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Mobile devices take an important part in everyday life. They are now cheaper and widespread, but still a lot of time is spent by the users to configure them: users adapt to their own device, not vice versa. Can our smart phones do something smarter? In this work, we propose a framework to support the development of context aware applications for Android devices: the goal of such applications is to reduce as much as possible the interaction with the user, making use of automatic and intelligent components. Moreover, these components should consume as less power and computational resources as possible, being them part of a mobile ecosystem whose battery and hardware are highly constrained. The work implies the study of a methodology that fits the Android framework and the design of a highly extensible software architecture. An open source framework based on the proposed methodology is then described. Some use cases are finally presented, analyzing the performances and the limitations of the proposed methodology.

Survival statistics are fundamental in outcome evaluation of clinical studies for modern cancer science. The use of survival statistics allows to compare results and to predict the effect of therapies, by using different statistical approaches that can be also combined together. Definition of survival statistics can be performed mainly in three different ways (Non-parametric-Kaplan-Meier, Parametric and Semi-parametric), each one having its own computational methods and being implemented in different ways. Modern cancer publications strengthen the value of diagnostic tools that can be used for predicting outcome of newly diagnosed patients. The nomograms are examples of these tools: they are usually drawn to facilitate physicians in manually solving complex equations required to calculate outcomes predicted by using Cox’s proportional hazards models. The use of models to predict the outcome must follow adequate procedures for reliability evaluation and testing, in order to prevent the erroneous application on unsuitable patient populations.

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In this work a system for analysing radiotherapy treatment planning dose-volume data is proposed. The work starts from the definition of a framework inside a statistical scripting environment (R) used for creating a software package. The analysis of dose-volume data in radiotherapy of malignant tumours is mandatory for evaluating the prescribed treatment and for feedback analysis of outcome, both in the direction of tumour control and in detection of parameters for estimating and predicting toxicity outcome. The statistical analysis of large amount of clinical data can be slowed by the lack of practice in statistical tools needed, by clinicians, to perform such kind of analysis. This is the reason that lead our working group in the creation of such a tool. Finally an example of clinical application of our software is given for the analysis of the outcome of patients undergoing to radiotherapy for prostate cancer.

The purpose of this study was to investigate the magnitude and dosimetric relevance of translational and rotational shifts on IGRT prostate volumetric‐modulated arc therapy (VMAT) using Protura six degrees of freedom (DOF) Robotic Patient Positioning System. Patients with cT3aN0M0 prostate cancer, treated with VMAT simultaneous integrated boost (VMAT‐SIB), were enrolled. PTV2 was obtained adding 0.7 cm margin to seminal vesicles base (CTV2), while PTV1 adding to prostate (CTV1) 0.7 cm margin in all directions, except 1.2 cm, as caudal margin. A daily CBCT was acquired before dose delivery. The translational and rotational displacements were corrected through Protura Robotic Couch, collected and applied to the simulation CT to obtain a translated CT (tCT) and a rototranslated CT (rtCT) on which we recalculated the initial treatment plan (TP). We analyzed the correlation between dosimetric coverage, organs at risk (OAR) sparing, and translational or rotational displacements. The dosimetric impact of a rototranslational correction was calculated. From October 2012 to September 2013, a total of 263 CBCT scans from 12 patients were collected. Translational shifts were