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.

PREDICTIVE VARIABLES FOR WEIGHT CHANGE 2 YEARS FOLLOWING PARTICIPATION IN A WEIGHT LOSS INTERVENTION PROGRAM

A historical prospective study was designed to assess the man weight status of subjects who participated in a behavioral weight reduction program in 1983 and to determine whether there was an association between the dependent variable weight change and any of 31 independent variables after a 2 year follow-up period. Data was obtained by abstracting the subjects records and from a follow-up questionnaire administered 2 years following program participation. Five hundred nine subjects (386 females and 123 males) of 1460 subjects who participated in the program, completed and returned the questionnaire. Results showed that mean weight was significantly different (p < 0.001) between the measurement at baseline and after a 2 year follow-up period. The mean weight loss of the group was 5.8 pounds, 10.7 pounds for males and 4.2 pounds for females after a 2 year follow-up period. A total of 63.9% of the group, 69.9% of males and 61.9% of females were still below their initial weight after the 2 year follow-up period. Sixteen of the 31 variables assessed utilizing bivariate analyses were found to be significantly (p (LESSTHEQ) 0.05) associated with weight change after a 2 year follow-up period. These variables were then entered into a multivariate linear regression model. A total of 37.9% of the variance of the dependent variable, weight change, was accounted for by all 16 variables. Eight of these variables were found to be significantly (p (LESSTHEQ) 0.05) predictive of weight change in the stepwise multivariate process accounting for 37.1% of the variance. These variables included: Two baseline variables (percent over ideal body weight at enrollment and occupation) and six follow-up variables (feeling in control of eating habits, percent of body weight lost during treatment, frequency of weight measurement, physical activity, eating in response to emotions, and number of pounds of weight gain needed to resume a diet). It was concluded that a greater amount of emphasis should be placed on the six follow-up variables by clinicians involved in the treatment of obesity, and by the subjects themselves to enhance their chances of success at long-term weight loss. ^

Food intake in nonpurging obese women who binge eat before and after a behavioral weight loss program

Little is known about the impact of behavioral programs to decrease binge eating in obese persons who do not purge. This study was conducted to compare the amount of change in the reduction of binge days and selected nutrients in women who had joined a behavioral weight loss program. Forty-six women in the behavioral self management (BSM) group and thirty-six women in the Wait List Control (WLC) groups completed seven day food records at baseline and six months. These records were analyzed for calories, percentage of calories from protein, carbohydrate, fat and dietary fiber/ 1000 calories and were marked as "binge" or "nonbinge" days. Foods were also divided into 12 food groups but only six contributing to fat intake were chosen for analysis: dairy; fat; grains and starchy vegetables; meat, fish, and poultry; meat, fish, and poultry combinations; snacks and desserts. At six months, there was no difference in the amount of change in any of the selected nutrients between the BSM and WLC groups or in the amount of change within each food group except in the meat, fish, and poultry combination and in the snacks and desserts groups because both groups experienced similar changes at six months. Binge and nonbinge day nutrient analysis by BSM and WLC showed that at baseline and six months within the BSM group, calories increased significantly on binge days. Within the WLC group at six months, percentage of calories from protein was significantly decreased on binge days.^ The significant finding of this study was the reduction in the amount of change in the number of binge days at six months between the BSM and WLC groups ($-$2.2 versus $-$1.1 respectively). These data suggest that behavioral programs can successful reduce binge days, but that significant change in food intake may require more intensive treatment. ^

EVALUATION OF THE EFFECTIVENESS OF ANISOTROPIC ANALYTICAL ALGORITHM IN FLATTENED AND FLATTENING-FILTER-FREE BEAMS FOR HIGH ENERGY LUNG DOSE DELIVERY USING THE RADIOLOGICAL PHYSICS CENTER LUNG PHANTOM

The effectiveness of the Anisotropic Analytical Algorithm (AAA) implemented in the Eclipse treatment planning system (TPS) was evaluated using theRadiologicalPhysicsCenteranthropomorphic lung phantom using both flattened and flattening-filter-free high energy beams. Radiation treatment plans were developed following the Radiation Therapy Oncology Group and theRadiologicalPhysicsCenterguidelines for lung treatment using Stereotactic Radiation Body Therapy. The tumor was covered such that at least 95% of Planning Target Volume (PTV) received 100% of the prescribed dose while ensuring that normal tissue constraints were followed as well. Calculated doses were exported from the Eclipse TPS and compared with the experimental data as measured using thermoluminescence detectors (TLD) and radiochromic films that were placed inside the phantom. The results demonstrate that the AAA superposition-convolution algorithm is able to calculate SBRT treatment plans with all clinically used photon beams in the range from 6 MV to 18 MV. The measured dose distribution showed a good agreement with the calculated distribution using clinically acceptable criteria of ±5% dose or 3mm distance to agreement. These results show that in a heterogeneous environment a 3D pencil beam superposition-convolution algorithms with Monte Carlo pre-calculated scatter kernels, such as AAA, are able to reliably calculate dose, accounting for increased lateral scattering due to the loss of electronic equilibrium in low density medium. The data for high energy plans (15 MV and 18 MV) showed very good tumor coverage in contrast to findings by other investigators for less sophisticated dose calculation algorithms, which demonstrated less than expected tumor doses and generally worse tumor coverage for high energy plans compared to 6MV plans. This demonstrates that the modern superposition-convolution AAA algorithm is a significant improvement over previous algorithms and is able to calculate doses accurately for SBRT treatment plans in the highly heterogeneous environment of the thorax for both lower (≤12 MV) and higher (greater than 12 MV) beam energies.

Targeting AKT/mTOR Signaling Pathways During Murine Skin Tumor Promotion and the Impact of Dietary Energy Balance Manipulation

The prevalence of obesity has continued to rise over the last several decades in the United States lending to overall increases in risk for chronic diseases including many types of cancer. In contrast, reduction in energy consumption via calorie restriction (CR) has been shown to be a potent inhibitor of carcinogenesis across a broad range of species and tumor types. Previous data has demonstrated differential signaling through Akt and mTOR via the IGF-1R and other growth factor receptors across the diet-induced obesity (DIO)/CR spectrum. Furthermore, mTORC1 is known to be regulated directly via nutrient availability, supporting its role in the link between epithelial carcinogenesis and diet-induced obesity. In an effort to better understand the importance of mTORC1 in the context of both positive and negative energy balance during epithelial carcinogenesis, we have employed the use of specific pharmacological inhibitors, rapamycin (mTORC1 inhibitor) and metformin (AMPK activator) to target mTORC1 or various components of this pathway during skin tumor promotion. Two-stage skin carcinogenesis studies demonstrated that mTORC1 inhibition via rapamycin, metformin or combination treatments greatly inhibited skin tumor development in normal, overweight and obese mice. Furthermore, mechanisms by which these chemopreventive agents may be exerting their anti-tumor effects were explored. In addition, the effect of these compounds on the epidermal proliferative response was analyzed and drastic decreases in epidermal hyperproliferation and hyperplasia were found. Rapamycin also inhibited dermal inflammatory cell infiltration in a dose-dependent manner. Both compounds also blocked or attenuated TPA-induced signaling through epidermal mTORC1 as well as several downstream targets. In addition, inhibition of this pathway by metformin appeared to be, at least in part, dependent on AMPK activation in the skin. Overall, the data indicate that pharmacological strategies targeting this pathway offset the tumor-enhancing effects of DIO and may serve as possible CR mimetics. They suggest that mTORC1 contributes significantly to the process of skin tumor promotion, specifically during dietary energy balance effects. Exploiting the mechanistic information underlying dietary energy balance responsive pathways will help translate decades of research into effective strategies for prevention of epithelial carcinogenesis.

Exposure to nonsteroidal anti-inflammatory drugs and weight loss and hospitalization in non-small cell lung cancer patients

Background. Cancer cachexia is a common syndrome complex in cancer, occurring in nearly 80% of patients with advanced cancer and responsible for at least 20% of all cancer deaths. Cachexia is due to increased resting energy expenditure, increased production of inflammatory mediators, and changes in lipid and protein metabolism. Non-steroidal anti-inflammatory drugs (NSAIDs), by virtue of their anti-inflammatory properties, are possibly protective against cancer-related cachexia. Since cachexia is also associated with increased hospitalizations, this outcome may also show improvement with NSAID exposure. ^ Design. In this retrospective study, computerized records from 700 non-small cell lung cancer patients (NSCLC) were reviewed, and 487 (69.57%) were included in the final analyses. Exclusion criteria were severe chronic obstructive pulmonary disease, significant peripheral edema, class III or IV congestive heart failure, liver failure, other reasons for weight loss, or use of research or anabolic medications. Information on medication history, body weight and hospitalizations was collected from one year pre-diagnosis until three years post-diagnosis. Exposure to NSAIDs was defined if a patient had a history of being treated with NSAIDs for at least 50% of any given year in the observation period. We used t-test and chi-square tests for statistical analyses. ^ Results. Neither the proportion of patients with cachexia (p=0.27) nor the number of hospitalizations (p=0.74) differed among those with a history of NSAID use (n=92) and those without (n=395). ^ Conclusions. In this study, NSAID exposure was not significantly associated with weight loss or hospital admissions in patients with NSCLC. Further studies may be needed to confirm these observations.^