Cumulative Advanced Breast Cancer Risk Prediction Model Developed in a Screening Mammography Population
Abstract Background Estimating 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 h...
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| Veröffentlicht in: | JNCI : Journal of the National Cancer Institute 2022-05, Vol.114 (5), p.676-685 |
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| creator | Kerlikowske, Karla Chen, Shuai Golmakani, Marzieh K Sprague, Brian L Tice, Jeffrey A Tosteson, Anna N A Rauscher, Garth H Henderson, Louise M Buist, Diana S M Lee, Janie M Gard, Charlotte C Miglioretti, Diana L |
| description | Abstract
Background
Estimating 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.
Methods
We 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 ( |
| doi_str_mv | 10.1093/jnci/djac008 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9086807</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><oup_id>10.1093/jnci/djac008</oup_id><sourcerecordid>2620082111</sourcerecordid><originalsourceid>FETCH-LOGICAL-c444t-ae2b1261b9f5193ff6a06f2ff63c8a9d9d18a1410760e4a5a8ae0dc81682026e3</originalsourceid><addsrcrecordid>eNp9kUtv1DAURi1ERYfCjjWyxAIWhNqOk9gbpDI8WqkVFY-1dce5mXpI7GAnI_Xf18MMFbDAmyvLR0f-7kfIM87ecKbL04237rTdgGVMPSALLmtWCM6qh2TBmGgKpRp5TB6ntGH5aCEfkeOyYqJmXC_IejkPcw-T2yI9a7fgLbb0XURIE13ubpF-cekHvY7YOju54OlVaLGn73GLfRgz7TwF-tVGRO_8ml7BMIR1hPHmll6H8Zc8-CfkqIM-4dPDPCHfP374tjwvLj9_ulieXRZWSjkVgGLFRc1Xuqu4LruuBlZ3Is_SKtCtbrkCLjlraoYSKlCArLWK10rkSFiekLd77zivBmwt-ilCb8boBoi3JoAzf794d2PWYWs0U7ViTRa8Oghi-DljmszgksW-B49hTkbUIi9acM4z-uIfdBPm6HO8TDWVrESldKZe7ykbQ0oRu_vPcGZ2DZpdg-bQYMaf_xngHv5dWQZe7oEwj_9X3QHho6bU</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2675452589</pqid></control><display><type>article</type><title>Cumulative Advanced Breast Cancer Risk Prediction Model Developed in a Screening Mammography Population</title><source>MEDLINE</source><source>Oxford University Press Journals All Titles (1996-Current)</source><source>EZB-FREE-00999 freely available EZB journals</source><source>Alma/SFX Local Collection</source><creator>Kerlikowske, Karla ; Chen, Shuai ; Golmakani, Marzieh K ; Sprague, Brian L ; Tice, Jeffrey A ; Tosteson, Anna N A ; Rauscher, Garth H ; Henderson, Louise M ; Buist, Diana S M ; Lee, Janie M ; Gard, Charlotte C ; Miglioretti, Diana L</creator><creatorcontrib>Kerlikowske, Karla ; Chen, Shuai ; Golmakani, Marzieh K ; Sprague, Brian L ; Tice, Jeffrey A ; Tosteson, Anna N A ; Rauscher, Garth H ; Henderson, Louise M ; Buist, Diana S M ; Lee, Janie M ; Gard, Charlotte C ; Miglioretti, Diana L</creatorcontrib><description>Abstract
Background
Estimating 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.
Methods
We 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%).
Results
Obesity, 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.
Conclusion
Most 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.</description><identifier>ISSN: 0027-8874</identifier><identifier>ISSN: 1460-2105</identifier><identifier>EISSN: 1460-2105</identifier><identifier>DOI: 10.1093/jnci/djac008</identifier><identifier>PMID: 35026019</identifier><language>eng</language><publisher>United States: Oxford University Press</publisher><subject>Biopsy ; Body mass ; Body mass index ; Body size ; Breast cancer ; Breast Density ; Breast Neoplasms - diagnostic imaging ; Breast Neoplasms - epidemiology ; Confidence intervals ; Density ; Early Detection of Cancer - methods ; Editor's Choice ; Female ; Genetics ; Health risks ; Humans ; Mammography ; Mammography - methods ; Mass Screening - methods ; Menopause ; Minority & ethnic groups ; Prediction models ; Risk ; Screening ; Time Factors</subject><ispartof>JNCI : Journal of the National Cancer Institute, 2022-05, Vol.114 (5), p.676-685</ispartof><rights>The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com 2022</rights><rights>The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.</rights><rights>The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c444t-ae2b1261b9f5193ff6a06f2ff63c8a9d9d18a1410760e4a5a8ae0dc81682026e3</citedby><cites>FETCH-LOGICAL-c444t-ae2b1261b9f5193ff6a06f2ff63c8a9d9d18a1410760e4a5a8ae0dc81682026e3</cites><orcidid>0000-0002-9857-2028 ; 0000-0001-7201-4450 ; 0000-0001-7718-8943 ; 0000-0002-5547-1833 ; 0000-0001-5408-2804 ; 0000-0001-8793-8779 ; 0000-0002-2111-0773 ; 0000-0002-4016-8217 ; 0000-0002-6452-8168</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,1578,27903,27904</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35026019$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kerlikowske, Karla</creatorcontrib><creatorcontrib>Chen, Shuai</creatorcontrib><creatorcontrib>Golmakani, Marzieh K</creatorcontrib><creatorcontrib>Sprague, Brian L</creatorcontrib><creatorcontrib>Tice, Jeffrey A</creatorcontrib><creatorcontrib>Tosteson, Anna N A</creatorcontrib><creatorcontrib>Rauscher, Garth H</creatorcontrib><creatorcontrib>Henderson, Louise M</creatorcontrib><creatorcontrib>Buist, Diana S M</creatorcontrib><creatorcontrib>Lee, Janie M</creatorcontrib><creatorcontrib>Gard, Charlotte C</creatorcontrib><creatorcontrib>Miglioretti, Diana L</creatorcontrib><title>Cumulative Advanced Breast Cancer Risk Prediction Model Developed in a Screening Mammography Population</title><title>JNCI : Journal of the National Cancer Institute</title><addtitle>J Natl Cancer Inst</addtitle><description>Abstract
Background
Estimating 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.
Methods
We 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%).
Results
Obesity, 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.
Conclusion
Most 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.</description><subject>Biopsy</subject><subject>Body mass</subject><subject>Body mass index</subject><subject>Body size</subject><subject>Breast cancer</subject><subject>Breast Density</subject><subject>Breast Neoplasms - diagnostic imaging</subject><subject>Breast Neoplasms - epidemiology</subject><subject>Confidence intervals</subject><subject>Density</subject><subject>Early Detection of Cancer - methods</subject><subject>Editor's Choice</subject><subject>Female</subject><subject>Genetics</subject><subject>Health risks</subject><subject>Humans</subject><subject>Mammography</subject><subject>Mammography - methods</subject><subject>Mass Screening - methods</subject><subject>Menopause</subject><subject>Minority & ethnic groups</subject><subject>Prediction models</subject><subject>Risk</subject><subject>Screening</subject><subject>Time Factors</subject><issn>0027-8874</issn><issn>1460-2105</issn><issn>1460-2105</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kUtv1DAURi1ERYfCjjWyxAIWhNqOk9gbpDI8WqkVFY-1dce5mXpI7GAnI_Xf18MMFbDAmyvLR0f-7kfIM87ecKbL04237rTdgGVMPSALLmtWCM6qh2TBmGgKpRp5TB6ntGH5aCEfkeOyYqJmXC_IejkPcw-T2yI9a7fgLbb0XURIE13ubpF-cekHvY7YOju54OlVaLGn73GLfRgz7TwF-tVGRO_8ml7BMIR1hPHmll6H8Zc8-CfkqIM-4dPDPCHfP374tjwvLj9_ulieXRZWSjkVgGLFRc1Xuqu4LruuBlZ3Is_SKtCtbrkCLjlraoYSKlCArLWK10rkSFiekLd77zivBmwt-ilCb8boBoi3JoAzf794d2PWYWs0U7ViTRa8Oghi-DljmszgksW-B49hTkbUIi9acM4z-uIfdBPm6HO8TDWVrESldKZe7ykbQ0oRu_vPcGZ2DZpdg-bQYMaf_xngHv5dWQZe7oEwj_9X3QHho6bU</recordid><startdate>20220509</startdate><enddate>20220509</enddate><creator>Kerlikowske, Karla</creator><creator>Chen, Shuai</creator><creator>Golmakani, Marzieh K</creator><creator>Sprague, Brian L</creator><creator>Tice, Jeffrey A</creator><creator>Tosteson, Anna N A</creator><creator>Rauscher, Garth H</creator><creator>Henderson, Louise M</creator><creator>Buist, Diana S M</creator><creator>Lee, Janie M</creator><creator>Gard, Charlotte C</creator><creator>Miglioretti, Diana L</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TO</scope><scope>7U7</scope><scope>7U9</scope><scope>C1K</scope><scope>H94</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-9857-2028</orcidid><orcidid>https://orcid.org/0000-0001-7201-4450</orcidid><orcidid>https://orcid.org/0000-0001-7718-8943</orcidid><orcidid>https://orcid.org/0000-0002-5547-1833</orcidid><orcidid>https://orcid.org/0000-0001-5408-2804</orcidid><orcidid>https://orcid.org/0000-0001-8793-8779</orcidid><orcidid>https://orcid.org/0000-0002-2111-0773</orcidid><orcidid>https://orcid.org/0000-0002-4016-8217</orcidid><orcidid>https://orcid.org/0000-0002-6452-8168</orcidid></search><sort><creationdate>20220509</creationdate><title>Cumulative Advanced Breast Cancer Risk Prediction Model Developed in a Screening Mammography Population</title><author>Kerlikowske, Karla ; Chen, Shuai ; Golmakani, Marzieh K ; Sprague, Brian L ; Tice, Jeffrey A ; Tosteson, Anna N A ; Rauscher, Garth H ; Henderson, Louise M ; Buist, Diana S M ; Lee, Janie M ; Gard, Charlotte C ; Miglioretti, Diana L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c444t-ae2b1261b9f5193ff6a06f2ff63c8a9d9d18a1410760e4a5a8ae0dc81682026e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Biopsy</topic><topic>Body mass</topic><topic>Body mass index</topic><topic>Body size</topic><topic>Breast cancer</topic><topic>Breast Density</topic><topic>Breast Neoplasms - diagnostic imaging</topic><topic>Breast Neoplasms - epidemiology</topic><topic>Confidence intervals</topic><topic>Density</topic><topic>Early Detection of Cancer - methods</topic><topic>Editor's Choice</topic><topic>Female</topic><topic>Genetics</topic><topic>Health risks</topic><topic>Humans</topic><topic>Mammography</topic><topic>Mammography - methods</topic><topic>Mass Screening - methods</topic><topic>Menopause</topic><topic>Minority & ethnic groups</topic><topic>Prediction models</topic><topic>Risk</topic><topic>Screening</topic><topic>Time Factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kerlikowske, Karla</creatorcontrib><creatorcontrib>Chen, Shuai</creatorcontrib><creatorcontrib>Golmakani, Marzieh K</creatorcontrib><creatorcontrib>Sprague, Brian L</creatorcontrib><creatorcontrib>Tice, Jeffrey A</creatorcontrib><creatorcontrib>Tosteson, Anna N A</creatorcontrib><creatorcontrib>Rauscher, Garth H</creatorcontrib><creatorcontrib>Henderson, Louise M</creatorcontrib><creatorcontrib>Buist, Diana S M</creatorcontrib><creatorcontrib>Lee, Janie M</creatorcontrib><creatorcontrib>Gard, Charlotte C</creatorcontrib><creatorcontrib>Miglioretti, Diana L</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>JNCI : Journal of the National Cancer Institute</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kerlikowske, Karla</au><au>Chen, Shuai</au><au>Golmakani, Marzieh K</au><au>Sprague, Brian L</au><au>Tice, Jeffrey A</au><au>Tosteson, Anna N A</au><au>Rauscher, Garth H</au><au>Henderson, Louise M</au><au>Buist, Diana S M</au><au>Lee, Janie M</au><au>Gard, Charlotte C</au><au>Miglioretti, Diana L</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cumulative Advanced Breast Cancer Risk Prediction Model Developed in a Screening Mammography Population</atitle><jtitle>JNCI : Journal of the National Cancer Institute</jtitle><addtitle>J Natl Cancer Inst</addtitle><date>2022-05-09</date><risdate>2022</risdate><volume>114</volume><issue>5</issue><spage>676</spage><epage>685</epage><pages>676-685</pages><issn>0027-8874</issn><issn>1460-2105</issn><eissn>1460-2105</eissn><abstract>Abstract
Background
Estimating 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.
Methods
We 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%).
Results
Obesity, 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.
Conclusion
Most 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.</abstract><cop>United States</cop><pub>Oxford University Press</pub><pmid>35026019</pmid><doi>10.1093/jnci/djac008</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-9857-2028</orcidid><orcidid>https://orcid.org/0000-0001-7201-4450</orcidid><orcidid>https://orcid.org/0000-0001-7718-8943</orcidid><orcidid>https://orcid.org/0000-0002-5547-1833</orcidid><orcidid>https://orcid.org/0000-0001-5408-2804</orcidid><orcidid>https://orcid.org/0000-0001-8793-8779</orcidid><orcidid>https://orcid.org/0000-0002-2111-0773</orcidid><orcidid>https://orcid.org/0000-0002-4016-8217</orcidid><orcidid>https://orcid.org/0000-0002-6452-8168</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | JNCI : Journal of the National Cancer Institute, 2022-05, Vol.114 (5), p.676-685 |
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| source | MEDLINE; Oxford University Press Journals All Titles (1996-Current); EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Biopsy Body mass Body mass index Body size Breast cancer Breast Density Breast Neoplasms - diagnostic imaging Breast Neoplasms - epidemiology Confidence intervals Density Early Detection of Cancer - methods Editor's Choice Female Genetics Health risks Humans Mammography Mammography - methods Mass Screening - methods Menopause Minority & ethnic groups Prediction models Risk Screening Time Factors |
| title | Cumulative Advanced Breast Cancer Risk Prediction Model Developed in a Screening Mammography Population |
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