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Introduction Personalised cancer screening aims to improve benefits, reduce harms and being more cost-effective than age-based screening. The objective of the DECIDO study is to assess the acceptability and feasibility of offering risk-based personalised breast cancer screening and its integration in regular clinical practice in a National Health System setting.Methods and analysis The study is designed as a single-arm proof-of-concept trial. The study sample will include 385 women aged 40–50 years resident in a primary care health area in Spain. The study intervention consists of (1) a baseline visit; (2) breast cancer risk estimation; (3) a second visit for risk communication and screening recommendations based on breast cancer risk and (4) a follow-up to obtain the study outcomes.A polygenic risk score (PRS) will be constructed as a composite likelihood ratio of 83 single nucleotide polymorphisms. The Breast Cancer Surveillance Consortium risk model, including age, race/ethnicity, family history of breast cancer, benign breast disease and breast density will be used to estimate a preliminary 5-year absolute risk of breast cancer. A Bayesian approach will be used to update this risk with the PRS value.The primary outcome measures will be attitude towards, intention to participate in and satisfaction with personalised breast cancer screening. Secondary outcomes will include the proportions of women who accept to participate and who complete the different phases of the study. The exact binomial and the Student’s t-test will be used to obtain 95% CIs.Ethics and dissemination The study protocol was approved by the Drug Research Ethics Committee of the University Hospital Arnau de Vilanova. The trial will be conducted in compliance with this study protocol, the Declaration of Helsinki and Good Clinical Practice.The results will be published in peer-reviewed scientific journals and disseminated in scientific conferences and media.Trial registration number NCT03791008.

Cloud systems are becoming a powerful tool for business. The evidence of the advantages of offering infrastructure as a service (IaaS), platform as a service (PaaS), or software as a service (SaaS) is overwhelming. Therefore, for SaaS providers, it is essential to know the virtual resources required to optimize the service offered and to keep the quality of service (QoS) at the desired levels. This paper presents an analytic model cloud availability and response time (CART) for obtaining the best tradeoff between performance, cost, and availability in a cloud system aimed at providing software as a service. The model aims to guarantee the predetermined availability and response time (RT) agreed with customers in a service-level agreement (SLA) contract while minimizing the cost of the system. A client-transparent error recovery strategy has been considered along with a Poisson traffic model. A sensitivity analysis of the simulated and real workloads is presented to study how a specific SLA influences the cloud service performance regarding the guaranteed RT and availability. The results corroborate the goodness of the model proposed, adjusting the real system accurately. Furthermore, the obtained results provide useful guidelines for cloud or Web-server designers.