Abstract-Decision Support Systems (DSSs) are increasingly exploited in the area of prognostic evaluations. For predicting the effect of therapies on patients, the trend is now to use image features, i.e. information that can be automatically computed by considering images resulting by analysis. The DSSs application as predictive tools is particularly suitable for cancer treatment, given the peculiarities of the disease -which is highly localised and lead to significant social costs-and the large number of images that are available for each patient.At the state of the art, there exists tools that allow to handle image features for prognostic evaluations, but they are not designed for medical experts. They require either a strong engineering or computer science background since they do not integrate all the required functions, such as image retrieval and storage. In this paper we fill this gap by proposing Moddicom, a user-friendly complete library specifically designed to be exploited by physicians. A preliminary experimental analysis, performed by a medical expert that used the tool, demonstrates the efficiency and the effectiveness of Moddicom.

The advances in diagnostic and treatment technology are responsible for a remarkable transformation in the internal medicine concept with the establishment of a new idea of personalized medicine. Inter- and intra-patient tumor heterogeneity and the clinical outcome and/or treatment's toxicity's complexity, justify the effort to develop predictive models from decision support systems. However, the number of evaluated variables coming from multiple disciplines: oncology, computer science, bioinformatics, statistics, genomics, imaging, among others could be very large thus making traditional statistical analysis difficult to exploit. Automated data-mining processes and machine learning approaches can be a solution to organize the massive amount of data, trying to unravel important interaction. The purpose of this paper is to describe the strategy to collect and analyze data properly for decision support and introduce the concept of an 'umbrella protocol' within the framework of 'rapid learning healthcare'.

SummaryThe interaction between implementation of new technologies and different outcomes can allow a broad range of researches to be expanded. The purpose of this paper is to introduce the VAlidation of high TEchnology based on large database analysis by learning machine (VATE) project that aims to combine new technologies with outcomes related to rectal cancer in terms of tumor control and normal tissue sparing. Using automated computer bots and the knowledge for screening data it is possible to identify the factors that can mostly influence those outcomes. Population-based observational studies resulting from the linkage of different datasets will be conducted in order to develop predictive models that allow physicians to share decision with patients into a wider concept of tailored treatment. KeywordSOver the past decade, remarkable advances in cancer care with the adoption of newest diagnostic and treatment technologies has created new challenges [1].The use and role of medical imaging technologies in clinical oncology has greatly expanded from a primarily diagnostic tool to award a central role in the context of individualized medicine. Multiple imaging features involving descriptors of intensity distribution, spatial relationships between the various intensity levels, texture heterogeneity patterns, descriptors of shape and the relations of the tumor with the surrounding tissues have been analyzed for their relationship with treatment outcomes or Practice points• Progress in individualized medicine has created new challenges.• New technology implementation.• Necessity to develop systems that allow shared decision making by the physicians and the patients and chose a tailored treatment.• Standardization of the data collection -ontology.• Sharing data: Semantic Web and Resource Description Framework.• Statistical analysis.• Privacy protection of individual patients.• Development of predictive models based on individual patients features which complement existing consensus or guidelines.For reprint orders, please contact: reprints@futuremedicine.com

The study will provide information on patients' quality of life and its variations over time in relation to the treatments received for the prostate cancer.

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