Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes

McCarthy, Anne Marie; Liu, Yi; Ehsan, Sarah; Guan, Zoe; Liang, Jane W.; Huang, Theodore; Hughes, Kevin S.; Semine, Alan; Kontos, Despina; Conant, Emily F.; Lehman, Constance; Armstrong, Katrina; Braun, Danielle; Parmigiani, Giovanni; Chen, Jinbo

doi:10.3390/cancers14010045

Cited by 15 publications

(19 citation statements)

References 33 publications

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“…There are several limitations to the updated BCSC model. cohorts, [5][6][7][8][9] which gives us confidence that the current model will be robust to external validation. Second, we used selfreported height and weight to calculate BMI.…”

Section: Discussionmentioning

confidence: 80%

“…36 However, in independent validation studies, the v2 BCSC model has consistently had equal or better discrimination compared with other models that include more detailed family history (BCRAT, BRCApro, Claus, and Tyrer-Cuzick). 6,7 The addition of BMI, extended family history, and age at first live birth enhances the ability of the BCSC model to discriminate between women who will and will not develop breast cancer.…”

Section: Discussionmentioning

confidence: 99%

“…3 The current BCSC model 4 (version 2 [v2]) uses information about a woman's age, race and ethnicity, first-degree family history of breast cancer, breast density, and benign breast biopsy results to estimate her 5-and 10-year absolute risks of developing invasive breast cancer. The model has been externally validated in five independent cohorts [5][6][7][8][9] and found to be well calibrated. In recent comparisons of the BRCAPRO, Gail, Tyrer-Cuzick, and BCSC risk models in women with breast density measurements, the BCSC model was found to have the highest discrimination.…”

Section: Introductionmentioning

confidence: 99%

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Extending the Breast Cancer Surveillance Consortium Model of Invasive Breast Cancer

Gard,

Tice,

Miglioretti

et al. 2024

JCO

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PURPOSE We extended the Breast Cancer Surveillance Consortium (BCSC) version 2 (v2) model of invasive breast cancer risk to include BMI, extended family history of breast cancer, and age at first live birth (version 3 [v3]) to better inform appropriate breast cancer prevention therapies and risk-based screening. METHODS We used Cox proportional hazards regression to estimate the age- and race- and ethnicity-specific relative hazards for family history of breast cancer, breast density, history of benign breast biopsy, BMI, and age at first live birth for invasive breast cancer in the BCSC cohort. We evaluated calibration using the ratio of expected-to-observed (E/O) invasive breast cancers in the cohort and discrimination using the area under the receiver operating characteristic curve (AUROC). RESULTS We analyzed data from 1,455,493 women age 35-79 years without a history of breast cancer. During a mean follow-up of 7.3 years, 30,266 women were diagnosed with invasive breast cancer. The BCSC v3 model had an E/O of 1.03 (95% CI, 1.01 to 1.04) and an AUROC of 0.646 for 5-year risk. Compared with the v2 model, discrimination of the v3 model improved most in Asian, White, and Black women. Among women with a BMI of 30.0-34.9 kg/m2, the true-positive rate in women with an estimated 5-year risk of 3% or higher increased from 10.0% (v2) to 19.8% (v3) and the improvement was greater among women with a BMI of ≥35 kg/m2 (7.6%-19.8%). CONCLUSION The BCSC v3 model updates an already well-calibrated and validated breast cancer risk assessment tool to include additional important risk factors. The inclusion of BMI was associated with the largest improvement in estimated risk for individual women.

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Section: Discussionmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Extending the Breast Cancer Surveillance Consortium Model of Invasive Breast Cancer

Gard,

Tice,

Miglioretti

et al. 2024

JCO

View full text Add to dashboard Cite

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“…To begin, our study is clearly a retrospective cohort study that is inevitably biased by patients’ selection in the SEER database. Second, there is a lack of some proven prognostic factors such as BRCA1/2 gene ( 43 , 44 ), body mass index (BMI) ( 45 , 46 ) and family history ( 47 ) in the SEER database, all of which have been found to be highly linked to poorer results for patients diagnosed with breast cancer. Third, we cannot operate further research on the roles of systematic treatments in YBC patients with RLNM, for the reason that detailed information on specific chemotherapy regimens and endocrine therapy was not accessible in the SEER database.…”

Section: Discussionmentioning

confidence: 99%

A nomogram for predicting overall survival of breast cancer with regional lymph node metastasis in young women

Sun,

Huang,

Chen

et al. 2024

Transl Cancer Res

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Background Young breast cancer (YBC) patients demonstrate a heightened propensity for regional lymph node metastasis (RLNM) in contrast to cohorts across varying age demographics. The aim of our study was to identify clinicopathologic prognostic variables in YBC patients with RLNM and construct a practical and reliable nomogram for the prediction of overall survival (OS) using the Surveillance, Epidemiology, and End Results (SEER) database. Methods Young individuals (≤40 years) with a diagnosis of breast cancer with RLNM were recognized from the SEER database between 2010 and 2015, and further randomly split into two cohorts: the training set (n=4,497) and the validation set (n=1,927). We first performed univariate and multivariate Cox regression analyses to confirm independent survival predictors of OS. A novel prognostic nomogram was developed and evaluated using Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). To make a clear distinction between high- and low-risk patients in terms of patient survival, Kaplan-Meier survival curves were assessed using the log-rank test. Results Nine risk factors were found as independent prognostic variables in predicting OS, including race, grade, histology, surgery, radiation, molecular subtype, American Joint Committee on Cancer (AJCC) stage 7 th edition, T stage, and N stage. The C-index values of our nomogram were 0.786 [95% confidence interval (CI): 0.767–0.805] and 0.791 (95% CI: 0.760–0.822) in our training and validation groups, respectively. The ROC curves demonstrated sufficient discriminating ability, while the predicted and real survival rates were fairly consistent, as shown by the calibration plots. The prediction model had a higher net benefit and acceptable clinical value, as shown by the DCA curves. Conclusions In YBC patients with RLNM, we successfully established a unique nomogram to forecast the 2-, 3-, and 5-year OS. Clinicians may utilize this nomogram to pinpoint patients at higher risk and provide them with appropriate customized therapies.

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“…The few studies evaluating differences in model performance at the level of the individual woman have used small sample sizes and only evaluate lifetime risk, 16 , 17 or report results for a 5-year threshold of ≥ 1.67%. 18 Since key screening and medical intervention decisions are based on a woman’s estimated risk of developing invasive breast cancer, a consistent and accurate risk estimate is essential.…”

Section: Introductionmentioning

confidence: 99%

Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman

et al. 2023

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Background Breast cancer risk models guide screening and chemoprevention decisions, but the extent and effect of variability among models, particularly at the individual level, is uncertain. Objective To quantify the accuracy and disagreement between commonly used risk models in categorizing individual women as average vs. high risk for developing invasive breast cancer. Design Comparison of three risk prediction models: Breast Cancer Risk Assessment Tool (BCRAT), Breast Cancer Surveillance Consortium (BCSC) model, and International Breast Intervention Study (IBIS) model. Subjects Women 40 to 74 years of age presenting for screening mammography at a multisite health system between 2011 and 2015, with 5-year follow-up for cancer outcome. Main Measures Comparison of model discrimination and calibration at the population level and inter-model agreement for 5-year breast cancer risk at the individual level using two cutoffs (≥ 1.67% and ≥ 3.0%). Key Results A total of 31,115 women were included. When using the ≥ 1.67% threshold, more than 21% of women were classified as high risk for developing breast cancer in the next 5 years by one model, but average risk by another model. When using the ≥ 3.0% threshold, more than 5% of women had disagreements in risk severity between models. Almost half of the women (46.6%) were classified as high risk by at least one of the three models (e.g., if all three models were applied) for the threshold of ≥ 1.67%, and 11.1% were classified as high risk for ≥ 3.0%. All three models had similar accuracy at the population level. Conclusions Breast cancer risk estimates for individual women vary substantially, depending on which risk assessment model is used. The choice of cutoff used to define high risk can lead to adverse effects for screening, preventive care, and quality of life for misidentified individuals. Clinicians need to be aware of the high false-positive and false-negative rates and variation between models when talking with patients.

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Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes

Cited by 15 publications

References 33 publications

Extending the Breast Cancer Surveillance Consortium Model of Invasive Breast Cancer

Extending the Breast Cancer Surveillance Consortium Model of Invasive Breast Cancer

A nomogram for predicting overall survival of breast cancer with regional lymph node metastasis in young women

Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman

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