Model for individualized prediction of breast cancer risk after a benign breast biopsy

V. Shane Pankratz, Amy C Degnim, Ryan D. Frank, Marlene H. Frost, Daniel W Visscher, Robert A. Vierkant, Tina J Hieken, Karthik Ghosh, Yaman Tarabishy, Celine M Vachon, Derek C Radisky, Lynn C. Hartmann

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Abstract

Purpose: Optimal early detection and prevention for breast cancer depend on accurate identification of women at increased risk. We present a risk prediction model that incorporates histologic features of biopsy tissues from women with benign breast disease (BBD) and compare its performance to the Breast Cancer Risk Assessment Tool (BCRAT). Methods: We estimated the age-specific incidence of breast cancer and death from the Mayo BBD cohort and then combined these estimates with a relative risk model derived from 377 patient cases with breast cancer and 734 matched controls sampled from the Mayo BBD cohort to develop the BBD-to-breast cancer (BBD-BC) risk assessment tool. We validated the model using an independent set of 378 patient cases with breast cancer and 728 matched controls from the Mayo BBD cohort and compared the risk predictions from our model with those from the BCRAT. Results: The BBD-BC model predicts the probability of breast cancer in women with BBD using tissue-based and other risk factors. The concordance statistic from the BBD-BC model was 0.665 in the model development series and 0.629 in the validation series; these values were higher than those from the BCRAT (0.567 and 0.472, respectively). The BCRAT significantly underpredicted breast cancer risk after benign biopsy (P = .004), whereas the BBD-BC predictions were appropriately calibrated to observed cancers (P = .247). Conclusion: We developed a model using both demographic and histologic features to predict breast cancer risk in women with BBD. Our model more accurately classifies a woman's breast cancer risk after a benign biopsy than the BCRAT.

Original languageEnglish (US)
Pages (from-to)923-929
Number of pages7
JournalJournal of Clinical Oncology
Volume33
Issue number8
DOIs
StatePublished - Mar 10 2015

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Breast Diseases
Breast
Breast Neoplasms
Biopsy
Demography

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

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Model for individualized prediction of breast cancer risk after a benign breast biopsy. / Pankratz, V. Shane; Degnim, Amy C; Frank, Ryan D.; Frost, Marlene H.; Visscher, Daniel W; Vierkant, Robert A.; Hieken, Tina J; Ghosh, Karthik; Tarabishy, Yaman; Vachon, Celine M; Radisky, Derek C; Hartmann, Lynn C.

In: Journal of Clinical Oncology, Vol. 33, No. 8, 10.03.2015, p. 923-929.

Research output: Contribution to journalArticle

Pankratz, V. Shane ; Degnim, Amy C ; Frank, Ryan D. ; Frost, Marlene H. ; Visscher, Daniel W ; Vierkant, Robert A. ; Hieken, Tina J ; Ghosh, Karthik ; Tarabishy, Yaman ; Vachon, Celine M ; Radisky, Derek C ; Hartmann, Lynn C. / Model for individualized prediction of breast cancer risk after a benign breast biopsy. In: Journal of Clinical Oncology. 2015 ; Vol. 33, No. 8. pp. 923-929.
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abstract = "Purpose: Optimal early detection and prevention for breast cancer depend on accurate identification of women at increased risk. We present a risk prediction model that incorporates histologic features of biopsy tissues from women with benign breast disease (BBD) and compare its performance to the Breast Cancer Risk Assessment Tool (BCRAT). Methods: We estimated the age-specific incidence of breast cancer and death from the Mayo BBD cohort and then combined these estimates with a relative risk model derived from 377 patient cases with breast cancer and 734 matched controls sampled from the Mayo BBD cohort to develop the BBD-to-breast cancer (BBD-BC) risk assessment tool. We validated the model using an independent set of 378 patient cases with breast cancer and 728 matched controls from the Mayo BBD cohort and compared the risk predictions from our model with those from the BCRAT. Results: The BBD-BC model predicts the probability of breast cancer in women with BBD using tissue-based and other risk factors. The concordance statistic from the BBD-BC model was 0.665 in the model development series and 0.629 in the validation series; these values were higher than those from the BCRAT (0.567 and 0.472, respectively). The BCRAT significantly underpredicted breast cancer risk after benign biopsy (P = .004), whereas the BBD-BC predictions were appropriately calibrated to observed cancers (P = .247). Conclusion: We developed a model using both demographic and histologic features to predict breast cancer risk in women with BBD. Our model more accurately classifies a woman's breast cancer risk after a benign biopsy than the BCRAT.",
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T1 - Model for individualized prediction of breast cancer risk after a benign breast biopsy

AU - Pankratz, V. Shane

AU - Degnim, Amy C

AU - Frank, Ryan D.

AU - Frost, Marlene H.

AU - Visscher, Daniel W

AU - Vierkant, Robert A.

AU - Hieken, Tina J

AU - Ghosh, Karthik

AU - Tarabishy, Yaman

AU - Vachon, Celine M

AU - Radisky, Derek C

AU - Hartmann, Lynn C.

PY - 2015/3/10

Y1 - 2015/3/10

N2 - Purpose: Optimal early detection and prevention for breast cancer depend on accurate identification of women at increased risk. We present a risk prediction model that incorporates histologic features of biopsy tissues from women with benign breast disease (BBD) and compare its performance to the Breast Cancer Risk Assessment Tool (BCRAT). Methods: We estimated the age-specific incidence of breast cancer and death from the Mayo BBD cohort and then combined these estimates with a relative risk model derived from 377 patient cases with breast cancer and 734 matched controls sampled from the Mayo BBD cohort to develop the BBD-to-breast cancer (BBD-BC) risk assessment tool. We validated the model using an independent set of 378 patient cases with breast cancer and 728 matched controls from the Mayo BBD cohort and compared the risk predictions from our model with those from the BCRAT. Results: The BBD-BC model predicts the probability of breast cancer in women with BBD using tissue-based and other risk factors. The concordance statistic from the BBD-BC model was 0.665 in the model development series and 0.629 in the validation series; these values were higher than those from the BCRAT (0.567 and 0.472, respectively). The BCRAT significantly underpredicted breast cancer risk after benign biopsy (P = .004), whereas the BBD-BC predictions were appropriately calibrated to observed cancers (P = .247). Conclusion: We developed a model using both demographic and histologic features to predict breast cancer risk in women with BBD. Our model more accurately classifies a woman's breast cancer risk after a benign biopsy than the BCRAT.

AB - Purpose: Optimal early detection and prevention for breast cancer depend on accurate identification of women at increased risk. We present a risk prediction model that incorporates histologic features of biopsy tissues from women with benign breast disease (BBD) and compare its performance to the Breast Cancer Risk Assessment Tool (BCRAT). Methods: We estimated the age-specific incidence of breast cancer and death from the Mayo BBD cohort and then combined these estimates with a relative risk model derived from 377 patient cases with breast cancer and 734 matched controls sampled from the Mayo BBD cohort to develop the BBD-to-breast cancer (BBD-BC) risk assessment tool. We validated the model using an independent set of 378 patient cases with breast cancer and 728 matched controls from the Mayo BBD cohort and compared the risk predictions from our model with those from the BCRAT. Results: The BBD-BC model predicts the probability of breast cancer in women with BBD using tissue-based and other risk factors. The concordance statistic from the BBD-BC model was 0.665 in the model development series and 0.629 in the validation series; these values were higher than those from the BCRAT (0.567 and 0.472, respectively). The BCRAT significantly underpredicted breast cancer risk after benign biopsy (P = .004), whereas the BBD-BC predictions were appropriately calibrated to observed cancers (P = .247). Conclusion: We developed a model using both demographic and histologic features to predict breast cancer risk in women with BBD. Our model more accurately classifies a woman's breast cancer risk after a benign biopsy than the BCRAT.

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