Marco Alfò

MACOP-B and Involved-Field Radiotherapy Is an Effective and Safe Therapy for Primary Mediastinal Large B Cell Lymphoma

PURPOSE To report the clinical findings and long-term results of front-line, third-generation MACOP-B (methotrexate, doxorubicin, cyclophosphamide, vincristine, prednisone, and bleomycin) chemotherapy and mediastinal involved-field radiotherapy (IFRT) in 85 consecutive, previously untreated patients with primary mediastinal large B cell lymphoma (PMLBCL) diagnosed and managed at a single institution. METHODS AND MATERIALS Between 1991 and April 2004, 92 consecutive, untreated patients with PMLBCL were treated at our institution. The median age was 33 years (range, 15-61 years), 46 patients (50%) showed a mediastinal syndrome at onset; 52 patients (57%) showed a low/low-intermediate (0 to 1) and 40 patients (43%) an intermediate-high/high (2 to 3) International Prognostic Index (IPI) score. Eighty-five patients were treated with standard chemotherapy (MACOP-B), and 80 underwent mediastinal IFRT at a dose of 30-36 Gy. RESULTS After a MACOP-B regimen, the overall response rate was 87% and the partial response rate 9%. After chemotherapy, (67)Ga scintigraphy/positron emission tomography results were positive in 43 of 52 patients (83%), whereas after IFRT 11 of 52 patients (21%) remained positive (p < 0.0001). After a median follow-up of 81 months (range, 2-196 months), progression or relapse was observed in 15 of 84 patients (18%). The projected 5-year overall survival and progression-free survival rates were 87% and 81%, respectively. The 5-year overall survival and progression-free survival rates were better for patients with an IPI of 0 to 1 than for those with an IPI of 2 to 3 (96% vs. 73% [p = 0.002] and 90% vs. 67% [p = 0.007], respectively). CONCLUSIONS Combined-modality treatment with intensive chemotherapy plus mediastinal IFRT induces high response and lymphoma-free survival rates. Involved-field RT plays an important role in inducing negative results on (67)Ga scintigraphy/positron emission tomography in patients responsive to chemotherapy.

Regression models for binary longitudinal responses

Some conditional models to deal with binary longitudinal responses are proposed, extending random effects models to include serial dependence of Markovian form, and hence allowing for quite general association structures between repeated observations recorded on the same individual. The presence of both these components implies a form of dependence between them, and so a complicated expression for the resulting likelihood. To handle this problem, we introduce, as a first instance, what Follmann and Wu (1995) called, in a different setting, an approximate conditional model, which represents an optimal choice for the general framework of categorical longitudinal responses. Then we define two more formally correct models for the binary case, with no assumption about the distribution of the random effect. All of the discussed models are estimated by means of an EM algorithm for nonparametric maximum likelihood. The algorithm, an adaptation of that used by Aitkin (1996) for the analysis of overdispersed generalized linear models, is initially derived as a form of Gaussian quadrature, and then extended to a completely unknown mixing distribution. A large scale simulation work is described to explore the behaviour of the proposed approaches in a number of different situations.

Long‐term evaluation of 164 patients with essential thrombocythaemia treated with pipobroman: occurrence of leukaemic evolution

Summary. Essential thrombocythaemia (ET) is usually considered an indolent disease, but it may progress during its natural course into acute leukaemia (AL); however, an influence of myelosuppressive agents in the blastic transformation of ET cannot be excluded. We performed a retrospective study to assess the incidence of AL in ET patients treated with pipobroman (PB) as first‐line therapy. One hundred and sixty‐four patients with ET were managed with PB at a dose of 1 mg/kg/d until a stable platelet count below 400 × 109/l was achieved. Maintenance therapy was given at a planned dose ranging between 0·2 and 1 mg/kg/d according to platelet count, in all cases, with a median daily dose of 25 mg (range 7–75 mg/d). The median treatment time was 100 months (range 25–243 months). The patients were evaluated for the occurrence of AL and/or secondary malignancies and survival end‐points. AL was observed in nine patients (5·5%) after a median treatment time of 153 months (range 79–227 months). The overall survival (OS) and the event‐free survival (EFS) at 120 months were 95% and 97%, whereas at 180 months, they were 84% and 76% respectively. In conclusion, this retrospective analysis shows a low incidence of AL in a large group of patients consecutively treated with PB as first‐line chemotherapy. Therefore, an investigation of the role of myelosuppressive agents in the blastic transformation of ET would be of interest.

Longitudinal Analysis of Repeated Binary Data Using Autoregressive and Random Effect Modelling

In this paper we extend random coefficient models for binary repeated responses to include serial dependence of Markovian form, with the aim of defining a general association structure among responses recorded on the same individual. We do not adopt a parametric specification for the random coefficients distribution and this allows us to overcome inconsistencies due to misspecification of this component. Model parameters are estimated by means of an EM algorithm for nonparametric maximum likelihood (NPML), which is extended to deal with serial correlation among repeated measures, with an explicit focus on those situations where short individual time series have been observed. The approach is described by presenting a reanalysis of the well-known Muscatine (Iowa) longitudinal study on childhood obesity.

A finite mixture model for image segmentation

Abstract In this paper, we propose a model for image segmentation based on a finite mixture of Gaussian distributions. For each pixel of the image, prior probabilities of class memberships are specified through a Gibbs distribution, where association between labels of adjacent pixels is modeled by a class-specific term allowing for different interaction strengths across classes. We show how model parameters can be estimated in a maximum likelihood framework using Mean Field theory. Experimental performance on perturbed phantom and on real benchmark images shows that the proposed method performs well in a wide variety of empirical situations.

Cytokines, Fatigue, and Cutaneous Erythema in Early Stage Breast Cancer Patients Receiving Adjuvant Radiation Therapy

We investigated the hypothesis that patients developing high-grade erythema of the breast skin during radiation treatment could be more likely to present increased levels of proinflammatory cytokines which may lead, in turn, to associated fatigue. Forty women with early stage breast cancer who received adjuvant radiotherapy were enrolled from 2007 to 2010. Fatigue symptoms, erythema, and cytokine levels (IL-1β, IL-2, IL6, IL-8, TNF-α, and MCP-1) were registered at baseline, during treatment, and after radiotherapy completion. Seven (17.5%) patients presented fatigue without associated depression/anxiety. Grade ≥2 erythema was observed in 5 of these 7 patients. IL-1β, IL-2, IL-6, and TNF-α were statistically increased 4 weeks after radiotherapy (P < 0.05). After the Heckman two-step analysis, a statistically significant influence of skin erythema on proinflammatory markers increase (P = 0.00001) was recorded; in the second step, these blood markers showed a significant impact on fatigue (P = 0.026). A seeming increase of fatigue, erythema, and proinflammatory markers was observed between the fourth and the fifth week of treatment followed by a decrease after RT. There were no significant effects of hormone therapy, breast volume, and anemia on fatigue. Our study seems to suggest that fatigue is related to high-grade breast skin erythema during radiotherapy through the increase of cytokines levels.

Gene expression profiles reveal homeostatic dynamics during interferon-β therapy in multiple sclerosis

Understanding the mechanisms that sustain the effects of disease modifying drugs in multiple sclerosis (MS) may help refine current therapies and improve our knowledge of disease pathogenesis. By using cDNA microarrays, we investigated gene expression in the peripheral blood mononuclear cells (PBMC) of 7 MS patients, at baseline (T0) as well as after 1 (T1) and 3 months (T3) of interferon beta-1a (IFN-β-1a; Rebif™ 44 μg) therapy. Gene expression changes involved genes of both immunological and non-immunological significance. We validated IL-10 up-regulation, which is in accordance with previous reports, and other novel changes that underscore the capacity of IFN-β to impair antigen presentation and migration of inflammatory elements into the central nervous system (up-regulation of filamin B and down-regulation of IL-16 and rab7). Overall, gene expression changes became less pronounced after 3 months of therapy, suggesting a homeostatic response to IFN-β. This may be of use for the design of new treatment schedules.

Random coefficient models for binary longitudinal responses with attrition

We extend the approach introduced by Aitkin and Alfò (1998, Statistics and Computing, 4, pp. 289–307) to the general framework of random coefficient models and propose a class of conditional models to deal with binary longitudinal responses, including unknown sources of heterogeneity in the regression parameters as well as serial dependence of Markovian form.Furthermore, we discuss the extension of the proposed approach to the analysis of informative drop-outs, which represent a central problem in longitudinal studies, and define, as suggested by Follmann and Wu (1995, Biometrics, 51, pp. 151–168), a conditional specification of the full shared parameter model for the primary response and the missingness indicator. The model is applied to a dataset from a methadone maintenance treatment programme held in Sydney in 1986 and previously analysed by Chan et al. (1998, Australian & New Zealand Journal of Statistics, 40, pp. 1–10).All of the proposed models are estimated by means of an EM algorithm for nonparametric maximum likelihood, without assuming any specific parametric distribution for the random coefficients and for the drop-out process.A small scale simulation work is described to explore the behaviour of the extended approach in a number of different situations where informative drop-outs are present.

Finite Mixture Models for Mapping Spatially Dependent Disease Counts

A vast literature has recently been concerned with the analysis of variation in disease counts recorded across geographical areas with the aim of detecting clusters of regions with homogeneous behavior. Most of the proposed modeling approaches have been discussed for the univariate case and only very recently spatial models have been extended to predict more than one outcome simultaneously. In this paper we extend the standard finite mixture models to the analysis of multiple, spatially correlated, counts. Dependence among outcomes is modeled using a set of correlated random effects and estimation is carried out by numerical integration through an EM algorithm without assuming any specific parametric distribution for the random effects. The spatial structure is captured by the use of a Gibbs representation for the prior probabilities of component membership through a Strauss-like model. The proposed model is illustrated using real data.

Generalized linear mixed joint model for longitudinal and survival outcomes

Longitudinal studies often entail categorical outcomes as primary responses. When dropout occurs, non-ignorability is frequently accounted for through shared parameter models (SPMs). In this context, several extensions from Gaussian to non-Gaussian longitudinal processes have been proposed. In this paper, we formulate an approach for non-Gaussian longitudinal outcomes in the framework of joint models. As an extension of SPMs, based on shared latent effects, we assume that the history of the response up to current time may have an influence on the risk of dropout. This history is represented by the current, expected, value of the response. Since the time a subject spends in the study is continuous, we parametrize the dropout process through a proportional hazard model. The resulting model is referred to as Generalized Linear Mixed Joint Model (GLMJM). To estimate model parameters, we adopt a maximum likelihood approach via the EM algorithm. In this context, the maximization of the observed data log-likelihood requires numerical integration over the random effect posterior distribution, which is usually not straightforward; under the assumption of Gaussian random effects, we compare Gauss-Hermite and Pseudo-Adaptive Gaussian quadrature rules. We investigate in a simulation study the behaviour of parameter estimates in the case of Poisson and Binomial longitudinal responses, and apply the GLMJM to a benchmark dataset.