The Colorectal cancer RISk Prediction (CRISP) trial: a randomised controlled trial of a decision support tool for risk-stratified colorectal cancer screening.

BACKGROUND: A risk-stratified approach to colorectal cancer (CRC) screening could result in a more acceptable balance of benefits and harms, and be more cost-effective. AIM: To determine the effect of a consultation in general practice using a computerised risk assessment and decision support tool (Colorectal cancer RISk Prediction, CRISP) on risk-appropriate CRC screening. DESIGN AND SETTING: Randomised controlled trial in 10 general practices in Melbourne, Australia, from May 2017 to May 2018. METHOD: Participants were recruited from a consecutive sample of patients aged 50-74 years attending their GP. Intervention consultations included CRC risk assessment using the CRISP tool and discussion of CRC screening recommendations. Control group consultations focused on lifestyle CRC risk factors. The primary outcome was risk-appropriate CRC screening at 12 months. RESULTS: A total of 734 participants (65.1% of eligible patients) were randomised (369 intervention, 365 control); the primary outcome was determined for 722 (362 intervention, 360 control). There was a 6.5% absolute increase (95% confidence interval [CI] = -0.28 to 13.2) in risk-appropriate screening in the intervention compared with the control group (71.5% versus 65.0%; odds ratio [OR] 1.36, 95% CI = 0.99 to 1.86, P = 0.057). In those due CRC screening during follow-up, there was a 20.3% (95% CI = 10.3 to 30.4) increase (intervention 59.8% versus control 38.9%; OR 2.31, 95% CI = 1.51 to 3.53, P<0.001) principally by increasing faecal occult blood testing in those at average risk. CONCLUSION: A risk assessment and decision support tool increases risk-appropriate CRC screening in those due screening. The CRISP intervention could commence in people in their fifth decade to ensure people start CRC screening at the optimal age with the most cost-effective test.

Exploring a novel method for optimising the implementation of a colorectal cancer risk prediction tool into primary care: a qualitative study.

BACKGROUND: We developed a colorectal cancer risk prediction tool ('CRISP') to provide individualised risk-based advice for colorectal cancer screening. Using known environmental, behavioural, and familial risk factors, CRISP was designed to facilitate tailored screening advice to patients aged 50 to 74 years in general practice. In parallel to a randomised controlled trial of the CRISP tool, we developed and evaluated an evidence-based implementation strategy. METHODS: Qualitative methods were used to explore the implementation of CRISP in general practice. Using one general practice in regional Victoria, Australia, as a 'laboratory', we tested ways to embed CRISP into routine clinical practice. General practitioners, nurses, and operations manager co-designed the implementation methods with researchers, focussing on existing practice processes that would be sustainable. Researchers interviewed the staff regularly to assess the successfulness of the strategies employed, and implementation methods were adapted throughout the study period in response to feedback from qualitative interviews. The Consolidated Framework for Implementation Research (CFIR) underpinned the development of the interview guide and intervention strategy. Coding was inductive and themes were developed through consensus between the authors. Emerging themes were mapped onto the CFIR domains and a fidelity checklist was developed to ensure CRISP was being used as intended. RESULTS: Between December 2016 and September 2019, 1 interviews were conducted, both face-to-face and via videoconferencing (Zoom). All interviews were transcribed verbatim and coded. Themes were mapped onto the following CFIR domains: (1) 'characteristics of the intervention': CRISP was valued but time consuming; (2) 'inner setting': the practice was open to changing systems; 3. 'outer setting': CRISP helped facilitate screening; (4) 'individual characteristics': the practice staff were adaptable and able to facilitate adoption of new clinical processes; and (5) 'processes': fidelity checking, and education was important. CONCLUSIONS: These results describe a novel method for exploring implementation strategies for a colorectal cancer risk prediction tool in the context of a parallel RCT testing clinical efficacy. The study identified successful and unsuccessful implementation strategies using an adaptive methodology over time. This method emphasised the importance of co-design input to make an intervention like CRISP sustainable for use in other practices and with other risk tools.

Ten-year prediction model for post-bronchodilator airflow obstruction and early detection of COPD: development and validation in two middle-aged population-based cohorts.

BACKGROUND: Classifying individuals at high chronic obstructive pulmonary disease (COPD)-risk creates opportunities for early COPD detection and active intervention. OBJECTIVE: To develop and validate a statistical model to predict 10-year probabilities of COPD defined by post-bronchodilator airflow obstruction (post-BD-AO; forced expiratory volume in 1 s/forced vital capacity<5th percentile). SETTING: General Caucasian populations from Australia and Europe, 10 and 27 centres, respectively. PARTICIPANTS: For the development cohort, questionnaire data on respiratory symptoms, smoking, asthma, occupation and participant sex were from the Tasmanian Longitudinal Health Study (TAHS) participants at age 41-45 years (n=5729) who did not have self-reported COPD/emphysema at baseline but had post-BD spirometry and smoking status at age 51-55 years (n=2407). The validation cohort comprised participants from the European Community Respiratory Health Survey (ECRHS) II and III (n=5970), restricted to those of age 40-49 and 50-59 with complete questionnaire and spirometry/smoking data, respectively (n=1407). STATISTICAL METHOD: Risk-prediction models were developed using randomForest then externally validated. RESULTS: Area under the receiver operating characteristic curve (AUCROC) of the final model was 80.8% (95% CI 80.0% to 81.6%), sensitivity 80.3% (77.7% to 82.9%), specificity 69.1% (68.7% to 69.5%), positive predictive value (PPV) 11.1% (10.3% to 11.9%) and negative predictive value (NPV) 98.7% (98.5% to 98.9%). The external validation was fair (AUCROC 75.6%), with the PPV increasing to 17.9% and NPV still 97.5% for adults aged 40-49 years with ≥1 respiratory symptom. To illustrate the model output using hypothetical case scenarios, a 43-year-old female unskilled worker who smoked 20 cigarettes/day for 30 years had a 27% predicted probability for post-BD-AO at age 53 if she continued to smoke. The predicted risk was 42% if she had coexistent active asthma, but only 4.5% if she had quit after age 43. CONCLUSION: This novel and validated risk-prediction model could identify adults aged in their 40s at high 10-year COPD-risk in the general population with potential to facilitate active monitoring/intervention in predicted 'COPD cases' at a much earlier age.

Accuracy of Risk Estimates from the iPrevent Breast Cancer Risk Assessment and Management Tool.

BACKGROUND: iPrevent is an online breast cancer (BC) risk management decision support tool. It uses an internal switching algorithm, based on a woman's risk factor data, to estimate her absolute BC risk using either the International Breast Cancer Intervention Study (IBIS) version 7.02, or Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm version 3 models, and then provides tailored risk management information. This study assessed the accuracy of the 10-year risk estimates using prospective data. METHODS: iPrevent-assigned 10-year invasive BC risk was calculated for 15 732 women aged 20-70 years and without BC at recruitment to the Prospective Family Study Cohort. Calibration, the ratio of the expected (E) number of BCs to the observed (O) number and discriminatory accuracy were assessed. RESULTS: During the 10 years of follow-up, 619 women (3.9%) developed BC compared with 702 expected (E/O = 1.13; 95% confidence interval [CI] =1.05 to 1.23). For women younger than 50 years, 50 years and older, and BRCA1/2-mutation carriers and noncarriers, E/O was 1.04 (95% CI = 0.93 to 1.16), 1.24 (95% CI = 1.11 to 1.39), 1.13 (95% CI = 0.96 to 1.34), and 1.13 (95% CI = 1.04 to 1.24), respectively. The C-statistic was 0.70 (95% CI = 0.68 to 0.73) overall and 0.74 (95% CI = 0.71 to 0.77), 0.63 (95% CI = 0.59 to 0.66), 0.59 (95% CI = 0.53 to 0.64), and 0.65 (95% CI = 0.63 to 0.68), respectively, for the subgroups above. Applying the newer IBIS version 8.0b in the iPrevent switching algorithm improved calibration overall (E/O = 1.06, 95% CI = 0.98 to 1.15) and in all subgroups, without changing discriminatory accuracy. CONCLUSIONS: For 10-year BC risk, iPrevent had good discriminatory accuracy overall and was well calibrated for women aged younger than 50 years. Calibration may be improved in the future by incorporating IBIS version 8.0b.

Benefits and harms of aspirin to reduce colorectal cancer risk: a cross-sectional study of methods to communicate risk in primary care.

BACKGROUND: New Australian guidelines recommend that GPs actively consider prescribing low-dose aspirin to patients aged 50-70 years to reduce their risk of developing colorectal cancer (CRC). Patients and GPs need to understand the relative benefits and harms to support informed decision making. AIM: To develop and examine different methods to communicate the benefits and harms of taking aspirin for CRC prevention. DESIGN AND SETTING: A cross-sectional, vignette study with patients aged 50-70 years consecutively recruited from general practices in Melbourne, Australia, between July and August 2018. METHOD: Summary estimates from meta-analyses of the effects of aspirin on the incidence of CRC, cardiovascular disease, gastrointestinal bleeding, and incidence rates in the Australian population to estimate outcomes in a hypothetical population of 10 000 people aged 50-70 years. These estimates were presented using four different risk communication formats. Participants were shown these different formats and asked if they would take aspirin to prevent CRC. RESULTS: A total of 313 participants were recruited (95.1% recruitment rate), of whom 304 completed the study. Most participants (71.7-75.3%) reported they would take aspirin irrespective of risk format presented. Bar charts (odds ratio [OR] 1.20, 95% confidence intervals [CI] = 1.01 to 1.44) and expected frequency trees (OR 1.18, 95% CI = 0.99 to 1.41) were more strongly associated with the intentions to take aspirin compared with icon arrays. Bar charts were most preferred for presenting risk information. CONCLUSION: A large proportion of participants in this study intended to take aspirin to reduce their CRC risk regardless of risk communication format. Bar charts and expected frequency trees were the preferred methods to present the benefits and harms of taking aspirin to prevent CRC.

Benefits and harms of selective oestrogen receptor modulators (SERMs) to reduce breast cancer risk: a cross-sectional study of methods to communicate risk in primary care.

BACKGROUND: In Australia, evidence-based guidelines recommend that women consider taking selective oestrogen receptor modulators (SERMs) to reduce their risk of breast cancer. In practice, this requires effective methods for communicating the harms and benefits of taking SERMs so women can make an informed choice. AIM: To evaluate how different risk presentations influence women's decisions to consider taking SERMs. DESIGN AND SETTING: Cross-sectional, correlational study of Australian women in general practice. METHOD: Three risk communication formats were developed that included graphics, numbers, and text to explain the reduction in breast cancer risk and risk of side effects for women taking SERMs (raloxifene or tamoxifen). Women aged 40-74 years in two general practices were shown the risk formats using vignettes of hypothetical women at moderate or high risk of breast cancer and asked to choose 'If this was you, would you consider taking a SERM?' Descriptive statistics and predictors (risk format, level of risk, and type of SERM) of choosing SERMs were determined by logistic regression. RESULTS: A total of 288 women were recruited (an 88% response rate) between March and May 2017. The risk formats that showed a government statement and an icon array were associated with a greater likelihood of considering SERMs relative to one that showed a novel expected frequency tree. Risk formats for raloxifene and for the high-risk vignettes were also more strongly associated with choosing to consider SERMs. No associations were found with any patient demographics. CONCLUSION: Specific risk formats may lead to more women considering taking SERMs to reduce breast cancer risk, especially if they are at high risk of the condition. Raloxifene may be a more acceptable SERM to patients.

The CRISP-P study: feasibility of a self-completed colorectal cancer risk prediction tool in primary care.

OBJECTIVE: Australia and New Zealand have the highest incidence of colorectal cancer (CRC) globally. Our research team has developed a CRC risk prediction tool for use in primary care to increase targeted screening. This study, Colorectal cancer RISk Prediction tool - patient ('CRISP-P'), aimed to determine the following to inform a future trial design: (i) the feasibility of self-reporting; (ii) the feasibility of recruitment methods; and (iii) the prevalence of CRC risk. METHODS: Participants aged between 40 and 75 years were recruited consecutively from three primary care waiting rooms. Participants input data into CRISP on a tablet without receiving clinical advice. Feasibility was evaluated using recruitment rate, timely completion, a self-reported 'ease-of-use', score and field notes. Prevalence of CRC risk was calculated using the CRISP model. RESULTS: Five hundred sixty-one (90%) patients agreed to use the tool and 424 (84%) rated the tool easy to use. Despite this, 41% of people were unable to complete the questions without assistance. Patients who were older, without tertiary education or with English as their second language were more likely to require assistance (P < 0.001). Thirty-nine percent of patients were low risk, 58% at slightly increased and 2.4% were at moderately increased risk of developing colorectal cancer in the next 5 years. CONCLUSIONS: The tool was perceived as easy to use, although older, less educated people, and patients with English as their second language needed help. The data support the recruitment methods but not the use of a self-completed tool for an efficacy trial.

Mortality after breast cancer as a function of time since diagnosis by estrogen receptor status and age at diagnosis.

Our aim was to estimate how long-term mortality following breast cancer diagnosis depends on age at diagnosis, tumor estrogen receptor (ER) status, and the time already survived. We used the population-based Australian Breast Cancer Family Study which followed-up 1,196 women enrolled during 1992-1999 when aged <60 years at diagnosis with a first primary invasive breast cancer, over-sampled for younger ages at diagnosis, for whom tumor pathology features and ER status were measured. There were 375 deaths (median follow-up = 15.7; range = 0.8-21.4, years). We estimated the mortality hazard as a function of time since diagnosis using a flexible parametric survival analysis with ER status a time-dependent covariate. For women with ER-negative tumors compared with those with ER-positive tumors, 5-year mortality was initially higher (p < 0.001), similar if they survived to 5 years (p = 0.4), and lower if they survived to 10 years (p = 0.02). The estimated mortality hazard for ER-negative disease peaked at ~3 years post-diagnosis, thereafter declined with time, and at 7 years post-diagnosis became lower than that for ER-positive disease. This pattern was more pronounced for women diagnosed at younger ages. Mortality was also associated with lymph node count (hazard ratio (HR) per 10 nodes = 2.52 [95% CI:2.11-3.01]) and tumor grade (HR per grade = 1.62 [95% CI:1.34-1.96]). The risk of death following a breast cancer diagnosis differs substantially and qualitatively with diagnosis age, ER status and time survived. For women who survive >7 years, those with ER-negative disease will on average live longer, and more so if younger at diagnosis.

The use of a risk assessment and decision support tool (CRISP) compared with usual care in general practice to increase risk-stratified colorectal cancer screening: study protocol for a randomised controlled trial.

BACKGROUND: Australia and New Zealand have the highest incidence rates of colorectal cancer worldwide. In Australia there is significant unwarranted variation in colorectal cancer screening due to low uptake of the immunochemical faecal occult blood test, poor identification of individuals at increased risk of colorectal cancer, and over-referral of individuals at average risk for colonoscopy. Our pre-trial research has developed a novel Colorectal cancer RISk Prediction (CRISP) tool, which could be used to implement precision screening in primary care. This paper describes the protocol for a phase II multi-site individually randomised controlled trial of the CRISP tool in primary care. METHODS: This trial aims to test whether a standardised consultation using the CRISP tool in general practice (the CRISP intervention) increases risk-appropriate colorectal cancer screening compared to control participants who receive standardised information on cancer prevention. Patients between 50 and 74 years old, attending an appointment with their general practitioner for any reason, will be invited into the trial. A total of 732 participants will be randomised to intervention or control arms using a computer-generated allocation sequence stratified by general practice. The primary outcome (risk-appropriate screening at 12 months) will be measured using baseline data for colorectal cancer risk and objective health service data to measure screening behaviour. Secondary outcomes will include participant cancer risk perception, anxiety, cancer worry, screening intentions and health service utilisation measured at 1, 6 and 12 months post randomisation. DISCUSSION: This trial tests a systematic approach to implementing risk-stratified colorectal cancer screening in primary care, based on an individual's absolute risk, using a state-of-the-art risk assessment tool. Trial results will be reported in 2020. TRIAL REGISTRATION: Australian and New Zealand Clinical Trial Registry, ACTRN12616001573448p . Registered on 14 November 2016.

The CRISP-Q study: Communicating the risks and benefits of colorectal cancer screening

Background and objectives: Many Australians at average risk of colorectal cancer (CRC) are undergoing unnecessary colonoscopic screening, while many at increased risk are getting inadequate screening. The aim of this study was to test different ways of communicating the risks and benefits of CRC screening, as part of the development of a CRC risk prediction (CRISP) tool. Method: General practice patients were shown five different risk presentations for hypothetical 'average' and 'increased' risk cases and were asked to choose the screening method they would undergo. Associations were explored between risk presentation type and 'risk-appropriate screening' choice. Results: All risk formats were associated with improved risk-appropriate screening by participants (n = 204); however, there was a statistical trend favouring absolute risk with a government recommendation and an 'expected frequency tree'. The icon array was most weakly associated with appropriate screening. Discussion: This research will inform approaches to communicating risk in CRISP and may be of wider relevance to supporting informed decisions about cancer screening.

The iPrevent Online Breast Cancer Risk Assessment and Risk Management Tool: Usability and Acceptability Testing.

BACKGROUND: iPrevent estimates breast cancer (BC) risk and provides tailored risk management information. OBJECTIVE: The objective of this study was to assess the usability and acceptability of the iPrevent prototype. METHODS: Clinicians were eligible for participation in the study if they worked in primary care, breast surgery, or genetics clinics. Female patients aged 18-70 years with no personal cancer history were eligible. Clinicians were first familiarized with iPrevent using hypothetical paper-based cases and then actor scenarios; subsequently, they used iPrevent with their patients. Clinicians and patients completed the System Usability Scale (SUS) and an Acceptability questionnaire 2 weeks after using iPrevent; patients also completed measures of BC worry, anxiety, risk perception, and knowledge pre- and 2 weeks post-iPrevent. Data were summarized using descriptive statistics. RESULTS: The SUS and Acceptability questionnaires were completed by 19 of 20 clinicians and 37 of 43 patients. Usability was above average (SUS score >68) for 68% (13/19) clinicians and 76% (28/37) patients. The amount of information provided by iPrevent was reported as "about right" by 89% (17/19) clinicians and 89% (33/37) patients and 95% (18/19) and 97% (36/37), respectively, would recommend iPrevent to others, although 53% (10/19) clinicians and 27% (10/37) patients found it too long. Exploratory analyses suggested that iPrevent could improve risk perception, decrease frequency of BC worry, and enhance BC prevention knowledge without changing state anxiety. CONCLUSIONS: The iPrevent prototype demonstrated good usability and acceptability. Because concerns about length could be an implementation barrier, data entry has been abbreviated in the publicly available version of iPrevent.

Testing for Gene-Environment Interactions Using a Prospective Family Cohort Design: Body Mass Index in Early and Later Adulthood and Risk of Breast Cancer.

The ability to classify people according to their underlying genetic susceptibility to a disease is increasing with new knowledge, better family data, and more sophisticated risk prediction models, allowing for more effective prevention and screening. To do so, however, we need to know whether risk associations are the same for people with different genetic susceptibilities. To illustrate one way to estimate such gene-environment interactions, we used prospective data from 3 Australian family cancer cohort studies, 2 enriched for familial risk of breast cancer. There were 288 incident breast cancers in 9,126 participants from 3,222 families. We used Cox proportional hazards models to investigate whether associations of breast cancer with body mass index (BMI; weight (kg)/height (m)2) at age 18-21 years, BMI at baseline, and change in BMI differed according to genetic risk based on lifetime breast cancer risk from birth, as estimated by BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) software, adjusted for age at baseline data collection. Although no interactions were statistically significant, we have demonstrated the power with which gene-environment interactions can be investigated using a cohort enriched for persons with increased genetic risk and a continuous measure of genetic risk based on family history.

Mammographic density defined by higher than conventional brightness thresholds better predicts breast cancer risk.

Background: Mammographic density defined by the conventional pixel brightness threshold, and adjusted for age and body mass index (BMI), is a well-established risk factor for breast cancer. We asked if higher thresholds better separate women with and without breast cancer. Methods: We studied Australian women, 354 with breast cancer over-sampled for early-onset and family history, and 944 unaffected controls frequency-matched for age at mammogram. We measured mammographic dense area and percent density using the CUMULUS software at the conventional threshold, which we call Cumulus , and at two increasingly higher thresholds, which we call Altocumulus and Cirrocumulus , respectively. All measures were Box-Cox transformed and adjusted for age and BMI. We estimated the odds per adjusted standard deviation (OPERA) using logistic regression and the area under the receiver operating characteristic curve (AUC). Results: Altocumulus and Cirrocumulus were correlated with Cumulus (r ∼ 0.8 and 0.6 , respectively) . For dense area, the OPERA was 1.62, 1.74 and 1.73 for Cumulus, Altocumulus and Cirrocumulus , respectively (all P < 0.001). After adjusting for Altocumulus and Cirrocumulus , Cumulus was not significant ( P > 0.6). The OPERAs for percent density were less but gave similar findings. The mean of the standardized adjusted Altocumulus and Cirrocumulus dense area measures was the best predictor; OPERA = 1.87 [95% confidence interval (CI): 1.64-2.14] and AUC = 0.68 (0.65-0.71). Conclusions: The areas of higher mammographically dense regions are associated with almost 30% stronger breast cancer risk gradient, explain the risk association of the conventional measure and might be more aetiologically important. This has substantial implications for clinical translation and molecular, genetic and epidemiological research.

The CRISP colorectal cancer risk prediction tool: an exploratory study using simulated consultations in Australian primary care.

BACKGROUND: In Australia, screening for colorectal cancer (CRC) with colonoscopy is meant to be reserved for people at increased risk, however, currently there is a mismatch between individuals' risk of CRC and the type of CRC screening they receive. This paper describes the development and optimisation of a Colorectal cancer RISk Prediction tool ('CRISP') for use in primary care. The aim of the CRISP tool is to increase risk-appropriate CRC screening. METHODS: CRISP development was informed by previous experience with developing risk tools for use in primary care and a systematic review of the evidence. A CRISP prototype was used in simulated consultations by general practitioners (GPs) with actors as patients. GPs were interviewed to explore their experience of using CRISP, and practice nurses (PNs) and practice managers (PMs) were interviewed after a demonstration of CRISP. Transcribed interviews and video footage of the 'consultations' were qualitatively analyzed. Themes arising from the data were mapped onto Normalization Process Theory (NPT). RESULTS: Fourteen GPs, nine PNs and six PMs were recruited from 12 clinics. Results were described using the four constructs of NPT: 1) Coherence: Clinicians understood the rationale behind CRISP, particularly since they were familiar with using risk tools for other conditions; 2) Cognitive participation: GPs welcomed the opportunity CRISP provided to discuss healthy and unhealthy behaviors with their patients, but many GPs challenged the screening recommendation generated by CRISP; 3) Collective Action: CRISP disrupted clinician-patient flow if the GP was less comfortable with computers. GP consultation time was a major implementation barrier and overall consensus was that PNs have more capacity and time to use CRISP effectively; 4) Reflexive monitoring: Limited systematic monitoring of new interventions is a potential barrier to the sustainable embedding of CRISP. CONCLUSIONS: CRISP has the potential to improve risk-appropriate CRC screening in primary care but was considered more likely to be successfully implemented as a nurse-led intervention.

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.

Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry.

BACKGROUND: The extent to which clinical breast cancer risk prediction models can be improved by including information on known susceptibility SNPs is not known. METHODS: Using 750 cases and 405 controls from the population-based Australian Breast Cancer Family Registry who were younger than 50 years at diagnosis and recruitment, respectively, Caucasian and not BRCA1 or BRCA2 mutation carriers, we derived absolute 5-year risks of breast cancer using the BOADICEA, BRCAPRO, BCRAT, and IBIS risk prediction models and combined these with a risk score based on 77 independent risk-associated SNPs. We used logistic regression to estimate the OR per adjusted SD for log-transformed age-adjusted 5-year risks. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. We also constructed reclassification tables and calculated the net reclassification improvement. RESULTS: The ORs for BOADICEA, BRCAPRO, BCRAT, and IBIS were 1.80, 1.75, 1.67, and 1.30, respectively. When combined with the SNP-based score, the corresponding ORs were 1.96, 1.89, 1.80, and 1.52. The corresponding AUCs were 0.66, 0.65, 0.64, and 0.57 for the risk prediction models, and 0.70, 0.69, 0.66, and 0.63 when combined with the SNP-based score. CONCLUSIONS: By combining a 77 SNP-based score with clinical models, the AUC for predicting breast cancer before age 50 years improved by >20%. IMPACT: Our estimates of the increased performance of clinical risk prediction models from including genetic information could be used to inform targeted screening and prevention.

Assessing and managing breast cancer risk: clinicians' current practice and future needs.

Decision support tools for the assessment and management of breast cancer risk may improve uptake of prevention strategies. End-user input in the design of such tools is critical to increase clinical use. Before developing such a computerized tool, we examined clinicians' practice and future needs. Twelve breast surgeons, 12 primary care physicians and 5 practice nurses participated in 4 focus groups. These were recorded, coded, and analyzed to identify key themes. Participants identified difficulties assessing risk, including a lack of available tools to standardize practice. Most expressed confidence identifying women at potentially high risk, but not moderate risk. Participants felt a tool could especially reassure young women at average risk. Desirable features included: evidence-based, accessible (e.g. web-based), and displaying absolute (not relative) risks in multiple formats. The potential to create anxiety was a concern. Development of future tools should address these issues to optimize translation of knowledge into clinical practice.

Misperceptions of ovarian cancer risk in women at increased risk for hereditary ovarian cancer.

This study assessed the sociodemographic, medical and psychological predictors of accuracy of perceived risk in women at increased genetic risk for ovarian cancer. Women participating in a large cohort study who were at increased risk of ovarian and fallopian tube cancer, had no personal history of cancer and had ≥1 ovary in situ at cohort enrollment, were eligible. Women completed self-administered questionnaires and attended an interview at enrollment. Of 2,868 women unaffected with cancer at cohort enrollment, 561 were eligible. 335 women (59.8 %) overestimated their ovarian cancer risk, while 215 women (38.4 %) accurately estimated their risk, and 10 (1.8 %) underestimated it. Women who did not know their mutation status were more likely to overestimate their risk (OR 1.74, 95 % CI 1.10, 2.77, p = 0.018), as were those with higher cancer-specific anxiety (OR 1.05, 95 % CI 1.02, 1.08, p < 0.001) and/or a mother who had been diagnosed with ovarian cancer (OR 1.98, 95 % CI 1.23, 3.18, p = 0.005). Amongst the group of women who did not know their mutation status, 63.3 % overestimated their risk and the mean perceived lifetime risk of developing ovarian cancer was 42.1 %, compared to a mean objective risk of 6.4 %. A large number of women at increased risk for ovarian cancer overestimate their risk. This is of concern especially in women who are at moderately increased risk only; for this sub-group of women, interventions are needed to reduce potentially unnecessary psychological distress and minimise engagement in unnecessary surgery or screening.

Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model.

It has been shown that, for women aged 50 years or older, the discriminatory accuracy of the Breast Cancer Risk Prediction Tool (BCRAT) can be modestly improved by the inclusion of information on common single nucleotide polymorphisms (SNPs) that are associated with increased breast cancer risk. We aimed to determine whether a similar improvement is seen for earlier onset disease. We used the Australian Breast Cancer Family Registry to study a population-based sample of 962 cases aged 35-59 years, and 463 controls frequency matched for age and for whom genotyping data was available. Overall, the inclusion of data on seven SNPs improved the area under the receiver operating characteristic curve (AUC) from 0.58 (95 % confidence interval [CI] 0.55-0.61) for BCRAT alone to 0.61 (95 % CI 0.58-0.64) for BCRAT and SNP data combined (p < 0.001). For women aged 35-39 years at interview, the corresponding improvement in AUC was from 0.61 (95 % CI 0.56-0.66) to 0.65 (95 % CI 0.60-0.70; p = 0.03), while for women aged 40-49 years at diagnosis, the AUC improved from 0.61 (95 % CI 0.55-0.66) to 0.63 (95 % CI 0.57-0.69; p = 0.04). Using previously used classifications of low, intermediate and high risk, 2.1 % of cases and none of the controls aged 35-39 years, and 10.9 % of cases and 4.0 % of controls aged 40-49 years were classified into a higher risk group. Including information on seven SNPs associated with breast cancer risk, improves the discriminatory accuracy of BCRAT for women aged 35-39 years and 40-49 years. Given, the low absolute risk for women in these age groups, only a small proportion are reclassified into a higher category for predicted 5-year risk of breast cancer.

Cancer risk in 680,000 people exposed to computed tomography scans in childhood or adolescence: data linkage study of 11 million Australians.

OBJECTIVE: To assess the cancer risk in children and adolescents following exposure to low dose ionising radiation from diagnostic computed tomography (CT) scans. DESIGN: Population based, cohort, data linkage study in Australia. COHORT MEMBERS: 10.9 million people identified from Australian Medicare records, aged 0-19 years on 1 January 1985 or born between 1 January 1985 and 31 December 2005; all exposures to CT scans funded by Medicare during 1985-2005 were identified for this cohort. Cancers diagnosed in cohort members up to 31 December 2007 were obtained through linkage to national cancer records. MAIN OUTCOME: Cancer incidence rates in individuals exposed to a CT scan more than one year before any cancer diagnosis, compared with cancer incidence rates in unexposed individuals. RESULTS: 60,674 cancers were recorded, including 3150 in 680,211 people exposed to a CT scan at least one year before any cancer diagnosis. The mean duration of follow-up after exposure was 9.5 years. Overall cancer incidence was 24% greater for exposed than for unexposed people, after accounting for age, sex, and year of birth (incidence rate ratio (IRR) 1.24 (95% confidence interval 1.20 to 1.29); P<0.001). We saw a dose-response relation, and the IRR increased by 0.16 (0.13 to 0.19) for each additional CT scan. The IRR was greater after exposure at younger ages (P<0.001 for trend). At 1-4, 5-9, 10-14, and 15 or more years since first exposure, IRRs were 1.35 (1.25 to 1.45), 1.25 (1.17 to 1.34), 1.14 (1.06 to 1.22), and 1.24 (1.14 to 1.34), respectively. The IRR increased significantly for many types of solid cancer (digestive organs, melanoma, soft tissue, female genital, urinary tract, brain, and thyroid); leukaemia, myelodysplasia, and some other lymphoid cancers. There was an excess of 608 cancers in people exposed to CT scans (147 brain, 356 other solid, 48 leukaemia or myelodysplasia, and 57 other lymphoid). The absolute excess incidence rate for all cancers combined was 9.38 per 100,000 person years at risk, as of 31 December 2007. The average effective radiation dose per scan was estimated as 4.5 mSv. CONCLUSIONS: The increased incidence of cancer after CT scan exposure in this cohort was mostly due to irradiation. Because the cancer excess was still continuing at the end of follow-up, the eventual lifetime risk from CT scans cannot yet be determined. Radiation doses from contemporary CT scans are likely to be lower than those in 1985-2005, but some increase in cancer risk is still likely from current scans. Future CT scans should be limited to situations where there is a definite clinical indication, with every scan optimised to provide a diagnostic CT image at the lowest possible radiation dose.