Primary chemotherapy for inoperable ovarian, fallopian tube, or primary peritoneal cancer with or without delayed debulking surgery.

OBJECTIVE: To describe the outcome of primary chemotherapy for women with advanced-stage epithelial ovarian or primary peritoneal cancer and delayed surgery when optimal debulking surgery cannot be achieved at diagnosis. METHODS: Between 1998 and 2006, we retrospectively reviewed the overall survival and examined prognostic markers in consecutive patients who were not suitable for initial radical surgery because of the extent of disease and/or poor performance status. They were treated with a policy of primary platinum-based chemotherapy, followed whenever possible in responding patients by debulking surgery. RESULTS: A total of 171 patients received least one cycle of chemotherapy. Eighty-six patients proceeded to surgery and 53 (31% of 171 and 62% of 86) had optimal (<1 cm) residual disease. Eighty-five patients did not undergo surgery because they remained unfit or had not responded sufficiently to chemotherapy. The median overall survival was 18.7 months (95% confidence interval [CI], 16.5-24.2). The median OS in the surgical group for optimal and suboptimal surgery was 40.8 (95% CI, 32.5-50.0) and 22.5 (95% CI, 17.7-37.1) months (P = 0.005). On multivariate analysis, interval surgery and optimal surgery were the only independent prognostic factors (hazard ratios, 0.45 and 0.43, respectively; P = 0.009). In the nonsurgical group, CA125 response was an independent prognostic factor (hazard ratio, 0.34; P = 0.001) with an OS of 21.7 months (95% CI, 14.0-35.4) in women with a normal CA125 after treatment compared with 6.7 (95% CI, 4.5-7.8) months. CONCLUSIONS: In one third of the women, the tumor was optimally debulked after primary chemotherapy and their median survival was 40.8 months. Suboptimal debulking surgery after primary chemotherapy did not result in a better survival than that achieved after a chemotherapy response alone, suggesting that surgery may be avoided when imaging after chemotherapy demonstrates residual disease that cannot be optimally debulked.

A review of graphical methods to map the natural hazard-to-wellbeing risk chain in a socio-ecological system.

The popular concept of wellbeing has added multiple dimensions to the current socio-economic measures of vulnerability from natural hazards. Due to the wellbeing concept's relevance in various policy agendas, there is a need for a stronger integration of what is predominantly a socio-economic concept into the natural hazards space. Graphical methods have been used as transdisciplinary engagement tools to translate verbal descriptions of socio-ecological systems into simulation models able to test hypotheses. The purpose of this article is to identify the graphical methods that have been used in the literature to graphically represent, structure and model different segments of the hazard risk chain. A thorough review of the literature on natural hazards was performed using a set of keywords and filters that resulted in a total of 94 articles, which were then categorised based on the graphical methods used, broad families, properties, hazard types, and segments along the risk chain considered. A case study on volcanic hazards in Mount Taranaki, New Zealand showcased ways forward by conceptually combining methods to link hazards to impacts on wellbeing. Out of the review it was identified that the most widely used methodologies in the natural hazards space are probabilistic graphs (e.g. Bayesian networks) representing the random nature of hazards while mapping methods based on System Dynamic principles (SD) (e.g. causal loop diagrams) are used to characterise the dynamically emergent behaviours of socio-economic agents. While studies linking hazards to wellbeing using graphs are scarce, there is a nascent literature on the characterisation of wellbeing's multi-dimensionality using networks and SD diagrams. Hence, the possibilities to use common methods, or combinations of these, are numerous potentially enabling the creation of graph-based, distilled simulation models that can be used by experts from different backgrounds to quantitatively model the wellbeing impacts exerted by natural hazards.