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- de Azambuja, Evandro, et al.
(author)
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The effect of body mass index on overall and disease-free survival in node-positive breast cancer patients treated with docetaxel and doxorubicin-containing adjuvant chemotherapy: the experience of the BIG 02-98 trial
- 2010
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In: Breast Cancer Research and Treatment. - : Springer Science and Business Media LLC. - 0167-6806 .- 1573-7217. ; 119:1, s. 145-153
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Journal article (peer-reviewed)abstract
- Background: Obesity has been shown to be an indicator of poor prognosis for patients with primary breast cancer (BC) regardless of the use of adjuvant systemic therapy. Patients and methods: This is a retrospective analysis of 2,887 node-positive BC patients enrolled in the BIG 02-98 adjuvant study, a randomised phase III trial whose primary objective was to evaluate disease-free survival (DFS) by adding docetaxel to doxorubicin-based chemotherapy. In the current analysis, the effect of body mass index (BMI) on DFS and overall survival (OS) was assessed. BMI was obtained before the first cycle of chemotherapy. Obesity was defined as a BMI a parts per thousand yen 30 kg/mA(2). Results: In total, 547 (19%) patients were obese at baseline, while 2,340 (81%) patients were non-obese. Estimated 5-year OS was 87.5% for non-obese and 82.9% for obese patients (HR 1.34; P = 0.013). Estimated 5-years DFS was 75.9% for non-obese and 70.0% for obese patients (HR 1.20; P = 0.041). In a multivariate model, obesity remained an independent prognostic factor for OS and DFS. Conclusions: In this study, obesity was associated with poorer outcome in node-positive BC patients. Given the increasing prevalence of obesity worldwide, more research on improving the treatment of obese BC patients is needed.
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- Siegmund, Florian, et al.
(author)
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A Comparative Study of Fast Adaptive Preference-Guided Evolutionary Multi-objective Optimization
- 2017
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In: Evolutionary Multi-Criterion Optimization. - Cham : Springer. - 9783319541563 - 9783319541570 ; , s. 560-574
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Conference paper (peer-reviewed)abstract
- In Simulation-based Evolutionary Multi-objective Optimization, the number of simulation runs is very limited, since the complex simulation models require long execution times. With the help of preference information, the optimization result can be improved by guiding the optimization towards relevant areas in the objective space with, for example, the Reference Point-based NSGA-II algorithm (R-NSGA-II). Since the Pareto-relation is the primary fitness function in R-NSGA-II, the algorithm focuses on exploring the objective space with high diversity. Only after the population has converged closeto the Pareto-front does the influence of the reference point distance as secondary fitness criterion increase and the algorithm converges towards the preferred area on the Pareto-front.In this paper, we propose a set of extensions of R-NSGA-II which adaptively control the algorithm behavior, in order to converge faster towards the reference point. The adaption can be based on criteria such as elapsed optimization time or the reference point distance, or a combination thereof. In order to evaluate the performance of the adaptive extensions of R-NSGA-II, a performance metric for reference point-based EMO algorithms is used, which is based on the Hypervolume measure called the Focused Hypervolume metric. It measures convergence and diversity of the population in the preferred area around the reference point. The results are evaluated on two benchmark problems ofdifferent complexity and a simplistic production line model.
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