Local control rate after the combination of re-irradiation and hyperthermia for irresectable recurrent breast cancer: Results in 248 patients.

BACKGROUND AND PURPOSE: Randomized studies have shown that adding hyperthermia (HT) to re-irradiation (re-RT) improves treatment outcome for patients with breast cancer recurrences. We evaluated the efficacy and side effects in patients treated with re-RT and HT for irresectable locoregional breast cancer recurrences. MATERIAL AND METHODS: From September 1996 to December 2011, 248 patients with a macroscopic breast cancer recurrence were treated with re-RT and HT. Radiotherapy (RT) was applied to a dose of 32 Gy in 4 Gy fractions, twice weekly. HT was prescribed once weekly after RT. Primary endpoints for this analysis were complete response (CR) and local control (LC). Secondary endpoints were overall survival (OS), and toxicity. Patient-, tumor-, and treatment-related characteristics predictive for the endpoints were identified in univariate and multivariate analyses. RESULTS: The median follow-up period was 32 months. The CR rate was 70%. At 1, 3, and 5 years LC was 53%, 40% and 39%, and OS was 66%, 32%, and 18%, respectively. OS after 10 years was 10%. Thermal burns developed in 23% patients, healing with conservative measures. The incidence of 5 years late grade 3 toxicity was 1%. A few patients survived more than 10 years without evidence of disease. CONCLUSIONS: The combination of re-RT and HT results in a high rate of long-term LC with acceptable late toxicity, and many patients remained locally controlled for the rest of their survival period.

Risk model and nomogram for dysphagia and xerostomia prediction in head and neck cancer patients treated by radiotherapy and/or chemotherapy.

In our randomized trial on hyperbaric oxygen (HBO), it was shown that HBO could reduce dysphagia and xerostomia, which are frequently encountered after (chemo-) radiotherapy (RT) and/or surgery for head and neck cancer (HNC). A risk model and nomogram are developed to select those patients who most likely will respond to HBO treatment. A total of 434 HNC patients treated from 2000 to 2008 were analyzed and filled out the EORTC QLQC-30 and H&N35 questionnaires. Age, gender, chemotherapy, T and N stages, site, radiotherapy technique, RT boost, surgery of the primary tumor and neck, bilateral RT, and dose were analyzed in a statistical model. The discriminative value of the model was evaluated based on receiver operating characteristics (ROC), the area under the curve (AUC), sensitivity, specificity, and proportion of correctly classified measures. Significant factors in predicting swallowing problems are age, follow-up duration, tumor site, chemotherapy, surgery of the primary tumor and neck, and dose. For dry mouth, the significant factors are age, gender, tumor site, N stage, chemotherapy, and bilateral irradiation. For dysphagia and xerostomia, the area under the ROC curve is 0.7034 and 0.7224, respectively, with a specificity of 89/77%, sensitivity of 27/58%, and a positive predictive value of 83/67% for dysphagia and xerostomia, respectively. The developed predictive risk model could be used to select patients for costly hyperbaric oxygen treatment to prevent or reduce severe late side effects of HNC treatment. Our model serves as a guideline for the Department of Radiation Oncology to reduce costs by excluding patients not amenable to hyperbaric oxygen protocols. The nomogram presented is a useful tool for clinicians in assessing patient risks when deciding on follow-up strategies (e.g., hyperbaric oxygen treatment) after RT or surgery for HNC.

Modelling the effect of baseline risk in meta-analysis: a review from the perspective of errors-in-variables regression.

In meta-analysis of clinical trials, investigating the relationship between the baseline risk and the treatment benefit is often of interest in order to explain the between trials heterogeneity with respect to treatment effect. The relationship is commonly described with a linear model taking into account the fact that the latent baseline risk is estimated from a finite sample and thus subjected to measurement error. Depending on the specific assumption about the latent baseline risks, two different classes of methods can be pursued. In the literature, it is commonly assumed that the latent baseline risks are sampled from a (normal) distribution. Such methods are often criticised for needing a distribution. Here, we propose the use of methods that require no distributional assumption on the baseline risks. A number of alternative methods are reviewed and are illustrated via simulation and by application to a published meta-analysis data.

The interaction between HLA shared epitope alleles and smoking and its contribution to autoimmunity against several citrullinated antigens.

OBJECTIVE: Recent data suggest that a gene-environment interaction between smoking and the HLA shared epitope alleles plays a role in shaping the autoimmune reaction to specific citrullinated antigens. This study was undertaken to determine the effects of HLA shared epitope alleles and tobacco exposure on the immune response against various citrullinated antigens. These associations were analyzed in the anti-citrullinated protein antibody (ACPA)-positive stratum to control for the possibility that the associations found are explained by the known interaction between HLA shared epitope alleles and tobacco exposure on ACPA status. METHODS: In 661 patients with rheumatoid arthritis, reactivity against several citrullinated antigens from vimentin, fibrinogen, enolase, and myelin basic protein was determined by enzyme-linked immunosorbent assay. The effects of the HLA shared epitope alleles and tobacco exposure were assessed by logistic regression analysis. Biologic interaction was analyzed by investigating whether the effects of the risk factors combined exhibited departure from additivity. RESULTS: A significant interaction between tobacco exposure and HLA shared epitope alleles was found for the presence of ACPA as reported previously. When these interaction effects were studied for several ACPA "fine specificities," significant interactions were noted for several citrullinated peptides. However, these interactions were not present after stratification for ACPA status, indicating that the interaction between tobacco exposure and HLA shared epitope alleles influences autoimmunity not to specific citrullinated antigens, but rather to ACPA development. CONCLUSION: Our data indicate that the gene-environment interaction between HLA shared epitope alleles and smoking does not appear to shape the reactivity of the ACPA response. These data suggest that smoking promotes nonspecific citrullination rather than citrullination of specific antigens.

A comparison of methods for estimating the random effects distribution of a linear mixed model.

This article reviews various recently suggested approaches to estimate the random effects distribution in a linear mixed model, i.e. (1) the smoothing by roughening approach of Shen and Louis,(1) (2) the semi-non-parametric approach of Zhang and Davidian,(2) (3) the heterogeneity model of Verbeke and Lesaffre( 3) and (4) a flexible approach of Ghidey et al. (4) These four approaches are compared via an extensive simulation study. We conclude that for the considered cases, the approach of Ghidey et al. (4) often shows to have the smallest integrated mean squared error for estimating the random effects distribution. An analysis of a longitudinal dental data set illustrates the performance of the methods in a practical example.

Smooth random effects distribution in a linear mixed model.

A linear mixed model with a smooth random effects density is proposed. A similar approach to P-spline smoothing of Eilers and Marx (1996, Statistical Science 11, 89-121) is applied to yield a more flexible estimate of the random effects density. Our approach differs from theirs in that the B-spline basis functions are replaced by approximating Gaussian densities. Fitting the model involves maximizing a penalized marginal likelihood. The best penalty parameters minimize Akaike's Information Criterion employing Gray's (1992, Journal of the American Statistical Association 87, 942-951) results. Although our method is applicable to any dimensions of the random effects structure, in this article the two-dimensional case is explored. Our methodology is conceptually simple, and it is relatively easy to fit in practice and is applied to the cholesterol data first analyzed by Zhang and Davidian (2001, Biometrics 57, 795-802). A simulation study shows that our approach yields almost unbiased estimates of the regression and the smoothing parameters in small sample settings. Consistency of the estimates is shown in a particular case.