{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:22:49Z","timestamp":1787019769966,"version":"build-2736575974"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2012,11,5]],"date-time":"2012-11-05T00:00:00Z","timestamp":1352073600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0\/"},{"start":{"date-parts":[[2012,11,5]],"date-time":"2012-11-05T00:00:00Z","timestamp":1352073600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Breast Cancer Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Clinicians use different breast cancer risk models for patients considered at average and above-average risk, based largely on their family histories and genetic factors. We used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the genetic and nongenetic covariates that differentiate the performance of two commonly used models that include nongenetic factors - BCRAT, also called Gail model, generally used for patients with average risk and IBIS, also called Tyrer Cuzick model, generally used for patients with above-average risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We evaluated the performance of the BCRAT and IBIS models as currently applied in clinical settings for 10-year absolute risk of breast cancer, using prospective data from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer. This cohort spans the continuum of breast cancer risk, with some subjects at lower than average population risk. Therefore, the wide variation in individual risk makes it an interesting population to examine model performance across subgroups of women. For model calibration, we divided the cohort into quartiles of model-assigned risk and compared differences between assigned and observed risks using the Hosmer-Lemeshow (HL) chi-squared statistic. For model discrimination, we computed the area under the receiver operator curve (AUC) and the case risk percentiles (CRPs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The 10-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to 79.5). The mean BCRAT- and IBIS-assigned risks of 3.18% and 5.49%, respectively, were lower than the cohort's 10-year cumulative probability of developing breast cancer (6.25%; 95% confidence interval (CI) = 5.0 to 7.8%). Agreement between assigned and observed risks was better for IBIS (HL X\n                      <jats:sub>4<\/jats:sub>\n                      <jats:sup>2<\/jats:sup>\n                      = 7.2,\n                      <jats:italic>P<\/jats:italic>\n                      value 0.13) than BCRAT (HL X\n                      <jats:sub>4<\/jats:sub>\n                      <jats:sup>2<\/jats:sup>\n                      = 22.0,\n                      <jats:italic>P<\/jats:italic>\n                      value &lt;0.001). The IBIS model also showed better discrimination (AUC = 69.5%, CI = 63.8% to 75.2%) than did the BCRAT model (AUC = 63.2%, CI = 57.6% to 68.9%). In almost all covariate-specific subgroups, BCRAT mean risks were significantly lower than the observed risks, while IBIS risks showed generally good agreement with observed risks, even in the subgroups of women considered at average risk (for example, no family history of breast cancer,\n                      <jats:italic>BRCA1\/2<\/jats:italic>\n                      mutation negative).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Models developed using extended family history and genetic data, such as the IBIS model, also perform well in women considered at average risk (for example, no family history of breast cancer,\n                      <jats:italic>BRCA1\/2<\/jats:italic>\n                      mutation negative). Extending such models to include additional nongenetic information may improve performance in women across the breast cancer risk continuum.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/bcr3352","type":"journal-article","created":{"date-parts":[[2012,11,5]],"date-time":"2012-11-05T18:14:17Z","timestamp":1352139257000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":95,"title":["Breast cancer risk assessment across the risk continuum: genetic and nongenetic risk factors contributing to differential model performance"],"prefix":"10.1186","volume":"14","author":[{"given":"Anne S","family":"Quante","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alice S","family":"Whittemore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom","family":"Shriver","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantin","family":"Strauch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary B","family":"Terry","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2012,11,5]]},"reference":[{"key":"3164_CR1","doi-asserted-by":"crossref","unstructured":"Smith RA, Cokkinides V, Brooks D, Saslow D, Shah M, Brawley OW: Cancer screening in the United States, 2011: A review of current American Cancer Society guidelines and issues in cancer screening. CA: A Cancer Journal for Clinicians. 61: 8-30.","DOI":"10.3322\/caac.20096"},{"key":"3164_CR2","doi-asserted-by":"publisher","first-page":"1652","DOI":"10.1093\/jnci\/dji372","volume":"97","author":"B Fisher","year":"2005","unstructured":"Fisher B, Costantino JP, Wickerham DL, Cecchini RS, Cronin WM, Robidoux A, Bevers TB, Kavanah MT, Atkins JN, Margolese RG, Runowicz CD, James JM, Ford LG, Wolmark N: Tamoxifen for the prevention of breast cancer: current status of the National Surgical Adjuvant Breast and Bowel Project P-1 study. J Natl Cancer Inst. 2005, 97: 1652-1662. 10.1093\/jnci\/dji372.","journal-title":"J Natl Cancer Inst"},{"key":"3164_CR3","doi-asserted-by":"publisher","first-page":"1879","DOI":"10.1093\/jnci\/81.24.1879","volume":"81","author":"MH Gail","year":"1989","unstructured":"Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C, Mulvihill JJ: Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst. 1989, 81: 1879-1886. 10.1093\/jnci\/81.24.1879.","journal-title":"J Natl Cancer Inst"},{"key":"3164_CR4","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF00666424","volume":"28","author":"EB Claus","year":"1993","unstructured":"Claus EB, Risch N, Thompson WD: The calculation of breast cancer risk for women with a first degree family history of ovarian cancer. Breast Cancer Res Treat. 1993, 28: 115-120. 10.1007\/BF00666424.","journal-title":"Breast Cancer Res Treat"},{"key":"3164_CR5","doi-asserted-by":"crossref","first-page":"1580","DOI":"10.1038\/sj.bjc.6602175","volume":"91","author":"AC Antoniou","year":"2004","unstructured":"Antoniou AC, Pharoah PP, Smith P, Easton DF: The BOADICEA model of genetic susceptibility to breast and ovarian cancer. Br J Cancer. 2004, 91: 1580-1590.","journal-title":"Br J Cancer"},{"key":"3164_CR6","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1002\/sim.1668","volume":"23","author":"J Tyrer","year":"2004","unstructured":"Tyrer J, Duffy SW, Cuzick J: A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. 2004, 23: 1111-1130. 10.1002\/sim.1668.","journal-title":"Stat Med"},{"key":"3164_CR7","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1002\/1097-0142(19940201)73:3<643::AID-CNCR2820730323>3.0.CO;2-5","volume":"73","author":"EB Claus","year":"1994","unstructured":"Claus EB, Risch N, Thompson WD: Autosomal dominant inheritance of early-onset breast cancer. Implications for risk prediction. Cancer. 1994, 73: 643-651. 10.1002\/1097-0142(19940201)73:3<643::AID-CNCR2820730323>3.0.CO;2-5.","journal-title":"Cancer"},{"key":"3164_CR8","doi-asserted-by":"publisher","first-page":"1782","DOI":"10.1093\/jnci\/djm223","volume":"99","author":"MH Gail","year":"2007","unstructured":"Gail MH, Costantino JP, Pee D, Bondy M, Newman L, Selvan M, Anderson GL, Malone KE, Marchbanks PA, McCaskill-Stevens W, Norman SA, Simon MS, Spirtas R, Ursin G, Bernstein L: Projecting individualized absolute invasive breast cancer risk in African American women. J Natl Cancer Inst. 2007, 99: 1782-1792. 10.1093\/jnci\/djm223.","journal-title":"J Natl Cancer Inst"},{"key":"3164_CR9","doi-asserted-by":"publisher","first-page":"1541","DOI":"10.1093\/jnci\/91.18.1541","volume":"91","author":"JP Costantino","year":"1999","unstructured":"Costantino JP, Gail MH, Pee D, Anderson S, Redmond CK, Benichou J, Wieand HS: Validation studies for models projecting the risk of invasive and total breast cancer incidence. J Natl Cancer Inst. 1999, 91: 1541-1548. 10.1093\/jnci\/91.18.1541.","journal-title":"J Natl Cancer Inst"},{"key":"3164_CR10","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1093\/jnci\/djr154","volume":"103","author":"RK Matsuno","year":"2011","unstructured":"Matsuno RK, Costantino JP, Ziegler RG, Anderson GL, Li H, Pee D, Gail MH: Projecting individualized absolute invasive breast cancer risk in Asian and Pacific Islander American women. J Natl Cancer Inst. 2011, 103: 951-961. 10.1093\/jnci\/djr154.","journal-title":"J Natl Cancer Inst"},{"key":"3164_CR11","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1093\/jnci\/djq088","volume":"102","author":"E Amir","year":"2010","unstructured":"Amir E, Freedman OC, Seruga B, Evans DG: Assessing women at high risk of breast cancer: a review of risk assessment models. J Natl Cancer Inst. 2010, 102: 680-691. 10.1093\/jnci\/djq088.","journal-title":"J Natl Cancer Inst"},{"key":"3164_CR12","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1093\/jnci\/86.8.620","volume":"86","author":"ML Bondy","year":"1994","unstructured":"Bondy ML, Lustbader ED, Halabi S, Ross E, Vogel VG: Validation of a breast cancer risk assessment model in women with a positive family history. J Natl Cancer Inst. 1994, 86: 620-625. 10.1093\/jnci\/86.8.620.","journal-title":"J Natl Cancer Inst"},{"key":"3164_CR13","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1093\/jnci\/86.8.600","volume":"86","author":"D Spiegelman","year":"1994","unstructured":"Spiegelman D, Colditz GA, Hunter D, Hertzmark E: Validation of the Gail et al. model for predicting individual breast cancer risk. J Natl Cancer Inst. 1994, 86: 600-607. 10.1093\/jnci\/86.8.600.","journal-title":"J Natl Cancer Inst"},{"key":"3164_CR14","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/s10549-008-0070-x","volume":"115","author":"CE Jacobi","year":"2009","unstructured":"Jacobi CE, de Bock GH, Siegerink B, van Asperen CJ: Differences and similarities in breast cancer risk assessment models in clinical practice: which model to choose?. Breast Cancer Res Treat. 2009, 115: 381-390. 10.1007\/s10549-008-0070-x.","journal-title":"Breast Cancer Res Treat"},{"key":"3164_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10549-011-1853-z","volume":"133","author":"T Anothaisintawee","year":"2012","unstructured":"Anothaisintawee T, Teerawattananon Y, Wiratkapun C, Kasamesup V, Thakkinstian A: Risk prediction models of breast cancer: a systematic review of model performances. Breast Cancer Res Treat. 2012, 133: 1-10. 10.1007\/s10549-011-1853-z.","journal-title":"Breast Cancer Res Treat"},{"key":"3164_CR16","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1136\/jmg.40.11.807","volume":"40","author":"E Amir","year":"2003","unstructured":"Amir E, Evans DG, Shenton A, Lalloo F, Moran A, Boggis C, Wilson M, Howell A: Evaluation of breast cancer risk assessment packages in the family history evaluation and screening programme. J Med Genet. 2003, 40: 807-814. 10.1136\/jmg.40.11.807.","journal-title":"J Med Genet"},{"key":"3164_CR17","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1046\/j.1524-4741.2002.08005.x","volume":"8","author":"DM Euhus","year":"2002","unstructured":"Euhus DM, Leitch AM, Huth JF, Peters GN: Limitations of the Gail model in the specialized breast cancer risk assessment clinic. Breast J. 2002, 8: 23-27. 10.1046\/j.1524-4741.2002.08005.x.","journal-title":"Breast J"},{"key":"3164_CR18","doi-asserted-by":"publisher","first-page":"5374","DOI":"10.1200\/JCO.2007.14.8833","volume":"26","author":"VS Pankratz","year":"2008","unstructured":"Pankratz VS, Hartmann LC, Degnim AC, Vierkant RA, Ghosh K, Vachon CM, Frost MH, Maloney SD, Reynolds C, Boughey JC: Assessment of the accuracy of the Gail model in women with atypical hyperplasia. J Clin Oncol. 2008, 26: 5374-5379. 10.1200\/JCO.2007.14.8833.","journal-title":"J Clin Oncol"},{"key":"3164_CR19","first-page":"8","volume":"6","author":"C Bellcross","year":"2009","unstructured":"Bellcross C: Approaches to applying breast cancer risk prediction models in clinical practice. Oncology Genetics. 2009, 6: 8-","journal-title":"Oncology Genetics"},{"key":"3164_CR20","unstructured":"Breast Cancer Risk Assessment Tool. [http:\/\/www.cancer.gov\/bcrisktool]"},{"key":"3164_CR21","doi-asserted-by":"publisher","first-page":"R375","DOI":"10.1186\/bcr801","volume":"6","author":"EM John","year":"2004","unstructured":"John EM, Hopper JL, Beck JC, Knight JA, Neuhausen SL, Senie RT, Ziogas A, Andrulis IL, Anton-Culver H, Boyd N, Buys SS, Daly MB, O'Malley FP, Santella RM, Southey MC, Venne VL, Venter DJ, West DW, Whittemore AS, Seminara D: The Breast Cancer Family Registry: an infrastructure for cooperative multinational, interdisciplinary and translational studies of the genetic epidemiology of breast cancer. Breast Cancer Res. 2004, 6: R375-389. 10.1186\/bcr801.","journal-title":"Breast Cancer Res"},{"key":"3164_CR22","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1093\/jnci\/dji013","volume":"97","author":"DO Kennedy","year":"2005","unstructured":"Kennedy DO, Agrawal M, Shen J, Terry MB, Zhang FF, Senie RT, Motykiewicz G, Santella RM: DNA repair capacity of lymphoblastoid cell lines from sisters discordant for breast cancer. J Natl Cancer Inst. 2005, 97: 127-132. 10.1093\/jnci\/dji013.","journal-title":"J Natl Cancer Inst"},{"key":"3164_CR23","doi-asserted-by":"publisher","first-page":"5538","DOI":"10.1158\/0008-5472.CAN-06-3490","volume":"67","author":"J Shen","year":"2007","unstructured":"Shen J, Terry MB, Gurvich I, Liao Y, Senie RT, Santella RM: Short telomere length and breast cancer risk: a study in sister sets. Cancer Res. 2007, 67: 5538-5544. 10.1158\/0008-5472.CAN-06-3490.","journal-title":"Cancer Res"},{"key":"3164_CR24","doi-asserted-by":"publisher","first-page":"2966","DOI":"10.1158\/0008-5472.CAN-08-3418","volume":"69","author":"J Zipprich","year":"2009","unstructured":"Zipprich J, Terry MB, Liao Y, Agrawal M, Gurvich I, Senie R, Santella RM: Plasma protein carbonyls and breast cancer risk in sisters discordant for breast cancer from the New York site of the Breast Cancer Family Registry. Cancer Res. 2009, 69: 2966-2972. 10.1158\/0008-5472.CAN-08-3418.","journal-title":"Cancer Res"},{"key":"3164_CR25","doi-asserted-by":"publisher","first-page":"1946","DOI":"10.1093\/carcin\/bgs201","volume":"33","author":"HC Wu","year":"2012","unstructured":"Wu HC, Delgado-Cruzata L, Flom JD, Perrin M, Liao Y, Ferris J, Santella RM, Terry MB: Repetitive element DNA methylation levels in white blood cell DNA from sisters discordant for breast cancer from the New York site of the BCFR. Carcinogenesis. 2012, 33: 1946-52. 10.1093\/carcin\/bgs201. Epub 2012 Jun 7","journal-title":"Carcinogenesis"},{"key":"3164_CR26","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.1158\/1055-9965.EPI-12-0300","volume":"21","author":"HC Wu","year":"2012","unstructured":"Wu HC, Wang Q, Delgado-Cruzata L, Santella RM, Terry MB: Genomic methylation changes over time in peripheral blood mononuclear cell DNA: differences by assay type and baseline values. Cancer Epidemiol Biomarkers Prev. 2012, 21: 1314-8. 10.1158\/1055-9965.EPI-12-0300. Epub 2012 Jun 4","journal-title":"Cancer Epidemiol Biomarkers Prev"},{"key":"3164_CR27","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/s10549-008-0153-8","volume":"116","author":"SL Neuhausen","year":"2009","unstructured":"Neuhausen SL, Ozcelik H, Southey MC, John EM, Godwin AK, Chung W, Iriondo-Perez J, Miron A, Santella RM, Whittemore A, Andrulis IL, Buys SS, Daly MB, Hopper JL, Seminara D, Senie RT, Terry MB: BRCA1 and BRCA2 mutation carriers in the Breast Cancer Family Registry: an open resource for collaborative research. Breast Cancer Res Treat. 2009, 116: 379-386. 10.1007\/s10549-008-0153-8.","journal-title":"Breast Cancer Res Treat"},{"key":"3164_CR28","unstructured":"Breast Cancer Risk Assessment Macro. [http:\/\/dceg.cancer.gov\/bb\/tools\/bcrasasmacro]"},{"key":"3164_CR29","unstructured":"IBIS Breast Cancer Risk Evaluation Tool. [http:\/\/www.ems-trials.org\/riskevaluator\/]"},{"key":"3164_CR30","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1093\/biostatistics\/kxi005","volume":"6","author":"MH Gail","year":"2005","unstructured":"Gail MH, Pfeiffer RM: On criteria for evaluating models of absolute risk. Biostatistics. 2005, 6: 227-239. 10.1093\/biostatistics\/kxi005.","journal-title":"Biostatistics"},{"key":"3164_CR31","doi-asserted-by":"publisher","DOI":"10.1002\/9781118032985","volume-title":"The statistical analysis of failure time data","author":"J Kalbfleisch","year":"2002","unstructured":"Kalbfleisch J, Prentice R: The statistical analysis of failure time data. 2002, New York: Wiley and Sons, Second","edition":"Second"},{"key":"3164_CR32","doi-asserted-by":"publisher","DOI":"10.1002\/0471722146","volume-title":"Applied logistic regression","author":"DW Hosmer","year":"2000","unstructured":"Hosmer DW, Lemeshow S: Applied logistic regression. 2000, New York: Wiley and Sons"},{"key":"3164_CR33","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1111\/j.0006-341X.2000.00337.x","volume":"56","author":"PJ Heagerty","year":"2000","unstructured":"Heagerty PJ, Lumley T, Pepe MS: Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics. 2000, 56: 337-344. 10.1111\/j.0006-341X.2000.00337.x.","journal-title":"Biometrics"},{"key":"3164_CR34","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1097\/01.ede.0000173041.03470.8b","volume":"16","author":"MS Pepe","year":"2005","unstructured":"Pepe MS, Longton G: Standardizing diagnostic markers to evaluate and compare their performance. Epidemiology. 2005, 16: 598-603. 10.1097\/01.ede.0000173041.03470.8b.","journal-title":"Epidemiology"},{"key":"3164_CR35","unstructured":"The 'rmap' package. [http:\/\/www.stanford.edu\/~ggong\/rmap\/index.html]"},{"key":"3164_CR36","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1002\/ijc.26004","volume":"130","author":"L Liu","year":"2012","unstructured":"Liu L, Zhang J, Wu AH, Pike MC, Deapen D: Invasive breast cancer incidence trends by detailed race\/ethnicity and age. Int J Cancer. 2012, 130: 395-404. 10.1002\/ijc.26004.","journal-title":"Int J Cancer"},{"key":"3164_CR37","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.1200\/JCO.2008.17.5869","volume":"27","author":"D Huo","year":"2009","unstructured":"Huo D, Senie RT, Daly M, Buys SS, Cummings S, Ogutha J, Hope K, Olopade OI: Prediction of BRCA mutations using the BRCAPRO model in clinic-based African American, Hispanic, and other minority families in the United States. J Clin Oncol. 2009, 27: 1184-1190. 10.1200\/JCO.2008.17.5869.","journal-title":"J Clin Oncol"},{"key":"3164_CR38","doi-asserted-by":"crossref","unstructured":"Boughey JC, Hartmann LC, Anderson SS, Degnim AC, Vierkant RA, Reynolds CA, Frost MH, Pankratz VS: Evaluation of the Tyrer-Cuzick (International Breast Cancer Intervention Study) model for breast cancer risk prediction in women with atypical hyperplasia. J Clin Oncol. 28: 3591-3596.","DOI":"10.1200\/JCO.2010.28.0784"}],"container-title":["Breast Cancer Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/bcr3352.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/bcr3352\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/bcr3352.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T06:48:57Z","timestamp":1728370137000},"score":1,"resource":{"primary":{"URL":"https:\/\/breast-cancer-research.biomedcentral.com\/articles\/10.1186\/bcr3352"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,11,5]]},"references-count":38,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2012,12]]}},"alternative-id":["3164"],"URL":"https:\/\/doi.org\/10.1186\/bcr3352","relation":{},"ISSN":["1465-542X"],"issn-type":[{"value":"1465-542X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,11,5]]},"assertion":[{"value":"17 May 2012","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2012","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 October 2012","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2012","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"R144"}}