DUAL ENERGY COMPUTED TOMOGRAPHY FOR PROTON THERAPY TREATMENT PLANNING

Proton radiation therapy is gaining popularity because of the unique characteristics of its dose distribution, e.g., high dose-gradient at the distal end of the percentage-depth-dose curve (known as the Bragg peak). The high dose-gradient offers the possibility of delivering high dose to the target while still sparing critical organs distal to the target. However, the high dose-gradient is a double-edged sword: a small shift of the highly conformal high-dose area can cause the target to be substantially under-dosed or the critical organs to be substantially over-dosed. Because of that, large margins are required in treatment planning to ensure adequate dose coverage of the target, which prevents us from realizing the full potential of proton beams. Therefore, it is critical to reduce uncertainties in the proton radiation therapy. One major uncertainty in a proton treatment is the range uncertainty related to the estimation of proton stopping power ratio (SPR) distribution inside a patient. The SPR distribution inside a patient is required to account for tissue heterogeneities when calculating dose distribution inside the patient. In current clinical practice, the SPR distribution inside a patient is estimated from the patient’s treatment planning computed tomography (CT) images based on the CT number-to-SPR calibration curve. The SPR derived from a single CT number carries large uncertainties in the presence of human tissue composition variations, which is the major drawback of the current SPR estimation method. We propose to solve this problem by using dual energy CT (DECT) and hypothesize that the range uncertainty can be reduced by a factor of two from currently used value of 3.5%. A MATLAB program was developed to calculate the electron density ratio (EDR) and effective atomic number (EAN) from two CT measurements of the same object. An empirical relationship was discovered between mean excitation energies and EANs existing in human body tissues. With the MATLAB program and the empirical relationship, a DECT-based method was successfully developed to derive SPRs for human body tissues (the DECT method). The DECT method is more robust against the uncertainties in human tissues compositions than the current single-CT-based method, because the DECT method incorporated both density and elemental composition information in the SPR estimation. Furthermore, we studied practical limitations of the DECT method. We found that the accuracy of the DECT method using conventional kV-kV x-ray pair is susceptible to CT number variations, which compromises the theoretical advantage of the DECT method. Our solution to this problem is to use a different x-ray pair for the DECT. The accuracy of the DECT method using different combinations of x-ray energies, i.e., the kV-kV, kV-MV and MV-MV pair, was compared using the measured imaging uncertainties for each case. The kV-MV DECT was found to be the most robust against CT number variations. In addition, we studied how uncertainties propagate through the DECT calculation, and found general principles of selecting x-ray pairs for the DECT method to minimize its sensitivity to CT number variations. The uncertainties in SPRs estimated using the kV-MV DECT were analyzed further and compared to those using the stoichiometric method. The uncertainties in SPR estimation can be divided into five categories according to their origins: the inherent uncertainty, the DECT modeling uncertainty, the CT imaging uncertainty, the uncertainty in the mean excitation energy, and SPR variation with proton energy. Additionally, human body tissues were divided into three tissue groups – low density (lung) tissues, soft tissues and bone tissues. The uncertainties were estimated separately because their uncertainties were different under each condition. An estimate of the composite range uncertainty (2s) was determined for three tumor sites – prostate, lung, and head-and-neck, by combining the uncertainty estimates of all three tissue groups, weighted by their proportions along typical beam path for each treatment site. In conclusion, the DECT method holds theoretical advantages in estimating SPRs for human tissues over the current single-CT-based method. Using existing imaging techniques, the kV-MV DECT approach was capable of reducing the range uncertainty from the currently used value of 3.5% to 1.9%-2.3%, but it is short to reach our original goal of reducing the range uncertainty by a factor of two. The dominant source of uncertainties in the kV-MV DECT was the uncertainties in CT imaging, especially in MV CT imaging. Further reduction in beam hardening effect, the impact of scatter, out-of-field object etc. would reduce the Hounsfeld Unit variations in CT imaging. The kV-MV DECT still has the potential to reduce the range uncertainty further.

Implications for Adoption and Implementation of Effective Sexual Health Education Programs

Research on school-based sexual health education programs is at a critical juncture. With the growing number of evidenced-based programs, more focus is needed on how to help schools adopt and implement these programs. The article in this issue titled “Sexual Health Education from the Perspectives of School Staff: Implications for Adoption and Implementation of Effective Programs in Middle School” provides data on individual cognitive factors that may influence adoption and implementation. This commentary explores another framework, Concerns Based Adoption Model, as a tool for examining and supporting change associated with adoption and implementation of sexual health education programs.

Effective interventions for lowering HbA1C in African Americans with type 2 diabetes: A review

This synthesis of the literature provides descriptive analysis and outlines current self-management interventions for African Americans with type 2 diabetes. Specifically, this study describes and explores the design of those studies whose interventions have been shown to lower HbA1C levels in this population by at least 0.5% points, an improvement that provides approximately 10% reduction in long term complications from this disease.^ Results. In total, 37 articles were reviewed and 17 articles met inclusion criteria for analysis. Analysis of each study's methodology and results was performed and selected studies with interventions that resulted in improvements in HbA1C outcomes equal to 0.5% or greater for both group 1 and 2 were summarized by intervention type in table format. Descriptive analysis, outlining the number and characteristics of proximal and distal mediating components addressed in Group 1 studies, was performed in order to determine whether mediating components may have had some relation to effectiveness of intervention on outcome HbA1C. Descriptive analysis revealed that no particular design is substantially more effective than another among Behavioral studies although, there may be an advantage in using culturally sensitive, group interventions that address greater numbers of distal mediating components. Among Process studies, structured approaches (i.e. algorithm care and scheduled follow up), as well as utilization of specialty and group care are represented as effective for African American populations. ^ Conclusions. It may be summarized that by targeting behavior and addressing provider delivery (i.e. algorithm use, group care, home care, and provider follow up) in this population, a greater yield in outcome improvements may be accomplished. However, many gaps exist in a review process that stratifies results and focuses on identifying group specific intervention successes and failures. Further research in different populations will aid researchers and practitioners in discovering the best evidence, and identifying models that could be utilized in practice to achieve the best diabetes management for at risk groups.^