Cancer 2015: a longitudinal whole-of-system study of genomic cancer medicine

Genomic cancer medicine promises revolutionary change in oncology. The impacts of 'personalized medicine', based upon a molecular classification of cancer and linked to targeted therapies, will extend from individual patient outcomes to the health economy at large. To address the 'whole-of-system' impact of genomic cancer medicine, we have established a prospective cohort of patients with newly diagnosed cancer in the state of Victoria, Australia, about whom we have collected a broad range of clinical, demographic, molecular, and patient-reported data, as well as data on health resource utilization. Our goal is to create a model for investigating public investment in genomic medicine that maximizes the cost:benefit ratio for the Australian community at large.

Heterogeneous copper concentrations in cancerous human prostate tissues

© 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc. Background Therapeutics that target copper for the treatment of prostate cancer are being evaluated in human clinical trials. Elevated intracellular copper is considered to sensitize prostate cancer cells to certain copper-coordination compounds, especially those with ionophoric properties. While there is compelling in vitro evidence that prostate cancer cells accumulate intracellular copper, a corresponding status for copper in patient tissues has not been corroborated. We therefore established whether copper concentrations increase in cancerous prostate tissues, and in sera, in patients throughout disease progression. Methods Human prostate tissue samples were obtained from patient prostatectomies (n=28), and together with patient-matched sera, were analyzed for copper content by inductively coupled plasma mass spectrometry. Results When grouped together, cancerous prostate tissues exhibiting moderate disease severity (Gleason Score 7) (n=10) had 1.6-fold more copper than age-matched normal tissues (n=10) (P<0.05). Those with more aggressive disease (Gleason Score 9) (n=8) had 1.8-fold more copper (P<0.05). In both disease stages however, the copper concentrations between individual samples were rather variable (0.55-3.02μg/g), with many clearly within the normal range (0.52-1.28μg/g). Additionally, we found that there was no change in serum copper concentrations in patients with either moderate or aggressive prostate cancer (Gleason Score 7 or 9), compared with reference intervals and to age-matched controls. Conclusions The heterogeneous nature of copper concentrations in cancerous prostate tissues, suggest that a small subset of patients may respond to treatments that target elevated intratumoral copper. Therefore, such approaches would likely require personalized treatment strategies. Prostate 75:1510-1517, 2015.

Randomized controlled trial of proactive web-based alcohol screening and brief intervention for university students

BACKGROUND: University students drink more heavily than their nonstudent peers and are often unaware that their drinking is risky and exceeds normative levels. We tested the efficacy of a proactive Web-based alcohol screening and brief intervention program. METHODS: A randomized controlled trial was conducted at an Australian university in 2007. Invitations were sent to 13 000 undergraduates (age range, 17-24 years) to complete a Web-based Alcohol Use Disorders Identification Test. Of 7237 students who responded, 2435 scored in the hazardous/harmful range (> or =8) and were randomized, and 2050 (84%) completed at least 1 follow-up assessment. Intervention was 10 minutes of Web-based motivational assessment and personalized feedback. Controls received only screening. Follow-up assessments were conducted at 1 and 6 months with observers and participants blinded to allocation. Outcome measures were drinking frequency, typical occasion quantity, overall volume, number of personal problems, an academic problems score, prevalence of binge drinking, and prevalence of heavy drinking. RESULTS: Mean (SD) baseline Alcohol Use Disorders Identification Test scores for control and intervention groups were 14.3 (5.1) and 14.2 (5.1), respectively. After 1 month, participants receiving intervention drank less often (rate ratio [RR], 0.89; 95% confidence interval [CI], 0.83-0.94), smaller quantities per occasion (RR, 0.93; 95% CI, 0.88-0.98), and less alcohol overall (RR, 0.83; 95% CI, 0.78-0.90) than did controls. Differences in alcohol-related harms were nonsignificant. At 6 months, intervention effects persisted for drinking frequency (RR, 0.91; 95% CI, 0.85-0.97) and overall volume (RR, 0.89; 95% CI, 0.82-0.96) but not for other variables. CONCLUSION: Proactive Web-based screening and intervention reduces drinking in undergraduates, and such a program could be implemented widely.

A graph-based model for context-aware recommendation using implicit feedback data

Recommender systems have been successfully dealing with the problem of information overload. However, most recommendation methods suit to the scenarios where explicit feedback, e.g. ratings, are available, but might not be suitable for the most common scenarios with only implicit feedback. In addition, most existing methods only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a graph-based generic recommendation framework, which constructs a Multi-Layer Context Graph (MLCG) from implicit feedback data, and then performs ranking algorithms in MLCG for context-aware recommendation. Specifically, MLCG incorporates a variety of contextual information into a recommendation process and models the interactions between users and items. Moreover, based on MLCG, two novel ranking methods are developed: Context-aware Personalized Random Walk (CPRW) captures user preferences and current situations, and Semantic Path-based Random Walk (SPRW) incorporates semantics of paths in MLCG into random walk model for recommendation. The experiments on two real-world datasets demonstrate the effectiveness of our approach.

A web- and mobile phone-based intervention to prevent obesity in 4-year-olds (MINISTOP): a population-based randomized controlled trial

BACKGROUND: Childhood obesity is an increasing health problem globally. Overweight and obesity may be established as early as 2-5 years of age, highlighting the need for evidence-based effective prevention and treatment programs early in life. In adults, mobile phone based interventions for weight management (mHealth) have demonstrated positive effects on body mass, however, their use in child populations has yet to be examined. The aim of this paper is to report the study design and methodology of the MINSTOP (Mobile-based Intervention Intended to Stop Obesity in Preschoolers) trial.

METHODS/DESIGN: A two-arm, parallel design randomized controlled trial in 300 healthy Swedish 4-year-olds is conducted. After baseline measures, parents are allocated to either an intervention- or control group. The 6- month mHealth intervention consists of a web-based application (the MINSTOP app) to help parents promote healthy eating and physical activity in children. MINISTOP is based on the Social Cognitive Theory and involves the delivery of a comprehensive, personalized program of information and text messages based on existing guidelines for a healthy diet and active lifestyle in pre-school children. Parents also register physical activity and intakes of candy, soft drinks, vegetables as well as fruits of their child and receive feedback through the application. Primary outcomes include body fatness and energy intake, while secondary outcomes are time spent in sedentary, moderate, and vigorous physical activity, physical fitness and intakes of fruits and vegetables, snacks, soft drinks and candy. Food and energy intake (Tool for Energy balance in Children, TECH), body fatness (pediatric option for BodPod), physical activity (Actigraph wGT3x-BT) and physical fitness (the PREFIT battery of five fitness tests) are measured at baseline, after the intervention (six months after baseline) and at follow-up (12 months after baseline).

DISCUSSION: This novel study will evaluate the effectiveness of a mHealth program for mitigating gain in body fatness among 4-year-old children. If the intervention proves effective it has great potential to be implemented in child-health care to counteract childhood overweight and obesity.

Privacy preserving data release for tagging recommender systems

Tagging recommender systems allow Internet users to annotate resources with personalized tags. The connection among users, resources and these annotations, often called a folksonomy, permits users the freedom to explore tags, and to obtain recommendations. Releasing these tagging datasets accelerates both commercial and research work on recommender systems. However, tagging recommender systems has been confronted with serious privacy concerns because adversaries may re-identify a user and her/his sensitive information from the tagging dataset using a little background information. Recently, several private techniques have been proposed to address the problem, but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes an private releasing algorithm to perturb users' profile in a strict privacy notion, differential privacy, with the goal of preserving a user's identity in a tagging dataset. The algorithm includes three privacy-preserving operations: Private Tag Clustering is used to shrink the randomized domain and Private Tag Selection is then applied to find the most suitable replacement tags for the original tags. To hide the numbers of tags, the third operation, Weight Perturbation, finally adds Laplace noise to the weight of tags. We present extensive experimental results on two real world datasets, De.licio.us and Bibsonomy. While the personalization algorithm is successful in both cases, our results further suggest the private releasing algorithm can successfully retain the utility of the datasets while preserving users' identity.

DeepCare: a deep dynamic memory model for predictive medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.

A mobile phone intervention increases physical activity in people with cardiovascular disease: Results from the HEART randomized controlled trial.

AIM: To determine the effectiveness and cost-effectiveness of a mobile phone intervention to improve exercise capacity and physical activity behaviour in people with ischaemic heart disease (IHD).

METHODS AND RESULTS: In this single-blind, parallel, two-arm, randomized controlled trial adults (n = 171) with IHD were randomized to receive a mobile phone delivered intervention (HEART; n = 85) plus usual care, or usual care alone (n = 86). Adult participants aged 18 years or more, with a diagnosis of IHD, were clinically stable as outpatients, able to perform exercise, able to understand and write English, and had access to the Internet. The HEART (Heart Exercise And Remote Technologies) intervention involved a personalized, automated package of text messages and a secure website with video messages aimed at increasing exercise behaviour, delivered over 24 weeks. All participants were able to access usual community-based cardiac rehabilitation, which involves encouragement of physical activity and an offer to join a local cardiac support club. All outcomes were assessed at baseline and 24 weeks and included peak oxygen uptake (PVO2; primary outcome), self-reported physical activity, health-related quality of life, self-efficacy and motivation (secondary outcomes). Results showed no differences in PVO2 between the two groups (difference -0.21 ml kg(-1)min(-1), 95% CI: -1.1, 0.7; p = 0.65) at 24 weeks. However significant treatment effects were observed for selected secondary outcomes, including leisure time physical activity (difference 110.2 min/week, 95% CI: -0.8, 221.3; p = 0.05) and walking (difference 151.4 min/week, 95% CI: 27.6, 275.2; p = 0.02). There were also significant improvements in self-efficacy to be active (difference 6.2%, 95% CI: 0.2, 12.2; p = 0.04) and the general health domain of the SF36 (difference 2.1, 95% CI: 0.1, 4.1; p = 0.03) at 24 weeks. The HEART programme was considered likely to be cost-effective for leisure time activity and walking.

CONCLUSIONS: A mobile phone intervention was not effective at increasing exercise capacity over and above usual care. The intervention was effective and probably cost-effective for increasing physical activity and may have the potential to augment existing cardiac rehabilitation services.

Text message and internet support for coronary heart disease self-management: results from the text4heart randomized controlled trial

BACKGROUND: Mobile technology has the potential to deliver behavior change interventions (mHealth) to reduce coronary heart disease (CHD) at modest cost. Previous studies have focused on single behaviors; however, cardiac rehabilitation (CR), a component of CHD self-management, needs to address multiple risk factors. OBJECTIVE: The aim was to investigate the effectiveness of a mHealth-delivered comprehensive CR program (Text4Heart) to improve adherence to recommended lifestyle behaviors (smoking cessation, physical activity, healthy diet, and nonharmful alcohol use) in addition to usual care (traditional CR). METHODS: A 2-arm, parallel, randomized controlled trial was conducted in New Zealand adults diagnosed with CHD. Participants were recruited in-hospital and were encouraged to attend center-based CR (usual care control). In addition, the intervention group received a personalized 24-week mHealth program, framed in social cognitive theory, sent by fully automated daily short message service (SMS) text messages and a supporting website. The primary outcome was adherence to healthy lifestyle behaviors measured using a self-reported composite health behavior score (≥3) at 3 and 6 months. Secondary outcomes included clinical outcomes, medication adherence score, self-efficacy, illness perceptions, and anxiety and/or depression at 6 months. Baseline and 6-month follow-up assessments (unblinded) were conducted in person. RESULTS: Eligible patients (N=123) recruited from 2 large metropolitan hospitals were randomized to the intervention (n=61) or the control (n=62) group. Participants were predominantly male (100/123, 81.3%), New Zealand European (73/123, 59.3%), with a mean age of 59.5 (SD 11.1) years. A significant treatment effect in favor of the intervention was observed for the primary outcome at 3 months (AOR 2.55, 95% CI 1.12-5.84; P=.03), but not at 6 months (AOR 1.93, 95% CI 0.83-4.53; P=.13). The intervention group reported significantly greater medication adherence score (mean difference: 0.58, 95% CI 0.19-0.97; P=.004). The majority of intervention participants reported reading all their text messages (52/61, 85%). The number of visits to the website per person ranged from zero to 100 (median 3) over the 6-month intervention period. CONCLUSIONS: A mHealth CR intervention plus usual care showed a positive effect on adherence to multiple lifestyle behavior changes at 3 months in New Zealand adults with CHD compared to usual care alone. The effect was not sustained to the end of the 6-month intervention. A larger study is needed to determine the size of the effect in the longer term and whether the change in behavior reduces adverse cardiovascular events. TRIAL REGISTRATION: ACTRN 12613000901707; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364758&isReview=true (Archived by WebCite at http://www.webcitation.org/6c4qhcHKt).

Does student engagement improve when 1:1 device technologies are used and adapted to cater for individual learning styles during online delivery of engineering courses?

BACKGROUND OR CONTEXT: A developing international engineering industry is dependent on competition and innovation, creating a market for highly skilled graduates from respected overseas and Australian Engineering universities. The delivery of engineering teaching and learning via blended faceto-face, problem based, research focused and online collaborative learning will continue to be the foundation of future engineering education, however, it will be those institutions who can reshape its learning spaces within a culture of innovation using 1:1 devices that will continue to attract the brightest minds. Investing in educational research that explores the preferred learning styles of learners and matching this to specifically designed 1:1 personalized web applications may be the ‘value add’ to improve student engagement. In this paper, a survey of Australian engineering education is presented and contrasted against a backdrop of internationally recognised educational pedagogy to demonstrate how engineering teaching and learning has changed over time. This paper draws on research and identifies a gap where a necessity to question the validity of 1:1 devices as the next step in the evolution of engineering education needs to be undertaken. How will teaching and learning look using 1:1 devices and will it drive student demand into engineering higher
education courses. Will this lead to improving professional standards within a dynamic engineering education context? How will current and future teaching and learning be influenced by constructivism using 1:1 device technologies? How will the engineering industry benefit from higher education investment in individualised engineering education
using 1:1 devices for teaching and learning?
PURPOSE OR GOAL: To review the current academic thinking around the topic of 1:1 devices within higher education engineering teaching and learning context in Australia. To identify any gaps in the current understandings and use of 1:1 devices within engineering courses in Australia. To generate discussion and better understanding about how the use of 1:1 devices may hinder and/or improve teaching and learning and student engagement.
APPROACH: A review covering the development of engineering education in Australia and a broader international review of engineering teaching methodology. To identify the extent of research into the use and effectiveness of online strategies within engineering education utilising 1:1 devices for teaching and learning. i.e. “Students must feel that they are part of a learning community and derive motivation to engage in the study material from the lecturer.’ (Lloyd et al., 2001) It is proposed to add to the current body of understandings and explore the effectiveness of a constructiveness teaching approach using course material specifically designed to cater for individual learning styles and delivered via the use of 1:1 devices in the classroom. It is anticipated the research will contrast current engineering teaching and learning practices and identify factors that will facilitate a greater understanding about student connectedness and engagement with the teaching and learning experience; where a constructiveness environment is supported with the use of 1:1 devices. Also, it is anticipated that the constructed learning environment will foster a culture of innovation and students will be empowered to take control of their own learning and be encouraged to contribute back to the discussion initiated by the lecture and/or course material with the aid of 1:1 device technologies. A gap has been identified in the academic literature that show there is a need to understand the relationship between engineering teaching, learning, students engagement and the use of 1:1 devices.
DISCUSSION: A review covering the development of engineering education in Australia and a broader international review of engineering teaching methodology. To identify the extent of research into the use and effectiveness of online strategies within engineering education utilising 1:1 devices for teaching and learning. i.e. “Students must feel that they are part of a learning community and derive motivation to engage in the study material from the lecturer.’ (Lloyd et al., 2001) It is proposed to add to the current body of understandings and explore the effectiveness of a constructiveness teaching approach using course material specifically designed to cater for individual learning styles and delivered via the use of 1:1 devices in the classroom.
ANTICIPATED OUTCOMES: It is anticipated the research will contrast current engineering teaching and learning practices and identify factors that will facilitate a greater understanding about student connectedness and engagement with the teaching and learning experience; where a constructiveness environment is supported with the use of 1:1 devices. Also, it is anticipated that the constructed learning environment will foster a culture of innovation and students will be empowered to take control of their own learning and be encouraged to contribute back to the discussion initiated by the lecture and/or course material with the aid of 1:1 device technologies. A gap has been identified in the academic literature that show there is a need to understand the relationship between engineering teaching, learning, students engagement and the use of 1:1 devices.
RECOMMENDATIONS/IMPLICATIONS/CONCLUSION: A gap exists in the current research about the effectiveness and use of 1:1 devices in engineering education; therefore, it is necessary to undertake further research in the area. It is proposed to hypothesize and conduct field research to identify any shortcomings and possible benefits for engineering educators and learners within a constructivist-teaching
context that explores the relationship between the use of personalized 1:1 devices for teaching and learning, adapting for individual learning styles, and identification and application of appropriate teaching and learning strategies within a constructiveness engineering course approach. Research is required to clarify the following research questions;
• What education teaching and learning strategies best facilitate the use of 1:1 devices for online teaching and learning?
• Does student engagement improve when 1:1 device technologies are used and adapted to cater for individual learning styles during online delivery of engineering courses?
• What are the factors within a university engineering faculty that may hinder and/or support the use of 1:1 devices for online teaching and learning?
• To what extent do 1:1 devices assist engineering educators and students to foster a culture of innovation? The study results will offer engineering educators and students an opportunity to reflect on
their current teaching and learning practice, and contextualise the use of 1:1 devices as a tool to improve student engagement. It is expected the learning benefits will outweigh the implementation costs and derive a unique learning experience that will empower engineering educators and students to inspire a culture of innovation.

Reproducibility of the online food4me food-frequency questionnaire for estimating dietary intakes across Europe

BACKGROUND: Accurate dietary assessment is key to understanding nutrition-related outcomes and is essential for estimating dietary change in nutrition-based interventions. OBJECTIVE: The objective of this study was to assess the pan-European reproducibility of the Food4Me food-frequency questionnaire (FFQ) in assessing the habitual diet of adults. METHODS: Participants from the Food4Me study, a 6-mo, Internet-based, randomized controlled trial of personalized nutrition conducted in the United Kingdom, Ireland, Spain, Netherlands, Germany, Greece, and Poland, were included. Screening and baseline data (both collected before commencement of the intervention) were used in the present analyses, and participants were included only if they completed FFQs at screening and at baseline within a 1-mo timeframe before the commencement of the intervention. Sociodemographic (e.g., sex and country) and lifestyle [e.g., body mass index (BMI, in kg/m(2)) and physical activity] characteristics were collected. Linear regression, correlation coefficients, concordance (percentage) in quartile classification, and Bland-Altman plots for daily intakes were used to assess reproducibility. RESULTS: In total, 567 participants (59% female), with a mean ± SD age of 38.7 ± 13.4 y and BMI of 25.4 ± 4.8, completed both FFQs within 1 mo (mean ± SD: 19.2 ± 6.2 d). Exact plus adjacent classification of total energy intake in participants was highest in Ireland (94%) and lowest in Poland (81%). Spearman correlation coefficients (ρ) in total energy intake between FFQs ranged from 0.50 for obese participants to 0.68 and 0.60 in normal-weight and overweight participants, respectively. Bland-Altman plots showed a mean difference between FFQs of 210 kcal/d, with the agreement deteriorating as energy intakes increased. There was little variation in reproducibility of total energy intakes between sex and age groups. CONCLUSIONS: The online Food4Me FFQ was shown to be reproducible across 7 European countries when administered within a 1-mo period to a large number of participants. The results support the utility of the online Food4Me FFQ as a reproducible tool across multiple European populations. This trial was registered at clinicaltrials.gov as NCT01530139.

Enabling fine-grained multi-keyword search supporting classified sub-dictionaries over encrypted cloud data

Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency.

Medical burden, body mass index and the outcome of psychosocial interventions for bipolar depression

Objective: Individuals with bipolar disorder experience a disproportionately high incidence of medical co-morbidity and obesity. These health-related problems are a barrier to recovery from mood episodes and have been linked with unfavorable responses to pharmacological treatment. However, little is known about whether and how these characteristics affect responses to adjunctive psychotherapy. Method: Embedded in the Systematic Treatment Enhancement Program for Bipolar Disorder was a randomized controlled trial of psychotherapy for bipolar depression comparing the efficacy of intensive psychotherapy plus pharmacotherapy with collaborative care (a three-session psycho-educational intervention) plus pharmacotherapy. We conducted a post-hoc analysis to evaluate whether medical burden and body mass index predicted and/or moderated the likelihood of recovery and time until recovery from a depressive episode among patients in the two treatments. Results: Participants who had medical co-morbidity and body mass index data constituted 199 of the 293 patients in the original Systematic Treatment Enhancement Program for Bipolar Disorder trial. Higher medical burden predicted a lower likelihood of recovery from depression in both treatment conditions (odds ratio = 0.89), but did not moderate responses to intensive psychotherapy vs collaborative care. Intensive psychotherapy yielded superior recovery rates for individuals of normal body mass index (odds ratio= 2.39) compared with collaborative care, but not among individuals who were overweight or obese. Conclusion: Medical co-morbidity and body weight impacts symptom improvement and attention to this co-morbidity may inform the development of more personalized treatments for bipolar disorder.

Immune biomarkers in the spectrum of childhood noncommunicable diseases

A biomarker is an accurately and reproducibly quantifiable biological characteristic that provides an objective measure of health status or disease. Benefits of biomarkers include identification of therapeutic targets, monitoring of clinical interventions, and development of personalized (or precision) medicine. Challenges to the use of biomarkers include optimizing sample collection, processing and storage, validation, and often the need for sophisticated laboratory and bioinformatics approaches. Biomarkers offer better understanding of disease processes and should benefit the early detection, treatment, and management of multiple noncommunicable diseases (NCDs). This review will consider the utility of biomarkers in patients with allergic and other immune-mediated diseases in childhood. Typically, biomarkers are used currently to provide mechanistic insight or an objective measure of disease severity, with their future role in risk stratification/disease prediction speculative at best. There are many lessons to be learned from the biomarker strategies used for cancer in which biomarkers are in routine clinical use and industry-wide standardized approaches have been developed. Biomarker discovery and validation in children with disease lag behind those in adults; given the early onset and therefore potential lifelong effect of many NCDs, there should be more studies incorporating cohorts of children. Many pediatric biomarkers are at the discovery stage, with a long path to evaluation and clinical implementation. The ultimate challenge will be optimization of prevention strategies that can be implemented in children identified as being at risk of an NCD through the use of biomarkers.

iPrevent®: a tailored, web-based, decision support tool for breast cancer risk assessment and management

We aimed to develop a user-centered, web-based, decision support tool for breast cancer risk assessment and personalized risk management. Using a novel model choice algorithm, iPrevent(®) selects one of two validated breast cancer risk estimation models (IBIS or BOADICEA), based on risk factor data entered by the user. Resulting risk estimates are presented in simple language and graphic formats for easy comprehension. iPrevent(®) then presents risk-adapted, evidence-based, guideline-endorsed management options. Development was an iterative process with regular feedback from multidisciplinary experts and consumers. To verify iPrevent(®), risk factor data for 127 cases derived from the Australian Breast Cancer Family Study were entered into iPrevent(®), IBIS (v7.02), and BOADICEA (v3.0). Consistency of the model chosen by iPrevent(®) (i.e., IBIS or BOADICEA) with the programmed iPrevent(®) model choice algorithm was assessed. Estimated breast cancer risks from iPrevent(®) were compared with those attained directly from the chosen risk assessment model (IBIS or BOADICEA). Risk management interventions displayed by iPrevent(®) were assessed for appropriateness. Risk estimation model choice was 100 % consistent with the programmed iPrevent(®) logic. Discrepant 10-year and residual lifetime risk estimates of >1 % were found for 1 and 4 cases, respectively, none was clinically significant (maximal variation 1.4 %). Risk management interventions suggested by iPrevent(®) were 100 % appropriate. iPrevent(®) successfully integrates the IBIS and BOADICEA risk assessment models into a decision support tool that provides evidence-based, risk-adapted risk management advice. This may help to facilitate precision breast cancer prevention discussions between women and their healthcare providers.