Investigating the Implications of a Variable RBE on Proton Dose Fractionation Across a Clinical Pencil Beam Scanned Spread-Out Bragg Peak

Purpose: To investigate the clinical implications of a variable relative biological effectiveness (RBE) on proton dose fractionation. Using acute exposures, the current clinical adoption of a generic, constant cell killing RBE has been shown to underestimate the effect of the sharp increase in linear energy transfer (LET) in the distal regions of the spread-out Bragg peak (SOBP). However, experimental data for the impact of dose fractionation in such scenarios are still limited.

Methods and Materials: Human fibroblasts (AG01522) at 4 key depth positions on a clinical SOBP of maximum energy 219.65 MeV were subjected to various fractionation regimens with an interfraction period of 24 hours at Proton Therapy Center in Prague, Czech Republic. Cell killing RBE variations were measured using standard clonogenic assays and were further validated using Monte Carlo simulations and parameterized using a linear quadratic formalism.

Results: Significant variations in the cell killing RBE for fractionated exposures along the proton dose profile were observed. RBE increased sharply toward the distal position, corresponding to a reduction in cell sparing effectiveness of fractionated proton exposures at higher LET. The effect was more pronounced at smaller doses per fraction. Experimental survival fractions were adequately predicted using a linear quadratic formalism assuming full repair between fractions. Data were also used to validate a parameterized variable RBE model based on linear α parameter response with LET that showed considerable deviations from clinically predicted isoeffective fractionation regimens.

Conclusions: The RBE-weighted absorbed dose calculated using the clinically adopted generic RBE of 1.1 significantly underestimates the biological effective dose from variable RBE, particularly in fractionation regimens with low doses per fraction. Coupled with an increase in effective range in fractionated exposures, our study provides an RBE dataset that can be used by the modeling community for the optimization of fractionated proton therapy.

Preoperative characteristics and compliance with follow-up after trabeculectomy surgery in rural southern China

Purpose: To evaluate preoperative characteristics and follow-up in rural China after trabeculectomy, the primary treatment for glaucoma there. Methods: Patients undergoing trabeculectomy at 14 rural hospitals in Guangdong and Guangxi Provinces and their doctors completed questionnaires concerning clinical and sociodemographic information, transportation, and knowledge and attitudes about glaucoma. Follow-up after surgery was assessed as cumulative score (1 week: 10 points, 2 weeks: 7 points, 1 month: 5 points). Results Among 212 eligible patients, mean preoperative presenting acuity in the operative eye was 6/120, with 61.3% (n=130) blind (≤6/60). Follow-up rates were 60.8% (129/212), 75.9% (161/212) and 26.9% (57/212) at 1 week, 2 weeks and 1 month, respectively. Patient predictors of poor follow-up included elementary education or less (OR=0.37, 95% CI 0.20 to 0.70, p=0.002), believing follow-up was not important (OR=0.62, 95% CI 0.41 to 0.94, p=0.02), lack of an accompanying person (OR=0.14, 95% CI 0.07 to 0.29, p<0.001), family annual income <US$800 (OR=0.28, 95% CI 0.11 to 0.72, p=0.008) and not requiring removal of scleral flap sutures postoperatively (OR=0.11, 95% CI 0.06 to 0.22, p<0.001). Age, sex, employment, travel distance/time/costs, patient preoperative clinical factors and physician factors were unassociated with follow-up. Conclusions: Follow-up after 2 weeks was inadequate to provide optimal clinical care, and surgery is being applied too late to avoid blindness in the majority of patients. Earlier surgery, support for return visits and better explanations of the importance of follow-up are needed. Directing all patients to return for possible scleral flap suture removal may be a valid strategy to improve follow-up.

A Review of Numerical Modelling of Wave Energy Converter Arrays

Large-scale commercial exploitation of wave energy is certain to require the deployment of wave energy converters (WECs) in arrays, creating ‘WEC farms’. An understanding of the hydrodynamic interactions in such arrays is essential for determining optimum layouts of WECs, as well as calculating the area of ocean that the farms will require. It is equally important to consider the potential impact of wave farms on the local and distal wave climates and coastal processes; a poor understanding of the resulting environmental impact may hamper progress, as it would make planning consents more difficult to obtain. It is therefore clear that an understanding the interactions between WECs within a farm is vital for the continued development of the wave energy industry.To support WEC farm design, a range of different numerical models have been developed, with both wave phase-resolving and wave phase-averaging models now available. Phase-resolving methods are primarily based on potential flow models and include semi-analytical techniques, boundary element methods and methods involving the mild-slope equations. Phase-averaging methods are all based around spectral wave models, with supra-grid and sub-grid wave farm models available as alternative implementations.The aims, underlying principles, strengths, weaknesses and obtained results of the main numerical methods currently used for modelling wave energy converter arrays are described in this paper, using a common framework. This allows a qualitative comparative analysis of the different methods to be performed at the end of the paper. This includes consideration of the conditions under which the models may be applied, the output of the models and the relationship between array size and computational effort. Guidance for developers is also presented on the most suitable numerical method to use for given aspects of WEC farm design. For instance, certain models are more suitable for studying near-field effects, whilst others are preferable for investigating far-field effects of the WEC farms. Furthermore, the analysis presented in this paper identifies areas in which the numerical modelling of WEC arrays is relatively weak and thus highlights those in which future developments are required.