Abstract
Background
Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC).
Methods
We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope.
Results
The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57–0.59) for CBCrisk; 0.60 (95% CI 0.59–0.61) for the Manchester formula; 0.63 (95% CI 0.59–0.66) and 0.59 (95% CI 0.56–0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51–1.32) for CBCrisk; 1.53 (95% CI 0.63–3.73) for the Manchester formula; 1.28 (95% CI 0.63–2.58) for PredictCBC-1A and 1.35 (95% CI 0.65–2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01–1.50) for CBCrisk; 0.90 (95% CI 0.79–1.02) for PredictCBC-1A; 0.81 (95% CI 0.63–0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34–0.43) for the Manchester formula.
Conclusions
Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.
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References
Langballe R, Frederiksen K, Jensen MB, Andersson M, Cronin-Fenton D, Ejlertsen B, Mellemkjaer L (2018) Mortality after contralateral breast cancer in Denmark. Breast Cancer Res Treat 171(2):489–499. https://doi.org/10.1007/s10549-018-4846-3
Xiong Z, Yang L, Deng G, Huang X, Li X, Xie X, Wang J, Shuang Z, Wang X (2018) Patterns of occurrence and outcomes of contralateral breast cancer: analysis of SEER data. J Clin Med. https://doi.org/10.3390/jcm7060133
Wong SM, Freedman RA, Sagara Y, Aydogan F, Barry WT, Golshan M (2017) Growing use of contralateral prophylactic mastectomy despite no improvement in long-term survival for invasive breast cancer. Ann Surg 265(3):581–589. https://doi.org/10.1097/SLA.0000000000001698
Kramer I, Schaapveld M, Oldenburg HSA, Sonke GS, McCool D, van Leeuwen FE, Van de Vijver KK, Russell NS, Linn SC, Siesling S, der Houven M-V, van Oordt CW, Schmidt MK (2019) The influence of adjuvant systemic regimens on contralateral breast cancer risk and receptor subtype. J Natl Cancer Inst. https://doi.org/10.1093/jnci/djz010
Giardiello D, Steyerberg EW, Hauptmann M, Adank MA, Akdeniz D, Blomqvist C, Bojesen SE, Bolla MK, Brinkhuis M, Chang-Claude J, Czene K, Devilee P, Dunning AM, Easton DF, Eccles DM, Fasching PA, Figueroa J, Flyger H, Garcia-Closas M, Haeberle L, Haiman CA, Hall P, Hamann U, Hopper JL, Jager A, Jakubowska A, Jung A, Keeman R, Kramer I, Lambrechts D, Le Marchand L, Lindblom A, Lubinski J, Manoochehri M, Mariani L, Nevanlinna H, Oldenburg HSA, Pelders S, Pharoah PDP, Shah M, Siesling S, Smit V, Southey MC, Tapper WJ, Tollenaar R, van den Broek AJ, van Deurzen CHM, van Leeuwen FE, van Ongeval C, Van't Veer LJ, Wang Q, Wendt C, Westenend PJ, Hooning MJ, Schmidt MK (2019) Prediction and clinical utility of a contralateral breast cancer risk model. Breast Cancer Res 21(1):144. https://doi.org/10.1186/s13058-019-1221-1
O'Donnell M (2018) Estimating Contralateral Breast Cancer Risk. Curr Breast Cancer Rep 10(2):91–97
Chowdhury M, Euhus D, Onega T, Biswas S, Choudhary PK (2017) A model for individualized risk prediction of contralateral breast cancer. Breast Cancer Res Treat 161(1):153–160. https://doi.org/10.1007/s10549-016-4039-x
Basu NN, Ross GL, Evans DG, Barr L (2015) The Manchester guidelines for contralateral risk-reducing mastectomy. World J Surg Oncol 13:237. https://doi.org/10.1186/s12957-015-0638-y
Chowdhury M, Euhus D, Arun B, Umbricht C, Biswas S, Choudhary P (2018) Validation of a personalized risk prediction model for contralateral breast cancer. Breast Cancer Res Treat. https://doi.org/10.1007/s10549-018-4763-5
Chowdhury M, Euhus D, Onega T, Choudhary P (2017) CBCRisk: contralateral breast cancer (CBC) risk predictor. https://cbc-predictor-utd.shinyapps.io/CBCRisk/
van den Broek AJ, Van’t Veer LJ, Hooning MJ, Cornelissen S, Broeks A, Rutgers EJ, Smit VT, Cornelisse CJ, van Beek M, Janssen-Heijnen ML, Seynaeve C, Westenend PJ, Jobsen JJ, Siesling S, Tollenaar RA, van Leeuwen FE, Schmidt MK (2016) Impact of age at primary breast cancer on contralateral breast cancer risk in BRCA1/2 mutation carriers. J Clin Oncol 34(5):409–418. https://doi.org/10.1200/JCO.2015.62.3942
Malone KE, Begg CB, Haile RW, Borg A, Concannon P, Tellhed L, Xue S, Teraoka S, Bernstein L, Capanu M, Reiner AS, Riedel ER, Thomas DC, Mellemkjaer L, Lynch CF, Boice JD Jr, Anton-Culver H, Bernstein JL (2010) Population-based study of the risk of second primary contralateral breast cancer associated with carrying a mutation in BRCA1 or BRCA2. J Clin Oncol 28(14):2404–2410. https://doi.org/10.1200/JCO.2009.24.2495
Austin PC, van Klaveren D, Vergouwe Y, Nieboer D, Lee DS, Steyerberg EW (2016) Geographic and temporal validity of prediction models: different approaches were useful to examine model performance. J Clin Epidemiol 79:76–85. https://doi.org/10.1016/j.jclinepi.2016.05.007
Michailidou K, Lindstrom S, Dennis J, Beesley J, Hui S, Kar S, Lemacon A, Soucy P, Glubb D, Rostamianfar A, Bolla MK, Wang Q, Tyrer J, Dicks E, Lee A, Wang Z, Allen J, Keeman R, Eilber U, French JD, Qing Chen X, Fachal L, McCue K, McCart Reed AE, Ghoussaini M, Carroll JS, Jiang X, Finucane H, Adams M, Adank MA, Ahsan H, Aittomaki K, Anton-Culver H, Antonenkova NN, Arndt V, Aronson KJ, Arun B, Auer PL, Bacot F, Barrdahl M, Baynes C, Beckmann MW, Behrens S, Benitez J, Bermisheva M, Bernstein L, Blomqvist C, Bogdanova NV, Bojesen SE, Bonanni B, Borresen-Dale AL, Brand JS, Brauch H, Brennan P, Brenner H, Brinton L, Broberg P, Brock IW, Broeks A, Brooks-Wilson A, Brucker SY, Bruning T, Burwinkel B, Butterbach K, Cai Q, Cai H, Caldes T, Canzian F, Carracedo A, Carter BD, Castelao JE, Chan TL, David Cheng TY, Seng Chia K, Choi JY, Christiansen H, Clarke CL, Collaborators N, Collee M, Conroy DM, Cordina-Duverger E, Cornelissen S, Cox DG, Cox A, Cross SS, Cunningham JM, Czene K, Daly MB, Devilee P, Doheny KF, Dork T, Dos-Santos-Silva I, Dumont M, Durcan L, Dwek M, Eccles DM, Ekici AB, Eliassen AH, Ellberg C, Elvira M, Engel C, Eriksson M, Fasching PA, Figueroa J, Flesch-Janys D, Fletcher O, Flyger H, Fritschi L, Gaborieau V, Gabrielson M, Gago-Dominguez M, Gao YT, Gapstur SM, Garcia-Saenz JA, Gaudet MM, Georgoulias V, Giles GG, Glendon G, Goldberg MS, Goldgar DE, Gonzalez-Neira A, Grenaker Alnaes GI, Grip M, Gronwald J, Grundy A, Guenel P, Haeberle L, Hahnen E, Haiman CA, Hakansson N, Hamann U, Hamel N, Hankinson S, Harrington P, Hart SN, Hartikainen JM, Hartman M, Hein A, Heyworth J, Hicks B, Hillemanns P, Ho DN, Hollestelle A, Hooning MJ, Hoover RN, Hopper JL, Hou MF, Hsiung CN, Huang G, Humphreys K, Ishiguro J, Ito H, Iwasaki M, Iwata H, Jakubowska A, Janni W, John EM, Johnson N, Jones K, Jones M, Jukkola-Vuorinen A, Kaaks R, Kabisch M, Kaczmarek K, Kang D, Kasuga Y, Kerin MJ, Khan S, Khusnutdinova E, Kiiski JI, Kim SW, Knight JA, Kosma VM, Kristensen VN, Kruger U, Kwong A, Lambrechts D, Le Marchand L, Lee E, Lee MH, Lee JW, Neng Lee C, Lejbkowicz F, Li J, Lilyquist J, Lindblom A, Lissowska J, Lo WY, Loibl S, Long J, Lophatananon A, Lubinski J, Luccarini C, Lux MP, Ma ESK, MacInnis RJ, Maishman T, Makalic E, Malone KE, Kostovska IM, Mannermaa A, Manoukian S, Manson JE, Margolin S, Mariapun S, Martinez ME, Matsuo K, Mavroudis D, McKay J, McLean C, Meijers-Heijboer H, Meindl A, Menendez P, Menon U, Meyer J, Miao H, Miller N, Taib NAM, Muir K, Mulligan AM, Mulot C, Neuhausen SL, Nevanlinna H, Neven P, Nielsen SF, Noh DY, Nordestgaard BG, Norman A, Olopade OI, Olson JE, Olsson H, Olswold C, Orr N, Pankratz VS, Park SK, Park-Simon TW, Lloyd R, Perez JIA, Peterlongo P, Peto J, Phillips KA, Pinchev M, Plaseska-Karanfilska D, Prentice R, Presneau N, Prokofyeva D, Pugh E, Pylkas K, Rack B, Radice P, Rahman N, Rennert G, Rennert HS, Rhenius V, Romero A, Romm J, Ruddy KJ, Rudiger T, Rudolph A, Ruebner M, Rutgers EJT, Saloustros E, Sandler DP, Sangrajrang S, Sawyer EJ, Schmidt DF, Schmutzler RK, Schneeweiss A, Schoemaker MJ, Schumacher F, Schurmann P, Scott RJ, Scott C, Seal S, Seynaeve C, Shah M, Sharma P, Shen CY, Sheng G, Sherman ME, Shrubsole MJ, Shu XO, Smeets A, Sohn C, Southey MC, Spinelli JJ, Stegmaier C, Stewart-Brown S, Stone J, Stram DO, Surowy H, Swerdlow A, Tamimi R, Taylor JA, Tengstrom M, Teo SH, Beth Terry M, Tessier DC, Thanasitthichai S, Thone K, Tollenaar R, Tomlinson I, Tong L, Torres D, Truong T, Tseng CC, Tsugane S, Ulmer HU, Ursin G, Untch M, Vachon C, van Asperen CJ, Van Den Berg D, van den Ouweland AMW, van der Kolk L, van der Luijt RB, Vincent D, Vollenweider J, Waisfisz Q, Wang-Gohrke S, Weinberg CR, Wendt C, Whittemore AS, Wildiers H, Willett W, Winqvist R, Wolk A, Wu AH, Xia L, Yamaji T, Yang XR, Har Yip C, Yoo KY, Yu JC, Zheng W, Zheng Y, Zhu B, Ziogas A, Ziv E, Investigators A, ConFab AI, Lakhani SR, Antoniou AC, Droit A, Andrulis IL, Amos CI, Couch FJ, Pharoah PDP, Chang-Claude J, Hall P, Hunter DJ, Milne RL, Garcia-Closas M, Schmidt MK, Chanock SJ, Dunning AM, Edwards SL, Bader GD, Chenevix-Trench G, Simard J, Kraft P, Easton DF (2017) Association analysis identifies 65 new breast cancer risk loci. Nature 551(7678):92–94. https://doi.org/10.1038/nature24284
Blanche P, Dartigues JF, Jacqmin-Gadda H (2013) Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med 32(30):5381–5397. https://doi.org/10.1002/sim.5958
Blanche P, Kattan MW, Gerds TA (2018) The c-index is not proper for the evaluation of $t$-year predicted risks. Biostatistics. https://doi.org/10.1093/biostatistics/kxy006
Pfeiffer RM, Park Y, Kreimer AR, Lacey JV Jr, Pee D, Greenlee RT, Buys SS, Hollenbeck A, Rosner B, Gail MH, Hartge P (2013) Risk prediction for breast, endometrial, and ovarian cancer in white women aged 50 y or older: derivation and validation from population-based cohort studies. PLoS Med 10(7):e1001492. https://doi.org/10.1371/journal.pmed.1001492
Van Calster B, Nieboer D, Vergouwe Y, De Cock B, Pencina MJ, Steyerberg EW (2016) A calibration hierarchy for risk models was defined: from utopia to empirical data. J Clin Epidemiol 74:167–176. https://doi.org/10.1016/j.jclinepi.2015.12.005
Collins GS, Ogundimu EO, Altman DG (2016) Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med 35(2):214–226. https://doi.org/10.1002/sim.6787
RDC Team (2017) A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Akdeniz D, Schmidt MK, Seynaeve CM, McCool D, Giardiello D, van den Broek AJ, Hauptmann M, Steyerberg EW, Hooning MJ (2018) Risk factors for metachronous contralateral breast cancer: a systematic review and meta-analysis. Breast 44:1–14. https://doi.org/10.1016/j.breast.2018.11.005
Armstrong N, Ryder S, Forbes C, Ross J, Quek RG (2019) A systematic review of the international prevalence of BRCA mutation in breast cancer. Clin Epidemiol 11:543–561. https://doi.org/10.2147/CLEP.S206949
Bueno-de-Mesquita JM, Nuyten DS, Wesseling J, van Tinteren H, Linn SC, van de Vijver MJ (2010) The impact of inter-observer variation in pathological assessment of node-negative breast cancer on clinical risk assessment and patient selection for adjuvant systemic treatment. Ann Oncol 21(1):40–47. https://doi.org/10.1093/annonc/mdp273
Whittle R, Peat G, Belcher J, Collins GS, Riley RD (2018) Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported. J Clin Epidemiol 102:38–49. https://doi.org/10.1016/j.jclinepi.2018.05.008
Luijken K, Groenwold RHH, Van Calster B, Steyerberg EW, van Smeden M (2019) Impact of predictor measurement heterogeneity across settings on the performance of prediction models: a measurement error perspective. Stat Med 38(18):3444–3459. https://doi.org/10.1002/sim.8183
Pflieger LT, Mason CC, Facelli JC (2017) Uncertainty quantification in breast cancer risk prediction models using self-reported family health history. J Clin Transl Sci 1(1):53–59. https://doi.org/10.1017/cts.2016.9
Groenwold RH, White IR, Donders AR, Carpenter JR, Altman DG, Moons KG (2012) Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis. CMAJ 184(11):1265–1269. https://doi.org/10.1503/cmaj.110977
Janssen KJ, Donders AR, Harrell FE Jr, Vergouwe Y, Chen Q, Grobbee DE, Moons KG (2010) Missing covariate data in medical research: to impute is better than to ignore. J Clin Epidemiol 63(7):721–727. https://doi.org/10.1016/j.jclinepi.2009.12.008
Janssen KJ, Vergouwe Y, Donders AR, Harrell FE Jr, Chen Q, Grobbee DE, Moons KG (2009) Dealing with missing predictor values when applying clinical prediction models. Clin Chem 55(5):994–1001. https://doi.org/10.1373/clinchem.2008.115345
Royston P, Altman DG (2013) External validation of a Cox prognostic model: principles and methods. BMC Med Res Methodol 13:33. https://doi.org/10.1186/1471-2288-13-33
van Houwelingen HC (2000) Validation, calibration, revision and combination of prognostic survival models. Stat Med 19(24):3401–3415
Pajouheshnia R, van Smeden M, Peelen LM, Groenwold RHH (2019) How variation in predictor measurement affects the discriminative ability and transportability of a prediction model. J Clin Epidemiol 105:136–141. https://doi.org/10.1016/j.jclinepi.2018.09.001
Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B (2019) A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 110:12–22. https://doi.org/10.1016/j.jclinepi.2019.02.004
Ming C, Viassolo V, Probst-Hensch N, Chappuis PO, Dinov ID, Katapodi MC (2019) Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models. Breast Cancer Res 21(1):75. https://doi.org/10.1186/s13058-019-1158-4
Torkamani A, Wineinger NE, Topol EJ (2018) The personal and clinical utility of polygenic risk scores. Nat Rev Genet 19(9):581–590. https://doi.org/10.1038/s41576-018-0018-x
Mellemkjaer L, Dahl C, Olsen JH, Bertelsen L, Guldberg P, Christensen J, Borresen-Dale AL, Stovall M, Langholz B, Bernstein L, Lynch CF, Malone KE, Haile RW, Andersson M, Thomas DC, Concannon P, Capanu M, Boice JD Jr, Group WSC, Bernstein JL (2008) Risk for contralateral breast cancer among carriers of the CHEK2*1100delC mutation in the WECARE Study. Br J Cancer 98(4):728–733. https://doi.org/10.1038/sj.bjc.6604228
Acknowledgements
We thank all individuals who took part in these studies and all researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. ABCFS thank Maggie Angelakos, Judi Maskiell, Gillian Dite. ABCS and BOSOM thanks all the collaborating hospitals and pathology departments and many individual that made this study possible, specifically, we wish to acknowledge: Annegien Broeks, Sten Cornelissen, Frans Hogervorst, Laura van ‘t Veer, Floor van Leeuwen, Emiel Rutgers. EMC thanks J.C. Blom-Leenheer, P.J. Bos,C.M.G. Crepin and M. van Vliet for data management. CGPS thanks staff and participants of the Copenhagen General Population Study. For the excellent technical assistance: Dorthe Uldall Andersen, Maria Birna Arnadottir, Anne Bank, Dorthe Kjeldgård Hansen. HEBCS thanks Taru A. Muranen, Kristiina Aittomäki, Karl von Smitten, Irja Erkkilä. KARMA thanks the Swedish Medical Research Counsel. LMBC thanks Gilian Peuteman, Thomas Van Brussel, EvyVanderheyden and Kathleen Corthouts. MARIE thanks Petra Seibold, Dieter Flesch-Janys, Judith Heinz, Nadia Obi, Alina Vrieling, Sabine Behrens, Ursula Eilber, Muhabbet Celik, Til Olchers and Stefan Nickels. ORIGO thanks E. Krol-Warmerdam, and J. Blom for patient accrual, administering questionnaires, and managing clinical information. The authors thank the registration team of the Netherlands Comprehensive Cancer Organisation (IKNL) for the collection of data for the Netherlands Cancer Registry as well as IKNL staff for scientific advice. PBCS thanks Louise Brinton, Mark Sherman, Neonila Szeszenia-Dabrowska, Beata Peplonska, Witold Zatonski, Pei Chao, Michael Stagner. The ethical approval for the POSH study is MREC /00/6/69, UKCRN ID: 1137. We thank the SEARCH team.
Funding
This work is supported by the Alpe d’HuZes/Dutch Cancer Society (KWF Kankerbestrijding) project 6253. BCAC is funded by Cancer Research UK [C1287/A16563, C1287/A10118], the European Union's Horizon 2020 Research and Innovation Programme (Grant Nos. 634935 and 633784 for BRIDGES and B-CAST respectively), and by the European Community´s Seventh Framework Programme under grant agreement number 223175 (Grant No. HEALTH-F2-2009-223175) (COGS). The EU Horizon 2020 Research and Innovation Programme funding source had no role in study design, data collection, data analysis, data interpretation or writing of the report. The Australian Breast Cancer Family Study (ABCFS) was supported by grant UM1 CA164920 from the National Cancer Institute (USA). The ABCFS was also supported by the National Health and Medical Research Council of Australia, the New South Wales Cancer Council, the Victorian Health Promotion Foundation (Australia) and the Victorian Breast Cancer Research Consortium. J.L.H. is a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellow. M.C.S. is a NHMRC Senior Research Fellow. The ABCS study was supported by the Dutch Cancer Society [grants NKI 2007-3839; 2009 4363]. The work of the BBCC was partly funded by ELAN-Fond of the University Hospital of Erlangen. BOSOM was supported by the Dutch Cancer Society grant numbers DCS-NKI 2001-2423, DCS-NKI 2007-3839, and DCSNKI 2009-4363; the Cancer Genomics Initiative; and notary office Spier & Hazenberg for the coding procedure. The EMC was supported by grants from Alpe d’HuZes/Dutch Cancer Society NKI2013-6253 and from Pink Ribbon 2012.WO39.C143. The HEBCS was financially supported by the Helsinki University Hospital Research Fund, the Finnish Cancer Society, and the Sigrid Juselius Foundation. Financial support for KARBAC was provided through the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet, the Swedish Cancer Society, The Gustav V Jubilee foundation and Bert von Kantzows foundation. The KARMA study was supported by Märit and Hans Rausings Initiative Against Breast Cancer. LMBC is supported by the 'Stichting tegen Kanker'. The MARIE study was supported by the Deutsche Krebshilfe e.V.[70-2892-BR I, 106332, 108253, 108419, 110826, 110828], the Hamburg Cancer Society, the German Cancer Research Center (DKFZ) and the Federal Ministry of Education and Research (BMBF) Germany [01KH0402]. MEC was support by NIH grants CA63464, CA54281, CA098758, CA132839 and CA164973. The ORIGO study was supported by the Dutch Cancer Society (RUL 1997-1505) and the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL CP16). The PBCS was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. Genotyping for PLCO was supported by the Intramural Research Program of the National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The POSH study is funded by Cancer Research UK (Grants C1275/A11699, C1275/C22524, C1275/A19187, C1275/A15956 and Breast Cancer Campaign 2010PR62, 2013PR044. PROCAS is funded from NIHR grant PGfAR 0707-10031. SEARCH is funded by Cancer Research UK [C490/A10124, C490/A16561] and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge. The University of Cambridge has received salary support for PDPP from the NHS in the East of England through the Clinical Academic Reserve. SKKDKFZS is supported by the DKFZ. The SZBCS (Szczecin Breast Cancer Study) was supported by Grant PBZ_KBN_122/P05/2004 and The National Centre for Research and Development (NCBR) within the framework of the international ERA-NET TRANSAN JTC 2012 application no. Cancer 12-054 (Contract No. ERA-NET-TRANSCAN / 07/2014).
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Author DG, MH, EW, MAA, DA, JCB, CB, SEB, MKB, JCC, KC, PD, AMD, DFE, JF, HF, MGC, LH, CAH, PH, UH, JLH, AJ, AJ2, AJ3, RK, LBK, IK, DL, LLN, AL, JL, MM, LM, HN, HSAO, SP, PDPP, MS, SS, VTHBMS, MCS, WJT, RAEMT, AJvdB, CHMvD, FEvL, CvO, LvV, QW, CW, PJW, MJH declares that he has no conflict of interest. Author DMM declares that she receives a lecture fee from Pierre Fabre and personal fees for consultancy from Astra Zeneca. Author PAF reports grants from Novartis, grants from Biontech, personal fees from Novartis, personal fees from Roche, personal fees from Pfizer, personal fees from Celgene, personal fees from Daiichi-Sankyo, personal fees from TEVA, personal fees from Astra Zeneca, personal fees from Merck Sharp & Dohme, personal fees from Myelo Therapeutics, personal fees from Macrogenics, personal fees from Eisai, personal fees from Puma, grants from Cepheid.
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Giardiello, D., Hauptmann, M., Steyerberg, E.W. et al. Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts. Breast Cancer Res Treat 181, 423–434 (2020). https://doi.org/10.1007/s10549-020-05611-8
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DOI: https://doi.org/10.1007/s10549-020-05611-8