Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive medical tests to define the full range of phenotypic variation associated with individual health. Such knowledge is critical for improved early intervention, for better treatment decisions, and for ameliorating the steadily worsening epidemic of chronic disease. We present proof-of-concept experiments to demonstrate how routinely acquired cross-sectional CT imaging may be used to predict patient longevity as a proxy for overall individual health and disease status using computer image analysis techniques. Despite the limitations of a modest dataset and the use of off-the-shelf machine learning methods, our results are comparable to previous 'manual' clinical methods for longevity prediction. This work demonstrates that radiomics techniques can be used to extract biomarkers relevant to one of the most widely used outcomes in epidemiological and clinical research -mortality, and that deep learning with convolutional neural networks can be usefully applied to radiomics research. Computer image analysis applied to routinely collected medical images offers substantial potential to enhance precision medicine initiatives. Measuring phenotypic variation in precision medicinePrecision medicine has become a key focus of modern bioscience and medicine, and involves "prevention and treatment strategies that take individual variability into account", through the use of "large-scale biologic databases … powerful methods for characterizing patients … and computational tools for analysing large sets of data" 1 . The variation within individuals that enables the identification of patient subgroups for precision medicine strategies is termed the "phenotype". The observable phenotype reflects both genomic variation and the accumulated lifestyle and environmental exposures that impact biological function -the exposome 2 .Precision medicine relies upon the availability of useful biomarkers, defined as "a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention" 3 . A 'good' biomarker has the following characteristics: it is sensitive, specific, predictive, robust, bridges clinical and preclinical health states, and is non-invasive 4 .Genomics can produce good biomarkers useful for precision medicine 5 . There has been significant success in exploring human genetic variation in the field of genomics, where data-driven methods have highlighted the role of human genetic variation in disease diagnosis, prognosis, and treatment response 6 . However, for the chronic and age-related diseases which account for the majority of morbidity and mortality in developed nations 7 and worldwide 8 , the majority (70-90%) of observable p...

Background:Early diagnosis and improved treatment outcomes have increased breast cancer survival rates that, in turn, have led to increased numbers of women undergoing follow-up after completion of primary treatment. The current workload growth is unsustainable for breast cancer specialists who also provide care for women newly diagnosed or with a recurrence. Appropriate and acceptable follow-up care is important; yet, currently we know little about patient preferences. The aim of this study was to explore the preferences of Australian breast cancer survivors for alternative modes of delivery of follow-up services.Methods:A self-administered questionnaire (online or paper) was developed. The questionnaire contained a discrete choice experiment (DCE) designed to explore patient preferences with respect to provider, location, frequency and method of delivery of routine follow-up care in years 3, 4 and 5 after diagnosis, as well as the perceived value of ‘drop-in' clinics providing additional support. Participants were recruited throughout Australia over a 6-month period from May to October 2012. Preference scores and choice probabilities were used to rank the top 10 most preferred follow-up scenarios for respondents.Results:A total of 836 women participated in the study, of whom 722 (86.4%) completed the DCE. In the absence of specialist follow-up, the 10 most valued surveillance scenarios all included a Breast Physician as the provider of follow-up care. The most preferred scenario is a face-to-face local breast cancer follow-up clinic held every 6 months and led by a Breast Physician, where additional clinics focused on the side effects of treatment are also provided.Conclusion:Beyond the first 2 years from diagnosis, in the absence of a specialist led follow-up, women prefer to have their routine breast cancer follow-up by a Breast Physician (or a Breast Cancer Nurse) in a dedicated local breast cancer clinic, rather than with their local General Practitioner. Drop-in clinics for the management of treatment related side effects and to provide advice to both develop and maintain good health are also highly valued by breast cancer survivors.

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Assessment of case note documentation has limitations. Clinician groups seem to differ in their capacity and willingness to change their practice. A multifaceted change strategy including a problem specific radiography request form can improve the selection of patients for radiography.