Validation of a breast cancer risk prediction model developed for Black women

Validation of the Nigerian Breast Cancer Study Model for Predicting Individual Breast Cancer Risk in Cameroon and Uganda

BACKGROUND

The Nigerian Breast Cancer Study (NBCS) model is a new risk assessment tool developed for predicting risk of invasive breast cancer in Nigeria. Its applicability outside of Nigeria remains uncertain as it has not been validated in other sub-Saharan Africa populations.

METHODS

We conducted a case-control study among women with breast cancer and controls ascertained in Cameroon and Uganda from 2011 to 2016. Structured questionnaire interviews were performed to collect risk factor characteristics. The NBCS model, the Gail model, the Gail model for Black population, and the Black Women's Health Study model were applied to the Cameroon and Uganda samples separately. Nigerian as well as local incidence rates were incorporated into the models. Receiver-Operating Characteristic analyses were performed to indicate discriminating capacity.

RESULTS

The study included 550 cases (mean age 46.8 ± 11.9) and 509 controls (mean age 46.3 ± 11.7). Compared with the other three models, the NBCS model performed best in both countries. The discriminating accuracy of the NBCS model in Cameroon (age-adjusted C-index = 0.602; 95% CI, 0.542-0.661) was better than in Uganda (age-adjusted C-index = 0.531; 95% CI, 0.459-0.603).

CONCLUSIONS

These findings demonstrate the potential clinical utility of the NBCS model for risk assessment in Cameroon. All currently available models performed poorly in Uganda, which suggests that the NBCS model may need further calibration before use in other regions of Africa.

IMPACT

Differences in risk profiles across the African diaspora underscores the need for larger studies and may require development of region-specific risk assessment tools for breast cancer.

Young Black Women May be More Likely to Have First Mammogram Cancers: A New Perspective in Breast Cancer Disparities

INTRODUCTION

Black women are diagnosed with breast cancer at earlier ages and are 42% more likely to die from the disease than White women. Recommendations for commencement of screening mammography remain discordant. This study sought to determine the frequency of first mammogram cancers among Black women versus other self-reported racial groups.

METHODS

In this retrospective cohort study, clinical and mammographic data were obtained from 738 women aged 40-45 years who underwent treatment for breast cancer between 2010 and 2019 within a single hospital system. First mammogram cancers were defined as those with tissue diagnoses within 3 months of baseline mammogram. Multivariate logistic regression was applied to assess variables associated with first mammogram cancer detection.

RESULTS

Black women were significantly more likely to have first mammogram cancer diagnoses (39/82, 47.6%) compared with White women (162/610, 26.6%) and other groups (16/46, 34.8%) [p < 0.001]. Black women were also more likely to have a body mass index > 30 (p < 0.001), higher clinical T categories (p = 0.02), and present with more advanced clinical stages (p = 0.03). Every month delay in mammographic screening beyond age 40 years (odds ratio [OR] 1.06, 95% confidence interval [CI] 1.05-1.07; p < 0.0001), Black race (OR 2.24, 95% CI 1.10-4.53; p = 0.03), and lack of private insurance (OR 2.41, 95% CI 1.22-4.73; p = 0.01) were associated with an increased likelihood of cancer detection on first mammogram.

CONCLUSION

Our findings suggests that Black women aged 40-45 years may be more likely to have cancer detected on their first mammogram and would benefit from starting screening mammography no later than age 40 years, and for those with elevated lifetime risk, even sooner.

A Validated Risk Prediction Model for Breast Cancer in US Black Women

PURPOSE

Breast cancer risk prediction models are used to identify high-risk women for early detection, targeted interventions, and enrollment into prevention trials. We sought to develop and evaluate a risk prediction model for breast cancer in US Black women, suitable for use in primary care settings.

METHODS

Breast cancer relative risks and attributable risks were estimated using data from Black women in three US population-based case-control studies (3,468 breast cancer cases; 3,578 controls age 30-69 years) and combined with SEER age- and race-specific incidence rates, with incorporation of competing mortality, to develop an absolute risk model. The model was validated in prospective data among 51,798 participants of the Black Women's Health Study, including 1,515 who developed invasive breast cancer. A second risk prediction model was developed on the basis of estrogen receptor (ER)-specific relative risks and attributable risks. Model performance was assessed by calibration (expected/observed cases) and discriminatory accuracy (C-statistic).

RESULTS

The expected/observed ratio was 1.01 (95% CI, 0.95 to 1.07). Age-adjusted C-statistics were 0.58 (95% CI, 0.56 to 0.59) overall and 0.63 (95% CI, 0.58 to 0.68) among women younger than 40 years. These measures were almost identical in the model based on estrogen receptor-specific relative risks and attributable risks.

CONCLUSION

Discriminatory accuracy of the new model was similar to that of the most frequently used questionnaire-based breast cancer risk prediction models in White women, suggesting that effective risk stratification for Black women is now possible. This model may be especially valuable for risk stratification of young Black women, who are below the ages at which breast cancer screening is typically begun.

Breast cancer risk prediction in African women using Random Forest Classifier

INTRODUCTION

One of the most important steps in combating breast cancer is early and accurate diagnosis. Unfortunately, breast cancer is asymptomatic at the early stage, although some symptoms are presented at a later time, but at symptomatic stage treatment could be complicated or even become impossible thereby leading to death. Proper risk assessment is hence very important in reducing mortality. Some computational techniques have been developed for breast cancer risk assessment in the developed world, but such techniques do not work well in Africa because of the difference in risk profiles of African women e.g. later menarche, low drug abuse and low smoking rate.

AIM

In this work, we propose a bespoke risk prediction model for African women using Random Forest Classifier (RFC) machine learning technique.

METHODS

A total of 180 subjects were studied out of which 90 were confirmed cases of breast cancer and 90 were benign. Twenty-five risk factors were included, for example, smoking, alcohol intake, occupational hazards and age at menopause. Four approaches were empirically used in the feature selection, these are the use of Chi-Square, mutual information gain, Spearman correlation and the entire features. RFC algorithm was used to develop the prediction model.

RESULTS

We found that family history of breast cancer, dense breast, deliberate abortion, age at first child, fruit intake and regular exercise are predictors of breast cancer. The RFC model gave an accuracy of 91.67%, sensitivity of 87.10%, specificity of 96.55% and Area under curve (AUC) of 92% when all the risk factors were included in the model while an accuracy of 96.67%, sensitivity of 93.75%, specificity of 100% and AUC of 97% were obtained when correlation-selected features were included in the model. The Chi-Square selected features gave the best performance with 98.33% accuracy, 100% sensitivity, 96.55 specificity and 98% AUC. Mutual information gain selected feature gave the same results as Chi-Square selected features.

CONCLUSION

Random Forest Classifier has a good potential at predicting the risk of breast cancer in African women. The study helped to identify the risk factors of breast cancer in African women. This is a valuable information which can help African women to pay attention to those risk factors with the intention of reducing the incidence of breast cancer in Africa.

Development of a Breast Cancer Risk Prediction Model for Women in Nigeria

Background: Risk prediction models have been widely used to identify women at higher risk of breast cancer. We aimed to develop a model for absolute breast cancer risk prediction for Nigerian women.Methods: A total of 1,811 breast cancer cases and 2,225 controls from the Nigerian Breast Cancer Study (NBCS, 1998-2015) were included. Subjects were randomly divided into the training and validation sets. Incorporating local incidence rates, multivariable logistic regressions were used to develop the model.Results: The NBCS model included age, age at menarche, parity, duration of breastfeeding, family history of breast cancer, height, body mass index, benign breast diseases, and alcohol consumption. The model developed in the training set performed well in the validation set. The discriminating accuracy of the NBCS model [area under ROC curve (AUC) = 0.703, 95% confidence interval (CI), 0.687-0.719] was better than the Black Women's Health Study (BWHS) model (AUC = 0.605; 95% CI, 0.586-0.624), Gail model for white population (AUC = 0.551; 95% CI, 0.531-0.571), and Gail model for black population (AUC = 0.545; 95% CI, 0.525-0.565). Compared with the BWHS and two Gail models, the net reclassification improvement of the NBCS model were 8.26%, 13.45%, and 14.19%, respectively.Conclusions: We have developed a breast cancer risk prediction model specific to women in Nigeria, which provides a promising and indispensable tool to identify women in need of breast cancer early detection in Sub-Saharan Africa populations.Impact: Our model is the first breast cancer risk prediction model in Africa. It can be used to identify women at high risk for breast cancer screening. Cancer Epidemiol Biomarkers Prev; 27(6); 636-43. ©2018 AACR.

Predicting Trajectories of Functional Decline in 60- to 70-Year-Old People

BACKGROUND

Early identification of people at risk of functional decline is essential for delivering targeted preventive interventions.

OBJECTIVE

The aim of this study is to identify and predict trajectories of functional decline over 9 years in males and females aged 60-70 years.

METHODS

We included 403 community-dwelling participants from the InCHIANTI study and 395 from the LASA study aged 60-70 years at baseline, of whom the majority reported no functional decline at baseline (median 0, interquartile range 0-1). Participants were included if they reported data on ≥2 measurements of functional ability during a 9-year follow-up. Functional ability was scored with 6 self-reported items on activities of daily living. We performed latent class growth analysis to identify trajectories of functional decline and applied multinomial regression models to develop prediction models of identified trajectories. Analyses were stratified for sex.

RESULTS

Three distinct trajectories were identified: no/little decline (219 males, 241 females), intermediate decline (114 males, 158 females), and severe decline (36 males, 30 females). Higher gait speed showed decreased risk of functional limitations in males (intermediate limitations, odds ratio [OR] 0.74, 95% CI 0.57-0.97; severe limitations, OR 0.42, 95% CI 0.26-0.66). The final model in males further included the predictors fear of falling and alcohol intake (no/little decline, area under the receiver operating curve [AUC] 0.68, 95% CI 0.62-0.73; intermediate decline, AUC 0.63, 95% CI 0.56-0.69; severe decline, AUC 0.79, 95% CI 0.71-0.87). In females, higher gait speed showed a decreased risk of intermediate limitations (OR 0.51, 95% CI 0.38-0.68) and severe limitations (OR 0.18, 95% CI 0.07-0.44). Other predictors in females were age, living alone, economic satisfaction, balance, physical activity, BMI, and cardiovascular disease (no/little decline, AUC 0.80, 95% CI 0.75-0.85; intermediate decline, AUC 0.74, 95% CI 0.69-0.79; severe decline, AUC 0.95, 95% CI 0.91-0.99).

CONCLUSION

Already in people aged 60-70 years, 3 distinct trajectories of functional decline were identified in these cohorts over a 9-year follow-up. Predictors of trajectories differed between males and females, except for gait speed. Identification of people at risk is the basis for targeting interventions.

Predictive risk models for proximal aortic surgery

Predictive risk models help improve decision making, information to our patients and quality control comparing results between surgeons and between institutions. The use of these models promotes competitiveness and led to increasingly better results. All these virtues are of utmost importance when the surgical operation entails high-risk. Although proximal aortic surgery is less frequent than other cardiac surgery operations, this procedure itself is more challenging and technically demanding than other common cardiac surgery techniques. The aim of this study is to review the current status of predictive risk models for patients who undergo proximal aortic surgery, which means aortic root replacement, supracoronary ascending aortic replacement or aortic arch surgery.

Breast cancer disparities: high-risk breast cancer and African ancestry

African American women have a lower lifetime incidence of breast cancer than white/Caucasian Americans yet have a higher risk of breast cancer mortality. African American women are also more likely to be diagnosed with breast cancer at young ages, and they have higher risk for the biologically more aggressive triple-negative breast cancers. These features are also more common among women from western, sub-Saharan Africa who share ancestry with African Americans, and this prompts questions regarding an association between African ancestry and inherited susceptibility for certain patterns of mammary carcinogenesis.

Prospective approach to breast cancer risk prediction in African American women: the black women's health study model

PURPOSE

Breast cancer risk prediction models have underestimated risk for African American women, contributing to lower recruitment rates in prevention trials. A model previously developed for African American women was found to underestimate risk in the Black Women's Health Study (BWHS).

METHODS

We developed a breast cancer risk model for African American women using relative risks derived from 10 years of follow-up of BWHS participants age 30 to 69 years at baseline. Using the subsequent 5 years of follow-up data, we evaluated calibration as the ratio of expected to observed number of breast cancers and assessed discriminatory accuracy using the concordance statistic.

RESULTS

The BWHS model included family history, previous biopsy, body mass index at age 18 years, age at menarche, age at first birth, oral contraceptive use, bilateral oophorectomy, estrogen plus progestin use, and height. There was good agreement between predicted and observed number of breast cancers overall (expected-to-observed ratio, 0.96; 95% CI, 0.88 to 1.05) and in most risk factor categories. Discriminatory accuracy was higher for women younger than age 50 years (area under the curve [AUC], 0.62; 95% CI, 0.58 to 0.65) than for women age ≥ 50 years (AUC, 0.56; 95% CI, 0.53 to 0.59). Using a 5-year predicted risk of 1.66% or greater as a cut point, 2.8% of women younger than 50 years old and 32.2% of women ≥ 50 years old were classified as being at elevated risk of invasive breast cancer.

CONCLUSION

The BWHS model was well calibrated overall, and the predictive ability was best for younger women. The proportion of women predicted to meet the 1.66% cut point commonly used to determine eligibility for breast cancer prevention trials was greatly increased relative to previous models.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.

Cross-sectional study to assess the association of population density with predicted breast cancer risk

The Gail and CARE models estimate breast cancer risk for white and African-American (AA) women, respectively. The aims of this study were to compare metropolitan and nonmetropolitan women with respect to predicted breast cancer risks based on known risk factors, and to determine if population density was an independent risk factor for breast cancer risk. A cross-sectional survey was completed by 15,582 women between 35 and 85 years of age with no history of breast cancer. Metropolitan and nonmetropolitan women were compared with respect to risk factors, and breast cancer risk estimates, using general linear models adjusted for age. For both white and AA women, tisk factors used to estimate breast cancer risk included age at menarche, history of breast biopsies, and family history. For white women, age at first childbirth was an additional risk factor. In comparison to their nonmetropolitan counterparts, metropolitan white women were more likely to report having a breast biopsy, have family history of breast cancer, and delay childbirth. Among white metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.44% and 1.32% (p < 0.001), and lifetime risks of breast cancer were 10.81% and 10.01% (p < 0.001), respectively. AA metropolitan residents were more likely than those from nonmetropolitan areas to have had a breast biopsy. Among AA metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.16% and 1.12% (p = 0.039) and lifetime risks were 8.94%, and 8.85% (p = 0.344). Metropolitan residence was associated with higher predicted breast cancer risks for white women. Among AA women, metropolitan residence was associated with a higher predicted breast cancer risk at 5 years, but not over a lifetime. Population density was not an independent risk factor for breast cancer.