The Risk of Cancer Risk Prediction: “What Is My Risk of Getting Breast Cancer?”

Development of Malignancy-Risk Gene Signature Assay for Predicting Breast Cancer Risk

BACKGROUND

Breast cancer (BC) risk assessment models are statistical estimates based on patient characteristics. We developed a gene expression assay to assess BC risk using benign breast biopsy tissue.

METHODS

A NanoString-based malignancy risk (MR) gene signature was validated for formalin-fixed paraffin-embedded (FFPE) tissue. It was applied to FFPE benign and BC specimens obtained from women who underwent breast biopsy, some of whom developed BC during follow-up to evaluate diagnostic capability of the MR signature. BC risk was calculated with MR score, Gail risk score, and both tests combined. Logistic regression and receiver operating characteristic curves were used to evaluate these 3 models.

RESULTS

NanoString MR demonstrated concordance between fresh frozen and FFPE malignant samples (r = 0.99). Within the validation set, 563 women with benign breast biopsies from 2007 to 2011 were identified and followed for at least 5 y; 50 women developed BC (affected) within 5 y from biopsy. Three groups were compared: benign tissue from unaffected and affected patients and malignant tissue from affected patients. Kruskal-Wallis test suggested difference between the groups (P = 0.09) with trend in higher predicted MR score for benign tissue from affected patients before development of BC. Neither the MR signature nor Gail risk score were statistically different between affected and unaffected patients; combining both tests demonstrated best predictive value (AUC = 0.71).

CONCLUSIONS

FFPE gene expression assays can be used to develop a predictive test for BC. Further investigation of the combined MR signature and Gail Model is required. Our assay was limited by scant cellularity of archived breast tissue.

Predicting breast cancer risk using personal health data and machine learning models

Among women, breast cancer is a leading cause of death. Breast cancer risk predictions can inform screening and preventative actions. Previous works found that adding inputs to the widely-used Gail model improved its ability to predict breast cancer risk. However, these models used simple statistical architectures and the additional inputs were derived from costly and / or invasive procedures. By contrast, we developed machine learning models that used highly accessible personal health data to predict five-year breast cancer risk. We created machine learning models using only the Gail model inputs and models using both Gail model inputs and additional personal health data relevant to breast cancer risk. For both sets of inputs, six machine learning models were trained and evaluated on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial data set. The area under the receiver operating characteristic curve metric quantified each model’s performance. Since this data set has a small percentage of positive breast cancer cases, we also reported sensitivity, specificity, and precision. We used Delong tests (p < 0.05) to compare the testing data set performance of each machine learning model to that of the Breast Cancer Risk Prediction Tool (BCRAT), an implementation of the Gail model. None of the machine learning models with only BCRAT inputs were significantly stronger than the BCRAT. However, the logistic regression, linear discriminant analysis, and neural network models with the broader set of inputs were all significantly stronger than the BCRAT. These results suggest that relative to the BCRAT, additional easy-to-obtain personal health inputs can improve five-year breast cancer risk prediction. Our models could be used as non-invasive and cost-effective risk stratification tools to increase early breast cancer detection and prevention, motivating both immediate actions like screening and long-term preventative measures such as hormone replacement therapy and chemoprevention.

Prediction and clinical utility of a contralateral breast cancer risk model

BACKGROUND

Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making.

METHODS

We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility.

RESULTS

In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.

CONCLUSIONS

We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.

Do population-level risk prediction models that use routinely collected health data reliably predict individual risks?

The objective of this study was to assess the reliability of individual risk predictions based on routinely collected data considering the heterogeneity between clinical sites in data and populations. Cardiovascular disease (CVD) risk prediction with QRISK3 was used as exemplar. The study included 3.6 million patients in 392 sites from the Clinical Practice Research Datalink. Cox models with QRISK3 predictors and a frailty (random effect) term for each site were used to incorporate unmeasured site variability. There was considerable variation in data recording between general practices (missingness of body mass index ranged from 18.7% to 60.1%). Incidence rates varied considerably between practices (from 0.4 to 1.3 CVD events per 100 patient-years). Individual CVD risk predictions with the random effect model were inconsistent with the QRISK3 predictions. For patients with QRISK3 predicted risk of 10%, the 95% range of predicted risks were between 7.2% and 13.7% with the random effects model. Random variability only explained a small part of this. The random effects model was equivalent to QRISK3 for discrimination and calibration. Risk prediction models based on routinely collected health data perform well for populations but with great uncertainty for individuals. Clinicians and patients need to understand this uncertainty.

Performance of the Breast Cancer Risk Assessment Tool Among Women Age 75 Years and Older

BACKGROUND

The Breast Cancer Risk Assessment Tool (BCRAT, "Gail model") is commonly used for breast cancer prediction; however, it has not been validated for women age 75 years and older.

METHODS

We used Nurses' Health Study (NHS) data beginning in 2004 and Women's Health Initiative (WHI) data beginning in 2005 to compare BCRAT's performance among women age 75 years and older with that in women age 55 to 74 years in predicting five-year breast cancer incidence. BCRAT risk factors include: age, race/ethnicity, age at menarche, age at first birth, family history, history of benign breast biopsy, and atypia. We examined BCRAT's calibration by age by comparing expected/observed (E/O) ratios of breast cancer incidence. We examined discrimination by computing c-statistics for the model by age. All statistical tests were two-sided.

RESULTS

Seventy-three thousand seventy-two NHS and 97 081 WHI women participated. NHS participants were more likely to be non-Hispanic white (96.2% vs 84.7% in WHI, P < .001) and were less likely to develop breast cancer (1.8% vs 2.0%, P = .02). E/O ratios by age in NHS were 1.16 (95% confidence interval [CI] = 1.09 to 1.23, age 57-74 years) and 1.31 (95% CI = 1.18 to 1.45, age ≥ 75 years, P = .02), and in WHI 1.03 (95% CI = 0.97 to 1.09, age 55-74 years) and 1.10 (95% CI = 1.00 to 1.21, age ≥ 75 years, P = .21). E/O ratio 95% confidence intervals crossed one among women age 75 years and older when samples were limited to women who underwent mammography and were without significant illness. C-statistics ranged between 0.56 and 0.58 in both cohorts regardless of age.

CONCLUSIONS

BCRAT accurately predicted breast cancer for women age 75 years and older who underwent mammography and were without significant illness but had modest discrimination. Models that consider individual competing risks of non-breast cancer death may improve breast cancer risk prediction for older women.

Risk Prediction for Local Breast Cancer Recurrence Among Women with DCIS Treated in a Community Practice: A Nested, Case-Control Study

BACKGROUND

Various patient, treatment, and pathologic factors have been associated with an increased risk of local recurrence (LR) following breast-conserving therapy (BCT) for ductal carcinoma in situ (DCIS). However, the strength and importance of individual factors has varied; whether combining factors improves prediction, particularly in community practice, is uncertain. In a large, population-based cohort of women with DCIS treated with BCT in three community-based practices, we assessed the validity of the Memorial Sloan-Kettering Cancer Center (MSKCC) DCIS nomogram, which combines clinical, pathologic, and treatment features to predict LR.

METHODS

We reviewed slides of patients with unilateral DCIS treated with BCT. Regression methods were used to estimate risks of LR. The MSKCC DCIS nomogram was applied to the study population to compare the nomogram-predicted and observed LR at 5 and 10 years.

RESULTS

The 495 patients in our study were grouped into quartiles and octiles to compare observed and nomogram-predicted LR. The 5-year absolute risk of recurrence for lowest and highest quartiles was 4.8 and 33.1 % (95 % CI 3.1-6.4 and 24.2-40.9, respectively; p < 0.0001). The overall correlation between 10-year nomogram-predicted recurrences and observed recurrences was 0.95. Compared with observed 10-year LR rates, the risk estimates provided by the nomogram showed good correlation, and reasonable discrimination with a c-statistic of 0.68.

CONCLUSIONS

The MSKCC DCIS nomogram provided good prediction of the 5- and 10-year LR when applied to a population of patients with DCIS treated with BCT in a community-based practice. This nomogram, therefore, is a useful treatment decision aid for patients with DCIS.

Breast cancer risk and early diagnosis applications in Turkish women aged 50 and over

BACKGROUND

The aim of the study was to determine breast cancer risk and early diagnosis applications in women aged ≥50.

MATERIALS AND METHODS

This cross-sectional, descriptive field study focused on a population of 4,815 in Mansurog?lu with a 55.1% participation rate in screening. In the study, body mass index (BMI) was also evaluated in the calculation of breast cancer risk by the Breast Cancer Risk Assessment Tool (BCRA) (also called the "Gail Risk Assessment Tool") . The interviewers had a three-hour training provided by the researchers, during which interactive training methods were used and applications were supported with role-plays.

RESULTS

The mean age of the women participating in the study was 60.1±8.80. Of these women, 57.3% were in the 50-59 age group, 71.7% were married, 57.3% were primary school graduates and 61.7% were housewives. Breast-cancer development rate was 7.4% in the women participating in the study. When they were evaluated according to their relationship with those with breast cancer, it was determined that 73.0% of them had first- degree relatives with breast cancer. According to the assessment based on the Gail method, the women's breast cancer development risk within the next 5 years was 17.6%, whereas their calculated lifetime risk was found to be as low as 0.2%. Statistically significant differences (P=0.000) were determined between performing BSE - CBE and socio-demographic factors.

CONCLUSIONS

It was determined that 17.6% of the participants had breast cancer risk. There was no statistically significant difference between the women with and without breast cancer risk in terms of early diagnosis practices, which can be regarded as a remarkable finding. It was planned to provide training about the early diagnosis and treatment of breast cancer for people with high-risk scores, and to conduct population-based breast cancer screening programs.

From cancer screening to treatment: service delivery and referral in the National Breast and Cervical Cancer Early Detection Program

The National Breast and Cervical Cancer Early Detection Program (NBCCEDP) provides breast and cervical cancer screening and diagnostic services to low-income and underserved women through a network of providers and health care organizations. Although the program serves women 40-64 years old for breast cancer screening and 21-64 years old for cervical cancer screening, the priority populations are women 50-64 years old for breast cancer and women who have never or rarely been screened for cervical cancer. From 1991 through 2011, the NBCCEDP provided screening and diagnostic services to more than 4.3 million women, diagnosing 54,276 breast cancers, 2554 cervical cancers, and 123,563 precancerous cervical lesions. A critical component of providing screening services is to ensure that all women with abnormal screening results receive appropriate and timely diagnostic evaluations. Case management is provided to assist women with overcoming barriers that would delay or prevent follow-up care. Women diagnosed with cancer receive treatment through the states' Breast and Cervical Cancer Treatment Programs (a special waiver for Medicaid) if they are eligible. The NBCCEDP has performance measures that serve as benchmarks to monitor the completeness and timeliness of care. More than 90% of the women receive complete diagnostic care and initiate treatment less than 30 days from the time of their diagnosis. Provision of effective screening and diagnostic services depends on effective program management, networks of providers throughout the community, and the use of evidence-based knowledge, procedures, and technologies.

t(14;18) Translocation: A predictive blood biomarker for follicular lymphoma

PURPOSE

The (14;18) translocation constitutes both a genetic hallmark and critical early event in the natural history of follicular lymphoma (FL). However, t(14;18) is also detectable in the blood of otherwise healthy persons, and its relationship with progression to disease remains unclear. Here we sought to determine whether t(14;18)-positive cells in healthy individuals represent tumor precursors and whether their detection could be used as an early predictor for FL.

PARTICIPANTS AND METHODS

Among 520,000 healthy participants enrolled onto the EPIC (European Prospective Investigation Into Cancer and Nutrition) cohort, we identified 100 who developed FL 2 to 161 months after enrollment. Prediagnostic blood from these and 218 controls were screened for t(14;18) using sensitive polymerase chain reaction-based assays. Results were subsequently validated in an independent cohort (65 case participants; 128 controls). Clonal relationships between t(14;18) cells and FL were also assessed by molecular backtracking of paired prediagnostic blood and tumor samples.

RESULTS

Clonal analysis of t(14;18) junctions in paired prediagnostic blood versus tumor samples demonstrated that progression to FL occurred from t(14;18)-positive committed precursors. Furthermore, healthy participants at enrollment who developed FL up to 15 years later showed a markedly higher t(14;18) prevalence and frequency than controls (P < .001). Altogether, we estimated a 23-fold higher risk of subsequent FL in blood samples associated with a frequency > 10(-4) (odds ratio, 23.17; 95% CI, 9.98 to 67.31; P < .001). Remarkably, risk estimates remained high and significant up to 15 years before diagnosis.

CONCLUSION

High t(14;18) frequency in blood from healthy individuals defines the first predictive biomarker for FL, effective years before diagnosis.

How well does family history predict who will get colorectal cancer? Implications for cancer screening and counseling

PURPOSE

Using a large, retrospective cohort from the Utah Population Database, we assess how well family history predicts who will acquire colorectal cancer during a 20-year period.

METHODS

Individuals were selected between ages 35 and 80 with no prior record of colorectal cancer diagnosis, as of the year 1985. Numbers of colorectal cancer-affected relatives and diagnosis ages were collected. Familial relative risk and absolute risk estimates were calculated. Colorectal cancer diagnoses in the cohort were counted between years 1986 and 2005. Cox regression and Harrell's C were used to measure the discriminatory power of resulting models.

RESULTS

A total of 431,153 individuals were included with 5,334 colorectal cancer diagnoses. Familial relative risk ranged from 0.83 to 12.39 and 20-year absolute risk from 0.002 to 0.21. With familial relative risk as the only predictor, Harrell's C = 0.53 and with age only, Harrell's C = 0.66. Familial relative risk combined with age produced a Harrell's C = 0.67.

CONCLUSION

Family history by itself is not a strong predictor of exactly who will acquire colorectal cancer within 20 years. However, stratification of risk using absolute risk probabilities may be more helpful in focusing screening on individuals who are more likely to develop the disease.

Breast cancer screening: a 35-year perspective

Screening for breast cancer has been evaluated by 9 randomized trials over 5 decades and recommended by major guideline groups for more than 3 decades. Successes and lessons for cancer screening from this history include development of scientific methods to evaluate screening, by the Canadian Task Force on the Periodic Health Examination and the U.S. Preventive Services Task Force; the importance of randomized trials in the past, and the increasing need to develop new methods to evaluate cancer screening in the future; the challenge of assessing new technologies that are replacing originally evaluated screening tests; the need to measure false-positive screening test results and the difficulty in reducing their frequency; the unexpected emergence of overdiagnosis due to cancer screening; the difficulty in stratifying individuals according to breast cancer risk; women's fear of breast cancer and the public outrage over changing guidelines for breast cancer screening; the need for population scientists to better communicate with the public if evidence-based recommendations are to be heeded by clinicians, patients, and insurers; new developments in the primary prevention of cancers; and the interaction between improved treatment and screening, which, over time, and together with primary prevention, may decrease the need for cancer screening.

Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information

BACKGROUND

The Gail model is widely used for the assessment of risk of invasive breast cancer based on recognized clinical risk factors. In recent years, a substantial number of single-nucleotide polymorphisms (SNPs) associated with breast cancer risk have been identified. However, it remains unclear how to effectively integrate clinical and genetic risk factors for risk assessment.

METHODS

Seven SNPs associated with breast cancer risk were selected from the literature and genotyped in white non-Hispanic women in a nested case-control cohort of 1664 case patients and 1636 control subjects within the Women's Health Initiative Clinical Trial. SNP risk scores were computed based on previously published odds ratios assuming a multiplicative model. Combined risk scores were calculated by multiplying Gail risk estimates by the SNP risk scores. The independence of Gail risk and SNP risk was evaluated by logistic regression. Calibration of relative risks was evaluated using the Hosmer-Lemeshow test. The performance of the combined risk scores was evaluated using receiver operating characteristic curves. The net reclassification improvement (NRI) was used to assess improvement in classification of women into low (<1.5%), intermediate (1.5%-2%), and high (>2%) categories of 5-year risk. All tests of statistical significance were two-sided.

RESULTS

The SNP risk score was nearly independent of Gail risk. There was good agreement between predicted and observed SNP relative risks. In the analysis for receiver operating characteristic curves, the combined risk score was more discriminating, with area under the curve of 0.594 compared with area under the curve of 0.557 for Gail risk alone (P < .001). Classification also improved for 5.6% of case patients and 2.9% of control subjects, showing an NRI value of 0.085 (P = 1.0 × 10⁻⁵). Focusing on women with intermediate Gail risk resulted in an improved NRI of 0.195 (P = 8.6 × 10⁻⁵).

CONCLUSIONS

Combining validated common genetic risk factors with clinical risk factors resulted in modest improvement in classification of breast cancer risks in white non-Hispanic postmenopausal women. Classification performance was further improved by focusing on women at intermediate risk.

Independent association of lobular involution and mammographic breast density with breast cancer risk

Background

Lobular involution, or age-related atrophy of breast lobules, is inversely associated with breast cancer risk, and mammographic breast density (MBD) is positively associated with breast cancer risk.

Methods

To evaluate whether lobular involution and MBD are independently associated with breast cancer risk in women with benign breast disease, we performed a nested cohort study among women (n = 2666) with benign breast disease diagnosed at Mayo Clinic between January 1, 1985, and December 31, 1991 and a mammogram available within 6 months of the diagnosis. Women were followed up for an average of 13.3 years to document any breast cancer incidence. Lobular involution was categorized as none, partial, or complete; parenchymal pattern was classified using the Wolfe classification as N1 (nondense), P1, P2 (ductal prominence occupying <25%, or >25% of the breast, respectively), or DY (extremely dense). Hazard ratios (HRs) and 95% confidence intervals (CIs) to assess associations of lobular involution and MBD with breast cancer risk were estimated using adjusted Cox proportional hazards model. All tests of statistical significance were two-sided.

Results

After adjustment for MBD, having no or partial lobular involution was associated with a higher risk of breast cancer than having complete involution (none: HR of breast cancer incidence = 2.62, 95% CI = 1.39 to 4.94; partial: HR of breast cancer incidence = 1.61, 95% CI = 1.03 to 2.53; P_trend = .002). Similarly, after adjustment for involution, having dense breasts was associated with higher risk of breast cancer than having nondense breasts (for DY: HR of breast cancer incidence = 1.67, 95% CI = 1.03 to 2.73; for P2: HR of breast cancer incidence = 1.96, 95% CI = 1.20 to 3.21; for P1: HR of breast cancer incidence = 1.23, 95% CI = 0.67 to 2.26; P_trend = .02). Having a combination of no involution and dense breasts was associated with higher risk of breast cancer than having complete involution and nondense breasts (HR of breast cancer incidence = 4.08, 95% CI = 1.72 to 9.68; P = .006).

Conclusion

Lobular involution and MBD are independently associated with breast cancer incidence; combined, they are associated with an even greater risk for breast cancer.

Limitations of diagnostic precision and predictive utility in the individual case: a challenge for forensic practice

Knowledge of group tendencies may not assist accurate predictions in the individual case. This has importance for forensic decision making and for the assessment tools routinely applied in forensic evaluations. In this article, we applied Monte Carlo methods to examine diagnostic agreement with different levels of inter-rater agreement given the distributional characteristics of PCL-R scores. Diagnostic agreement and score agreement were substantially less than expected. In addition, we examined the confidence intervals associated with individual predictions of violent recidivism. On the basis of empirical findings, statistical theory, and logic, we conclude that predictions of future offending cannot be achieved in the individual case with any degree of confidence. We discuss the problems identified in relation to the PCL-R in terms of the broader relevance to all instruments used in forensic decision making.

The National Cancer Institute of Canada Clinical Trials Group MAP.3 trial: an international breast cancer prevention trial

Several large phase iii trials have demonstrated that tamoxifen—and more recently, raloxifene—can effectively reduce the incidence of invasive breast cancer by 50%. However, these selective estrogen receptor modulators can also be associated with several rare, but serious, adverse events. Recently, the third-generation aromatase inhibitors (ais) have demonstrated excellent efficacy in adjuvant breast cancer trials, and they show particular promise in the breast cancer prevention setting. The National Cancer Institute of Canada Clinical Trials Group (ncic ctg) has developed a randomized phase iii study to determine the efficacy of an ai (exemestane) to reduce the incidence of invasive breast cancer in postmenopausal women at an increased risk for developing breast cancer. The ncic ctg map.3 (ExCel) trial is a double-blind placebo-controlled multicentre, multinational trial. Based on the known preclinical and clinical profile of the ais, a greater reduction in breast cancer incidence with fewer side effects is hypothesized with this class of agents than with tamoxifen or raloxifene.

Novel breast tissue feature strongly associated with risk of breast cancer

PURPOSE

Accurate, individualized risk prediction for breast cancer is lacking. Tissue-based features may help to stratify women into different risk levels. Breast lobules are the anatomic sites of origin of breast cancer. As women age, these lobular structures should regress, which results in reduced breast cancer risk. However, this does not occur in all women.

METHODS

We have quantified the extent of lobule regression on a benign breast biopsy in 85 patients who developed breast cancer and 142 age-matched controls from the Mayo Benign Breast Disease Cohort, by determining number of acini per lobule and lobular area. We also calculated Gail model 5-year predicted risks for these women.

RESULTS

There is a step-wise increase in breast cancer risk with increasing numbers of acini per lobule (P = .0004). Adjusting for Gail model score, parity, histology, and family history did not attenuate this association. Lobular area was similarly associated with risk. The Gail model estimates were associated with risk of breast cancer (P = .03). We examined the individual accuracy of these measures using the concordance (c) statistic. The Gail model c statistic was 0.60 (95% CI, 0.50 to 0.70); the acinar count c statistic was 0.65 (95% CI, 0.54 to 0.75). Combining acinar count and lobular area, the c statistic was 0.68 (95% CI, 0.58 to 0.78). Adding the Gail model to these measures did not improve the c statistic.

CONCLUSION

Novel, tissue-based features that reflect the status of a woman's normal breast lobules are associated with breast cancer risk. These features may offer a novel strategy for risk prediction.

Overview of risk prediction models in cardiovascular disease research

Many risk prediction models have been developed for cardiovascular diseases in different countries during the past three decades. However, there has not been consistent agreement regarding how to appropriately assess a risk prediction model, especially when new markers are added to an established risk prediction model. Researchers often use the area under the receiver operating characteristic curve (ROC) to assess the discriminatory ability of a risk prediction model. However, recent studies suggest that this method has serious limitations and cannot be the sole approach to evaluate the usefulness of a new marker in clinical and epidemiological studies. To overcome the shortcomings of this traditional method, new assessment methods have been proposed. The aim of this article is to overview various risk prediction models for cardiovascular diseases, to describe the receiver operating characteristic curve method and discuss some new assessment methods proposed recently. Some of the methods were illustrated with figures from a cardiovascular disease study in Australia.

Impact of socioeconomic, behavioral and clinical risk factors on mortality

This study investigates the relative contributions of socioeconomic status (SES), behavioral and clinical risk factors on mortality. The Third National Health and Nutrition Survey Linked Mortality File was used to examine the association of SES (race, insurance, education, income), behavioral (smoking, obesity, physical activity), and clinical (elevated blood pressure, triglyceride level, lipid levels, C-reactive protein (CRP)) risk factors with 6-12-year all-cause mortality. Respondents were stratified by known chronic diseases into one of the following categories: no chronic disease, non-cardiovascular chronic disease, cardiovascular disease, and diabetes. The overall weighted mortality rate was 9.5% with the highest mortality rate among diabetics. Race, insurance coverage, income, smoking status, inadequate physical activity, elevated blood pressure and elevated CRP were independently associated with mortality in the overall population. When stratified by chronic disease, SES factors remained associated with mortality, most strongly in the healthy population. Current smoking and inadequate physical activity were also associated with mortality across disease groups while clinical risk factors were less consistent. SES factors, health behaviors and clinical risk factors were all associated with mortality even when baseline health status and chronic diseases are taken into account. Efforts to reduce mortality will require a multi-faceted approach incorporating healthy behaviors and accessible health care systems in addition to clinical advances.

Conceptual problems in laypersons' understanding of individualized cancer risk: a qualitative study

OBJECTIVE

To explore laypersons' understanding of individualized cancer risk estimates, and to identify conceptual problems that may limit this understanding.

BACKGROUND

Risk prediction models are increasingly used to provide people with information about their individual risk of cancer and other diseases. However, laypersons may have difficulty understanding individualized risk information, because of conceptual as well as computational problems.

DESIGN

A qualitative study was conducted using focus groups. Semi-structured interviews explored participants' understandings of the concept of risk, and their interpretations of a hypothetical individualized colorectal cancer risk estimate.

SETTING AND PARTICIPANTS

Eight focus groups were conducted with 48 adults aged 50-74 years residing in two major US metropolitan areas. Participants had high school or greater education, some familiarity with information technology, and no personal or family history of cancer.

RESULTS

Several important conceptual problems were identified. Most participants thought of risk not as a neutral statistical concept, but as signifying danger and emotional threat, and viewed cancer risk in terms of concrete risk factors rather than mathematical probabilities. Participants had difficulty acknowledging uncertainty implicit to the concept of risk, and judging the numerical significance of individualized risk estimates. The most challenging conceptual problems related to conflict between subjective and objective understandings of risk, and difficulties translating aggregate-level objective risk estimates to the individual level.

CONCLUSIONS

Several conceptual problems limit laypersons' understanding of individualized cancer risk information. These problems have implications for future research on health numeracy, and for the application of risk prediction models in clinical and public health settings.

American Society of Clinical Oncology policy statement: the role of the oncologist in cancer prevention and risk assessment

Oncologists have a critical opportunity to utilize risk assessment and cancer prevention strategies to interrupt the initiation or progression of cancer in cancer survivors and individuals at high risk of developing cancer. Expanding knowledge about the natural history and prognosis of cancers positions oncologists to advise patients regarding the risk of second malignancies and treatment-related cancers. In addition, as recognized experts in the full spectrum of cancer care, oncologists are afforded opportunities for involvement in community-based cancer prevention activities. Although oncologists are currently providing many cancer prevention and risk assessment services to their patients, economic barriers exist, including inadequate or lack of insurance, that may compromise uniform patient access to these services. Additionally, insufficient reimbursement for existing and developing interventions may discourage patient access to these services. The American Society of Clinical Oncology (ASCO), the medical society representing cancer specialists involved in patient care and clinical research, is committed to supporting oncologists in their wide-ranging involvement in cancer prevention. This statement on risk assessment and prevention counseling, although not intended to be a comprehensive overview of cancer prevention describes the current role of oncologists in risk assessment and prevention; provides examples of risk assessment and prevention activities that should be offered by oncologists; identifies potential opportunities for coordination between oncologists and primary care physicians in prevention education and coordination of care for cancer survivors; describes ASCO's involvement in education and training of oncologists regarding prevention; and proposes improvement in the payment environment to encourage patient access to these services.

Assessment of the accuracy of the Gail model in women with atypical hyperplasia

PURPOSE

An accurate estimate of a woman's breast cancer risk is essential for optimal patient counseling and management. Women with biopsy-confirmed atypical hyperplasia of the breast (atypia) are at high risk for breast cancer. The Gail model is widely used in these women, but has not been validated in them.

PATIENTS AND METHODS

Women with atypia were identified from the Mayo Benign Breast Disease (BBD) cohort (1967 to 1991). Their risk factors for breast cancer were obtained, and the Gail model was used to predict 5-year-and follow-up-specific risks for each woman. The predicted and observed numbers of breast cancers were compared, and the concordance between individual risk levels and outcomes was computed.

RESULTS

Of the 9,376 women in the BBD cohort, 331 women had atypia (3.5%). At a mean follow-up of 13.7 years, 58 of 331 (17.5%) patients had developed invasive breast cancer, 1.66 times more than the 34.9 predicted by the Gail model (95% CI, 1.29 to 2.15; P < .001). For individual women, the concordance between predicted and observed outcomes was low, with a concordance statistic of 0.50 (95% CI, 0.44 to 0.55).

CONCLUSION

The Gail model significantly underestimates the risk of breast cancer in women with atypia. Its ability to discriminate women with atypia into those who did and did not develop breast cancer is limited. Health care professionals should be cautious when using the Gail model to counsel individual patients with atypia.

Differences and similarities in breast cancer risk assessment models in clinical practice: which model to choose?

To show differences and similarities between risk estimation models for breast cancer in healthy women from BRCA1/2-negative or untested families. After a systematic literature search seven models were selected: Gail-2, Claus Model, Claus Tables, BOADICEA, Jonker Model, Claus-Extended Formula, and Tyrer-Cuzick. Life-time risks (LTRs) for developing breast cancer were estimated for two healthy counsellees, aged 40, with a variety in family histories and personal risk factors. Comparisons were made with guideline thresholds for individual screening. Without a clinically significant family history LTRs varied from 6.7% (Gail-2 Model) to 12.8% (Tyrer-Cuzick Model). Adding more information on personal risk factors increased the LTRs and yearly mammography will be advised in most situations. Older models (i.e. Gail-2 and Claus) are likely to underestimate the LTR for developing breast cancer as their baseline risk for women is too low. When models include personal risk factors, surveillance thresholds have to be reformulated. For current clinical practice, the Tyrer-Cuzick Model and the BOADICEA Model seem good choices.

Mammographic density, breast cancer risk and risk prediction

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models.