Validation of an Abridged Breast Cancer Risk Prediction Model for the General Population.
Ontology highlight
ABSTRACT: Prevention relevance
In this prospective population-based cohort study, we show the improved performance of a new risk assessment model compared with a gold-standard model (BCRAT). The classification of at-risk women using this new model highlights the opportunity to improve risk stratification and implement existing clinical risk-reduction interventions.
SUBMITTER: Spaeth EL
PROVIDER: S-EPMC10150247 | biostudies-literature | 2023 May
REPOSITORIES: biostudies-literature
Publications
Validation of an Abridged Breast Cancer Risk Prediction Model for the General Population.
Spaeth Erika L EL Dite Gillian S GS Hopper John L JL Allman Richard R
Cancer prevention research (Philadelphia, Pa.) 20230501 5
<h4>Prevention relevance</h4>In this prospective population-based cohort study, we show the improved performance of a new risk assessment model compared with a gold-standard model (BCRAT). The classification of at-risk women using this new model highlights the opportunity to improve risk stratification and implement existing clinical risk-reduction interventions. ...[more]
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Project description:BackgroundsIn contrast to developed countries, breast cancer in China is characterized by a rapidly escalating incidence rate in the past two decades, lower survival rate, and vast geographic variation. However, there is no validated risk prediction model in China to aid early detection yet.MethodsA large nationwide prospective cohort, China Kadoorie Biobank (CKB), was used to evaluate relative and attributable risks of invasive breast cancer. A total of 300,824 women free of any prior cancer were recruited during 2004-2008 and followed up to Dec 31, 2016. Cox models were used to identify breast cancer risk factors and build a relative risk model. Absolute risks were calculated by incorporating national age- and residence-specific breast cancer incidence and non-breast cancer mortality rates. We used an independent large prospective cohort, Shanghai Women's Health Study (SWHS), with 73,203 women to externally validate the calibration and discriminating accuracy.ResultsDuring a median of 10.2 years of follow-up in the CKB, 2287 cases were observed. The final model included age, residence area, education, BMI, height, family history of overall cancer, parity, and age at menarche. The model was well-calibrated in both the CKB and the SWHS, yielding expected/observed (E/O) ratios of 1.01 (95% confidence interval (CI), 0.94-1.09) and 0.94 (95% CI, 0.89-0.99), respectively. After eliminating the effect of age and residence, the model maintained moderate but comparable discriminating accuracy compared with those of some previous externally validated models. The adjusted areas under the receiver operating curve (AUC) were 0.634 (95% CI, 0.608-0.661) and 0.585 (95% CI, 0.564-0.605) in the CKB and the SWHS, respectively.ConclusionsBased only on non-laboratory predictors, our model has a good calibration and moderate discriminating capacity. The model may serve as a useful tool to raise individuals' awareness and aid risk-stratified screening and prevention strategies.
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Project description:Genome-wide association studies (GWASs) have identified many single nucleotide polymorphisms (SNPs) that are significantly associated with pancreatic cancer susceptibility. We sought to replicate the associations of 61 GWAS-identified SNPs at 42 loci with pancreatic cancer in Japanese and to develop a risk model for the identification of individuals at high risk for pancreatic cancer development in the general Japanese population. The model was based on data including directly determined or imputed SNP genotypes for 664 pancreatic cancer case and 664 age- and sex-matched control subjects. Stepwise logistic regression uncovered five GWAS-identified SNPs at five loci that also showed significant associations in our case-control cohort. These five SNPs were included in the risk model and also applied to calculation of the polygenic risk score (PRS). The area under the curve determined with the leave-one-out cross-validation method was 0.63 (95% confidence interval, 0.60-0.66) or 0.61 (0.58-0.64) for versions of the model that did or did not include cigarette smoking and family history of pancreatic cancer in addition to the five SNPs, respectively. Individuals in the lowest and highest quintiles for the PRS had odds ratios of 0.62 (0.42-0.91) and 1.98 (1.42-2.76), respectively, for pancreatic cancer development compared with those in the middle quintile. We have thus developed a risk model for pancreatic cancer that showed moderately good discriminatory ability with regard to differentiation of pancreatic cancer patients from control individuals. Our findings suggest the potential utility of a risk model that incorporates replicated GWAS-identified SNPs and established demographic or environmental factors for the identification of individuals at increased risk for pancreatic cancer development.
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Project description:Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) is a risk prediction algorithm that can be used to compute estimates of age-specific risk of breast cancer. It is uncertain whether BOADICEA performs adequately for populations outside the United Kingdom.Using a batch mode version of BOADICEA that we developed (BOADICEACentre), we calculated the cumulative 10-year invasive breast cancer risk for 4176 Australian women of European ancestry unaffected at baseline from 1601 case and control families in the Australian Breast Cancer Family Registry. Based on 115 incident breast cancers, we investigated calibration, discrimination (using receiver-operating characteristic (ROC) curves) and accuracy at the individual level.The ratio of expected to observed number of breast cancers was 0.92 (95% confidence interval (CI) 0.76-1.10). The E/O ratios by subgroups of the participant's relationship to the index case and by the reported number of affected relatives ranged between 0.83 and 0.98 and all 95% CIs included 1.00. The area under the ROC curve was 0.70 (95% CI 0.66-0.75) and there was no evidence of systematic under- or over-dispersion (P=0.2).BOADICEA is well calibrated for Australian women, and had good discrimination and accuracy at the individual level.
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Project description:BackgroundEstimating advanced breast cancer risk in women undergoing annual or biennial mammography could identify women who may benefit from less or more intensive screening. We developed an actionable model to predict cumulative 6-year advanced cancer (prognostic pathologic stage II or higher) risk according to screening interval.MethodsWe included 931 186 women aged 40-74 years in the Breast Cancer Surveillance Consortium undergoing 2 542 382 annual (prior mammogram within 11-18 months) or 752 049 biennial (prior within 19-30 months) screening mammograms. The prediction model includes age, race and ethnicity, body mass index, breast density, family history of breast cancer, and prior breast biopsy subdivided by menopausal status and screening interval. We used fivefold cross-validation to internally validate model performance. We defined higher than 95th percentile as high risk (>0.658%), higher than 75th percentile to 95th or less percentile as intermediate risk (0.380%-0.658%), and 75th or less percentile as low to average risk (<0.380%).ResultsObesity, high breast density, and proliferative disease with atypia were strongly associated with advanced cancer. The model is well calibrated and has an area under the receiver operating characteristics curve of 0.682 (95% confidence interval = 0.670 to 0.694). Based on women's predicted advanced cancer risk under annual and biennial screening, 69.1% had low or average risk regardless of screening interval, 12.4% intermediate risk with biennial screening and average risk with annual screening, and 17.4% intermediate or high risk regardless of screening interval.ConclusionMost women have low or average advanced cancer risk and can undergo biennial screening. Intermediate-risk women may consider annual screening, and high-risk women may consider supplemental imaging in addition to annual screening.
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Project description:BackgroundWorldwide, gastric cancer is a leading cause of cancer incidence and mortality. This study aims to devise and validate a scoring system based on readily available clinical data to predict the risk of gastric cancer in a large Chinese population.MethodsWe included a total of 6,209,697 subjects aged between 18 and 70 years who have received upper digestive endoscopy in Hong Kong from 1997 to 2018. A binary logistic regression model was constructed to examine the predictors of gastric cancer in a derivation cohort (n = 4,347,224), followed by model evaluation in a validation cohort (n = 1,862,473). The algorithm's discriminatory ability was evaluated as the area under the curve (AUC) of the mathematically constructed receiver operating characteristic (ROC) curve.ResultsAge, male gender, history of Helicobacter pylori infection, use of proton pump inhibitors, non-use of aspirin, non-steroidal anti-inflammatory drugs (NSAIDs), and statins were significantly associated with gastric cancer. A scoring of ≤8 was designated as "average risk (AR)". Scores at 9 or above were assigned as "high risk (HR)". The prevalence of gastric cancer was 1.81% and 0.096%, respectively, for the HR and LR groups. The AUC for the risk score in the validation cohort was 0.834, implying an excellent fit of the model.ConclusionsThis study has validated a simple, accurate, and easy-to-use scoring algorithm which has a high discriminatory capability to predict gastric cancer. The score could be adopted to risk stratify subjects suspected as having gastric cancer, thus allowing prioritized upper digestive tract investigation.
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Project description:BackgroundMost sudden cardiac death (SCD) events occur in the general population among persons who do not have any prior history of clinical heart disease. We sought to develop a predictive model of SCD among US adults.MethodsWe evaluated a series of demographic, clinical, laboratory, electrocardiographic, and echocardiographic measures in participants in the ARIC study (Atherosclerosis Risk in Communities) (n=13 677) and the CHS (Cardiovascular Health Study) (n=4207) who were free of baseline cardiovascular disease. Our initial objective was to derive a SCD prediction model using the ARIC cohort and validate it in CHS. Independent risk factors for SCD were first identified in the ARIC cohort to derive a 10-year risk model of SCD. We compared the prediction of SCD with non-SCD and all-cause mortality in both the derivation and validation cohorts. Furthermore, we evaluated whether the SCD prediction equation was better at predicting SCD than the 2013 American College of Cardiology/American Heart Association Cardiovascular Disease Pooled Cohort risk equation.ResultsThere were a total of 345 adjudicated SCD events in our analyses, and the 12 independent risk factors in the ARIC study included age, male sex, black race, current smoking, systolic blood pressure, use of antihypertensive medication, diabetes mellitus, serum potassium, serum albumin, high-density lipoprotein, estimated glomerular filtration rate, and QTc interval. During a 10-year follow-up period, a model combining these risk factors showed good to excellent discrimination for SCD risk (c-statistic 0.820 in ARIC and 0.745 in CHS). The SCD prediction model was slightly better in predicting SCD than the 2013 American College of Cardiology/American Heart Association Pooled Cohort risk equations (c-statistic 0.808 in ARIC and 0.743 in CHS). Only the SCD prediction model, however, demonstrated similar and accurate prediction for SCD using both the original, uncalibrated score and the recalibrated equation. Finally, in the echocardiographic subcohort, a left ventricular ejection fraction <50% was present in only 1.1% of participants and did not enhance SCD prediction.ConclusionsOur study is the first to derive and validate a generalizable risk score that provides well-calibrated, absolute risk estimates across different risk strata in an adult population of white and black participants without a clinical diagnosis of cardiovascular disease.
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Project description:PurposeWe evaluated the performance of the Gail model for a Korean population and developed a Korean breast cancer risk assessment tool (KoBCRAT) based upon equations developed for the Gail model for predicting breast cancer risk.MethodsUsing 3,789 sets of cases and controls, risk factors for breast cancer among Koreans were identified. Individual probabilities were projected using Gail's equations and Korean hazard data. We compared the 5-year and lifetime risk produced using the modified Gail model which applied Korean incidence and mortality data and the parameter estimators from the original Gail model with those produced using the KoBCRAT. We validated the KoBCRAT based on the expected/observed breast cancer incidence and area under the curve (AUC) using two Korean cohorts: the Korean Multicenter Cancer Cohort (KMCC) and National Cancer Center (NCC) cohort.ResultsThe major risk factors under the age of 50 were family history, age at menarche, age at first full-term pregnancy, menopausal status, breastfeeding duration, oral contraceptive usage, and exercise, while those at and over the age of 50 were family history, age at menarche, age at menopause, pregnancy experience, body mass index, oral contraceptive usage, and exercise. The modified Gail model produced lower 5-year risk for the cases than for the controls (p = 0.017), while the KoBCRAT produced higher 5-year and lifetime risk for the cases than for the controls (p<0.001 and <0.001, respectively). The observed incidence of breast cancer in the two cohorts was similar to the expected incidence from the KoBCRAT (KMCC, p = 0.880; NCC, p = 0.878). The AUC using the KoBCRAT was 0.61 for the KMCC and 0.89 for the NCC cohort.ConclusionsOur findings suggest that the KoBCRAT is a better tool for predicting the risk of breast cancer in Korean women, especially urban women.
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Project description:Because existing risk prediction models for lung cancer were developed in white populations, they may not be appropriate for predicting risk among African-Americans. Therefore, a need exists to construct and validate a risk prediction model for lung cancer that is specific to African-Americans. We analyzed data from 491 African-Americans with lung cancer and 497 matched African-American controls to identify specific risks and incorporate them into a multivariable risk model for lung cancer and estimate the 5-year absolute risk of lung cancer. We performed internal and external validations of the risk model using data on additional cases and controls from the same ongoing multiracial/ethnic lung cancer case-control study from which the model-building data were obtained as well as data from two different lung cancer studies in metropolitan Detroit, respectively. We also compared our African-American model with our previously developed risk prediction model for whites. The final risk model included smoking-related variables [smoking status, pack-years smoked, age at smoking cessation (former smokers), and number of years since smoking cessation (former smokers)], self-reported physician diagnoses of chronic obstructive pulmonary disease or hay fever, and exposures to asbestos or wood dusts. Our risk prediction model for African-Americans exhibited good discrimination [75% (95% confidence interval, 0.67-0.82)] for our internal data and moderate discrimination [63% (95% confidence interval, 0.57-0.69)] for the external data group, which is an improvement over the Spitz model for white subjects. Existing lung cancer prediction models may not be appropriate for predicting risk for African-Americans because (a) they were developed using white populations, (b) level of risk is different for risk factors that African-American share with whites, and (c) unique group-specific risk factors exist for African-Americans. This study developed and validated a risk prediction model for lung cancer that is specific to African-Americans and thus more precise in predicting their risks. These findings highlight the importance of conducting further ethnic-specific analyses of disease risk.
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Project description:BackgroundThe incidence of oropharynx cancers has increased substantially in the United States. However, risk stratification tools for the identification of high-risk individuals do not exist. In this study, an individualized risk prediction model was developed and validated for oropharynx cancers in the US population.MethodsA synthetic, US population-based case-control study was conducted. Oropharynx cancer cases diagnosed at Ohio State University (n = 241) were propensity-weighted to represent oropharynx cancers occurring annually in the United States during 2009-2014 (n = 12,656). Controls (n = 9327) included participants in the National Health and Nutrition Examination Survey (2009-2014) and represented the annual US population aged 30 to 69 years (n = 154,532,508). The individualized 1-year absolute risk of oropharynx cancer was estimated with weighted logistic regression.ResultsThe risk prediction model included age, sex, race, smoking, alcohol use, lifetime sexual partners, and oral oncogenic human papillomavirus (HPV) status. The model had good discrimination and calibration in split-sample validation (area under the curve [AUC], 0.94; 95% confidence interval [CI], 0.92-0.97; observed/expected [O/E], 1.01; 95% CI, 0.70-1.32) and external validation (AUC, 0.87; 95% CI, 0.84-0.90; O/E, 1.08; 95% CI, 0.77-1.39). In the US population, 1-year predicted risks of oropharynx cancer were highest for older individuals (21.1/100,000 for 65- to 69-year-olds), men (13.9/100,000), whites (10.4/100,000), smokers (18.0/100,000 for >20 pack-years), heavy alcohol users (18.4/100,000), and those with prevalent oral oncogenic HPV (140.4/100,000). The risk prediction model provided substantial risk stratification, with approximately 77% of all oropharynx cancers and approximately 99% of HPV-positive oropharynx cancers occurring in the 10% of the US population with the highest model-predicted risk.ConclusionsThis risk prediction model will enable the efficient design of studies to address the outstanding questions pertaining to the natural history, screening, and secondary prevention of oropharynx cancers.
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