent DCA therapy, the times required to treat 1, 2, 3, and 4 isocenters were 23.9, 24.8, 33.1, and 37.8 minutes, respectively. Average beam-on time for these patients was 11.4 minutes. There has been no significant reduction in treatment delivery with the use of 1,000 monitor units/min, reflecting the fact that beam-on time is not the major determinant of overall treatment time. CONCLUSIONS: Multileaf collimator-based linac radiosurgery can be delivered efficiently in <30 minutes in the vast majority of patients. Given the limited treatment room utilization required for stereotactic radiosurgery treatments, this study calls into question the need for a dedicated radiosurgery unit for even busy treatment centers.