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Purpose: To present the principles and rationale of the Proton Priority System (PROPS), a priority points framework that assigns higher scores to patients thought to more likely benefit from proton therapy, and the distribution of PROPS scores by patient characteristics, Methods and Materials: We performed multivariable logistic regression to evaluate the association between PROPS scores and receipt of proton therapy, adjusted for insurance status, gender, race, geography, and the domains that inform the PROPS score., Results: Among 1529 adult patients considered for proton therapy prioritization during our Center's ramp-up phase of treatment availability, PROPS scores varied by age, diagnosis, site, and other PROPS domains. In adjusted analyses, receipt of proton therapy was lower for patients with non-Medicare relative to Medicare health insurance (commercial vs Medicare: adjusted odds ratio [OR] 0.47, 95% confidence interval [CI] 0.34-0.64; managed care vs Medicare: OR 0.40, 95% CI 0.28-0.56; Medicaid vs Medicare: OR 0.24, 95% CI 0.13-0.44). Proton Priority System score and age were not significantly associated with receipt of proton therapy., Conclusions: The Proton Priority System is a rationally designed and transparent system for allocation of proton therapy slots based on the best available evidence and expert opinion. Because the actual allocation of treatment slots depends mostly on insurance status, payers may consider incorporating PROPS, or its underlying principles, into proton therapy coverage policies., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Random-digit dialing (RDD) using landline telephone numbers is the historical gold standard for control recruitment in population-based epidemiologic research. However, increasing cell-phone usage and diminishing response rates suggest that the effectiveness of RDD in recruiting a random sample of the general population, particularly for younger target populations, is decreasing. In this study, we compared landline RDD with alternative methods of control recruitment, including RDD using cell-phone numbers and address-based sampling (ABS), to recruit primarily white men aged 18-55 years into a study of testicular cancer susceptibility conducted in the Philadelphia, Pennsylvania, metropolitan area between 2009 and 2012. With few exceptions, eligible and enrolled controls recruited by means of RDD and ABS were similar with regard to characteristics for which data were collected on the screening survey. While we find ABS to be a comparably effective method of recruiting young males compared with landline RDD, we acknowledge the potential impact that selection bias may have had on our results because of poor overall response rates, which ranged from 11.4% for landline RDD to 1.7% for ABS.

Testicular germ cell tumors (TGCT) have been expected to have a strong underlying genetic component. We conducted a genome-wide scan among 277 TGCT cases and 919 controls and found that seven markers at 12p22 within KITLG (c-KIT ligand) reached genome-wide significance (P < 5.0 x 10(-8) in discovery). In independent replication, TGCT risk was increased threefold per copy of the major allele at rs3782179 and rs4474514 (OR = 3.08, 95% CI = 2.29-4.13; OR = 3.07, 95% CI = 2.29-4.13, respectively). We found associations with rs4324715 and rs6897876 at 5q31.3 near SPRY4 (sprouty 4; P < 5.0 x 10(-6) in discovery). In independent replication, risk of TGCT was increased nearly 40% per copy of the major allele (OR = 1.37, 95% CI = 1.14-1.64; OR = 1.39, 95% CI = 1.16-1.66, respectively). All of the genotypes were associated with both seminoma and nonseminoma TGCT subtypes. These results demonstrate that common genetic variants affect TGCT risk and implicate KITLG and SPRY4 as genes involved in TGCT susceptibility.