High-Risk Merkel Cell Carcinoma of the Skin Treated With Synchronous Carboplatin/Etoposide and Radiation: A Trans-Tasman Radiation Oncology Group Study-TROG 96:07

Purpose: The effectiveness of synchronous carboplatin, etoposide, and radiation therapy was prospectively assessed in a group of patients with high-risk Merkel cell carcinoma (MCC) of the skin. Patients and Methods: Patients were eligible if they had disease localized to the primary site and nodes, and were required to have at least one of the following high risk features: recurrence after initial therapy, involved nodes, primary tumor size greater than 1 cm, gross residual disease after surgery, or occult primary with nodes. Radiation was delivered to the primary site and nodes to a dose of 50 Gy in 25 fractions over 5 weeks and synchronous carboplatin (area under the curve, 4.5) and intravenous etoposide 80 mg/m(2) days 1 to 3 was given in weeks 1, 4, 7, and 10. The median age of the group was 67 (range, 43-86) years, and there were 39 males and 14 females. Involved nodes (stage II) were present in 33 cases (62%). The sites involved were head and neck (22 patients), occult primary (13 patients), upper limb (eight patients), lower limb (eight patients), and trunk (two patients). Results: Fifty-three patients were entered between 1996 and 2001. The median potential follow-up was 48 months. There were no treatment related deaths. The 3-year overall survival, locoregional control, and distant control were 76%, 75%, and 76%, respectively. Tumor site and the presence of nodes were factors that were predictive for local control and survival. Multivariate analysis indicated that the major factor influencing survival was the presence of nodes; however, this was not a significant factor in locoregional control. Conclusion: High levels of locoregional control and survival have been achieved with the addition of chemotherapy to radiation treatment for high-risk MCC of the skin. The role of chemoradiotherapy for high-risk MCC warrants further investigation. (C) 2003 by American Society of Clinical Oncology.

A general-purpose tunable landscape generator

The research literature on metalieuristic and evolutionary computation has proposed a large number of algorithms for the solution of challenging real-world optimization problems. It is often not possible to study theoretically the performance of these algorithms unless significant assumptions are made on either the algorithm itself or the problems to which it is applied, or both. As a consequence, metalieuristics are typically evaluated empirically using a set of test problems. Unfortunately, relatively little attention has been given to the development of methodologies and tools for the large-scale empirical evaluation and/or comparison of metaheuristics. In this paper, we propose a landscape (test-problem) generator that can be used to generate optimization problem instances for continuous, bound-constrained optimization problems. The landscape generator is parameterized by a small number of parameters, and the values of these parameters have a direct and intuitive interpretation in terms of the geometric features of the landscapes that they produce. An experimental space is defined over algorithms and problems, via a tuple of parameters for any specified algorithm and problem class (here determined by the landscape generator). An experiment is then clearly specified as a point in this space, in a way that is analogous to other areas of experimental algorithmics, and more generally in experimental design. Experimental results are presented, demonstrating the use of the landscape generator. In particular, we analyze some simple, continuous estimation of distribution algorithms, and gain new insights into the behavior of these algorithms using the landscape generator.

On building a principled framework for evaluating and testing evolutionary algorithms: A continuous landscape generator

In this paper, we address some issue related to evaluating and testing evolutionary algorithms. A landscape generator based on Gaussian functions is proposed for generating a variety of continuous landscapes as fitness functions. Through some initial experiments, we illustrate the usefulness of this landscape generator in testing evolutionary algorithms.